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- Nature neuroscience 13(4):401 (2010)
Mobile devices are emerging as a viable way to maximize time to read and search our ever-expanding body of scientific information. We encourage our readers to download and use our new nature.com app and to provide us with feedback. - Preventing dehydration during sleep
- Nature neuroscience 13(4):403-404 (2010)
Vasopressin release increases late in sleep. Suprachiasmatic clock neurons modulate osmosensory synapses onto vasopressin neurons to facilitate osmoregulated vasopressin release, reports a study in this issue. This explains the increased late-night vasopressin release, and such facilitation prevents dehydration during sleep. - Manipulating the brain with epigenetics
- Nature neuroscience 13(4):405-406 (2010)
A study finds that the DNA methylation enzymes Dnmt1 and Dnmt3a are needed to maintain the epigenetic landscape in nondividing, postmitotic neurons and that this process is required for normal learning and memory. - Mouse brains wired for empathy?
- Nature neuroscience 13(4):406-408 (2010)
A study in this issue reports that mice can be fear conditioned through observation of other mice receiving aversive stimuli and identifies some of the brain regions involved in this observational fear learning. - Protecting endangered memories
- Nature neuroscience 13(4):408-410 (2010)
Memories are continually adapted by ongoing experience. A study now suggests that the reactivation of previously stored memories during the formation of new memories is a critical mechanism for determining memory survival. - Speedy rod signaling
- Nature neuroscience 13(4):410 (2010)
Rod photoreceptors in the mammalian retina allow vision under dim light conditions, when cones are not sufficiently activated. The rod light response, however, is relatively slow. - Microglial Cx3cr1 knockout prevents neuron loss in a mouse model of Alzheimer's disease
Fuhrmann M Bittner T Jung CK Burgold S Page RM Mitteregger G Haass C Laferla FM Kretzschmar H Herms J - Nature neuroscience 13(4):411-413 (2010)
Nature Neuroscience | Brief Communication Microglial Cx3cr1 knockout prevents neuron loss in a mouse model of Alzheimer's disease * Martin Fuhrmann1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Tobias Bittner1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Christian K E Jung1 Search for this author in: * NPG journals * PubMed * Google Scholar * Steffen Burgold1 Search for this author in: * NPG journals * PubMed * Google Scholar * Richard M Page2 Search for this author in: * NPG journals * PubMed * Google Scholar * Gerda Mitteregger1 Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Haass2 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank M LaFerla3 Search for this author in: * NPG journals * PubMed * Google Scholar * Hans Kretzschmar1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jochen Herms1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:411–413Year published:(2010)DOI:doi:10.1038/nn.2511Received15 October 2009Accepted28 January 2010Published online21 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Microglia, the immune cells of the brain, can have a beneficial effect in Alzheimer's disease by phagocytosing amyloid-β. Two-photon in vivo imaging of neuron loss in the intact brain of living Alzheimer's disease mice revealed an involvement of microglia in neuron elimination, indicated by locally increased number and migration velocity of microglia around lost neurons. Knockout of the microglial chemokine receptor Cx3cr1, which is critical in neuron-microglia communication, prevented neuron loss. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to the work. * Martin Fuhrmann & * Tobias Bittner Affiliations * Center of Neuropathology and Prion Research, Ludwig-Maximilians-University, Munich, Germany. * Martin Fuhrmann, * Tobias Bittner, * Christian K E Jung, * Steffen Burgold, * Gerda Mitteregger, * Hans Kretzschmar & * Jochen Herms * German Center for Neurodegenerative Diseases Munich, Ludwig-Maximilians-University, Munich, Germany. * Richard M Page & * Christian Haass * Department of Neurobiology and Behavior, University of California, Irvine, California, USA. * Frank M LaFerla Contributions M.F. and T.B. conducted the experiments and wrote the manuscript. C.K.E.J. and S.B. provided technical assistance. R.M.P. performed Aβ measurements. G.M., H.K., C.H. and F.M.L. provided mouse models and helpful discussion. J.H. coordinated the research and supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jochen Herms (jochen.herms@med.uni-muenchen.de) Supplementary information * Author information * Supplementary information Movies * Supplementary Video 1 (4M) Example of a z stack acquired by two-photon in vivo imaging. Z stack from 650 μm depth to the surface in a living mouse brain. Neurons are labeled with YFP and microglia with GFP. * Supplementary Video 2 (2M) Tracking of microglia migration in vivo. 5-week time-lapse example of microglia migration around a neuron. Colored lines representing the tracks of the microglia are superimposed. * Supplementary Video 3 (2M) Screening behavior of microglia with extension and retraction of fine processes. The video consists of z stack projections (40 μm) of fluorescence images recorded with a time interval of 5 min 150 μm below the brain surface. * Supplementary Video 4 (2M) Turnover rate (TOR) of microglia. Red/green overlay of subsequent time points to visualize gained (green) and lost (red) as well as stable (yellow) areas of microglial processes. PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–5 and Supplementary Methods Additional data - A fast rod photoreceptor signaling pathway in the mammalian retina
Li W Chen S Devries SH - Nature neuroscience 13(4):414-416 (2010)
Nature Neuroscience | Brief Communication A fast rod photoreceptor signaling pathway in the mammalian retina * Wei Li1 Search for this author in: * NPG journals * PubMed * Google Scholar * Shan Chen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Steven H DeVries2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:414–416Year published:(2010)DOI:doi:10.1038/nn.2507Received19 August 2009Accepted15 January 2010Published online28 February 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Rod photoreceptors were recently shown to contact 'Off' cone bipolar cells, providing an alternative pathway for rod signal flow in the mammalian retina. By recording from pairs of rods and Off cone bipolar cells in the ground squirrel (Spermophilus tridecemlineatus), we measured the synaptic responses of mammalian rods unfiltered by the slow kinetics of the rod bipolar cell response. We show that vesicle fusion and turnover in mammalian rods is fast, and that this new pathway can mediate rapid signaling. View full text Figures at a glance * Figure 1: Anatomical contacts between rods and b2 Off cone bipolar cells. () Rod outer segments (numbered) labeled with an antibody to rhodopsin in a flat-mounted retina. () A different image plane shows the corresponding small clusters (squares) of puncta labeled for GluR4 and GluR5. (,) A b2 Off cone bipolar cell was labeled with neurobiotin (NB). The b2 cell contacted all the terminals within its dendritic field, including those of a rod (square) and an S-cone (circle). (,) The dendritic endings at the rod terminal (square in ,) colocalize with GluR4 puncta. () A tracer-injected rod (Alexa Fluor 568) and b2 cell in a retinal slice (n = 3). The rod outer segment was labeled with an antibody to rhodopsin (Rhod). The b2 cell was identified by its stratum of axon termination. () Magnified image of the rod terminal showing a contact (arrowhead) with a b2 cell dendrite. Experimental use of animals was approved by the Institutional Animal Care and Use Committee at Northwestern University and at the National Eye Institute. * Figure 2: Synaptic transmission between rods and b2 bipolar cells. () Left, current response of a b2 cell to a 1 ms depolarization from −70 to −30 mV in a rod (black trace) and subsequently in a nearby M-cone (green trace). The cone was depolarized in the 'loose seal' configuration to elicit a maximal response. Inset, response transients normalized and superimposed. Right, morphology of the recorded rod (Alexa Fluor 568, red) and b2 cell (neurobiotin (NB), green), labeled with an antibody to rhodopsin (Rhod, blue). () Left, Ca2+ current (Co2+-subtracted) during a rod step from −70 to −20 mV. Right, recorded rod filled with Alexa Fluor 568 and labeled with antibody to rhodopsin. () Two 15-ms depolarizations were applied to either a rod or a cone with increasing interpulse intervals. Black, responses in a b2 cell to the first pulses; gray, to the second pulses. () Normalized rod-initiated (black) and cone-initiated (green) responses plotted against interpulse interval (mean ± s.d.). () A current injection 'ramp' (bottommost trace) in! a presynaptic cone produced a steady voltage change (blue) and a transient current in a postsynaptic b2 cell. The line between the two arrows (from −50 to −40 mV) was superimposed on the corresponding voltage range of a rod light response measured in current clamp (inset). Flash (10 ms) intensity, 4,400 photons μm−2 at an equivalent wavelength of 505 nm. Rod membrane potential in darkness is −30.7 mV. A cone-triggered response was used for illustration because it is larger than the rod-triggered response. The kinetics of rod- and cone-triggered responses should be the same. Author information * Author information * Supplementary information Affiliations * Unit on Retinal Neurophysiology, National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA. * Wei Li & * Shan Chen * Departments of Ophthalmology and Physiology, Northwestern University Medical School, Chicago, Illinois, USA. * Steven H DeVries Contributions The authors contributed equally to this work. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Steven H DeVries (s-devries@northwestern.edu) or * Wei Li (liwei2@nei.nih.gov) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (13M) Supplementary Figures 1–8 and Supplementary Methods Additional data - Cortical representations of bodies and faces are strongest in commonly experienced configurations
Chan AW Kravitz DJ Truong S Arizpe J Baker CI - Nature neuroscience 13(4):417-418 (2010)
Nature Neuroscience | Brief Communication Cortical representations of bodies and faces are strongest in commonly experienced configurations * Annie W-Y Chan1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Dwight J Kravitz1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Sandra Truong1 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph Arizpe1 Search for this author in: * NPG journals * PubMed * Google Scholar * Chris I Baker1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:417–418Year published:(2010)DOI:doi:10.1038/nn.2502Received05 October 2009Accepted14 January 2010Published online07 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Faces and bodies are perhaps the most salient and evolutionarily important visual stimuli. Using human functional imaging, we found that the strength of face and body representations depends on long-term experience. Representations were strongest for stimuli in their typical combinations of visual field and side (for example, left field, right body), although all conditions were simply reflections and translations of one another. Thus, high-level representations reflect the statistics with which stimuli occur. View full text Figures at a glance * Figure 1: Experimental design. () Sample stimuli from right (top row) or left side (bottom row) of the body. Left and right side stimuli are mirror images. () fMRI experiment. On each trial, participants indicated whether the color of the fixation cross matched that of a small circle placed on top of each stimulus (average accuracy, 94%). () Behavioral experiment. The same stimuli, sizes and locations were used as in the fMRI experiment. On each trial, participants viewed two masked presentations of exemplars from a given stimulus type and made an eye movement to the either the green or red target to indicate whether the exemplars were the same or different, respectively. * Figure 2: Discrimination of body parts and half-faces. (,) Similarity matrices and summary plots for the right side of the body in the left visual field in rEBA () and rFFA (). Each element in the similarity matrices shows the correlation between two conditions. The within-condition correlations (white bars) are plotted against the average between-condition correlations (gray bars) for each condition. Discrimination indices were calculated as the difference between these bars. Error bars indicate the between-subjects s.e. *P < 0.05. (–) Interaction of field and side for discrimination indices in rEBA () and rFFA () and for behavioral performance (). Error bars indicate the between-subjects s.e. # indicates significant difference from zero (P < 0.05) and thus significant discrimination. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Annie W-Y Chan & * Dwight J Kravitz Affiliations * Laboratory of Brain and Cognition, National Institute of Mental Health, US National Institutes of Health, Bethesda, Maryland, USA. * Annie W-Y Chan, * Dwight J Kravitz, * Sandra Truong, * Joseph Arizpe & * Chris I Baker Contributions A.W.-Y.C., D.J.K., S.T. and C.I.B. designed the fMRI study. A.W.-Y.C., D.J.K. and S.T. collected and analyzed the fMRI data. A.W.-Y.C., D.J.K., J.A. and C.I.B. designed the behavioral study. J.A. collected and analyzed the behavioral data with help from A.W.-Y.C., D.J.K. and C.I.B. A.W.-Y.C., D.J.K. and C.I.B. wrote the paper with contributions from S.T. and J.A. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Annie W-Y Chan (chanannie@mail.nih.gov) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–15, Supplementary Data 1–11 and Supplementary Methods Additional data - Mesolimbic dopamine reward system hypersensitivity in individuals with psychopathic traits
Buckholtz JW Treadway MT Cowan RL Woodward ND Benning SD Li R Ansari MS Baldwin RM Schwartzman AN Shelby ES Smith CE Cole D Kessler RM Zald DH - Nature neuroscience 13(4):419-421 (2010)
Nature Neuroscience | Brief Communication Mesolimbic dopamine reward system hypersensitivity in individuals with psychopathic traits * Joshua W Buckholtz1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael T Treadway1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ronald L Cowan1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Neil D Woodward3 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen D Benning1 Search for this author in: * NPG journals * PubMed * Google Scholar * Rui Li4 Search for this author in: * NPG journals * PubMed * Google Scholar * M Sib Ansari4 Search for this author in: * NPG journals * PubMed * Google Scholar * Ronald M Baldwin4 Search for this author in: * NPG journals * PubMed * Google Scholar * Ashley N Schwartzman1 Search for this author in: * NPG journals * PubMed * Google Scholar * Evan S Shelby1 Search for this author in: * NPG journals * PubMed * Google Scholar * Clarence E Smith4 Search for this author in: * NPG journals * PubMed * Google Scholar * David Cole5 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert M Kessler4 Search for this author in: * NPG journals * PubMed * Google Scholar * David H Zald1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:419–421Year published:(2010)DOI:doi:10.1038/nn.2510Received03 December 2009Accepted29 January 2010Published online14 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Psychopathy is a personality disorder that is strongly linked to criminal behavior. Using [18F]fallypride positron emission tomography and blood oxygen level–dependent functional magnetic resonance imaging, we found that impulsive-antisocial psychopathic traits selectively predicted nucleus accumbens dopamine release and reward anticipation-related neural activity in response to pharmacological and monetary reinforcers, respectively. These findings suggest that neurochemical and neurophysiological hyper-reactivity of the dopaminergic reward system may comprise a neural substrate for impulsive-antisocial behavior and substance abuse in psychopathy. View full text Figures at a glance * Figure 1: Impulsive-antisocial traits predict nucleus accumbens DA release. () Statistical parametric map (SPM) showing that PPI-IA factor scores selectively predicted increased amphetamine-induced DA release in bilateral NAcc (left NAcc: −16, 10, −10, pfalse discovery rate = 0.003, z = 3.74, cluster size (k) = 56; right NAcc: 16, 18, −6; pfalse discovery rate = 0.002, z = 4.21, k = 44). All coordinates reference the coordinate system of the Montreal Neurological Institute. SPM thresholded at P < 0.05 (uncorrected) for visualization purposes. Color bar indicates t statistic value. (,) Scatter plot depicting the relationship between PPI-IA factor scores and amphetamine-induced DA release in left () and right () NAcc. DA release values were extracted from clusters defined by a pFDR < 0.05 threshold. * Figure 2: Impulsive-antisocial factor scores are selectively associated with NAcc BOLD signal during monetary reward anticipation. () Image depicts the Harvard-Oxford nucleus accumbens anatomical ROI from which BOLD signal estimates were obtained. () Scatter plot depicts the relationship between PPI-IA factor scores and reward anticipation–related BOLD signal in the right NAcc. Author information * Author information * Supplementary information Affiliations * Department of Psychology, Vanderbilt University, Nashville, Tennessee, USA. * Joshua W Buckholtz, * Michael T Treadway, * Ronald L Cowan, * Stephen D Benning, * Ashley N Schwartzman, * Evan S Shelby & * David H Zald * Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee, USA. * Joshua W Buckholtz * Department of Psychiatry, Vanderbilt University, Nashville, Tennessee, USA. * Ronald L Cowan, * Neil D Woodward & * David H Zald * Department of Radiology, Vanderbilt University, Nashville, Tennessee, USA. * Rui Li, * M Sib Ansari, * Ronald M Baldwin, * Clarence E Smith & * Robert M Kessler * Department of Psychology and Human Development, Vanderbilt University, Nashville, Tennessee, USA. * David Cole Contributions J.W.B., R.M.K. and D.H.Z. designed the study. E.S.S. and A.N.S. recruited participants into the study and collected PET and personality data. J.W.B. collected fMRI data with assistance from E.S.S., and A.N.S., R.L., N.D.W. and R.M.K. performed single-subject PET data analysis and quality control. J.W.B. performed group level PET data analysis with assistance from M.T.T. J.W.B. analyzed fMRI data at all stages. M.S.A. and R.M.B. synthesized radio-labeled fallypride for PET scanning. S.D.B. provided conceptual advice, statistical support and supplementary analyses for the PPI data. R.L.C. oversaw all medical aspects of the protocol. C.E.S. and R.M.K. provided medical support for PET scanning. D.C. provided conceptual support and statistical advice for the study. J.W.B., M.T.T. and D.H.Z. wrote the manuscript with assistance from R.L.C. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Joshua W Buckholtz (joshua.buckholtz@vanderbilt.edu) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (880K) Supplementary Figures 1–8, Supplementary Data, Supplementary Discussion and Supplementary Methods Additional data - Dnmt1 and Dnmt3a maintain DNA methylation and regulate synaptic function in adult forebrain neurons
