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153 results for “brain connectivity”
The brains of elite soccer players are subject to experience-dependent alterations in white matter connectivity
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Data from: Brain states govern the spatio-temporal dynamics of resting-state functional connectivity
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Evolution of the speech‐ready brain: The voice/jaw connection in the human motor cortex
<p>A prominent model of the origins of speech, known as the "frame/content" theory, posits that oscillatory lowering and raising of the jaw provided an evolutionary scaffold for the development of syllable structure in speech. Because such oscillations are non‐vocal in most non‐human primates, the evolution of speech required the addition of vocalization onto this scaffold in order to turn such jaw oscillations into vocalized syllables. In the present functional MRI study, we demonstrate overlapping somatotopic representations between the larynx and the jaw muscles in the human primary motor cortex. This proximity between the larynx and jaw in the brain might support the coupling between vocalization and jaw oscillations to generate syllable structure. This model suggests that humans inherited voluntary control of jaw oscillations from ancestral species, but added voluntary control of vocalization onto this via the evolution of a new brain area that came to be situated near the jaw region in the human motor cortex.</p>
Data from: Cannabis analgesia in chronic neuropathic pain is associated with altered brain connectivity
Objective: The purpose of this study was to characterize the functional brain changes involved in THC modulation of chronic neuropathic pain. Methods: Fifteen patients with chronic radicular neuropathic pain participated in a randomized, double-blind, placebo-controlled trial employing a counterbalanced, within-subjects design. Pain assessments and functional resting state brain scans were performed at baseline and after sublingual THC administration. We examined functional connectivity of the anterior cingulate cortex (ACC) and pain related network dynamics using graph theory measures. Results: THC significantly reduced patients' pain compared to placebo. THC-induced analgesia was correlated with a reduction in functional connectivity between the anterior cingulate cortex (ACC) and the sensorimotor cortex. Moreover, the degree of reduction was predictive of the response to THC. Graph theory analyses of local measures demonstrated reduction in network connectivity in areas involved in pain processing, and specifically in the dorsolateral prefrontal cortex (DLPFC), which were correlated with individual pain reduction. Conclusions: These results suggest that the ACC and DLPFC, two major cognitive-emotional modulation areas, and their connections to somatosensory areas, are functionally involved in the analgesic effect of THC in chronic pain. This effect may therefore be mediated through induction of functional disconnection between regulatory high-order affective regions and the sensorimotor cortex. Moreover, baseline functional connectivity between these brain areas may serve as a predictor for the extent of pain relief induced by THC.
FlyWire Whole-brain Connectome Connectivity Data
<p>This repository contains the connectivity data for the FlyWire Connectome release. Currently, the latest release is version 783 (see also codex.flywire.ai).</p> <p>The synapses represent a combination of four different data releases, which are combined by the FlyWire whole-brain connectome release. The synapses (as points in space) were detected and published by <a href="https://www.nature.com/articles/s41592-021-01183-7">Buhmann et al., 2021</a> who made use of a cleft segmentation produced by <a href="https://link.springer.com/chapter/10.1007/978-3-030-00934-2_36">Heinrich et al., 2018</a>. The neurotransmitter for these synapses was then predicted and released by <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)00307-6">Eckstein, Bates et al., 2024</a>. The segmentation and neuron IDs (= root IDs) were proofread by the FlyWire consortium and are released by <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a> as part of the <a href="https://www.nature.com/collections/hgcfafejia">FlyWire connectome paper package</a>. </p> <p>Because multiple methods were involved in the production of this resource, the description of the methods is distributed across these manuscripts. We provide a summary in Dorkenwald et al., 2024 (Methods->Synaptic connections).</p> <p>Some of the files made available use feather as a file format. See a code example for reading these files, including a chunk-wise streaming to handle the large file.</p> <h3>flywire_synapses_783.feather</h3> <p>a pandas dataframe with all ~130 million synapses, their locations, neurotransmitter predictions and pre and postsynaptic partners (=root ids). This table contains all synapses that passed the thresholds (see methods section in <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a>), but not all synapses were associated with proofread neurons (e.g., see Discussion->Limitations of our reconstruction in <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a>). Hence, not all root IDs in this table will have a match in the proofread root IDs array. This table is provided for completeness, and to allow calculations about total synaptic input and output of neurons independent of other limitations. </p> <p>Columns:</p> <ul> <li>id: synapse ID</li> <li>pre_pt_root_id: presynaptic neuron ID</li> <li>post_pt_root_id: postsynaptic neuron ID</li> <li>connection_score: score assigned by Buhmann et al.; higher is better. We did not use this score to threshold synapses in any analysis</li> <li>cleft_score: score derived from the cleft segmentation by Heinrich et al.; higher is better. We used a threshold of 50 for all analyses and the released dataset. Synapses with lower score are not made available but can be made available on demand.</li> <li>gaba: probability for neurotransmitter=GABA</li> <li>ach: probability for neurotransmitter=Acetylcholine</li> <li>glut: probability for neurotransmitter=Glutamate</li> <li>oct: probability for neurotransmitter=Octopamine</li> <li>ser: probability for neurotransmitter=Serotonin</li> <li>da: probability for neurotransmitter=Dopamine</li> <li>neuropil: the name of the neuropil associated with this synapse. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>post_pt_position_{x,y,z}: Coordinate within the postsynaptic neuron (synapses were identified with two points, one in each neuron). Coordinates are in nanometers.