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332 results for “Brain network”
The language network reemerges during recovery from severe traumatic brain injury
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Structural brain network of gifted children
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Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids
<p><strong>ABSTACT</strong></p> <p>Placebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.</p> <p>We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups’ neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.</p>
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
Dynamic reconfiguration of macaque brain networks during natural vision
<p>Raw data acquired under awake imaging conditions in the macaque monkey during free-viewing on natural scenes. The movie presented is also shared which is based on 30 sec 0N and OFF periods. Time-series echo planar imaging (EPI) data for each subject (AL, DP, FL and VL). Data is named based on the subject.session.run. The format structure is on NIFTI. Anatomical files are also named according to the same nomenclature. EPI mask are also available for each in-session subject. </p>
Dataset of behavioral and neurophysiological data of a virtual sailing task published in: "Providing task instructions during motor training enhances performance and modulates attentional brain networks"
<p>Dataset belonging to the behavioral and neurophysiological data of the publication: "Providing task instructions during motor training enhances performance and modulates attentional brain networks". The two uploaded Zip files contain kinematic and electroencephalographic data of 36 participants for the Obstacle and HorizonTask.</p>
Node-layer duality in networked systems: multilayer brain networks dataset
<p>This dataset contains the processed data used in<a href="https://doi.org/10.1038/s41467-024-50176-5"> Presigny, Corsi and De Vico Fallani (2024)</a>. It is made available for replication purposes. The code to treat these data is available <a href="https://github.com/Presigny/multilayer_duality">here</a>.</p> <p>Multilayer brain networks are obtained from the experimental data published in <a>Guillon <em>et al. </em>(2017)</a>. 23 Alzheimer’s diseased (AD) patients and 27 healthy age-matched control (HC) subjects, participated in the study. For each subject, 6 minutes resting-state eyes-closed brain activity was recorded noninvasively using a whole-head MEG system with 102 magnetometers and 204 planar gradiometers (Elekta Neuromag TRIUX MEG system) at a sampling rate of 1000Hz. Signal artefacts were removed using different techniques including removed signal space separation, principal component analysis, and visual inspection. Finally, source-imaging was used to project the signals from the sensor to the source space consisting of N = 70 regions of interest (ROI) defined by the Lausanne cortical atlas parcellation (see file name_of_ROIs.txt for the order). Here, we used spectral bicoherence to estimate functional connectivity between ROIs and between frequencies of brain activity. Specifically, we considered M = 77 layers corresponding to frequencies in the 2 − 40 Hz range with a resolution of 0.5 Hz. Other parameters were non overlapping windows of 2s averaged according to the Welch method. The resulting networks are full-multilayer consisting of both intralayer and interlayer connections, including weighted links between replica nodes.</p>
Functional Brain Networks of Picture Naming in Broca's Aphasia and Healthy Controls
<p>The data were from the picture-naming task with MEG scanning. </p> <p>Brain networks of ".net" format: Each network has 776 regions that were derived by subdividing USCBrain Atlas. Phase-locking values (PLV) were calculated between the 776 regions in a gamma-band of 30-45Hz. PLVs were normalized (z-PLVs) by using the mean and standard deviation of the 200-ms pre-stimulus baseline. The edges were weighted by z-PLVs. </p> <p>Vector files of ".vec" format: Each file contains activations, viz., amplitude, of regions. There are two types of amplitude. One is the estimated electric density in a physical unit of picoampere. Another is the z-score of amplitude calculated through comparison with a baseline of –200 ms.</p> <p>We provided both the group-averaged files (named as b999 for the Broca group and c999 for the control group) and the individuals' files (b1 to b5 for the Broca's aphasia and c1 to c5 for the control persons).</p> <p>We also provided two ".clu" files. One is the partition file of eight functional modules in two hemispheres. Another is the partition file of two hemispheres. </p> <p>The .net, .vec., and .clu files can be imported to Pajek for further interpretations and visualizations. </p> <p> </p>
Consensus between pipelines in structural brain networks
<p>Weighted brain network matrices for all subjects reconstructed using each pipeline. Filename letters denote the combination of pipeline stages used to reconstruct the matrix, whereas the number represents the subject. Pipeline stages are described in filename_key.txt. The brain region names and parcellation labels corresponding to the network node row/column are in X_region_names.txt and X_parcellation_labels.txt, where X is the atlas abbreviation described in filename_key.txt.<br /> </p>
Data for: Brain control of bimanual movement enabled by recurrent neural networks
<p>Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. In this study, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders.</p> <p>This dataset represents all neural activity recorded during these experiments. This includes the neural activity corresponding to unimanual and bimanual hand movements during (1) instructed delay experiments and (2) real-time BCI control of two cursors. </p> <p>Code associated with the data can be found here: <a href="https://github.com/d-r-deo/bimanualBCI" target="_blank" rel="noopener">https://github.com/d-r-deo/bimanualBCI</a></p>