Feng J Zhou Y Campbell SL Le T Li E Sweatt JD Silva AJ Fan G - Nature neuroscience 13(4):423-430 (2010)
Nature Neuroscience | Article Dnmt1 and Dnmt3a maintain DNA methylation and regulate synaptic function in adult forebrain neurons * Jian Feng1 Search for this author in: * NPG journals * PubMed * Google Scholar * Yu Zhou2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Susan L Campbell3 Search for this author in: * NPG journals * PubMed * Google Scholar * Thuc Le1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * En Li5 Search for this author in: * NPG journals * PubMed * Google Scholar * J David Sweatt3 Search for this author in: * NPG journals * PubMed * Google Scholar * Alcino J Silva2 Search for this author in: * NPG journals * PubMed * Google Scholar * Guoping Fan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:423–430Year published:(2010)DOI:doi:10.1038/nn.2514Received14 December 2009Accepted15 February 2010Published online14 March 2010 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Dnmt1 and Dnmt3a are important DNA methyltransferases that are expressed in postmitotic neurons, but their function in the CNS is unclear. We generated conditional mutant mice that lack Dnmt1, Dnmt3a or both exclusively in forebrain excitatory neurons and found that only double knockout (DKO) mice showed abnormal long-term plasticity in the hippocampal CA1 region together with deficits in learning and memory. Although we found no neuronal loss, hippocampal neurons in DKO mice were smaller than in the wild type; furthermore, DKO neurons showed deregulated expression of genes, including the class I MHC genes and Stat1, that are known to contribute to synaptic plasticity. In addition, we observed a significant decrease in DNA methylation in DKO neurons. We conclude that Dnmt1 and Dnmt3a are required for synaptic plasticity, learning and memory through their overlapping roles in maintaining DNA methylation and modulating neuronal gene expression in adult CNS neurons. View full text Figures at a glance * Figure 1: Mice with conditional deletion of Dnmt1 and Dnmt3a have small hippocampi without cell loss. (,) Relative expression of Dnmt1 () and Dnmt3a () in hippocampi of DKO and wild-type littermate control (Con) mice were compared at various postnatal ages. Three pairs of samples were used for each postnatal age. () Stereological analysis of the volume of hippocampus and dentate gyrus in DKO and control mice. Data are presented as mean ± s.e.m. *P < 0.05, **P < 0.01. * Figure 2: Impaired neural plasticity in DKO mice. () Field EPSP (fEPSP) slopes in control (squares) versus DKO mice (triangles) recorded before and after tetanic stimulation (100 Hz, 1 s, twice with 20-s interval). *P < 0.05. n = 28 slices from 9 control mice; n = 13 slices from 7 DKO mice. () LTD was enhanced in adult DKO mice. fEPSP slopes were recorded before and after stimulation (1 Hz, 15 min). *P < 0.05. n = 10 slices from 5 control mice and n = 8 slices from 4 DKO mice. Representative recordings are shown in insets (,). (,) The basal synaptic transmission from the DKO and control mice are identical, as shown by plotting varying stimulus intensity (10–100 μA) against the presynaptic fiber volley amplitudes () and postsynaptic fEPSP slope (). n = 33 slices from 8 DKO mice and n = 28 slices from 8 control mice. () PPF studies across different ISIs revealed no difference between DKO and control mice. n = 16 slices from 4 DKO mice and n = 14 slice from 4 control mice. Slice numbers were used for statistical analysis. D! ata are presented as mean ± s.e.m. * Figure 3: Impaired learning and memory in DKO mice. (–) Morris water maze test. () Schematic drawing of the Morris water maze test design. () Escape latency (time to find the hidden platform) plotted against training day. Genotype F(1, 30) = 6.25, P = 0.019, two-way ANOVA. () Percentage time spent in target quadrant during three probe trials. () Swimming speeds measured in DKO (n = 13) and control (n = 17) mice. Three-month-old mice were used in –. (–) Contextual fear conditioning test. () Schematic drawing of the contextual fear conditioning test design. Mice were trained and tested immediately (IM/average for 3 min) and 24 h later in a conditioning chamber. () Contextual memory consolidation was measured by freezing frequency at 24 h. The acquisition was normal in DKO immediately after shock. Baseline, freezing before shock presentation; IM, freezing immediately after shock presentation. n = 21 DKO and n = 13 control mice in . Data are presented as mean ± s.e.m. *P < 0.05; **P < 0.01. * Figure 4: Induction of immune genes in DKO mouse brain. () Neuronal induction of MHC I gene expression in DKO. In situ hybridization of H2D was post-stained with cresyl violet to reveal the nuclei. Silver grains appeared black. In the cortex and hippocampus of DKO mice, MHC I signal is highly concentrated in the neurons whose nuclei are large and lightly stained with cresyl violet. By contrast, in the white matter, where most cells are glia whose nuclei are small and dark stained, there was no significant difference compare to controls. Scale bar at bottom right, 100 μm, applies to all panels. () Real-time PCR analysis of the immune genes Stat1, B2M, H2M2, H2Q7, C3 and C4 in hippocampi of 2–3 months adults. (,) The induction of immune genes in the hippocampus was found as early at P14 () but not at P3 (). n = 3–4 pairs of samples for each experiment. () Real-time PCR analysis of Stat1, B2M, H2M2 and H2Q7 as well as expression of Dnmt3a and Dnmt1 in Dnmt12lox/2loxDnmt3a2lox/2lox mouse hippocampal neuronal cultures 5 d after a! deno-cre virus infection. Hippocampal neuronal culture with adeno-GFP viruses infection was used as control. Data are presented as mean ± s.e.m. *P < 0.05; **P < 0.01. * Figure 5: Stat1 protein increase in DKO mouse brain in conjunction with promoter demethylation in neuronal cells. () Immunohistochemistry of DKO cortex showing increase phosphorylated Stat1, mainly in cells with neuronal shapes. Scale bar, 50 μm, applies to both panels. () Bisulfite sequencing of Stat1 promoter of control and DKO forebrains at 3 months old. Schematic gene promoter structure is shown with each CpG site marked with a vertical slash. The blue bar highlights the promoter region subject to methylation analysis. A summary of methylation frequency at individual CpG sites is shown in a line chart and the mean of DNA methylation levels of individual alleles is shown in bar chart23. () Bisulfite sequencing of Stat1 promoter showed enhanced DNA demethylation within DKO tissue at P14. () Bisulfite sequencing of Stat1 promoter of FAC-sorted NeuN positive and negative nucleus sub-populations from DKO and control mouse forebrains. Here the demethylation of Stat1 promoter is restricted to NeuN+ DKO cells. We analyzed 30 clones from 3 samples. Data are presented as mean ± s.e.m. Unpai! red student t-test; *P < 0.05, **P < 0.01. * Figure 6: Gene ontology and bisulfite sequencing verification of MeDIP-chip analysis. () Quantitative analysis of 5-methylcytosine using liquid chromatography–electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) 5-Methylcytosine content is expressed as the percentage of 5-methylcytosine in the total cytosine pool. Data are the mean ± s.e.m. from replicates of four separate experiments (n = 16). () Gene ontology classifications of MeDIP–chip result based on categories of biological process and molecular function plotted against the P value of enrichment significance. (–) Four gene loci (Dhh (), Kcne1 (), Pten region 1 () and Pten region 2 ()) were verified for DNA demethylation. The results are displayed in the same manner as in Figure 5. Data as shown were from DKO and control mouse forebrains at 2–3 months old. Data are presented as mean ± s.e.m. Unpaired student t-test; *P < 0.05, **P < 0.01. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE19367 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA. * Jian Feng, * Thuc Le & * Guoping Fan * Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA. * Yu Zhou & * Alcino J Silva * Department of Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA. * Susan L Campbell & * J David Sweatt * Interdepartmental Neuroscience Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA. * Thuc Le * Epigenetics Program, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA. * En Li * Present address: Department of Physiology, Medical College of Qingdao University, Qingdao, China. * Yu Zhou Contributions The studies were directed by G.F. and conceived and designed by J.F. and G.F. G.F., A.J.S. and J.D.S. coordinated the project. J.F. performed the behavioral tests, morphology analysis, gene expression and DNA methylation analysis. Y.Z. performed the fear-conditioning test, LTD and synaptic transmission experiments. S.L.C. performed the LTP experiments. T.L. carried out DNA hydrolysis/LC-ESI-MS/MS experiments and contributed to the DNA methylation analysis. E.L. contributed Dnmt3a2lox/2lox mice. The paper was written by J.F. and G.F. and was commented on by all the authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Guoping Fan (gfan@mednet.ucla.edu) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–10 and Supplementary Tables 1–3 Additional data - Presynaptic GABAA receptors enhance transmission and LTP induction at hippocampal mossy fiber synapses
Ruiz A Campanac E Scott RS Rusakov DA Kullmann DM - Nature neuroscience 13(4):431-438 (2010)
Nature Neuroscience | Article Presynaptic GABAA receptors enhance transmission and LTP induction at hippocampal mossy fiber synapses * Arnaud Ruiz1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Emilie Campanac1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Ricardo S Scott1, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Dmitri A Rusakov1 Search for this author in: * NPG journals * PubMed * Google Scholar * Dimitri M Kullmann1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:431–438Year published:(2010)DOI:doi:10.1038/nn.2512Received25 September 2009Accepted05 February 2010Published online21 March 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Presynaptic GABAA receptors (GABAARs) occur at hippocampal mossy fiber synapses. Whether and how they modulate orthodromic signaling to postsynaptic targets is poorly understood. We found that an endogenous neurosteroid that is selective for high-affinity δ subunit–containing GABAARs depolarized rat mossy fiber boutons, enhanced action potential–dependent Ca2+ transients and facilitated glutamatergic transmission to pyramidal neurons. Conversely, blocking GABAARs hyperpolarized mossy fiber boutons, increased their input resistance, decreased spike width and attenuated action potential–dependent presynaptic Ca2+ transients, indicating that a subset of presynaptic GABA receptors are tonically active. Blocking GABAARs also interfered with the induction of long-term potentiation at mossy fiber–CA3 synapses. Presynaptic GABAARs therefore facilitate information flow to the hippocampus both directly and by enhancing LTP. View full text Figures at a glance * Figure 1: THDOC modulates GABAA receptors in outside-out patches from mossy fiber boutons. () Traces obtained in one patch at different holding potentials (−120 to −20 mV) showing single-channel currents evoked by muscimol (10 μM, musc) and their reversible modulation by 10 nM THDOC (THDOC, wash) and block by 100 μM picrotoxin (PiTx). () High magnification of a portion of the traces shown in demonstrating an increased open channel probability when THDOC was added to muscimol. C, closed; O, open. () The single-channel conductance calculated from the slope of the open channel current-voltage relationship was unaltered by THDOC (example from one patch). * Figure 2: Noninvasive measurement of mossy fiber membrane potential and modulation via GABAA receptors. () Three examples of bouton-attached recordings. Depolarizing voltage ramps (−100 to +200 mV, 3 mV ms−1) induced a current that reversed polarity, superimposed on a leak current (fitted with the gray dashed line). The corresponding voltage ramp protocol is indicated below (not to scale). The vertical dashed line indicates the intersection between the current trace and the linear fit to the leak current corresponding to the reversal potential for the K+ current (the apparent resting membrane potential, Emapp). Single K+ channels were seen in some traces (middle). A straight line fitted through the open state current also crosses the intersection. Single-channel currents are shown at higher magnification below. Action potentials were occasionally detected (right). Vp, pipette potential. () Summary plot of estimated membrane potential (Em apparent) measured in bouton- and soma-attached recordings (error bars represent s.e.m.). The estimated membrane potential in mossy fiber! boutons was more depolarized than that measured at granule cell somata (**P = 0.03, Mann-Whitney U test). () Left, experimental design used to measure the effect of muscimol or gabazine on bouton membrane potential. Right, apparent membrane potential in two mossy fiber boutons showing the effects of pressure application of muscimol (musc puff) or gabazine (gabazine puff), as indicated. (,) Effects of muscimol () and gabazine () on Em apparent, with sample traces before (control, black) and immediately after (gray) drug application (averages of five consecutive trials). () Summary data for the effect of pressure application of ACSF (n = 3), muscimol (n = 6), muscimol in a background of picrotoxin (n = 6), THDOC (n = 3), GBX (n = 8) and gabazine (n = 4). (*P = 0.01, paired t test.) * Figure 3: Blocking GABAA receptors reveals a tonic current in mossy fiber boutons. () Whole-bouton voltage-clamp recording (−70 mV) showing a 4.5 pA tonic current in the presence of gabazine (5 μM). Right, all-point histograms and Gaussian fits derived from the traces. (,) Time course of holding current in two mossy fiber boutons recorded at 22–24 °C () and at 32 °C () showing the effect of blocking GABAA receptors with picrotoxin (100 μM) or gabazine (5 μM). () Summary of changes in holding current measured in mossy fiber boutons in experiments performed at 22–24 °C and 32 °C (± s.e.m., *P < 0.05, paired t test). * Figure 4: Tonic GABAA receptor–mediated currents modulate the electrical properties of mossy fiber boutons. () Current-clamp recording from a mossy fiber bouton at 32 °C (155 mM KCl). Left, the bouton fired with high fidelity in response to five consecutive depolarizing pulses at 200 Hz (average of ten traces). Right, sample traces (averages of ten consecutive trials) showing the action potential before (black) and after (gray) superfusion of gabazine (5 μM). () Top, response to hyperpolarizing current injection showing an increase in input resistance in gabazine. Bottom, summary of changes in input resistance in mossy fiber boutons recorded with 155 mM [Cl−]i. () Current-clamp recording from a mossy fiber bouton with 20 mM [Cl−]i. Sample traces show the action potential (averages of ten consecutive trials) before (black) and after (gray) superfusion of gabazine (5 μM). () Response to hyperpolarizing current injection showing the increase in input resistance in gabazine (recording obtained with 20 mM [Cl−]i). () Summary plot of the effect of gabazine on input resistance. ! (–) Summary plots showing the effects of gabazine on action potential parameters measured in mossy fiber boutons with 20 mM [Cl−]i. Bars indicate mean ± s.e.m. and gray symbols indicate individual experiments (*P < 0.05, **P < 0.01, paired t test). * Figure 5: Tonically active neurosteroid-sensitive GABAA receptors enhance presynaptic action potential–dependent Ca2+ transients in giant mossy fiber boutons. () Reconstruction of a dentate granule cell and its intact axon (Alexa Fluor 594 channel, λx = 800 nm). The somatic patch pipette is seen at left. The collage was obtained from Kalman-filtered averages of 10–15-μm stacks. Arrows indicate giant boutons equipped with characteristic thin filopodia. () Blocking GABAA receptors with gabazine reduced spike-dependent presynaptic Ca2+ entry. Left, representative giant bouton (red Alexa channel, dotted arrow shows position of line scan). Line scans and traces are Ca2+ responses in the mossy fiber bouton shown on the left (green Fluo-4 channel, 10-trace average, averaging window for ΔF/R is indicated by the gray area) following a single action potential induced at the soma in control conditions and in 1-μM gabazine. () THDOC (10 nM) enhanced spike-dependent presynaptic Ca2+ entry; data are presented as in . (,) Summary of the effects of gabazine (n = 7, ) and THDOC (n = 8, ) on the average spike-evoked Ca2+ response. Each point ! is the average of two trials. Gray symbols indicate the average ΔF/R time course in control conditions (n = 7). Error bars represent s.e.m. * Figure 6: Bidirectional modulation of synaptic transmission via tonically active GABAA receptors at mossy fibers. () Top, schematic illustrating the experimental design used to study presynaptic GABAA receptors at mossy fiber–CA3 synapses. Postsynaptic GABAA receptors in CA3 pyramidal cells were blocked using an intracellular pipette solution containing CsF and DIDS. A pressure-application pipette containing THDOC (50 nM, left) was positioned ~50–200 μm from the apical dendrite of the recorded neurons. Bottom, time course of the effects of pressure application of THDOC (n = 8). Traces are averages of ten consecutive EPSCs before and after drug application. NMDA and GABAB receptors were blocked throughout. () Effect of gabazine (10 μM) applied as in (n = 6). () Plot of evoked postsynaptic current amplitude against time in control experiments showing the complete blockade of synaptic transmission on NBQX (20 μM) application. Sample traces (averages of ten consecutive EPSCs) were taken as indicated on the graph. () Time course of the effects of pressure application of ACSF (arrow) f! ollowed by superfusion of DCG-IV (1 μM) on evoked EPSCs recorded with a pipette solution containing CsF and DIDS (n = 5). Sample traces show the average of ten consecutive EPSCs before and after superfusion of DCG-IV. Error bars represent s.e.m. * Figure 7: GABAA receptors facilitate mossy fiber LTP. () Schematic (left) and infrared differential interference contrast and fluorescence images (middle), illustrating the Fura2 imaging. A recording electrode (rec) was used to monitor mossy fiber excitatory postsynaptic potentials before glutamate receptor blockade and imaging. Mossy fibers were loaded (Fura2-AM) and stimulated (stim) in the dentate gyrus. Gabazine, zolpidem or vehicle were pressure applied just outside the imaged field (drugs). Scale bar represents 100 μm. Right, sample Ca2+ fluorescence trace showing a transient increase in the fluorescence ratio excited at 340 and 380 nm evoked by HFS. () Gabazine was pressure applied immediately before the first (left) or second (middle) of three trains delivered with 4-min intervals. The HFS-evoked fluorescence ratio transient (ΔR340/380) was significantly attenuated (P < 0.02) by gabazine (G), as revealed by comparing with control conditions (C, right). *P < 0.05. () Pressure-applied zolpidem (Z) increased the HFS-evok! ed fluorescence ratio transient (ΔR340/380). *P < 0.05. () EPSC amplitudes recorded in a CA3 pyramidal neuron in response to stimulation of two pathways. HFS was delivered to one pathway (filled symbols) at the times indicated, either together with local pressure application of gabazine in stratum lucidum (HFS + gabazine) or alone (HFS). DCG-IV was applied at the end of the experiment to confirm that the responses were profoundly depressed, typical of mossy fibers. The sample traces were obtained in the test pathway, aligned with the times at which they were recorded. () Example of a cell in which the LTP induction protocols were applied in reverse order. () Average time course of 11 experiments; data are presented as in . () Average time course of seven experiments; data are presented as in . Error bars indicate s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Emilie Campanac & * Ricardo S Scott Affiliations * Institute of Neurology, University College London, London, UK. * Arnaud Ruiz, * Emilie Campanac, * Ricardo S Scott, * Dmitri A Rusakov & * Dimitri M Kullmann * Department of Pharmacology, School of Pharmacy, University of London, London, UK. * Arnaud Ruiz * Present address: Instituto de Neurociencias, CSIC & Universidad Miguel Hernández, Alicante, Spain. * Ricardo S Scott Contributions A.R., E.C. and R.S.S. conducted the experiments. A.R. and E.C. analyzed the electrophysiology and epifluorescence imaging data. R.S.S. and D.A.R. analyzed the multi-photon imaging data. A.R., R.S.S., D.A.R. and D.M.K. conceived the study. D.M.K. wrote the first draft of the manuscript, which was revised by all of the authors. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Arnaud Ruiz (arnaud.ruiz@pharmacy.ac.uk) or * Dimitri M Kullmann (d.kullmann@ion.ucl.ac.uk) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–7 Additional data - Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe
Chou YH Spletter ML Yaksi E Leong JC Wilson RI Luo L - Nature neuroscience 13(4):439-449 (2010)
Nature Neuroscience | Article Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe * Ya-Hui Chou1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Maria L Spletter1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Emre Yaksi2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan C S Leong1 Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel I Wilson2 Search for this author in: * NPG journals * PubMed * Google Scholar * Liqun Luo1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:439–449Year published:(2010)DOI:doi:10.1038/nn.2489Received28 September 2009Accepted22 December 2009Published online07 February 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Local interneurons are essential in information processing by neural circuits. Here we present a comprehensive genetic, anatomical and electrophysiological analysis of local interneurons (LNs) in the Drosophila melanogaster antennal lobe, the first olfactory processing center in the brain. We found LNs to be diverse in their neurotransmitter profiles, connectivity and physiological properties. Analysis of >1,500 individual LNs revealed principal morphological classes characterized by coarsely stereotyped glomerular innervation patterns. Some of these morphological classes showed distinct physiological properties. However, the finer-scale connectivity of an individual LN varied considerably across brains, and there was notable physiological variability within each morphological or genetic class. Finally, LN innervation required interaction with olfactory receptor neurons during development, and some individual variability also likely reflected LN–LN interactions. Our result! s reveal an unexpected degree of complexity and individual variation in an invertebrate neural circuit, a result that creates challenges for solving the Drosophila connectome. View full text Figures at a glance * Figure 1: Antennal lobe LNs. Expression patterns of the ten Gal4 lines used in this study. Green, Gal4-driven mCD8-GFP; red, Gal4-driven nuclear β-galactosidase or nuclear RFP; blue, neuropil staining by monoclonal antibody nc82, specific for a neuropil marker. Scale bars, 20 μm. (Scale is the same for lines 1 and 3–10.) * Figure 2: Diversity of LN morphology. (–) Representative arborization patterns of single-cell MARCM clones of ipsilaterally projecting LNs. Each row presents examples from categories of LNs that innervate all glomeruli (), all but a few glomeruli (), continuous () or patchy () regions of the antennal lobe, and a few glomeruli (). () Representative arborization patterns of bilaterally projecting LNs. Green, mCD8-GFP, labeling LN processes; red, synaptotagmin-HA, a marker for presynaptic terminals; blue, nc82 or N-cadherin staining, highlighting glomerular structures. Some images in (left and fourth and fifth from left) and (third, fifth and sixth from top left) do not include synaptotagmin-HA staining. Arrowheads, cell bodies; dashed lines, outline of the antennal lobe. Scale bar, 20 μm. Information about corresponding Gal4 lines in this and subsequent figures can be found in Supplementary Table 4. * Figure 3: Statistical analysis of glomerular innervation patterns. () Binary glomerular innervation patterns of 1,532 singly labeled LNs organized by hierarchical clustering. Rows represent innervation patterns of individual cells; columns represent 54 glomeruli. 1,489 were ipsilateral projecting LNs. Of the 43 bilaterally projecting LNs, only the ipsilateral patterns were included in the clustering analysis. Yellow, glomeruli innervated; blue, glomeruli not innervated. () Antennal lobe model. The 54 glomeruli we scored for this study are outlined in three sections of the antennal lobe from anterior to posterior. This model is modified after a number of sources3, 43 and derived from tracing nc82-stained brains. (–) Three representative images from three selected regions of the LN cluster diagram indicated on the right side in . Green, mCD8-GFP staining; blue, nc82 or N-cadherin staining. Dashed lines in mark pheromonal glomeruli DA1, VA1d and VA1l/m. Scale bar, 20 μm. () The LN innervation probability of a glomerulus correlated positivel! y with the mean odor-evoked firing rate of the ORNs presynaptic to that glomerulus (r = 0.63, P < 0.005, n = 23 glomeruli). ORN data from ref. 25. Firing rates averaged across 110 odors. Filled symbols represent trichoid glomeruli. () Principal component analysis of LN glomerular innervation patterns. In the second dimension, the dumbbell subclass of LNs (bracket) distinctly separates from all other cell types. LNs labeled by different Gal4 lines are marked with different colors. See Supplementary Figure 8 for color code of Gal4 lines and histograms of cell distributions in PC1 and PC2. * Figure 4: Functional stereotypy and diversity among genetic classes. () Rasters showing the similar spiking responses of two line 7 LNs. These LNs are typical of line 7 in having high spontaneous firing rates, weak odor-evoked excitation and strong odor-evoked inhibition. Gray box, the nominal odor stimulus period; there is a delay of about 100 ms before odor reaches the fly. () Dissimilar responses of two line 9 LNs. The first of these innervated almost all glomeruli (52 of 54) and was mainly inhibited by all odors, whereas the second innervated a smaller subset of glomeruli and was excited by all odors. Overall, line 9 LNs were diverse. () Functional properties of lines 5–9 (mean ± s.e.m.). All these properties were significantly dependent on the Gal4 line of the recorded LN (one-way ANOVA, P < 0.0001). For each property, post hoc Tukey's tests yielded significant differences (P < 0.05) between some but not all of the ten pair-wise comparisons between Gal4 lines. Odor-evoked firing rates are expressed as a change from the spontaneous fir! ing rate. * Figure 5: Functional differences between morphological classes. () Pan-glomerular LNs (n = 26) had significantly higher spontaneous activity (P < 0.01) and weaker mean and maximum odor responses (P < 0.05, t-tests) as compared to all other LNs that were successfully reconstructed (n = 67). () Odor responses of three pan-glomerular LNs. () Left: pheromone-avoiding LNs (n = 9) fired a significantly higher percentage of their spikes during the first 100 ms of the odor response as compared to all other LNs (n = 84, P < 0.01, t-test). (Because there is a delay of about 100 ms before odor actually reaches the fly, spikes were counted during the period 100–200 ms after nominal odor onset; this was divided by total spikes during the 1-s period shown in rasters.) Right: odor response time course was more transient for these LNs than for other LNs (mean peristimulus time histogram, ± s.e.m. across cells). () Odor responses of three pheromone-avoiding LNs. * Figure 6: Variability and stereotypy of line 6 LNs. () Hierarchical clustering of innervation patterns, as in Figure 3a but only for line 6 LNs (n = 131). Cells in – are indicated. Glomeruli innervated by trichoid ORNs (pheromonal glomeruli) are highlighted in orange. k-means clustering verified that line 6 binary innervation patterns form a single cluster. Some cells were from flies in which antennae, maxillary palps or both had been removed for 10 d before fixation (Fig. 8e,f), but these treatments did not affect the number or the variability of glomerular innervation (Supplementary Fig. 11). (–) Odor response of four line 6 LNs. Shaded regions of plots denote odor stimulus period (500 ms). () Comparison of experimental (blue dots) and theoretical frequency of innervation if glomeruli were randomly innervated (center black line). The envelope of ± 2% is the standard error assuming a binomial distribution of innervation frequencies. In the experimental frequency, many glomeruli were almost always innervated, significant! ly above the theoretical distribution; other glomeruli were innervated significantly less frequently than the theoretical distribution. The glomerular identities to the right of DA1 are DM5, VA1m, VA1l, DL3 and VA1d; all except DM5 are pheromonal glomeruli. () Box plot quantification of innervation density of DM1, DA1 and VA1d from ten randomly chosen LNs of each class that innervated all three glomeruli. Center line denotes the average, the box encloses to the limits of the top and bottom quartiles and the whiskers extend to the maximum and minimum values. Innervation density = total dendritic length in glomerulus / glomerulus volume. Innervation of DA1 and VA1d by line 6 LNs (LN6) was significantly lower (Tukey's test, *P < 0.05) than that of control LNs (panglomerular (pan) LNs randomly selected from lines 1, 3 and 5). Dotted lines separate measurements from different glomeruli. * Figure 7: Variability of patchy LNs. () Clustering of 161 patchy cell innervation patterns, as in Figure 3a. No two cells had identical innervation patterns. () Schematic of MARCM–FLP-out, which allows two sister cells to be labeled by different colors. UAS-FRT-CD2-FRT-mCD8GFP (ref. 40) serves as a reporter of Gal4. After FLP-mediated mitotic recombination causes the loss of Gal80 in the ganglion mother cell (GMC), CD2 (red) should be expressed in both daughter cells derived from this GMC. However, if an additional FLP-out event occurs in one of the two daughter cells, this cell will express mCD8-GFP (green) instead of CD2. (–) Examples of two sister patchy cells (–) and two sister control cells (–) labeled by MARCM–FLP-out, shown with N-cadherin (blue), GFP (green) and CD2 (red) staining. (,) Projection of the entire antennal lobe. (,,,) High magnification of 5-μm projections from anterior (,) and middle (,) antennal lobe sections, showing nonoverlapping processes from two sister cells. Dashed lines! , boundaries of glomeruli VA1l/m () and DL1 and DC2 (). Scale bars, 20 μm. () Sister cell overlap index as a function of edge length of the dilation kernel (see Online Methods). Dashed lines indicate the mean for patchy sister cells (right-shifted) and non-patchy sister cells (left-shifted). Colored lines indicate individual pairs of sister cells. A quantitative distinction between patchy and non-patchy classes was verified by k-means clustering into two clusters. * Figure 8: Development but not maintenance of LN arborization depends on ORNs. () Top: normal adult fly head with two antennae (2 AT, arrows) and two maxillary palps (MP, arrowheads). Bottom: occasionally, eyFLP-induced smo clones eliminate both MPs (open arrowheads). Scale bars, 100 μm. (,) Brains from normal (top) and 0-MP (bottom) flies carrying Line5-Gal4 () or GH146-Gal4 () labeled by nc82 and Gal4-driven mCD8GFP as indicated. The MP ORN target VA7l glomerulus (dotted circle) was not innervated by line 5 processes in 0-MP flies but was still innervated by PN processes. Arrows designate glomeruli that are innervated by GH146-negative PNs. Scale bars, 20 μm. () Quantification of glomerular innervation by Line5-Gal4 LN and GH146-Gal4 PN processes in the presence or absence of ORN innervation of MP target glomeruli VA7l (top) and VC2 (bottom). () Representative single sections of line 6 LN single-cell clones after adult removal of MPs or of both MPs and ATs. Control samples (uncut) show line 6 LN innervation of VA7l (top) and VC2 (bottom). After adu! lt removal of MPs or of both MPs and ATs, line 6 LNs still innervated VA7l and VC2. Blue, nc82; green, mCD8-GFP; red, synaptotagmin (Syt)-HA. () Quantification of glomerular volume, LN process length and the number of Syt-HA puncta in control (WT), MP cut (–M) or MP and AT cut (–A–M) brains. The VA7l volume was significant lower when ORN processes were removed, but neither process length nor the number of synaptotagmin-HA puncta in VA7l and VC2 was significantly reduced. Error bars, s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Ya-Hui Chou, * Maria L Spletter & * Emre Yaksi Affiliations * Howard Hughes Medical Institute, Department of Biology, Stanford University, Stanford, California, USA. * Ya-Hui Chou, * Maria L Spletter, * Jonathan C S Leong & * Liqun Luo * Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA. * Emre Yaksi & * Rachel I Wilson Contributions Y.-H.C. and M.L.S. performed the anatomical and developmental experiments. E.Y. performed the physiological experiments. J.C.S.L. helped with statistical analysis. L.L. and R.I.W. supervised the project and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Liqun Luo (lluo@stanford.edu) or * Rachel I Wilson (rachel_wilson@hms.harvard.edu) Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Table 2 (640K) Raw data for ipsilateral glomerular innervation patterns * Supplementary Table 3 (56K) Raw data for contralateral glomerular innervation patterns of bilateral projection LNs PDF files * Supplementary Text and Figures (2M) Supplementary Tables 1 and 4, Supplementary Figures 1–11 Additional data - Loss of Arc renders the visual cortex impervious to the effects of sensory experience or deprivation
McCurry CL Shepherd JD Tropea D Wang KH Bear MF Sur M - Nature neuroscience 13(4):450-457 (2010)
Nature Neuroscience | Article Loss of Arc renders the visual cortex impervious to the effects of sensory experience or deprivation * Cortina L McCurry1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Jason D Shepherd1, 2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniela Tropea1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kuan H Wang3 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark F Bear1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Mriganka Sur1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:450–457Year published:(2010)DOI:doi:10.1038/nn.2508Received15 December 2009Accepted27 January 2010Published online14 March 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A myriad of mechanisms have been suggested to account for the full richness of visual cortical plasticity. We found that visual cortex lacking Arc is impervious to the effects of deprivation or experience. Using intrinsic signal imaging and chronic visually evoked potential recordings, we found that Arc−/− mice did not exhibit depression of deprived-eye responses or a shift in ocular dominance after brief monocular deprivation. Extended deprivation also failed to elicit a shift in ocular dominance or open-eye potentiation. Moreover, Arc−/− mice lacked stimulus-selective response potentiation. Although Arc−/− mice exhibited normal visual acuity, baseline ocular dominance was abnormal and resembled that observed after dark-rearing. These data suggest that Arc is required for the experience-dependent processes that normally establish and modify synaptic connections in visual cortex. View full text Figures at a glance * Figure 1: Loss of Arc does not affect V1 responsiveness and organization. () Intrinsic signal imaging of V1 (left) in wild-type and Arc−/− mice. Top, ocular dominance map of V1 in a wild-type mouse (WT, left) and an Arc−/− mouse (right). BZ, binocular zone; MZ, monocular zone. Scale illustrates binocularity index of pixels. Scale bar represents 500 μm. V1 in Arc−/− mice was similar to that in wild-type mice in total area (wild type, n = 6, area = 1.401 ± 0.07 mm2; Arc−/−, n = 10, area = 1.270 ± 0.15 mm2; P > 0.5, t test). Bottom, retinotopic organization of V1 in a wild-type mouse (left) and an Arc−/− mouse (right). Each image shows the mapping of elevation according to the scale bar on the right. () Scatter analysis of 50 × 50 pixel area in white box in A for wild-type and Arc−/− mice. The receptive field center (phase) differences between sets of five adjacent pixels are shown in the histograms on the right. The precision of local mapping was comparable between wild-type and Arc−/− mice. * Figure 2: Intrinsic signal imaging after monocular deprivation illustrates a requirement for Arc in deprived-eye depression after short-term monocular deprivation. () Top, monocular deprivation was initiated near the peak of the critical period for 3–4 d. Control mice were age-matched to deprived mice. Bottom, ODIs for individual mice are shown as circles. Horizontal bars represent group averages (wild type: control, n = 9, ODI = 0.28 ± 0.03; deprived, n = 14, ODI = −0.05 ± 0.03, P < 0.0001, t test; Arc−/−: control, n = 10, ODI = 0.19 ± 0.02; deprived, n = 11, ODI = 0.13 ± 0.02, P > 0.1, t test). () Response magnitude in wild-type mice driven by the