</li> <li>pre_pt_position_{x,y,z}: Coordinate within the presynaptic neuron (synapses were identified with two points, one in each neuron). Coordinates are in nanometers.</li> </ul> <p> </p> <h3>per_neuron_neuropil_count_post_783.feather</h3> <p>a pandas dataframe containing the number of postsynapses per neuropil and segment id, i.e. this is a summarized version of <em>flywire_synapses_783.feather</em></p> <p>Columns:</p> <ul> <li>post_pt_root_id: segment ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For a mapping to long names see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>Count: number of synapses for this segment ID and neuropil </li> </ul> <h3> </h3> <h3>per_neuron_neuropil_count_pre_783.feather</h3> <p>a pandas dataframe containing the number of presynapses per neuropil and segment id, i.e. this is a summarized version of <em>flywire_synapses_783.feather</em></p> <p>Columns:</p> <ul> <li>pre_pt_root_id: segment ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>Count: number of synapses for this segment ID and neuropil </li> </ul> <h3> </h3> <h3>proofread_root_ids_783.npy</h3> <p>an array of all proofread neuron ids (=root ids)</p> <p> </p> <h3>proofread_connections_783.feather</h3> <p>a pandas dataframe containing the proofread subset from <em>flywire_synapses_783.feather </em>and summarized per neuron-neuron pair and neuropil, i.e. this table contains one entry per neuron-neuron pair and neuropil if there is 1 or more synapses for a given combination</p> <p>Columns:</p> <ul> <li>pre_pt_root_id: presynaptic neuron ID</li> <li>post_pt_root_id: postsynaptic neuron ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>syn_count: number of synapses between these two neurons in this neuropil</li> <li>gaba_avg: average probability across the synapses for neurotransmitter=GABA</li> <li>ach_avg: average probability across the synapses for neurotransmitter=Acetylcholine</li> <li>glut_avg: average probability across the synapses for neurotransmitter=Glutamate</li> <li>oct_avg: average probability across the synapses for neurotransmitter=Octopamine</li> <li>ser_avg: average probability across the synapses for neurotransmitter=Serotonin</li> <li>da_avg: average probability across the synapses for neurotransmitter=Dopamine</li> </ul> <p> </p> <h3>Code for reading and streaming feather files</h3> <p>Read feather files with pandas:</p> <p><code>import pandas as pd</code></p> <p><code>df = pd.read_feather(path)</code></p> <p> </p> <p>Stream large feather files in chunks:</p> <p><code>import pyarrow.feather as feather</code></p> <p><code>table = feather.read_table(path)</code></p> <p><code># Total number of rows in the Feather file</code><br><code>num_rows = table.num_rows</code></p> <p><code># Define chunk size</code><br><code>chunk_size = 1000</code></p> <p><code># Read and process the data in chunks</code><br><code>for i in range(0, num_rows, chunk_size):</code><br><code> end_row = min(i + chunk_size, num_rows)</code><br><code> chunk = table.slice(i, end_row - i) # Slice the table from i to end_row</code><br><code> </code></p> <p><code> # Convert to pandas DataFrame if needed</code><br><code> df_chunk = chunk.to_pandas()</code><br><code> </code><br><code> # Now you can process each chunk DataFrame as needed</code><br><code> print(df_chunk.head())</code></p>
Brain Connections and Blood Pressure
ClinicalTrials.gov study NCT03736434. IPD Sharing: NO. Countries: 1. Publications: 0.
5HT2CR Balance in Brain Connectivity in Cocaine Dependence
ClinicalTrials.gov study NCT03921151. IPD Sharing: NO. Countries: 1. Publications: 0.
Brain Connectivity Between Visual Input and Movement
ClinicalTrials.gov study NCT00376545. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The Brain-Heart-Gut Connection
ClinicalTrials.gov study NCT06748274. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Cannabis analgesia in chronic neuropathic pain is associated with altered brain connectivity
Open the record for dataset details and reuse information.
Evolution of the speech‐ready brain: The voice/jaw connection in the human motor cortex
Open the record for dataset details and reuse information.
Sox2 is required for functional chromatin connectivity in brain-derived neural stem cells.
GEO Series GSE90561. Mus musculus. 65 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Dysfunctions in nonsense-mediated decay, protein homeostasis, OXPHOS function, and brain connectivity in ALS-FUS mice with cognitive deficits
GEO Series GSE157713. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
The Hao-Fountain syndrome protein USP7 regulates neuronal connectivity in the brain via a novel p53-independent ubiquitin signaling pathway
GEO Series GSE280944. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Neural connectivity molecules best identify the heterogeneous clock and dopaminergic cell types in the Drosophila adult brain
GEO Series GSE198948. Drosophila melanogaster. 25 samples. Type: Expression profiling by high throughput sequencing.
Altered Neocortical Gene Expression, Brain Overgrowth and Functional Over-Connectivity in Chd8 Haploinsufficient Mice
GEO Series GSE81103. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
alphaT-catenin in restricted brain cell types and its potential connection to autism
GEO Series GSE83084. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Promoter Decommissioning by the NuRD Chromatin Remodeling Complex Triggers Synaptic Connectivity in the Mammalian Brain
GEO Series GSE57758. Mus musculus. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Single nuclei RNAseq datasets supporting the impact of primary cilia deficiency on astrocytes’ intercellular connectivity and neuronal transcriptomes in the brain.
GEO Series GSE253642. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Data from: Integrated analysis and visualization of group differences in structural and functional brain connectivity: applications in typical ageing and schizophrenia
Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, "bi-modal comparison plots" show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using "worm plots". Group differences in connections are examined with an existing visualization, the "connectogram". These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the "Statistical Analysis of Minimum cost path based Structural Connectivity" method and the average fractional anisotropy along the fiber. The functional measures were Pearson's correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.
ScienceDex guides
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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.