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231 results for “functional connectivity”
The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity
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Functional Connectivity of Music-Induced Analgesia in Fibromyalgia
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Subcortical DMN functional connectivity
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Dataset for Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity
<p>Per-trial dataset accompanying the publication Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity.<br> Additionaly, preprocessing code is provided as Codebase.zip.</p>
Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS
<p>A portable fNIRS system Brite MKII (Artinis, NE) was placed on the PFC of the self-experimenting participant (Male, 43 years). Ten sources and eight detectors are combined into 22 long separation channels (30mm) and two short-separation channels (SSC) to cover the PFC (Figure 1A). The experiment lasted 392s where the participant was subject to pornographic video clips (V) and performed genital self-stimulation (M) until orgasm was reached (O).<br>Citation of the article related to this dataset:</p> <div> <div><strong>Guevara, E.</strong> (2024). <em>Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS</em> [Preprint]. OSF. <a href="https://doi.org/10.31219/osf.io/6y2ze">https://doi.org/10.31219/osf.io/6y2ze</a></div> </div>
Functional Near-Infrared Spectroscopy Reveals Delayed Hemodynamic Changes in the Primary Motor Cortex During Fine Motor Tasks and Decreased Interhemispheric Connectivity in Parkinson's Disease Patients
<p>This dataset contains functional near-infrared spectroscopy (fNIRS) data from 20 patients with Parkinson’s disease and 20 age- and sex-matched healthy subjects without movement disorders. There are 3 folders, each corresponding to a different task: a 10-second finger-tapping task, a 2-minute walking task, and a 6-minute resting-state. When using this dataset, please cite our work:</p> <div> <div>Guevara, E., Rivas-Ruvalcaba, F. J., Kolosovas-Machuca, E. S., Ramírez-Elías, M., Zapata, R. D. de L., Ramirez-GarciaLuna, J. L., & Rodríguez-Leyva, I. (2024). Parkinson’s disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: A functional near-infrared spectroscopy study. <em>Neurophotonics</em>, <em>11</em>(2), 025004. <a href="https://doi.org/10.1117/1.NPh.11.2.025004">https://doi.org/10.1117/1.NPh.11.2.025004</a></div> <div> <div> <div>Guevara, E., Solana-Lavalle, G., & Rosas-Romero, R. (2024). Integrating fNIRS and machine learning: Shedding light on Parkinson’s disease detection. <em>EXCLI Journal</em>, <em>23</em>, 763–771. <a href="https://doi.org/10.17179/excli2024-7151">https://doi.org/10.17179/excli2024-7151</a></div> <div> <div> <div> <div> <div>Guevara, E., Kolosovas-Machuca, E. S., & Rodríguez-Leyva, I. (2024). Exploring motor cortex functional connectivity in Parkinson’s disease using fNIRS. <em>Brain Organoid and Systems Neuroscience Journal</em>, <em>2</em>, 23–30. <a href="https://doi.org/10.1016/j.bosn.2024.04.001">https://doi.org/10.1016/j.bosn.2024.04.001</a></div> </div> </div> </div> </div> </div> </div> </div>
Data for: Brain structural connectivity predicts brain functional complexity
<p>Data used in analyses for "Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal"</p>
The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity
<p>This upload comprises supplementary material and data for the paper "The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity" Albrecht et al. (2015), Human Brain Mapping DOI: 10.1002/hbm.23052</p> <p>1) The cleaned and group ICA resting state data in EEGLAB format.</p> <p>2) Basic demographics for the participants. Drug order 1 = placebo first, then dexamphetamine second. Drug order 2 = dexamphetamine first, then placebo second. Gender 1 = Female, Gender 2 = Male.</p> <p>3) Bayesian hierarchical modelling functions for R and Stan (through rstan). See paper for more details.</p>
Functional Lake-to-Channel Connectivity Impacts Lake Ice in the Colville Delta, Alaska
<p>This data is public for a manuscript accepted in JGR Earth Surface. The article will be linked here once it is published. </p> <p>Corresponding code can be found on Github: <a href="https://github.com/whyana/colvilleConnectivity">https://github.com/whyana/colvilleConnectivity</a></p> <p>File and variable descriptions can be found in ReadMe_zenodo.docx OR on Github: <a href="https://github.com/whyana/colvilleConnectivity">https://github.com/whyana/colvilleConnectivity</a></p>
Data generated from: Functional connectivity of the world's protected areas