contralateral eye and ipsilateral eye, plotted as average ΔR/R × 10−3. There was a depression in the contralateral eye response amplitude (control = 2.9 ± 0.27, deprived = 1.62 ± 0.23, *P < 0.001, t test). No change in the ipsilateral eye response was detected (control = 1.56 ± 0.21, deprived = 1.68 ± 0.19, P > 0.8, t test). () No change in contralateral response occurred in Arc−/− mice after deprivation (control = 2.25 ± 0.28, deprived = 2.5 ± 0.26, P > 0.2, t tes! t); similarly, no change in ipsilateral response was detected (control = 1.35 ± 0.23, deprived = 1.64 ± 0.19, P > 0.2, t test). ΔR/R is the change in reflectance over baseline reflectance. Error bars represent s.e.m. * Figure 3: Chronic VEP recordings show that Arc−/− mice do not exhibit ocular dominance plasticity after short-term monocular deprivation. () Wild-type mice exhibited a significant depression in contralateral (deprived eye) responses (n = 11; day 0 = 149 ± 8.8 μV, 3-d monocular deprivation = 75.4 ± 8.8 μV, *P << 0.0001, paired t test). No significant change was observed in ipsilateral responses (n = 11; day 0 = 70.4 ± 6.4 μV, 3-d monocular deprivation = 68.8 ± 8 μV, P > 0.8, paired t test). Averaged waveforms across all mice are shown at top. MD, monocular deprivation. () Arc−/− mice exhibited no changes in contralateral responses (n = 8; day 0 = 121 ± 14.7 μV, 3-d monocular deprivation = 111.3 ± 13.5 μV, P > 0.2, paired t test) or in ipsilateral responses (n = 8; day 0 = 92.5 ± 15 μV, 3-d monocular deprivation = 85.8 ± 10.7 μV, P > 0.7, paired t test). Averaged waveforms are shown at top. () Wild-type mice exhibited a significant shift in the contralateral to ipsilateral eye ratio (n = 11; day 0 = 2.2 ± 0.16, 3-d monocular deprivation = 1.2 ± 0.16, *P << 0.0001, paired t test), whereas Ar! c−/− mice exhibited no significant shift in the contralateral to ipsilateral eye ratio (n = 8; day 0 = 1.4 ± 0.12, 3-d monocular deprivation = 1.5 ± 0.33, P > 0.8, paired t test). Arc−/− mice exhibited a significantly smaller baseline contralateral to ipsilateral eye ratio than wild-type mice (wild type, n = 11, contralateral to ipsilateral eye ratio 2.22 ± 0.16; Arc−/−, n = 8, contralateral to ipsilateral eye ratio 1.37 ± 0.12, #P < 0.001, t test). Error bars represent s.e.m. * Figure 4: Arc is required for the decrease in surface AMPARs after short-term monocular deprivation. () Schematic of mouse brain showing the segments of V1 dissected for biochemical analysis. Because V1 is dominated by contralateral eye responses, cortex contralateral to the deprived eye was termed 'deprived' and cortex ipsilateral to the deprived eye was termed 'control'. () Example immunoblots of total and biotinylated surface proteins in the visual cortex of Arc−/− and wild-type mice. Full blots are presented in Supplementary Figure 6. GAPDH was used as an internal control to show that biotin specifically labeled surface proteins. In addition, a control image (bottom) shows the specificity of the biotinylation assay. No band can be detected in the surface lane of protein samples that were not exposed to biotin. () Summary of changes in surface and total protein levels occurring after deprivation (wild type, n = 5; Arc−/−, n = 7). Surface levels of GluR1 were significantly lower in the deprived hemisphere of wild-type mice than in controls (*P < 0.0001, t test) bu! t not in Arc−/− mice (P > 0.2, t test). Error bars represent s.e.m. * Figure 5: Arc−/− mice do not show a shift in ocular dominance after extended deprivation, as assessed by intrinsic signal imaging. () Top, monocular deprivation was initiated near the peak of the critical period for 7 d. Control mice were age-matched to deprived mice. ODIs for individual mice are shown as circles. Horizontal bars represent group averages (wild type: control, n = 9, ODI = 0.28 ± 0.03; deprived, n = 7, ODI = −0.063 ± 0.02, *P < 0.0001; Arc−/−: control, n = 10, ODI = 0.19 ± 0.02; deprived, n = 8, ODI = 0.13 ± 0.02, P = 0.17). () Response magnitude in wild-type mice driven by the contralateral eye and ipsilateral eye, plotted as average ΔR/R × 10−3. Some depression was observed in the contralateral eye response amplitude, although it was not significant (control = 2.9 ± 0.27, deprived = 2.1 ± 0.23, P > 0.05). Lid suture resulted in an increase in the ipsilateral eye response (control = 1.56 ± 0.21, deprived = 2.49 ± 0.17, *P < 0.05). () No change in contralateral response occurred in Arc−/− mice after deprivation (control = 2.25 ± 0.28, deprived = 2.2 ± 0.21, P > 0.6! ); similarly, no change was detected in ipsilateral response (control = 1.35 ± 0.23, deprived = 1.5 ± 0.21, P > 0.6). ΔR/R is the change in reflectance over baseline reflectance. Error bars represent s.e.m. Statistical analyses for – conducted using one-way ANOVA with Bonferroni correction. * Figure 6: Arc−/− mice exhibit no ocular dominance plasticity as assessed by chronic VEP recordings after long-term monocular deprivation. () Wild-type mice exhibited a significant depression in contralateral (deprived eye) responses (n = 7; day 0 = 152 ± 9.2 μV, 7-d monocular deprivation = 89.5 ± 11.5 μV, *P < 0.003, paired t test) and a significant potentiation in ipsilateral responses (n = 7; day 0 = 84.9 ± 9.8 μV, 7-d monocular deprivation = 114.2 ± 10.1 μV, #P < 0.05, paired t test). Averaged waveforms are shown at top. () Arc−/− mice exhibited no changes in contralateral (n = 6; day 0 = 112 ± 2.2 μV, 7-d monocular deprivation = 100 ± 6 μV, P > 0.1, paired t test) or in ipsilateral responses (n = 8; day 0 = 96 ± 8.6 μV, 3-d monocular deprivation = 84 ± 10 μV, P > 0.4, paired t test). Averaged waveforms are shown at top. () Wild-type mice exhibited a significant shift in the contralateral to ipsilateral eye ratio (n = 7; day 0 = 1.9 ± 0.14, 7-d monocular deprivation = 0.8 ± 0.06, *P < 0.0001, paired t test), whereas the contralateral to ipsilateral eye ratio did not significantly shift! in Arc−/− mice (n = 6; day 0 = 1.2 ± 0.1, 7-d monocular deprivation = 1.25 ± 0.11, P > 0.7, paired t test). Arc−/− mice had a significantly smaller baseline contralateral to ipsilateral eye ratio than wild-type mice (wild type n = 7, contralateral to ipsilateral eye ratio 1.87 ± 0.14; Arc−/−n = 6, contralateral to ipsilateral eye ratio 1.2 ± 0.1, #P < 0.003). Error bars represent s.e.m. * Figure 7: Dark-rearing wild-type mice from birth mimics the contralateral to ipsilateral ratio observed Arc−/− mice. () Arc−/− and dark-reared (DR) mice both had significant decreases in the contralateral to ipsilateral eye ratio in layer 4 VEPs as compared with wild-type mice (wild type: n = 16, 2.1 ± 0.1; Arc−/−: n = 16, 1.35 ± 0.08, *P << 0.0001, t test; dark-reared: n = 11, 1.29 ± 0.1, *P << 0.0001, t test). () The change in ocular dominance ratio in Arc−/− and dark-reared mice was mainly the result of a significant depression in contralateral (C) responses (wild type, 146 ± 6 μV; Arc−/−, 116 ± 7 μV, *P < 0.006, t test; dark reared, 74 ± 9 μV, **P << 0.0001, t test), as ipsilateral responses (I) were not significantly different (wild type, 72 ± 5 μV; Arc−/−, 90 ± 8 μV, P > 0.07, t test; dark reared, 59 ± 8 μV, P > 0.2, t test). Error bars represent s.e.m. * Figure 8: Arc−/− mice lack stimulus-selective response potentiation (SRP), whereas dark-reared mice exhibit enhanced SRP in V1. () Wild-type mice exhibited large and sustained potentiation of binocular VEPs over many days of exposure to the same stimulus orientation (n = 11, day 1 = 195 ± 10 μV, day 6 = 369 ± 14 μV, P << 0.0001, paired t test). Responses to a control orthogonal stimulus (90°, open black circle) shown at day 6 were not significantly potentiated (day 6 (90°) = 170 ± 9 μV, P > 0.09, t test). Dark-reared mice had small VEPs at baseline, which became markedly potentiated after exposure to the same stimulus orientation (n = 12, day 1 = 83 ± 9 μV, day 6 = 304 ± 43 μV, P < 0.001, paired t test). Responses to a control orthogonal stimulus (90°, open red triangle) were significantly increased compared with baseline VEPs (day 6 (90°) = 161 ± 29 μV, P < 0.03, t test) but were also significantly smaller than the SRP orientation at day 6 (P < 0.04). In contrast, we did not observe significant potentiation of responses to the same stimulus in Arc−/− mice (n = 16, day 1 = 170 ± ! 9 μV, day 6 = 180 ± 23 μV, P > 0.7, paired t test). Responses to the control orthogonal stimulus (90°, blue square) were also not significantly different from baseline (day 6 (90°) = 159 ± 12 μV, P > 0.1, t test), suggesting that there was no general decrease in responses over time. () VEPs normalized to baseline values indicated that there was a relative enhancement of potentiation as compared in dark-reared compared with light-reared mice, whereas Arc−/− mice had no relative potentiation of VEPs. () Average VEP waveforms at baseline (day 1) and after 5 d of repeated exposure to the same orientation (day 6). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Cortina L McCurry & * Jason D Shepherd Affiliations * Department of Brain and Cognitive Sciences, The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Cortina L McCurry, * Jason D Shepherd, * Daniela Tropea, * Mark F Bear & * Mriganka Sur * Department of Brain and Cognitive Sciences, The Picower Institute for Learning and Memory, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Jason D Shepherd & * Mark F Bear * National Institute of Mental Health, Bethesda, Maryland, USA. * Kuan H Wang Contributions C.L.M. and J.D.S. conducted experiments and data analysis and wrote the manuscript. D.T. assisted with optical imaging experiments. K.H.W. provided the Arc−/− mouse line. M.S. and M.F.B. helped design experiments and supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Mriganka Sur (msur@mit.edu) or * Mark F Bear (mbear@mit.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–9 Additional data - Control of sexual differentiation and behavior by the doublesex gene in Drosophila melanogaster
Rideout EJ Dornan AJ Neville MC Eadie S Goodwin SF - Nature neuroscience 13(4):458-466 (2010)
Nature Neuroscience | Article Control of sexual differentiation and behavior by the doublesex gene in Drosophila melanogaster * Elizabeth J Rideout1, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony J Dornan1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Megan C Neville1, 2, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Suzanne Eadie1 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen F Goodwin1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:458–466Year published:(2010)DOI:doi:10.1038/nn.2515Received11 November 2009Accepted09 February 2010Published online21 March 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Doublesex proteins, which are part of the structurally and functionally conserved Dmrt gene family, are important for sex determination throughout the animal kingdom. We inserted Gal4 into the doublesex (dsx) locus of Drosophila melanogaster, allowing us to visualize and manipulate cells expressing dsx in various tissues. In the nervous system, we detected differences between the sexes in dsx-positive neuronal numbers, axonal projections and synaptic density. We found that dsx was required for the development of male-specific neurons that coexpressed fruitless (fru), a regulator of male sexual behavior. We propose that dsx and fru act together to form the neuronal framework necessary for male sexual behavior. We found that disrupting dsx neuronal function had profound effects on male sexual behavior. Furthermore, our results suggest that dsx-positive neurons are involved in pre- to post-copulatory female reproductive behaviors. View full text Figures at a glance * Figure 1: Expression of GAL4 from the dsxGal4 locus recapitulates endogenous dsx expression. () Schematic of dsx (dsx+) and Gal4 knock-in allele (dsxGal4). Arrows indicate transcriptional start sites and black boxes indicate exons. () Male and female dsxGal4 locus predicted transcripts. (,) dsxGal4, UAS-nlacZ 2-d-old male pupa brain () and VNC () stained with antibodies to Dsx (green) and β-galactosidase (magenta) (ventral views; anterior is up). The scale bar represents 50 μm. (–) dsxGal4 transformation of secondary sexual characteristics, dorsal abdominal cuticular pigmentation, external genitalia and T1 leg basitarsal detail. Shown are XY () and XX () wild types, XX dsx-null intersexual (), XX tra-null pseudo-male (), XY UAS-dsxF;dsxGal4 pseudo-female (), XX dsxGal4/UAS-dsxM pseudo-male (), XX UAS-traIR;dsxGal4 () and XX dsxGal4/UAS-tra2IR (). * Figure 2: Sexually dimorphic expression of dsxGal4-neurons and associated projections in 5-d adult CNS. () Male brain, dsx neuronal clusters (arrowheads), male-specific SN neurons (only one cell in plane of focus, arrow). The cell bodies of pC1, pC2 and pC3 were located in the dorsal inferomedial, inferolateral and superomedial protocerebral areas, respectively. (,) Male brain (SN cells position is shown in the box). (–) Female brain; dsx neuronal clusters (arrowheads, ). () Male VNC, Abg cluster (arrowhead), male-specific TN1 and TN2 neurons (arrow). () Female VNC, Abg cluster (arrowhead). (,) Male VNC. () Female VNC, hindleg contralateral projection (arrow). () Female VNC. Neuronal cell bodies expressing UAS-pStingerII (nGFP) are shown in , , and . GFP staining is shown in green. Neuronal projections expressing UAS-mCD8GFP (membrane-bound GFP) are shown in , , and . mCD8 staining is shown in green. Expression of UAS-synaptotagmin (pre-synaptic marker tagged with HA) is shown in , , and . HA staining is shown in green. Neuropil was counterstained with antibody to nC82 (mage! nta). Ventral views; anterior top. (,) UAS-RedStinger;dsxGal4, UAS-mCD8GFP male () and female brain (). Dsx neuronal clusters are indicated by arrowheads. GFP, green; RFP, magenta. Horizontal view, ventral top. Scale bars represent 50 μm. * Figure 3: Sex-specific dsxGal4 expression in the foreleg and effect of basitarsal amputations on axonal projections. (–) Sexually dimorphic expression in T1 foreleg. () Male T1 leg (medial aspect), sex comb (boxed). We found 96 ± 14.4 (n = 7) dsxGal4-expressing cells in the metatarsus and 72 ± 10.0 (n = 7) in tarsi 2–5. (,) Medial () and lateral () aspect male T1 tarsi and metatarsus, sex comb (boxed). () Female T1 tarsi and metatarsus (lateral aspect), area consistent with sex comb (boxed). We found 77 ± 12.4 (n = 8) dsxGal4-expressing cells in the metatarsus and 58 ± 9.7 (n = 7) in tarsi 2–5. () Male T1 tarsi and metatarsus (medial aspect), elav-Gal80 repression in subset of dsxGal4 cells, metatarsal sex comb (boxed). (,) Wild-type male () and wild-type female () prothoracic axonal projections (close-up from Fig. 2k). (,) Atrophied male () and atrophied female () prothoracic axonal projections, post-amputation. The point of amputation is indicated by arrowheads in . Scale bars represent 50 μm. * Figure 4: Colocalization of FruM neuronal cells and dsxGal4-expressing cells (expressing UAS-nGFP) in 3-d-old adult male flies. (–) Brain (dorsal view, anterior up, dsx and FruM neuronal cells and clusters are designated). (–) VNC (ventral view, anterior up, dsx and FruM neuronal cells and clusters that colocalized are designated). FruM clusters as previously described20. Scale bars represent 50 μm. FruM, magenta; nGFP, green. * Figure 5: dsxGal4 expression in CNS of FruM-null males and females expressing FruM, DsxM or the anti-apoptotic transgene p35. (,) Schematic of dsxGal4-nGFP expression in wild-type adult male () and female () CNS. Individual neuronal clusters are designated. (,) nGFP () and membrane-bound GFP () in FruM-null adult male brain. (,) nGFP () and membrane-bound GFP () in FruM-null adult male VNC, reduction in prothoracic contralateral (boxed area) and cervical connection projections (arrowhead). (,) nGFP () and membrane-bound GFP () in adult female brain expressing FruM. (,) nGFP () and membrane-bound GFP () in adult female VNC expressing FruM. (,) nGFP () and membrane-bound GFP () in adult female brain expressing both DsxF and DsxM. Supernumerary SN cells are shown in the boxed area. () nGFP adult female VNC expressing both DsxF and DsxM. Supernumerary male-specific TN1 and TN2 cells are shown in the boxed area, indicated by the arrow. () Membrane-bound GFP in adult female brain expressing both DsxF and DsxM. Prothoracic contralateral projections (boxed area). () UAS-p35; dsxGal4 adult female brain, sup! ernumerary SN cells (boxed area). () UAS-p35;dsxGal4 adult female VNC, supernumerary TN1 (arrowheads) and TN2 (boxed area) neurons. (–) UAS-nLacZ,-mCD8GFP,-p35;dsxGal4 adult female Prg. Staining with membrane-bound GFP (), nuclear β-galactosidase () and a merged image are shown (). Supernumerary TN1 (arrowheads) and TN2 cells (boxed area) and ectopic contralateral projections (ventral views, anterior up). Scale bars represent 50 μm. * Figure 6: dsxGal4 neurons control male sexual behavior. () Male fertility (*P < 0.05, **P < 0.0001, Fisher exact test). () Courtship initiation (mean ± s.e.m., *P < 0.05, Tukey-Kramer HSD statistical test). () Courtship index (mean ± s.e.m., *P < 0.05, Tukey-Kramer HSD test). () Percentage males mating in 4 h (**P < 0.0001, Fisher exact test). Data represent mean ± s.e.m. n values are shown in parentheses. () Song recording, 5–7-d-old males. Pulse (P) and sine (S) song components are indicated above traces and courtship is shown below. Each trace represents a fraction of a 10-min recording. Scale bar represents 200 ms. Genotypes indicate males. Target females were wild type. For 15 wild-type males, there were 18.6 ± 2.2 sine bouts per min, 19.9 ± 1.4 pulse trains per min, 8.1 ± 0.3 mean pulses per train and a 31.7 ± 3.0–ms interpulse interval. For 10 UAS-TNTG; dsxGal4 males, there was no recordable data. For 10 UAS-TNTG; dsxGal4, elav-GAL80 males, there were 18.4 ± 1.5 sine bouts per min, 26.6 ± 3.8 pulse trains per ! min, 10.0 ± 0.4 mean pulses per train and a 34.0 ± 0.4–ms interpulse interval. * Figure 7: dsxGal4 neurons control female sexual behavior. () Female fertility (**P < 0.0001, Fisher exact test). () Egg-laying (mean ± s.e.m., *P <0.0001, Dunnett's test). () Percent copulation over time (10-min intervals for 1 h). () Line crossings during copulation (mean ± s.e.m., *P <0.05, Tukey-Kramer HSD test). () Copulation duration (mean ± s.e.m. *P < 0.05, Tukey-Kramer HSD test). () Percentage females re-mating with the same male in 4 h (*P < 0.05, Tukey-Kramer HSD test). Genotypes indicate females. Target males were wild type. n values are shown in parentheses. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Elizabeth J Rideout, * Anthony J Dornan & * Megan C Neville Affiliations * Faculty of Biomedical and Life Sciences, Integrative and Systems Biology, University of Glasgow, Glasgow, UK. * Elizabeth J Rideout, * Anthony J Dornan, * Megan C Neville, * Suzanne Eadie & * Stephen F Goodwin * Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK. * Megan C Neville & * Stephen F Goodwin * Present address: Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada. * Elizabeth J Rideout Contributions E.J.R., A.J.D., M.C.N. and S.F.G. designed experiments and wrote the paper. E.J.R., A.J.D. and M.C.N. all contributed equally to performing the experiments. S.E. provided technical assistance. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Stephen F Goodwin (stephen.goodwin@dpag.ox.ac.uk) Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (4M) Sexually dimorphic dsxGAL4 neural circuitry in 5 d adult brains. Movies generated from confocal optical slice stacks, moving ventrally to dorsally (anterior top), of dsxGAL4 driving UAS-mCD8::GFP (membrane-bound GFP) male (top) and female (bottom) whole sample brains. Presenting comparative representations of the sexually dimorphic axonal projection patterns associated with the dsxGAL4-expressing neuronal cell bodies with respect to surrounding anatomical landmarks. These stacks were used to generate the maximal Z projections represented in figures 2b and 2e. Neuronal projections expressing UAS-mCD8::GFP (membrane-bound GFP) stained with anti-mCD8 antibody (green). Neuropil counterstained with anti-nC82 Mab (magenta). * Supplementary Video 2 (4M) Sexually dimorphic dsxGAL4 neural circuitry in 5 day adult ventral nerve cords. Movies generated from confocal optical slice stacks, moving ventrally to dorsally (anterior top), of dsxGAL4 driving UAS-mCD8::GFP (membrane-bound GFP) male (left) and female (right) whole sample ventral nerve cords. Presenting comparative representations of the sexually dimorphic axonal projection patterns associated with the dsxGAL4-expressing neuronal cell bodies with respect to surrounding anatomical landmarks. These stacks were used to generate the maximal Z projections represented in figures 2i and 2k. Neuronal projections expressing UAS-mCD8::GFP (membrane-bound GFP) stained with anti-mCD8 antibody (green). Neuropil counterstained with anti-nC82 Mab (magenta). * Supplementary Video 3 (15M) Wild-type male and UAS-TNTG;dsxGAL4 female. A wild-type male is shown courting and then copulating with a UAS-TNTG;dsxGAL4 female. PDF files * Supplementary Text and Figures (11M) Supplementary Figures 1–6 and Supplementary Tables 1 and 2 Additional data - Central clock excites vasopressin neurons by waking osmosensory afferents during late sleep