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
Data supporting the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence"
<p>This data supports the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence" by Dorfschmidt et al. Part of these <a href="https://doi.org/10.6084/m9.figshare.11551602">data</a> were initially released by Váša et al. (2020) as part of their <a href="https://doi.org/10.1073/pnas.1906144117">manuscript</a>. Please cite them when using these data. </p> <p> </p>
The Virtual Macaque Brain: A macaque connectome for large-scale network simulations in TheVirtualBrain
<p>A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing and diffusion-weighted imaging data. Created for modeling brain dynamics using TheVirtualBrain platform. Website: thevirtualbrain.org</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 9. Modular Hierarchical Organization of Perceptual Neuro-Symbolic Networks
<p>In analogy to how it is reported for the brain by A. Luria, connections of the lowest levels of the architecture of Figure 9 are predefined. Higher-level connections are set via a learning process, concretely via a supervised learning process that was described in detail in . More recent research findings indicate that learning could also already take place at lower levels of<br> perception and that unsupervised learning could be crucial for setting these connections. In, first attempts have been made to develop an unsupervised learning strategy for the model.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 14. Number of artificial creatures that could survive in the external environment
<p>Artificial creatures and the virtual environment are designed and implemented in a C++ platform in which simulations are performed. In the GA algorithm, at First step a population of 100 complex artificial creatures that each of them had 150 neurons were tested and evaluated by GA; each neuron connected to 15 post-synaptic neurons with different axonal conduction delays between every two neurons. In every generation fitness function has been calculated for all population. The initial energy level for each creature is considered as 50. Figure 14 the number of survived chromosome increases with<br> generation progressing.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 11. Crossover with two cut points
<p>Then next generation are produced by combination of the elites (15%), crossover (55%) and mutation (30%) of the initial population. Elites are the best chromosomes which are directly transferred to the next generation. Because of long chromosome length, for crossover, five points are randomly chosen in each parent as cut points. Figure 11 shows a typical crossover with two cut points and Figure 12 illustrates a flowchart for the proposed evolutionary model. Selections are based on Roulette Wheel selection, more detailed information can be found in.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 10: A typical neuron and postsynaptic connections with weights and delays
<p>First, an initial random population of creatures is generated where the neural networks of the creatures are coded as chromosomes, as shown in Figure 10a and Figure 10b. Each chromosome consists of four parts: A1, A2, A3 and A4. Each part consists of N segments for N neurons of a typical neural network structure. The first part, A1, denotes a, b, c and d parameters of neurons Izhikevich model (discussed in (1) and (2)). Each segment of A2 shows postsynaptic weights and connections for corresponding neuron and each segment of A3 indicates postsynaptic delays of theconnections. Segment A4 shows postsynaptic neurons that are connected to corresponding neuron, as shown in Figure 10b.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 5. The periphery of circular area around of artificial creature in each position
<p>It is assumed in the simulation that the artificial creature in each location stands in the center of a circular area with a radius of its vision range, i.e. 2 meters. If the creature turns around itself; it only can see objects in its visual range. When a food object appears in the periphery of this circular area, a creature should find this food. Moving around, another food appears in a random place on periphery of the circular area, and this procedure repeat for 5 iterations. As shown in Figure 5 the periphery of the circular area around the artificial creature in each position is divided to 48 slots, which can be considered as a rectangular. In the simulation program it is implemented as an array of 48 × 3.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 2. Example of different axonal conduction delay between presynaptic neuron A0 and postsynaptic neuron A1 and A2
<p>Different axonal conduction delays between every two neurons are applied as follows:<br> For instance, consider presynaptic neuron A0 and postsynaptic neurons A1 and A2 in Figure 2. If neuron A0 fires spike S0 in t = 9 and S1 in t = 11 and with a time step equal to 0.5 ms, S0 arrives to A1 in t = 13 and to A2 in t = 14 where S1 arrives to A1 in t = 15 and to A2 in t = 16. This is implemented using counters assigned to each spike which increases with each time step in the time window. If counters arrive to axonal delay values, the effect of spikes from presynaptic neuron applies to the post synaptic neurons.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 8. Flowchart for movements of creature
<p>Window time has a fixed length between and and is an appropriate time interval [5]. So in each time window the total number of spikes in each three neurons is compared with other three neurons and artificial creature moves toward direction that the respective neurons fired maximum number of spikes. These fixed time<br> windows consist of 600 time-steps. Each time step is 0.5 ms. Flowchart in Figure 8 shows details.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.