<p>Here, we provide the two primary global connectivity datasets generated in the study titled "Functional connectivity of the world's protected areas", including the protected area isolation (PAI) metric for all included protected areas (i.e., effective resistance), provided as a csv file, and the map of global mammal movement probability (i.e., electrical current density), provided as a tif. We also include the nationally aggregated PAI values in National_PAI.csv. National PAI represents the median PAI value for each country, after excluding values equal to -1.</p> <p>Generation of these datasets relied on the following three external data sources:</p> <p>- Observed mammal movement data (0.95 quantile displacement distances over 10-days), predictor variables and the linear mixed effects model presented in: M. A. Tucker <em>et al.</em>, <em>Science</em>. <strong>359</strong>, 466–469 (2018). </p> <p>- The 2009 Global Human Footprint map presented in: O. Venter <em>et al.</em>, <em>Nat. Communications.</em> <strong>7</strong>, 1–11 (2016). </p> <p>- The May 2020 and April 2018 versions of the World Database on Protected Areas, found at: UNEP-WCMC and IUCN, Protected Planet: the World Database on Protected Areas (WDPA), Cambridge, UK, (available at www.protectedplanet.com). </p> <p>Please read the Readme.txt for file details and cite the following paper if you use these data: Brennan, A., R. Naidoo, L. Greenstreet, Z. Mehrabi, N. Ramankutty and C. Kremen. Functional connectivity of the world's protected areas. Science (2022).</p> <p> </p>
Brain functional connectivity data in anesthetized participants and patients with neuropathological or psychiatric diagnoses
<p>Five fMRI datasets were collected from independent research sites including: propofol deep sedation (PDS; drug effect site concentration= ~2.4 μg/ml) in Dataset-1, propofol general anesthesia (PGA; drug effect site concentration= 4.0 μg/ml) in Dataset-2, ketamine anesthesia (KA) in Dataset-3, unresponsive wakefulness syndrome (UWS) in Dataset-4, and schizophrenia (SCHZ), bipolar disorder (BD), and attentional deficit hyperactivity disorder (ADHD) in Dataset-5. Following fMRI data preprocessing, the fMRI time courses were extracted from 400 cortical areas according to a well-established brain parcellation scheme (Schaefer's 400 ROIs). A connectivity matrix was then calculated using Pearson correlation resulting in a 400x400 connectivity matrix for each participant and each condition.</p>
Yoga Asana Increases Pre-Frontal Cortex Activity and Reduces Resting State Functional Connectivity
<p>This dataset characterizes changes in the prefrontal cortex (PFC) before, during and after Yoga Asana (physical postures) with the mobile neuroimaging technique of functional near-infrared spectroscopy (fNIRS). Measurements were conducted with twenty-seven healthy adults executing four basic Asanas for 23 minutes with each Asana maintained for 25 -30 seconds. All postures significantly increased PFC activity versus baseline and resting state functional connectivity showed a significant decrease post Yoga Asana.</p> <p>Files 8, 15 and 24 were removed due to poor signal quality.</p> <p>During the measurement process of Asana the following stim marks were used to distinguish between postures:</p> <p>Posture A (Tadasana): A</p> <p>Posture B (Uttanasana): B</p> <p>Posture C (Adho Mukah Svasana): C</p> <p>Posture D (Urdhva Muka Svasana): D</p> <p>Results of the repeated measures ANOVA are presented for each combination of Asana. Those showing a significant difference are highlighted in green in the second to last tab of the file (Final Table). Demographics of volunteers are outlined in the last tab of the excel file (Demographics Volunteers). </p>
EEG functional connectivity analysis for the study of the brain maturation in the first year of life
<p>This dataset is related to 146 typically developing infants who underwent baseline electrophysiological (EEG) data recording at 6 (T6) and 12 (T12) months of age. The recordings were made using a dense-array EGI system (Geodesic EEG System (GES) 300 or 400, Electric Geodesic, In., Eugene, Oregon, USA) equipped with 60/64-electrode or 128-electrode caps (HydroCel Geodesic Sensor net).<br>Whole-brain functional connectivity (FC) metrics were extracted, for multiple frequency bands with the aim to evaluate brain maturation in the first year of life in terms of EEG functional connectivity. In addition, Bayley test was administered at 24 months of age to explore possible relation between brain network connectivity and cognitive functions.</p> <p>The dataset includes subjects’ sociodemographic and individual factors (such as age, sex, socioeconomic status, gestational week and birth weight); functional connectivity metrics (such as the magnitude-squared coherence index, phase lag index (PLI), and parameters characterizing the minimum spanning tree built from the PLI index) computed in the delta (2-4 Hz), theta (4-6 Hz), low-alpha (6-9-Hz), high-alpha (9-13 Hz), beta (13-30 Hz) and gamma (30-45 Hz) frequency bands; and the Bayley test raw scores, assessed at 24 months of age. </p> <p>In particular, each row in the database corresponds to a subject and each column to a different variable.