Trudel E Bourque CW - Nature neuroscience 13(4):467-474 (2010)
Nature Neuroscience | Article Central clock excites vasopressin neurons by waking osmosensory afferents during late sleep * Eric Trudel1 Search for this author in: * NPG journals * PubMed * Google Scholar * Charles W Bourque1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:467–474Year published:(2010)DOI:doi:10.1038/nn.2503Received16 November 2009Accepted08 January 2010Published online28 February 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Osmoregulated vasopressin release is facilitated during the late sleep period (LSP) to prevent dehydration and enuresis. Previous work has shown that clock neurons in the suprachiasmatic nucleus (SCN) have low firing rates during the LSP, but it is not known how this reduced activity enhances vasopressin release. We found that synaptic excitation of rat supraoptic nucleus neurons by osmosensory afferents is facilitated during the LSP. Stimulation of the SCN at this time inhibited excitatory synaptic currents induced in supraoptic neurons by activation of osmosensory afferents. This effect was associated with an increased rate of synaptic failures and occurred without changes in frequency facilitation, quantal size or in the ratio of postsynaptic responses mediated by AMPA and NMDA receptors. We conclude that clock neurons mediate an activity-dependent presynaptic silencing of osmosensory afferent synapses onto vasopressin neurons and that osmoregulatory gain is enhanced by r! emoval of this effect during late sleep. View full text Figures at a glance * Figure 1: Increased osmoregulatory gain during LSP. () Diagram showing the positions and axonal projections of the OVLT, SCN and SON relative to the third ventricle (3V) and optic chiasma (OC) in the angled horizontal slice of rat hypothalamus. Whole-cell recordings were made from MNCs in the SON and hyperosmotic stimuli (excess mannitol) were delivered locally to the OVLT via a small pipette. () Traces show examples of action potential (AP) firing recorded from an MNC before (control) and during hyperosmotic stimulation of the OVLT. The top traces are from a cell recorded during the MSP and the lower traces are from a cell during the LSP. () Traces (arranged as in ) show examples of sEPSC activity recorded under voltage clamp (Vhold = −70 mV). () Bar graphs showing the mean (±s.e.m.) normalized osmotically induced increases in action potential and sEPSC frequency recorded from cells tested during the MSP or the LSP. *P < 0.05. * Figure 2: Firing rate of SCN neurons during the MSP and LSP in angled horizontal slices of rat hypothalamus. () Consecutive traces obtained during extracellular recordings of spontaneous action potential firing in representative SCN neurons recorded during the MSP (left) and LSP (right). () Cumulative probability distributions of inter-action potential intervals recorded from the cells shown in . The plots are constructed from 448 action potentials recorded from the MSP cell and 195 action potentials recorded from the LSP cell. () Bar histogram comparing the mean (±s.e.m.) firing rates recorded from SCN neurons sampled during the MSP (n = 16) and LSP (n = 20). **P < 0.005. * Figure 3: Electrical stimulation of SCN inhibits OVLT-MNC synapses. () Upper traces are examples of EPSCs evoked in an MNC by stimulation of the OVLT (arrows, 0.1 Hz). Each trace is an average of 15 consecutive sweeps recorded before (control), during (SCN 5 Hz, black bar in lower panel) and after (recovery) continuous repetitive electrical stimulation of the SCN at 5 Hz. The lower panel is a plot of the absolute peak amplitudes of all single EPSCs recorded during this experiment. () Plot showing the mean (±s.e.m.) changes in EPSC amplitude at OVLT-MNC synapses evoked by SCN stimulation at 5 Hz (gray bars) observed in eight cells. Values are expressed as a percent of baseline (dotted line, mean of the values recorded 100 s before SCN stimulation in each cell). () Graph showing the mean (±s.e.m.) percent inhibition of the evoked OVLT-MNC EPSC induced by repetitive stimulation of the SCN at various frequencies. The solid line is a best fit through the data points using a three parameter logistic equation, where a = 57.1%, b = −3.84 and xo ! = 3.05 Hz (r2 = 0.993, see Online Methods). * Figure 4: Excitation of SCN neurons inhibits OVLT-MNC synapses. () Diagram illustrating the experimental arrangement (see Online Methods and Supplementary Fig. 1). () Traces are averages of 15 consecutive EPSCs evoked in an MNC by electrical stimulation of the OVLT (arrows, 0.1 Hz, Vhold = −70 mV) recorded before (control), during (glutamate on SCN) and after (wash) exciting SCN neurons by local application of glutamate. () Plot of the average time course of changes in EPSC amplitude at OVLT-MNC synapses during the excitation of SCN neurons with glutamate (gray bar). Each point is the mean (±s.e.m.) value obtained from six cells (the number obtained from each cell was the average of the EPSC amplitude recorded during four consecutive sweeps), expressed as a percent of baseline. * Figure 5: Excitation of SCN neurons inhibits activation of MNCs by osmotic stimulation of OVLT. () Diagram illustrating the experimental arrangement. () Voltage traces recorded in current clamp showed the effects of hyperosmotic stimulation of the OVLT (right) on action potential firing in MNCs compared with controls (left) when tested either before (upper) or during (lower) local application of glutamate to the SCN. Note the smaller excitatory effect of the osmotic stimulus when glutamate was applied to the SCN. () Traces (arranged as in ) are examples of sEPSC activity recorded from another MNC under voltage clamp (Vhold = −70 mV) during the same protocol as in . () Bar graphs of the mean (±s.e.m.) normalized osmotically induced changes in action potential and sEPSC frequency (expressed as percent of baseline) recorded from cells tested before (control) and during the application of glutamate to the SCN. *P < 0.05, paired t test. * Figure 6: SCN activation inhibits OVLT-MNC synapses at a presynaptic locus. () Examples of OVLT-MNC EPSCs recorded at +40 mV (upper) and −60 mV (lower) before (control), during (SCN stim) and after (recovery) stimulation of the SCN at 5 Hz. Each trace is an average of 15 consecutive sweeps. () Plot showing the relationship between inhibition of the synaptic NMDAR and AMPAR responses during the dynamic (that is, rising and falling) phases of the SCN effect observed in eight cells. For each cell, the normalized inhibition of the NMDAR (current measured at +40 mV, 75 ms after the OVLT stimulus) and AMPAR (peak current measured at −60 mV) responses were measured during each of nine sweeps recorded following onset and offset of SCN stimulation (that is, 18 sweeps in total). The average (±s.e.m.) values of NMDAR versus AMPAR inhibition during corresponding sweeps are plotted. The dashed line is a linear regression fit (slope = 0.96 ± 0.10, r2 = 0.842, n = 18, P < 0.0001). () Examples of aEPSCs recorded during the 200 ms following EPSCs (data not sho! wn) evoked before (control) and during excitation of SCN neurons by local application of glutamate. () Cumulative probability distribution of aEPSC amplitudes (same cell as in ) in control and during SCN stimulation with glutamate. () Mean normalized (±s.e.m.) changes in aEPSC amplitude and frequency during SCN stimulation with glutamate. Data are expressed as percent of control (*P < 0.05; n.s. indicates no statistically significant difference, P = 0.2794). * Figure 7: Effects of SCN stimulation on synaptic failures and frequency facilitation. () Each panel superimposes ten consecutive sweeps recorded in an MNC with electrical stimulation of the OVLT (arrows) before (control), during (SCN stim) and after (recovery) repetitive SCN stimulation (5 Hz). Synaptic failures during SCN stimulation are denoted by an asterisk. () Percentage of cells (n = 63) with synaptic failures during each of 15 sweeps recorded after onset and offset (30 sweeps in total) of SCN stimulation at 5 Hz. Each point is a function of the average inhibition of the AMPAR responses during corresponding sweeps. The dashed line is a regression fit (slope = 0.166, r2 = 0.645). () EPSCs evoked by two stimuli delivered to the OVLT 50 ms apart (arrows). Traces are averages of 15 consecutive sweeps taken before (control, gray traces) or during SCN stimulation at 5 Hz (SCN, black traces). Left, superimposed traces at absolute amplitudes. Right, the same traces scaled to the amplitude of the first EPSC. () Mean (±s.e.m.) values of the PPR as a function of ! the percentage inhibition of AMDAR responses (n = 25 cells) computed from single sweeps recorded at corresponding time points during 2.5-min periods following the onset and offset of SCN stimulation. The dashed line is a linear regression through the data (slope = −0.004 ± 0.003, not significantly different from 0, P = 0.192). * Figure 8: SCN neurons modulate sEPSC frequency in MNCs. () Left, examples of sEPSC activity during the MSP (left) and LSP (right). Right, superimposed cumulative probability distributions of inter-event intervals from the MSP (318 events) and LSP (538 events) cells at left. () Mean (±s.e.m.) sEPSC frequencies recorded during the MSP (n = 32) and LSP (n = 44) (*P < 0.05). () Left, examples of sEPSC activity during the LSP (gray bar) before (control, left) and during application of glutamate to the SCN (glut/SCN, right). Right, superimposed cumulative probability distributions of inter-event intervals from the same cell in the absence (control) and presence of glutamate on the SCN (glut/SCN). () Mean (±s.e.m.) sEPSC frequencies recorded before (control) or during SCN glutamate from 12 MNCs tested during the LSP. () Left, examples of sEPSC activity recorded from an MNC during the MSP in the absence of bicuculline (gray bar) before (control) and during application of GABA to the SCN (GABA/SCN). Right, superimposed cumulative probab! ility distributions of inter-event intervals recorded from the same cell in the absence (control) and presence of GABA/SCN. () Mean (±s.e.m.) sEPSC frequencies recorded before and during GABA/SCN in seven MNCs tested during the LSP. Author information * Abstract * Author information * Supplementary information Affiliations * Centre for Research in Neuroscience, Research Institute of the McGill University Health Center, Montreal, Quebec, Canada. * Eric Trudel & * Charles W Bourque Contributions All of the electrophysiological experiments, technical development and data analysis were performed by E.T. C.W.B. designed the experiments and wrote the paper in close consultation with E.T. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Charles W Bourque (charles.bourque@mcgill.ca) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (476K) Supplementary Figures 1–9 Additional data - Methylphenidate facilitates learning-induced amygdala plasticity
Tye KM Tye LD Cone JJ Hekkelman EF Janak PH Bonci A - Nature neuroscience 13(4):475-481 (2010)
Nature Neuroscience | Article Methylphenidate facilitates learning-induced amygdala plasticity * Kay M Tye1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Lynne D Tye1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jackson J Cone1 Search for this author in: * NPG journals * PubMed * Google Scholar * Evelien F Hekkelman1 Search for this author in: * NPG journals * PubMed * Google Scholar * Patricia H Janak1, 2, 4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Antonello Bonci1, 2, 4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:475–481Year published:(2010)DOI:doi:10.1038/nn.2506Received24 September 2009Accepted04 January 2010Published online07 March 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although methylphenidate (Ritalin) has been used therapeutically for nearly 60 years, the mechanisms by which it acutely modifies behavioral performance are poorly understood. Here we combined intra–lateral amygdala in vivo pharmacology and ex vivo electrophysiology to show that acute administration of methylphenidate, as well as a selective dopamine transporter inhibitor, facilitated learning-induced strengthening of cortico-amygdala synapses through a postsynaptic increase in AMPA receptor–mediated currents, relative to those in saline-treated rats. Furthermore, local administration of methylphenidate in the lateral amygdala enhanced cue-reward learning through dopamine D1 receptor–dependent mechanisms and suppressed task-irrelevant behavior through D2 receptor–dependent mechanisms. These findings reveal critical and distinct roles for dopamine receptor subtypes in mediating methylphenidate-induced enhancements of neural transmission and learning performance. View full text Figures at a glance * Figure 1: MPH enhances task performance by altering different aspects of behavior through distinct D1 and D2 receptor–dependent mechanisms. () Intra-LA drug infusion alters reward earning (F6,47 = 5.161, P < 0.001). Relative to saline-treated rats, MPH and GBR groups earned significantly more rewards per minute, whereas SCH-treated rats earned a significantly fewer. MPH+SCH-treated, but not MPH+RAC-treated, rats earned significantly fewer than MPH-treated alone. () Task efficiency was altered by intra-LA drug infusion (F6,47 = 3.886, P = 0.004). Relative to saline, MPH, GBR and MPH+RAC-treated groups all showed significantly higher task efficiency. The MPH+SCH group, but not the MPH+RAC, showed lower task efficiency than the group treated with MPH alone. () Relative to saline, SCH-treated rats showed significantly lower task accuracy, and MPH+SCH-treated rats showed an attenuation of the enhancements induced by MPH alone, but MPH+RAC-treated rats did not differ from those treated with MPH alone (F6,47 = 3.806, P = 0.019). () Relative to the saline-treated group, MPH and GBR-treated groups showed significantly le! ss off-task behavior, whereas RAC-treated rats showed significantly more (F6,47 = 8.024, P < 0.001). In –, numbers in bars indicate rats per group. All values are mean ± s.e.m. One-way analysis of variance followed by all-pairwise multiple comparison procedure (Fisher least significant difference method; *P < 0.05, **P < 0.01). * Figure 2: Intra-LA NXT before training enhances memory retention but not acute task performance. Left column, performance on the initial training session; right column, performance during a 20-min memory retrieval test. No significant differences were observed during training, but NXT dose-dependently enhanced task accuracy (F3,26 = 4.209, P = 0.002) during a test session on the next day, on which no infusions were performed, suggesting that NET blockade may enhance memory retention in this task. Saline vehicle, N = 8; NXT 2 μg per side, N = 6; NXT 4 μg per side, N = 7; NXT 8 μg per side, N = 6. **P < 0.01. * Figure 3: Inhibition of the dopamine transporter gates cortico-amygdala synaptic potentiation. () Representative traces of AMPAR/NMDAR ratios evoked from thalamic and cortical afferents for each group. (,) AMPAR/NMDAR ratios evoked from thalamic (F8,62 = 3.471, P = 0.003) or cortical (F8,59 = 3.557, P = 0.002) afferents for each drug-treatment group after training were significantly altered. Inset shows rats treated with saline, MPH and GBR that were returned to their home cages in lieu of training. Numbers in bars indicate the number of cells per group. () SCH-treated rats show a significant decrease in thalamo-amygdala AMPAR/NMDAR. () MPH and GBR groups show significant increases in cortico-amygdala AMPAR/NMDAR. *P < 0.05, **P < 0.01. * Figure 4: Dopamine modulates learning-induced increases in mEPSC amplitude but not frequency. () Sample mEPSCs from each drug-treatment group. () Mean mEPSC amplitude for each group varied (F8,113 = 10.177, P < 0.001) with treatment. MPH, GBR and MPH+RAC groups had higher, whereas SCH-treated rats had lower, mEPSC amplitude than did saline controls. Inset: saline-, MPH- and GBR-treated home-cage controls. () Cumulative probability plot of mEPSC amplitude for representative cells from each group; 1 pA bins. () No significant change in mEPSC frequency of any groups relative to saline (F8,113 = 0.202, P = 0.990). () Cumulative probability plot of mEPSC frequency; 20-ms bins. *P < 0.05, **P < 0.01, ***P < 0.001. * Figure 5: D1R antagonism in the lateral amygdala attenuates learning-induced synaptic changes. (–) Rats given, before training, bilateral infusions of SCH compared with rats given unilateral infusions of SCH and contralateral infusions of saline. () Unilateral infusion of SCH and saline significantly decreased reward earning (F2,19 = 5.107, P = 0.018) relative to bilateral saline infusion (P = 0.009) and did not differ from bilateral SCH. (–) Rats treated with unilateral SCH and saline infusions did not show significant differences from rats treated with bilateral saline in task efficiency, task accuracy or off-task behavior. (,) Unilateral infusions of SCH and saline provide a within-subjects (N = 6 rats; n = 11 cells from saline-treated side; n = 9 cells from SCH-treated side) comparison of the effects of D1 receptor antagonism on learning-induced plasticity. (–) For both between-subjects and within-subjects comparisons, treatment with SCH significantly attenuated learning-induced increases in mEPSC amplitude (*P < 0.05, **P < 0.01, ***P < 0.001, Student's t-t! est) relative to saline (,), with no change in mEPSC frequency (,). * Figure 6: Dopamine signaling in the amygdala is necessary for mediating enhancements of learning performance induced by systemic administration of MPH. Behavioral measures of four groups of rats treated before training with (1) i.p. saline and intra-LA saline (N = 8 rats), (2) i.p. MPH and intra-LA saline (N = 8 rats), (3) i.p. MPH and intra-LA SCH (N = 7 rats) and (4) i.p. MPH and intra-LA RAC (N = 9 rats). (–) Intra-LA infusion of SCH significantly attenuated, whereas RAC spared, enhancements induced by systemic MPH in reward earning (F3,31 = 8.568, P < 0.001), task efficiency (F3,31 = 20.194, P < 0.001) and task accuracy (F3,31 = 6.004, P = 0.003). () Intra-LA infusion of RAC or SCH significantly attenuated reductions induced by systemic MPH in off-task behavior (F3,31 = 4.48, P = 0.011). In –, *P < 0.05, **P < 0.01,***P < 0.001. Author information * Abstract * Author information * Supplementary information Affiliations * Ernest Gallo Clinic & Research Center, University of California, San Francisco, Emeryville, California, USA. * Kay M Tye, * Lynne D Tye, * Jackson J Cone, * Evelien F Hekkelman, * Patricia H Janak & * Antonello Bonci * Program in Neuroscience, University of California, San Francisco, California, USA. * Kay M Tye, * Patricia H Janak & * Antonello Bonci * Program in Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Lynne D Tye * Department of Neurology, University of California, San Francisco, California, USA. * Patricia H Janak & * Antonello Bonci * Wheeler Center for the Neurobiology of Addiction, University of California, San Francisco, California, USA. * Patricia H Janak & * Antonello Bonci Contributions K.M.T. supervised experiments and performed all whole-cell recordings. K.M.T., A.B. and P.H.J. contributed to study design, results analysis, interpretation and manuscript writing. K.M.T., L.D.T., J.J.C. and E.F.H. surgically implanted guide cannulae, performed intra-LA drug infusions, conducted behavioral experiments, sectioned acute slice preparations and performed data entry and analyses. A.B. and P.H.J. provided mentorship and resources. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Antonello Bonci (antonello.bonci@ucsf.edu) or * Patricia H Janak (pjanak@gallo.ucsf.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–19 Additional data - Observational fear learning involves affective pain system and Cav1.2 Ca2+ channels in ACC
Jeon D Kim S Chetana M Jo D Ruley HE Lin SY Rabah D Kinet JP Shin HS - Nature neuroscience 13(4):482-488 (2010)
Nature Neuroscience | Article Observational fear learning involves affective pain system and Cav1.2 Ca2+ channels in ACC * Daejong Jeon1, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Sangwoo Kim1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Mattu Chetana1 Search for this author in: * NPG journals * PubMed * Google Scholar * Daewoong Jo3 Search for this author in: * NPG journals * PubMed * Google Scholar * H Earl Ruley4 Search for this author in: * NPG journals * PubMed * Google Scholar * Shih-Yao Lin5 Search for this author in: * NPG journals * PubMed * Google Scholar * Dania Rabah5 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Pierre Kinet5 Search for this author in: * NPG journals * PubMed * Google Scholar * Hee-Sup Shin1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:482–488Year published:(2010)DOI:doi:10.1038/nn.2504Received29 October 2009Accepted15 January 2010Published online28 February 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Fear can be acquired vicariously through social observation of others suffering from aversive stimuli. We found that mice (observers) developed freezing behavior by observing other mice (demonstrators) receive repetitive foot shocks. Observers had higher fear responses when demonstrators were socially related to themselves, such as siblings or mating partners. Inactivation of anterior cingulate cortex (ACC) and parafascicular or mediodorsal thalamic nuclei, which comprise the medial pain system representing pain affection, substantially impaired this observational fear learning, whereas inactivation of sensory thalamic nuclei had no effect. The ACC neuronal activities were increased and synchronized with those of the lateral amygdala at theta rhythm frequency during this learning. Furthermore, an ACC-limited deletion of Cav1.2 Ca2+ channels in mice impaired observational fear learning and reduced behavioral pain responses. These results demonstrate the functional involvement! of the affective pain system and Cav1.2 channels of the ACC in observational social fear. View full text Figures at a glance * Figure 1: Observational fear learning in the mouse. () Diagram of the apparatus used for observational fear conditioning and the scheme of the behavioral assay. (,) Observational fear learning in the mouse (nonsiblings) using a transparent (n = 21) or opaque (n = 8) partition. A significant difference in the level of freezing behavior was apparent depending on whether a transparent or an opaque partition was used for the conditioning experiment on both the training day () and 24 h after training (). *P < 0.01, Scheffe's post hoc test. (,) Observational fear learning with siblings. We examined freezing behavior on the day of training (F1, 45 = 9.41, P = 0.0036, two-way repeated ANOVA, ) and 24 h after training (F1, 45 = 11.48, P = 0.0015, two-way repeated ANOVA, ) in siblings (n = 26) and nonsiblings (n = 21) using a transparent partition. *P < 0.05, **P < 0.01, Scheffe's post hoc test. Error bars represent s.e.m. * Figure 2: Observational fear learning with female mating partners as demonstrators: effect of the duration of co-housing period (familiarity). (,) Observational fear conditioning after 1 week of co-housing (couple, n = 10; noncouple, n = 10). There was no difference in the observational fear response () and the 24-h contextual memory () between couple and noncouple experiments. (,) Observational fear conditioning after a 4–5-week co-housing period (couple, n = 6; noncouple, n = 9). There was no difference in the observational training () and the 24-h contextual memory () between couple and noncouple experiments. (,) Observational fear conditioning after 10–15 weeks of co-housing (couple, n = 12; noncouple, n = 7). There were significant differences in the observational training (F1,17 = 11.41, P = 0.0036, two-way repeated ANOVA, ) and the 24-h contextual memory (F1,17 = 11.77, P = 0.0032, two-way repeated ANOVA, ) between couple and noncouple experiments. (,) Observational fear conditioning after 20–36 weeks of co-housing (couple, n = 9; noncouple, n = 7). There were significant differences in the observation! al training (F1,14 = 8.62, P = 0.0109, two-way repeated ANOVA, ) and the 24-h contextual memory (F1,14 = 17.21, P = 0.001, two-way repeated ANOVA, ) between couple and noncouple experiments. () The strength of the fear response was increased with as the duration of the co-housing periods increased. ANOVA (F3,33 = 3.38, P = 0.029) of the total freezing time revealed a graded effect of the duration of co-housing period on the development of observational fear and there was a significant difference in total freezing time between 1-week co-housing period group and 10–15-week or 20–36-week groups. *P < 0.05, **P < 0.01, Scheffe's post hoc test. Error bars represent s.e.m. * Figure 3: The ACC and MITN are involved in observational fear learning. () Mice with lidocaine injections into the ACC (n = 12) before training failed to acquire fear compared with those receiving saline injections (n = 11) (F1, 21 = 19.20, P = 0.0003, two-way repeated ANOVA). () Contextual memory 24 h after the training in (F1, 21 = 16.43, P = 0.0006, two-way repeated ANOVA). () Mice with lidocaine injections into the ACC did not efficiently acquire fear by observation of siblings and mating partners (couples; F1, 30 = 18.25, P = 0.0002, two-way repeated ANOVA). (,) Administration of lidocaine into the parafascicular (PF) thalamic nuclei (n = 8) before training led to impaired observational fear learning during training (F1, 15 = 43.84, P < 0.0001, two-way repeated ANOVA, ) and 24 h after training (F1, 15 = 8.55, P = 0.0105, two-way repeated ANOVA, ) as compared with those receiving saline injections (n = 9). (,) Administration of lidocaine into the mediodorsal (MD) thalamic nuclei (n = 12) before training caused impaired observational fear lea! rning during training (F1, 28 = 24.11, P < 0.0001, two-way repeated ANOVA, ) and 24 h after training (F1, 28 = 5.19, P = 0.0306, two-way repeated ANOVA, ) as compared with those receiving saline injections (n = 18). (,) Administration of lidocaine into the VPL/VPM before training had no influence on the acquisition of observational fear () and 24-h contextual memory () (lidocaine, n = 10; saline, n = 12). *P < 0.05, **P < 0.01, Scheffe's post hoc test. Error bars represent s.e.m. * Figure 4: The ACC is involved in the acquisition of observational fear, but not in memory retrieval of observational fear and in classical fear conditioning. () Mice (n = 9) were trained with observational fear learning. () Local inactivation of the ACC 8 min before the 24-h contextual memory test did not affect the expression of fear in observational fear–conditioned mice as compared with fear expression by saline-injected mice (n = 14) (F1, 21 = 0.001, P = 0.99, two-way repeated ANOVA). (,) The contribution of the lateral amygdala (LA) to observational fear conditioning. Mice with lidocaine injections into the ACC (n = 8) before training failed to acquire fear compared with those receiving saline injections (n = 10) (F1, 16 = 11.46, P = 0.004, two-way repeated ANOVA, ); the same was true for contextual memory 24 h after training (F1, 16 = 21.34, P = 0.0003, two-way repeated ANOVA, ). (,) Local inactivation of the lateral amygdala (n = 8) before the 24-h contextual memory test () disrupted the expression of fear in observational fear-conditioned mice (), as compared with fear shown by saline-injected mice (n = 7) (F1, 13 = 7.6! 6, P = 0.016, two-way repeated ANOVA). *P < 0.05, **P < 0.01, Scheffe's post hoc test. Error bars represent s.e.m. * Figure 5: Synchronized theta activity between the ACC and lateral amygdala during learning of fear by observation. () Representative original traces of field potential recordings (8 s) in the ACC (upper) and lateral amygdala (bottom) during habituation. () Colored power spectra of the traces shown in . () Cross-correlation analysis revealed no correlated neuronal activity in the two brain areas. () Representative original traces of field potential recordings in the ACC (upper) and lateral amygdala (bottom) during training. () Colored power spectra of the traces shown in . Note the increased theta rhythms at 4–7 Hz. () Cross-correlation analysis revealed correlated neuronal activities in the two brain areas. (,) Averaged power spectra of neuronal activities (n = 7) in the ACC () and lateral amygdala () taken over an 8-s period just before delivery of the first foot shock (habituation) and after the last foot shock (conditioning). *P < 0.05, one-way ANOVA. () Averaged cross-correlograms of neuronal activities in the ACC and lateral amygdala taken over an 8-s period just before delivery o! f the first foot shock (habituation) and after the last foot shock (conditioning) (n = 7). ** indicates a significant difference in the amplitude of the second peaks between the two (P < 0.05, Student's t test). * Figure 6: Cav1.2ACC/Cre mice showed impaired observational fear learning and reduced pain responses. (,) Observational fear conditioning of 2ACC/Cre (n = 22), 2ACC/PBS (n = 22) and 2loxP/loxP (n = 13) mice. Similar freezing levels were seen during training (F1, 33 = 0.48, P = 0.49, two-way repeated ANOVA) and in the 24-h contextual memory test (F1,33 = 0.95, P = 0.34, two-way repeated ANOVA) between 2ACC/PBS (PBS injected) and 2loxP/loxP (non-injected) observers, and the results were pooled for analysis. The 2ACC/Cre observers exhibited impaired observational fear learning during training (F1, 55 = 17.47, P < 0.0001, two-way repeated ANOVA, ) and 24-h contextual memory (F1, 55 = 20.85, P < 0.0001, ). *P < 0.01, Scheffe's post hoc test. (,) Reduced inflammatory pain responses to formalin in 2ACC/Cre mice. Behavioral responses to a formalin injection, plotted in 5-min intervals, in 2ACC/PBS mice (n = 9) compared with 2ACC/Cre mice (n = 15) are shown in . Data from were grouped into five time intervals (). *P < 0.05, **P < 0.0001, one-way ANOVA. (,) Reduced behavioral response! s to acetic acid–induced visceral pain in 2ACC/Cre mice. Behavioral responses to acetic acid, plotted in 5-min intervals, in 2ACC/PBS mice (n = 7) and 2ACC/Cre mice (n = 6) are shown in . The total numbers of writhing events over 60 min are shown in . *P < 0.05, one-way ANOVA. Error bars represent s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Center for Neural Science, Korea Institute of Science and Technology, Seoul, Korea. * Daejong Jeon, * Sangwoo Kim, * Mattu Chetana & * Hee-Sup Shin * Department of Neuroscience, University of Science and Technology, Daejeon, Korea. * Sangwoo Kim & * Hee-Sup Shin * ProCell Therapeutics, ProCell R&D Center, Seoul, Korea. * Daewoong Jo * Department of Immunology and Microbiology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. * H Earl Ruley * Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA. * Shih-Yao Lin, * Dania Rabah & * Jean-Pierre Kinet * Present address: Department of Neurology, Seoul National University Hospital, Seoul, Korea. * Daejong Jeon Contributions D. Jeon and H.-S.S. designed the experiments. D. Jeon purified Cre protein. D. Jo and H.E.R. made and provided the vector containing His6-NLS-Cre-MTS. D. Jeon, S.K. and M.C. performed surgeries, microinjections and immunostainings and analyzed the data. D. Jeon and S.K. performed in vivo electrophysiology. S.-Y.L., D.R. and J.-P. K. generated the Cav1.2 conditional mice. D. Jeon and H.-S.S. wrote the manuscript. All of the authors commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Hee-Sup Shin (shin@kist.re.kr) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (620K) Supplementary Figures 1–11 and Supplementary Discussion Additional data - Synaptic correlates of fear extinction in the amygdala
Amano T Unal CT Paré D - Nature neuroscience 13(4):489-494 (2010)
Nature Neuroscience | Article Synaptic correlates of fear extinction in the amygdala * Taiju Amano1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Cagri T Unal1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Denis Paré1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:489–494Year published:(2010)DOI:doi:10.1038/nn.2499Received03 December 2009Accepted11 January 2010Published online07 March 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Anxiety disorders such as post-traumatic stress are characterized by an impaired ability to learn that cues previously associated with danger no longer represent a threat. However, the mechanisms underlying fear extinction remain unclear. We found that fear extinction in rats was associated with increased levels of synaptic inhibition in fear output neurons of the central amygdala (CEA). This increased inhibition resulted from a potentiation of fear input synapses to GABAergic intercalated amygdala neurons that project to the CEA. Enhancement of inputs to intercalated cells required prefrontal activity during extinction training and involved an increased transmitter release probability coupled to an altered expression profile of ionotropic glutamate receptors. Overall, our results suggest that intercalated cells constitute a promising target for pharmacological treatment of anxiety disorders. View full text Figures at a glance * Figure 1: Increased inhibition of CEm neurons in extinction and conditioned inhibition. () Experimental setup. EC, external capsule; LA, lateral amygdala; Rec, recording pipette. () Control and experimental groups. () Proportion of time spent freezing (average ± s.e.m.) during the various phases of the behavioral protocol (x axis). During habituation, no CSt was presented and the data shown are of freezing during randomly selected 30-s periods. During the conditioning phase, all groups were presented with four CSts, but they were paired with foot shocks only in the fear conditioning (black) and fear conditioning plus extinguished (red) groups. Nevertheless, the data shown represent the time spent freezing during the CSt for all groups. During the extinction training phase, the fear conditioning (black) and unpaired (blue line and filled circles) groups were not presented with the CSt. The time spent freezing during corresponding 30-s periods is shown. The fear conditioning plus extinguished (red) groups were presented with 20 CSts. () Representative examples o! f BLA-evoked responses in CEm cells recorded with 10 mM QX-314 in pipette solution. Three superimposed responses elicited by 300, 400 and 500 μA BLA stimuli are shown. () Intensity dependence of BLA-evoked IPSPs in CEm neurons (average ± s.e.m.). Inset, rising phase of BLA-evoked EPSPs (400 μA). We tested 16 fear-conditioned, 16 fear-extinguished, 12 naive and 10 unpaired CEm cells. * Figure 2: Group-related differences in CEm EPSP slopes and orthodromic spiking in response to BLA stimulation. (,) Slope of BLA-evoked EPSPs (initial 2 ms, from −70 mV, average ± s.e.m.; ) and percent BLA stimuli (400 μA) eliciting orthodromic spikes (average ± s.e.m., from rest; ) in CEm cells from the various groups (FC, fear conditioned; Nai, naive; Ext, fear conditioning plus extinguished; Unp, unpaired). Inset, normalized frequency distribution of BLA-evoked spike latencies in CEm neurons of the fear-conditioned group. (–) Representative examples of BLA-evoked responses (ten superimposed stimuli) in CEm cells from the various groups. Red arrows indicate the average time of EPSP-IPSP transition in CEm cells from the extinction group studied at −45 mV. * Figure 3: Increased recruitment of CEl neurons by BLA inputs in conditioned inhibition. () Representative examples of BLA-evoked responses in CEl cells in control artificial cerebrospinal fluid (aCSF). Four responses elicited by 200–500-μA BLA stimuli, increasing in 100-μA steps, are superimposed. () Intensity dependence of BLA-evoked EPSP peak amplitudes in CEl neurons (average ± s.e.m.). We tested 14 fear-conditioned, 14 fear-extinguished, 13 naive and 14 unpaired CEl cells. () Slope of BLA-evoked (400 μA stimuli) EPSPs (first 2 ms) in CEl neurons from the various groups (average ± s.e.m.). Inset, rising phase of BLA-evoked EPSPs. () Percent BLA stimuli (400 μA) eliciting orthodromic spikes from rest (average ± s.e.m.) in CEl cells from the various groups (x axis). * Figure 4: Enhanced efficacy of BLA synapses onto ITC cells in extinction. () Intensity dependence of BLA-evoked EPSPs in ITC neurons (average ± s.e.m.) in control aCSF. Inset, representative ITC cells from the extinction (red) and fear-conditioning (black) groups (300 μA). () Slope of BLA-evoked (400-μA stimuli) EPSPs (first 2 ms) in ITC neurons from the various groups (average ± s.e.m.). Rats from the U+CS group were treated similar to rats from the unpaired group except that they received 20 unpaired presentations of the CSt on day 3. () Percent BLA stimuli (400 μA) eliciting orthodromic spikes from rest (average ± s.e.m.) in ITC cells from the various groups (x axis). * Figure 5: Mechanisms underlying increased BLA responsiveness of ITC cells in extinction. () Left, paired pulse ratio (average ± s.e.m.) in ITC cells from the control (n = 34) and extinction (n = 9) groups. Right, representative examples of ITC responses to paired BLA stimuli (50-ms inter-stimulus interval; 500 μA). () Left, nonNMDA to NMDA ratio (average ± s.e.m.) in ITC cells from the control (n = 27) and extinction (n = 8) groups. Right, representative examples of ITC responses to BLA stimuli (500 μA) at −80 and 55 mV. * Figure 6: Infralimbic (IL) inactivation blocks extinction-related changes in the efficacy of BLA synapses onto ITC cells. () Experimental procedure. () Intensity-dependence of BLA-evoked responses in ITC cells from the vehicle (n = 15) and muscimol (n = 11) groups (average ± s.e.m.). Dashed line indicates data from unpaired group reproduced from Figure 4. Inset, extent of fluorophore-conjugated muscimol diffusion in the infralimbic cortex. () NonNMDA to NMDA ratio (average ± s.e.m.) in ITC cells from the vehicle (n = 9) and muscimol (n = 8) groups. () Representative examples of ITC responses to BLA stimuli (500 μA) at −80 and 55 mV. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Taiju Amano & * Cagri T Unal Affiliations * Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, New Jersey, USA. * Taiju Amano, * Cagri T Unal & * Denis Paré Contributions T.A. and C.T.U. performed all of the electrophysiological experiments and most of the analyses on ITC and CEA cells, respectively. T.A. performed the behavioral training and C.T.U. scored the behavior. D.P. designed the experiments, wrote the paper and contributed to data analysis. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Denis Paré (pare@andromeda.rutgers.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (516K) Supplementary Figures 1–5, Supplementary Tables 1–14 and Supplementary Results Additional data - Intention and attention: different functional roles for LIPd and LIPv
Liu Y Yttri EA Snyder LH - Nature neuroscience 13(4):495-500 (2010)
Nature Neuroscience | Article Intention and attention: different functional roles for LIPd and LIPv * Yuqing Liu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric A Yttri1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lawrence H Snyder1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:495–500Year published:(2010)DOI:doi:10.1038/nn.2496Received17 December 2009Accepted05 January 2010Published online28 February 2010Corrected online07 March 2010 Abstract * Abstract * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Establishing the circuitry underlying attentional and oculomotor control is a long-standing goal of systems neuroscience. The macaque lateral intraparietal area (LIP) has been implicated in both processes, but numerous studies have produced contradictory findings. Anatomically, LIP consists of a dorsal and ventral subdivision, but the functional importance of this division remains unclear. We injected muscimol, a GABAA agonist, and manganese, a magnetic resonance imaging lucent paramagnetic ion, into different portions of LIP, examined the effects of the resulting reversible inactivation on saccade planning and attention, and visualized each injection using anatomical magnetic resonance imaging. We found that dorsal LIP (LIPd) is primarily involved in oculomotor planning, whereas ventral LIP (LIPv) contributes to both attentional and oculomotor processes. Additional testing revealed that the two functions were dissociable, even in LIPv. Using our technique, we found a clear ! structure-function relationship that distinguishes LIPv from LIPd and found dissociable circuits for attention and eye movements in the posterior parietal cortex. View full text Figures at a glance * Figure 1: Behavioral tasks and example injections. () Schematic of the memory-guided saccade and the visual search task. Saccades were directed to remembered target locations after a 1–1.6-s memory period. The visual search task was based on a previously described task13, 15. On two-thirds of trials, monkeys performed a single saccade directly to a purple target (a square) lying in a radial array of seven purple distractors (ellipses, crosses and triangles). For the remaining trials, the target appeared alone, without distractors (data not shown), as a control for the oculomotor effect. (,) MRIs, reaction times (RT) and error rates from two example injections placed at different depths in the lateral bank of the IPS. Manganese mixed with muscimol resulted in a bright halo, seen here in coronal slices. Scale bars represent 5 mm. The mean of saccade (gray) and search (black) reaction times from each injection (solid circles) and their matched controls (hollow circles) are plotted against their corresponding error rates. Arro! ws indicate the effects of each inactivation in the reaction time and error rate domains. Upward and rightward directions indicate impaired behavior. * Figure 2: LIP lesion effects as a function of depth. () An illustration of how the full IPS depth and the lesion depth were measured for an LIPv injection from a coronal MRI slice. () Lesion-induced contralateral search error rate as a function of normalized injection depth. Filled circles represent injection sites with significant effects of either reaction time or errors (P < 0.025 before correction for the two independent comparisons). The vertical dashed line approximates the LIPd/v border. The mean LIPd effect was a change in error rate of −0.2 ± 1.4% and the mean LIPv effect was 15.7 ± 3.4%. () Contralateral adjusted saccade reaction time effect (see Supplementary Text) as a function of injection depth. Mean effects in LIPd and LIPv were 11.1 ± 1.8 ms and 10.1 ± 2.0 ms, respectively. Dotted lines are least-squares regression fits for the data, respectively. Four data points were shifted slightly to avoid overlap in . * Figure 3: Performance of memory-guided saccades and visual search before and after LIPd and LIPv inactivations. (,) Prevalence of significant saccadic (gray) and/or search (black) effects following LIPd and LIPv lesions. (–) Mean search error rate (black) and adjusted saccade reaction time (gray) by target direction for LIPd and LIPv controls (dashed line) and injections (solid line). Large outer circles indicate 30% (search error rate) or 30 ms (saccade reaction time) beyond the center value. Error bars are ± s.e. of the difference between the control and injection values. Solid data points indicate significant effects (P < 0.05). * Figure 4: Initial eye position modulates search, but not saccade, effect. () Initial eye position and all visual stimuli were displaced either 5° to the left or right for both memory-guided saccade task and visual search task. (,) The mean effect with different eye positions on saccade reaction time (gray) and search error rate (black) in LIPd and LIPv. Error bars are 1 s.e.m. *P < 0.05. * Figure 5: Lesion overlap maps. (,) The estimated areas of inactivation (see Supplementary Text) for all search-negative () and search-positive () injections from two fascicularis monkeys are shown on coronal brain slices. The color palette indicates the percentage of search-negative or search-positive lesions that involved each voxel. Data are thresholded at 20%. () The voxel-wise percentage effects from and were subtracted and projected, without thresholding, onto the inflated cortical surface. A, anterior; L, lateral; M, medial; P, posterior; STS, superior temporal sulcus. * Figure 6: MRIs and search effects of control injections. (–) Control injections were grouped into large medial bank (), focal medial bank (), large LIPd + 7a (), LIPd () and LIPv () injections. Bars and error bars show the population effects of each injection type on search error rates in the contralateral hemifield. Error bars are one s.e.m. Scale bars represent 5 mm. Only LIPv injections produced a significant increase in search error rate (*P < 0.0001). Data from the five large LIPd + 7a injections are included in the LIPd data. Change history * Abstract * Change history * Author information * Supplementary informationCorrected online 07 March 2010In the version of this article initially published online, the scale bar in Figure 2a was not the correct size. A scale bar and several arrows were missing from Figure 6. The error has been corrected for the print, PDF and HTML versions of this article. Author information * Abstract * Change history * Author information * Supplementary information Affiliations * Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri, USA. * Yuqing Liu, * Eric A Yttri & * Lawrence H Snyder Contributions Y.L. performed all aspects of this study, including the experimental design, data collection of two monkeys, analysis and writing of the manuscript. E.A.Y. assisted in data collection and analysis. L.H.S. oversaw the experiments and assisted in data analysis and manuscript preparation. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Yuqing Liu (yuqing@eye-hand.wustl.edu) Supplementary information * Abstract * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (944K) Supplementary Figures 1–9, Supplementary Tables 1 and 2 and Supplementary Text Additional data - Resistance to forgetting associated with hippocampus-mediated reactivation during new learning
Kuhl BA Shah AT Dubrow S Wagner AD - Nature neuroscience 13(4):501-506 (2010)
Nature Neuroscience | Article Resistance to forgetting associated with hippocampus-mediated reactivation during new learning * Brice A Kuhl1 Search for this author in: * NPG journals * PubMed * Google Scholar * Arpeet T Shah1 Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah DuBrow1 Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony D Wagner1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:501–506Year published:(2010)DOI:doi:10.1038/nn.2498Received13 October 2009Accepted08 January 2010Published online28 February 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg One of the reasons why we forget past experiences is because we acquire new memories in the interim. Although the hippocampus is thought to be important for acquiring and retaining memories, there is little evidence linking neural operations during new learning to the forgetting (or remembering) of earlier events. We found that, during the encoding of new memories, responses in the human hippocampus are predictive of the retention of memories for previously experienced, overlapping events. This brain-behavior relationship is evident in neural responses to individual events and in differences across individuals. We found that the hippocampus accomplishes this function by reactivating older memories as new memories are formed; in this case, reactivating neural responses that represented monetary rewards associated with older memories. These data reveal a fundamental mechanism by which the hippocampus tempers the forgetting of older memories as newer memories are acquired. View full text Figures at a glance * Figure 1: Experimental design and behavioral results. () During the encoding rounds, subjects studied pairs of items. Pairs consisted of either a novel cue paired with a novel associate (AB pair; for example, 'watch-sink') or a repeated cue paired with a novel associate (AC pair; for example, 'watch-pipe'). Two-thirds of all AB pairs were followed by a corresponding AC pair in the subsequent encoding round; the remaining one-third of AB pairs were not associated with corresponding AC pairs. Trials began with the presentation of a reward value ($2.00 or $0.10, high reward or low reward, respectively), indicating the potential value for later remembering the upcoming pair (see Online Methods and Supplementary Fig. 1). Thus, every AB pair was associated with either high or low reward and was later followed by a corresponding high reward AC pair, a low reward AC pair or no AC pair. Each encoding round was followed by an 'immediate test' round, during which subjects were shown each cue (A terms) from the immediately preceding encodi! ng round and attempted to recall the corresponding associate. In this manner, each AB pair was encoded and tested before the corresponding AC pair was encoded. () After eight alternating rounds of encoding and immediate test, a critical post-test was administered outside of the scanner, during which subjects were cued to recall each previously encoded AB pair, both those that had been followed by corresponding AC pairs (interference condition) and those that were not followed by corresponding AC pairs (no interference condition). () Performance on the post-test revealed that AB pairs followed by AC pairs were more likely to be forgotten, reflecting the deleterious effect of retroactive interference. Error bars indicate ± within-subject error. * Figure 2: Relationship between AC encoding and AB forgetting. () Activation in the posterior hippocampus (Montreal Neurological Institute coordinates: −30, −33, −9) extending into parahippocampal cortex (–30, −30, −18) during AC encoding was associated with spared forgetting of AB pairs (PFDR < 0.05 for both regions, small volume correction at voxel level; for complete results, see Supplementary Table 7). () Beta values showing the relationship between AC encoding activation and subsequent AB memory separately for AB pairs associated with high and low reward; drawn from the hippocampal ROI from contrast depicted in . Error bars indicate ± within-subject error. FDR, false discovery rate. Small volume correction was conducted using Anatomical Automatic Labeling atlas (http://www.cyceron.fr/web/aal_anatomical_automatic_labeling.html) to generate mask of entire medial temporal lobe, including hippocampus, parahippocampal cortex, perirhinal cortex and entorhinal cortex. * Figure 3: Hippocampal responses during encoding and susceptibility to retroactive interference. () Between-subject regression of AC encoding activation against proportionalized retroactive interference (RI) revealed a negative relationship between activation in hippocampus (–36, −30, −12), parahippocampal cortex (–30, −36, −15) and the magnitude of retroactive interference (PFDR ≤ 0.05 for both regions, small volume correction at voxel level; for complete results, see Supplementary Table 9). () Scatter plot showing activation in hippocampus as function of retroactive interference; higher activation during AC encoding was associated with reduced retroactive interference. () Conjunction of between-subject regression analysis (described in and ; shown in red) and within-subject analysis (described in Fig. 2a,b; shown in yellow) revealed overlap (orange) in the hippocampus and parahippocampal cortex (for display purposes only, each contrast is thresholded at P < 0.005, uncorrected). () Hippocampal activation during AC encoding (extracted from the region obser! ved in the between-subject regression analysis) as a function of initial learning (Pre-AC) and later memory (Post-AC) revealed a selective increase in hippocampal response during AC encoding when AB pairs were initially learned and subsequently retained. () Voxel-level contrast of AC encoding trials associated with initial AB learning and subsequent AB retention versus initial AB learning and subsequent AB forgetting revealed activation in hippocampus (27, −30, −6; P < 0.005, uncorrected). Error bars indicate ± within-subject error. * Figure 4: ROI analysis of reward-sensitive regions, as defined from independent reward-localizer task. () Contrast of high-reward anticipation versus low-reward anticipation from the reward-localizer task revealed activation in dorsal and ventral striatum (P < 0.001, uncorrected). Inset shows anatomical mask applied to functional data to obtain ventral striatum ROI, which was then applied to the encoding data (Supplementary Results). (b) Activation in ventral striatum during AC encoding predicted subsequent memory for AB pairs that were associated with high reward (*P < 0.05). () Across subjects, a relationship was observed between AC encoding responses in ventral striatum and hippocampus. Specifically, the greater the bias in ventral striatum toward predicting subsequent memory for high versus low reward AB pairs ((High ABremember − High ABforget) − (Low ABremember − Low ABforget)), the greater the bias in hippocampus (correlation coefficient r = 0.473, P < 0.05). () Contrast of hits versus misses from the reward-localizer task (for details see Supplementary Results) r! evealed activation in vmPFC (P < 0.005, uncorrected). All vmPFC voxels that showed this effect were combined into a single region of interest that was then applied to the encoding data (see Supplementary Results). () Data are presented as in but for activation in vmPFC (*P < 0.05). () Data are presented as in but for the relationship between activation in vmPFC and hippocampus (r = 0.478, P < 0.05). Error bars indicate ± within-subject error. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Psychology, Stanford University, Stanford, California, USA. * Brice A Kuhl, * Arpeet T Shah, * Sarah DuBrow & * Anthony D Wagner * Neurosciences Program, Stanford University, Stanford, California, USA. * Anthony D Wagner Contributions B.A.K. and A.D.W. designed the experiments and prepared the manuscript. B.A.K., A.T.S. and S.D. contributed to data collection and analysis. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Brice A Kuhl (brice.kuhl@yale.edu) or * Anthony D Wagner (awagner@stanford.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–5, Supplementary Tables 1–14 and Supplementary Results Additional data - A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention
Asplund CL Todd JJ Snyder AP Marois R - Nature neuroscience 13(4):507-512 (2010)