<br>Column A, Subject code;<br>Column B, Time point: 1 = data related to the EEG recording performed at six months of age; 2 = data related to the EEG recording performed at twelve months of age;<br>Column C, Sex: 0= males; 1=females;<br>Column D, Age (expressed in days) at T6;<br>Column E, Age (expressed in days) at T12;<br>Column F, Family socio-economic status;<br>Column G, Gestational age expressed in weeks;<br>Column H, Birth weight expressed in grams;<br>Column I and J, Bayley Cognitive Composite Score and Griffiths developmental quotient both assessed at 6 months of age;<br>Column K and L, Number of electrodes of the used electrode-caps for T6 and T12, respectively;<br>Columns from M to BM, FC metrics for all frequency bands;<br>Columns from BO to BQ, raw cognitive, receptive and expressive Bayley test scores assessed at 24 months of age;<br>Column BR, Composite language metric derived from the expressive and receptive Bayley scores.</p> <p> </p> <p>If you use this dataset please cite the following manucript: Falivene, A.; Cantiani, C.; Dondena, C.; Riboldi, E.M.; Riva, V.; Piazza, C. EEG Functional Connectivity Analysis for the Study of the Brain Maturation in the First Year of Life. Sensors 2024, 24, 4979. All details about subjects, data acquisition and signal processing pipeline are described in the manuscript.</p>
Figure 7. Adding an Acting Module, its configuration values and its input connections-Designing a Growing Functional Modules "Artificial Brain"
<p>The fourth step consists of adding an Acting Module, its configuration values and input<br> connection as shown in figure 7. A type “CI” is assigned because it functionality will consist of<br> triggering a steering command in accordance with the perception from the Sensing Module and in<br> order to satisfy the input request from the Global Goal. Consequently, the feedback is set to “1 18”<br> where “1” is the reference to the Sensation “free” and “18” to the perception in output of the<br> Sensing Module. The identifier “18” for this perception is computed as at the total number of<br> Sensation plus one (first sensing module). Identifiers and their references are automatically updated<br> when a Sensation is added or deleted.</p>
Figure 5. Adding connections from Sensations 2-17 to the Sensing Module 1-Designing a Growing Functional Modules "Artificial Brain"
<p>The next step consists of connecting the sixteen sensations in the input of the Sensing<br> Module. To do this, the user must right-click on each Sensation, then on “new connection” and<br> indicate the Sensing Module identifier. The resulting design is presented on figure 5. Finally, the<br> Acting Module's field is set to “1” (indicating the number of the Acting Module that later will assess<br> the correctness of the perception) and the unique extra-parameter set to “20” (related with the<br> module's behavior).</p>
The dataset of article "Early Detection of Cognitive Impairment in End-Stage Renal Disease Patients Undergoing Hemodialysis: Insights from Resting-State Functional Connectivity Analysis"
<p>This is a file as dataset of the article "Early Detection of Cognitive Impairment in End-Stage Renal Disease Patients Undergoing Hemodialysis: Insights from Resting-State Functional Connectivity Analysis".</p> <p>It includes fMRI brain imaging data of subjects included in the case group (ESRD group) and healthy control group (HC group).</p>
Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity
<p><strong>GENERAL INFORMATION</strong></p> <p>This data is described in the following publication: </p> <p><strong>Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity</strong>, Somayeh Shahsavarani<sup>1,2,5</sup>, David N. Thibodeaux<sup>1,5</sup>, Weihao Xu<sup>1</sup>, Sharon H. Kim<sup>1</sup>, Fatema Lodgher<sup>1</sup>, Chinwendu Nwokeabia<sup>1</sup>, Morgan Cambareri<sup>1</sup>, Alexis J. Yagielski<sup>1</sup>, Hanzhi T. Zhao<sup>1</sup>, Daniel A. Handwerker<sup>2</sup>, Javier Gonzalez-Castillo<sup>2</sup>, Peter A. Bandettini<sup>2,3</sup>, Elizabeth M. C. Hillman<sup>1,4,6,*</sup> Cell Reports (2023): <a href="https://doi.org/10.1016/j.celrep.2023.112527">https://doi.org/10.1016/j.celrep.2023.112527</a></p> <p><br> 1. Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA<br> 2. Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA<br> 3. Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA<br> 4. Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA<br> 5. These authors contributed equally<br> 6. Lead contact<br> *Correspondence: elizabeth.hillman@columbia.edu</p> <p>Preprocessing and analysis code that generated / can be used with this data is posted at: <br> GitHub: <a href="https://doi.org/10.5281/zenodo.7860561">https://doi.org/10.5281/zenodo.7860561</a></p> <p><strong>DATA OVERVIEW </strong></p> <p>This dataset comprises simultaneous neuronal and hemodynamic data collected using wide-field optical mapping (WFOM) techniques. The data were obtained from head-fixed mice that were allowed to behave spontaneously without any external stimulation. For more detail, please refer to the Readme file.</p>
Infrequent strong connections constrain connectomic predictions of neuronal function (2/3)
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Data from: Functional connectivity and home range inferred at a microgeographic landscape genetics scale in a desert-dwelling rodent
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Infrequent strong connections constrain connectomic predictions of neuronal function (3/3)
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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.