Nature Neuroscience | Article A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention * Christopher L Asplund1 Search for this author in: * NPG journals * PubMed * Google Scholar * J Jay Todd1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Andy P Snyder1 Search for this author in: * NPG journals * PubMed * Google Scholar * René Marois1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume:13,Pages:507–512Year published:(2010)DOI:doi:10.1038/nn.2509Received16 September 2009Accepted29 January 2010Published online07 March 2010Corrected online14 March 2010 Abstract * Abstract * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Attention is the process that selects which sensory information is preferentially processed and ultimately reaches our awareness. Attention, however, is not a unitary process; it can be captured by unexpected or salient events (stimulus driven) or it can be deployed under voluntary control (goal directed), and these two forms of attention are implemented by largely distinct ventral and dorsal parieto-frontal networks. For coherent behavior and awareness to emerge, stimulus-driven and goal-directed behavior must ultimately interact. We found that the ventral, but not dorsal, network can account for stimulus-driven attentional limits to conscious perception, and that stimulus-driven and goal-directed attention converge in the lateral prefrontal component of that network. Although these results do not rule out dorsal network involvement in awareness when goal-directed task demands are present, they point to a general role for the lateral prefrontal cortex in the control of atte! ntion and awareness. View full text Figures at a glance * Figure 1: SiB experiment (Experiment 1). () Trial design. Participants searched for a target letter in a rapid serial visual presentation (RSVP) stream of distractor letters. In a small proportion of trials (surprise trials), a surprise face stimulus was shown before the target. () Group target detection performance. Black bars represent accuracy in surprise trials, and gray bars represent accuracy in trials immediately preceding the surprise trials (surprise-1). Dashed line corresponds to the average target hit rate for search trials (target only). Dotted line corresponds to the false alarm rate. * Figure 2: SiB experiment (Experiment 1) SPM. Brain regions showing rapid attenuation of surprise stimulus-related activation. The SPM highlights brain regions that responded to all six surprise trials (see Online Methods), specifically the IFJs (Talairach coordinates45 37, 5, 29 and −40, 8, 25) and TPJs (Talairach coordinates 46, −56, 27 and −49, −56, 23). The time courses illustrate the brain regions from the SPM that showed greater activity in the first pair of Surprise trials than in the two other pairs of surprise trials. The surprise stimulus appears at approximately time zero. Error bars represent s.e.m. * Figure 3: Stimulus-driven and goal-directed attention activity in Experiment 1. () Dorsal brain regions that are active during search trials. () Surprise stimulus-specific waveform in dorsal (FEF, IPS) and ventral (IFJ, TPJ) regions of interest (ROIs) defined in individual participants (see Online Methods). Each time course was constructed by subtracting the search trial time course from the time course for the first two surprise stimulus trials. The surprise stimulus appears at approximately time zero. () Search trial activation time course over the same period of time as in for the same ROIs. Arrows mark each trial's onset. Note that the activation pattern is cyclical, mirroring the trial structure (one trial every 8 s). The observed hemodynamic responses in IPS, FEF and IFJ match the predicted responses for the hypothesized search-related activity (Supplementary Fig. 3). * Figure 4: Spatial SiB experiment (Experiment 2). () Trial design. The procedure was identical to that in Experiment 1 save that in a small proportion of trials, a colorful surprise stimulus was shown before the target away from fixation (Fig. 1a). () Surprise stimulus-specific waveforms in dorsal and ventral attention network ROIs defined in individual participants. Time courses were constructed in the same fashion as those in Experiment 1 (see Fig. 3b). Note that all four regions show an immediate response to the surprise stimulus presentations. * Figure 5: Endogenous cueing task experiment (Experiment 3). () Trial design. A color cue predicted the location of an upcoming target, to which the participant then responded in a speeded manner. () Cue-related activity in dorsal and ventral attention network ROIs isolated from Experiment 2 (see Online Methods). The arrow marks cue onset. ITI, inter-stimulus interval. Change history * Abstract * Change history * Author information * Supplementary informationCorrected online 14 March 2010In the version of this article initially published online, the last sentence of the abstract read "suggest to" instead of "point to". The error has been corrected for all versions of this article. Author information * Abstract * Change history * Author information * Supplementary information Affiliations * Department of Psychology, Vanderbilt Vision Research Center, Vanderbilt University, Nashville, Tennessee, USA. * Christopher L Asplund, * J Jay Todd, * Andy P Snyder & * René Marois * Department of Psychology, University of Chicago, Chicago, Illinois, USA. * J Jay Todd Contributions C.L.A. designed and performed experiments, analyzed data, and wrote the manuscript. J.J.T. and A.P.S. designed and performed experiments. R.M. designed experiments and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Christopher L Asplund (chris.asplund@vanderbilt.edu) or * René Marois (rene.marois@vanderbilt.edu) Supplementary information * Abstract * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (336K) Supplementary Figures 1–3, Supplementary Tables 1 and 2, and Supplementary Data Additional data - Monitoring neural activity with bioluminescence during natural behavior
Naumann EA Kampff AR Prober DA Schier AF Engert F - Nature neuroscience 13(4):513-520 (2010)
Nature Neuroscience | Technical Report Monitoring neural activity with bioluminescence during natural behavior * Eva A Naumann1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Adam R Kampff1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * David A Prober2 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander F Schier1 Search for this author in: * NPG journals * PubMed * Google Scholar * Florian Engert1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume:13,Pages:513–520Year published:(2010)DOI:doi:10.1038/nn.2518Received09 November 2009Accepted22 February 2010Published online21 March 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Existing techniques for monitoring neural activity in awake, freely behaving vertebrates are invasive and difficult to target to genetically identified neurons. We used bioluminescence to non-invasively monitor the activity of genetically specified neurons in freely behaving zebrafish. Transgenic fish with the Ca2+-sensitive photoprotein green fluorescent protein (GFP)-Aequorin in most neurons generated large and fast bioluminescent signals that were related to neural activity, neuroluminescence, which could be recorded continuously for many days. To test the limits of this technique, we specifically targeted GFP-Aequorin to the hypocretin-positive neurons of the hypothalamus. We found that neuroluminescence generated by this group of ~20 neurons was associated with periods of increased locomotor activity and identified two classes of neural activity corresponding to distinct swim latencies. Our neuroluminescence assay can report, with high temporal resolution and sensitivit! y, the activity of small subsets of neurons during unrestrained behavior. View full text Figures at a glance * Figure 1: Monitoring the neural activity of freely behaving zebrafish. () Dorsal (left) and lateral (right) fluorescence/bright-field micrographs of a 7-dpf Nβt–gfp-apoAequorin transgenic zebrafish larva. Scale bar represents 0.20 mm. () Neuroluminescence setup. A large-area (25 mm diameter) photon-counting PMT was situated above a transparent behavior chamber (12.5-mm diameter). The highly sensitive light detector was protected by an infrared-blocking filter such that a ring of 880-nm light-emitting diodes could be used to illuminate the behavioral chamber. Fish were imaged with an infrared-sensitive CCD camera positioned below the chamber. The large spectral separation between GFP-Aequorin bioluminescence and the infrared illumination allowed the simultaneous recording of neuroluminescence signals and the behavior of freely swimming zebrafish larvae. () Exemplary neuroluminescence recording of a 7-dpf Nβt–gfp-Aequorin transgenic zebrafish larva previously exposed to CLZN. Photon emission and behavior (swim speed in millimeters moved per! frame period (mm per 16.67 ms)) are shown for a 30-s recording. A mechanical stimulus was delivered at 15 s (), inducing a fast startle response and a large increase in neuroluminescence. () An expanded view of the boxed region in showing a neuroluminescence event not associated with locomotion (arrowhead). () Raw image acquired by the infrared CCD camera during neuroluminescence recording (scale bar: 1.5 mm). () Superposition (inverted grayscale) of all frames acquired during the 30-s recording period shown in ; the entire fish trajectory is shown. () The fish trajectory shown in is overlaid with a colored line for which the neuroluminescence amplitude at each segment is coded as the line width. * indicates the time of the mechanical stimulus. * Figure 2: Neuroluminescence and behavior of Nβt–GFP-Aequorin zebrafish. () Neuroluminescence signals and behavior could be monitored continuously for several days; a 16-h excerpt of the recording from a 6-dpf Nβt–gfp-Aequorin transgenic zebrafish, following 24 h of exposure to CLZN, is shown. Despite the constant dark conditions of the assay, an increase in locomotor activity, measured as the number of active seconds in a 10-min sliding window (bold line), and a corresponding increase in neuroluminescent events occurred soon after the previously experienced light-on time (9 a.m.) of the zebrafish light-dark rearing cycle. This was expected from a circadian modulation of spontaneous swimming. () Expanding the bracketed region in reveals the range of neuroluminescence signal amplitudes that occurred during spontaneous behavior. () By aligning all of the signals detected during the 16-h recording to each signal's onset time and color coding each event by the number of photons arriving in a 50-ms window (0 to >2,000, see color bar), we found that! neuroluminescence events consisted of a fast rise and slower decay in light emission with a large range of peak amplitudes. () Histogram of signal amplitudes observed from Nβt–GFP-Aequorin zebrafish (n = 6 fish, 3,125 events), normalized to the maximum signal detected from each individual, showing the frequent occurrence of small amplitude events and a long tail of the distribution populated by increasingly large and rare events. () A mechanical stimulus was delivered to a group of freely swimming zebrafish (n = 6) by tapping the recording chamber (stimulus times indicated by the asterisk). The stimulus resulted in neuroluminescence signals coincident with the evoked startle responses, surrounded by intermittent and variable spontaneous signals. () The fish shown in were paralyzed with α-bungarotoxin and received the same mechanical stimulus (*). Paralysis allowed us to isolate the sensory component of the neuroluminescence event from the full escape response behavior ! elicited in freely swimming fish. () The aligned stimulus-driv! en events in each condition were compared and we found an attenuated, but clearly detectable, sensory signal in paralyzed zebrafish. () PTZ-induced epileptic behavior, characterized by uncoordinated rapid swimming, was associated with large, fast bursts of neuroluminescence, consistent with the strong neural activation expected during seizure episodes (t0 = 1 min after initial PTZ exposure). () Following extended exposure to PTZ (t0 > 17 min), long, slow neuroluminescence events were observed independently of swimming. () Paralyzed zebrafish exposed to PTZ also exhibit long, slow neuroluminescence events, suggesting that motor activity may modulate the amplitude and timescale of PTZ-induced epileptic episodes. * Figure 3: Targeted GFP-apoAequorin expression in Hypocretin neurons. () Expression of GFP-apoAequorin in the ~20 Hypocretin (HCRT) neurons of a transgenic 4-dpf zebrafish larva was imaged with a wide-field fluorescence microscope, demonstrating their position within the posterior diencephalon. Scale bar represents 100 μm. () GFP-apoAequorin–expressing HCRT neurons shown in a maximum intensity projection of image sections acquired with a two-photon microscope (imaged region indicated by red rectangle in ); note the long, dorsal-caudal projecting axons with an expansive arborization near the zebrafish otic vesicle. Scale bar represents 50 μm. * Figure 4: Activity in Hypocretin neurons during natural behavior. () A freely behaving 4-dpf zebrafish larva exhibited periods of increased spontaneous locomotor activity. The longest active period occurred soon after the light-on time (9 a.m.) of the normal-rearing light cycle. Neuroluminescence events primarily occurred during these periods of heightened activity. () Expanding the bracketed region in revealed that these neural signals fell into two distinct amplitude classes. Manually determined thresholds (200 photons per 50 ms in ) were used to classify individual events into large and small amplitude groups. () The amplitude-classified signals from the entire recording of the larva shown in were aligned and the thick lines indicate the average signal time course in each class. () Histogram of the amplitudes for all HCRT neuroluminescence events (n = 1,064, 8 fish), normalized to the maximum response in each fish, were compared with the response amplitude of Nβt–GFP-Aequorin fish (Nβt) shown in Figure 2d. Signals that were classifi! ed as large and small are colored accordingly and are clearly distinct. () The mean distance swum, aligned to the position of the fish at the time of a HCRT signal (0 ms), was plotted for the frames immediately before and after HCRT signals of each amplitude class (error bars represent s.e.m.). Notably, fish swam sooner and further following small HCRT events than following large HCRT events. () A double exponential fit of neuroluminescence signals was used to identify the peak of the event. Example fits (solid curves) are shown for events (open circles) from the two amplitude classes along with the corresponding swim velocities. Latency was measured as the time from the peak of the response to time at which the zebrafish achieved a threshold swim velocity (0.25 mm per 16 ms). () Histograms of event-to-behavior latencies for the large and small HCRT events as well as events analyzed for Nβt–GFP-Aequorin zebrafish (Nβt); the distributions were distinct. * Figure 5: Bioluminescent photons are generated by the GFP-Aequorin-targeted HCRT neurons. () Schematic diagram of photon-counting imaging apparatus: an intensified CCD camera, custom epi-fluorescence microscope and excitation light (UV LED) were assembled in a light-tight enclosure. () The rectangle overlay indicates the region imaged to localize Aequorin expression via GFP fluorescence in a HCRT-GFP-Aequorin larva immobilized in low melting–point agarose and paralyzed with α-bungarotoxin. The arrow indicates the HCRT somata. Scale bar represents 100 μm. () When epileptic-like neural activity was induced by the addition of PTZ (10 mM), transient increases in the total number of photons arriving throughout the entire image field were observed (brackets). () The positional origin of the detected photons during these transient events was plotted. The majority of photons arrived from the region containing the HCRT neurons; the spread was likely caused by scattering in the biological tissue, whereas the homogenous background signal resulted from dark counts at the! detector. Scale bar represents 100 μm, arrow shown at same position as . () The photon flux arriving from four regions of interest (see inset): the HCRT somata, the imaged portion of the zebrafish head excluding the HCRT somata, the rostral tail and the background. The number of photons arriving from nonHCRT region of the zebrafish head was only slightly above the background dark counts and may represent photons originating from the axonal processes of the HCRT neurons (see Fig. 3). Error bars represent s.e.m. However, after adjusting for the dark count signal, we still found that >90% of photons arrived from the region containing the HCRT somata. * Figure 6: Temporally gated detection for monitoring neuroluminescence during visual stimulation. () Schematic of timing protocol for stroboscopic visual stimulation and gating of a CPM during a light ON to light OFF transition. Close ups of the 100 ms surrounding the transition and 10 ms of a light ON gate cycle demonstrating the synchronous control of the bioluminescence detection and behavior monitoring. When visual stimulation was required, the visible LED was switched on for 0.8 ms while the CPM was off gated. IR, infrared. () Example of neuroluminescence and visually evoked behavior recorded during periodic changes in whole-field illumination. We found reduced locomotor activity and Nβt–GFP-Aequorin neuroluminescence signal during "light ON" periods in 6-dpf Nβt–GFP-Aequorin transgenic zebrafish larvae that were previously exposed to CLZN. () The mean neuroluminescence and behavioral response surrounding an step increase in whole-field illumination (63 light transitions, 7 experiments, 49 fish). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Eva A Naumann & * Adam R Kampff Affiliations * Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA. * Eva A Naumann, * Adam R Kampff, * Alexander F Schier & * Florian Engert * Division of Biology, California Institute of Technology, Pasadena, California, USA. * David A Prober Contributions E.A.N. and A.R.K. designed the assay and performed the experiments. E.A.N., A.R.K. and F.E. analyzed the data. D.A.P. and A.F.S. generated the HCRT–GFP-apoAequorin transgenic line and assisted with behavioral analysis. E.A.N., A.R.K., D.A.P., A.F.S. and F.E. prepared the manuscript. E.A.N. suffered the most. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Florian Engert (florian@mcb.harvard.edu) Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (1M) Spontaneous and evoked neuroluminescence in freely swimming zebrafish. * Supplementary Video 2 (2M) Neuroluminescence shortly after exposure to PTZ. * Supplementary Video 3 (3M) Neuroluminescence after 20 min exposure to PTZ. * Supplementary Video 4 (2M) Neuroluminescence after exposure to PTZ in paralysed zebrafish. * Supplementary Video 5 (2M) Fluorescence changes in HuC:GCaMP2 zebrafish exposed to PTZ. PDF files * Supplementary Text and Figures (9M) Supplementary Figures 1–13 Additional data
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