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153 results for “brain connectivity”
The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity
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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>
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>
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>
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>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</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>
Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
<p>A fundamental neuroscience topic is the link between the brain's molecular, cellular and cytoarchitectonic properties and structural connectivity (SC). Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the Matrix Inversion and Subset Selection (MISS) algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell type densities, demonstrating surprisingly strong prediction accuracy of cell types in general and particular cells like oligodendrocytes. We found evidence of a strong distance-dependency in the cell-connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia are salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.</p>
Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
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Neonatal brain dynamic functional connectivity: impact of preterm birth and association with early childhood neurodevelopment (data)
<p>Neonatal brain dynamic functional connectivity: impact of preterm birth and association with early childhood neurodevelopment</p>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
Brain functional connectivity in chronic tic disorders and Gilles de la Tourette syndrome
<p>The pathophysiology of chronic tic disorder (cTD) and Gilles de la Tourette syndrome (GTS) is characterized by the dysfunction of both motor and non - motor cortico - striatal - thalamo - cortical (CSTC) circuitries, which leads to tic release and comorbids. A role of fronto - parietal network (FPN) connectivity breakdown has been postulated for tic pathogenesis, given that the FPN entertain connections with limbic, paralimbic, and CSTC networks. Our study was aimed at characterizing the FPN functional connectivity in cTD and GTS in order to assess the role of its deterioration in tic severity and the degree of comorbids. We recorded scalp EEG during resting state in patients with cTD and GTS. The eLORETA current source densities were analyzed, and the lagged phase synchronization (LPS) was calculated to estimate nonlinear functional connectivity between cortical areas. We found that the FPN functional connectivity in delta band was more detrimental in more severe GTS patients. Also, the sensorimotor functional connectivity in beta2 band was stronger in more severe cTD and GTS patients. FPN functional connectivity deterioration correlated with comorbids presence and severity in patients with GTS. Our data suggest that a FPN disconnection may contribute to the motoric symptomatology and comorbid severity in GTS, whereas sensorimotor disconnection may contribute to tic severity in cTD and GTS. Although preliminary, our study points out a differently disturbed brain connectivity between patients with cTD and GTS. This may serve as diagnostic marker and potentially interesting base to develop pharmacological and noninvasive neuromodulation trials aimed at reducing tic symptomatology.</p>
Brain Connectivity and Response to Tai Chi in Geriatric Depression
ClinicalTrials.gov study NCT02460666. IPD Sharing: NO. Countries: 1. Publications: 6.
Effects of Photobiomodularion on Brain Connectivity and Cognitive Function in Cognitive Impairment
ClinicalTrials.gov study NCT07287527. IPD Sharing: NO. Countries: 1. Publications: 1.
Mission Connect Mild Traumatic Brain Injury (TBI) Integrated Clinical Protocol
ClinicalTrials.gov study NCT01013870. IPD Sharing: YES. Countries: 1. Publications: 3.
Effects of Sertraline on Brain Connectivity in Adolescents With OCD
ClinicalTrials.gov study NCT02797808. IPD Sharing: NO. Countries: 1. Publications: 2.
Community-Based Social Connection Intervention Program to Improve Cardiovascular and Brain Health
ClinicalTrials.gov study NCT07319663. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Brain states govern the spatio-temporal dynamics of resting-state functional connectivity
<p>Previously, using simultaneous resting-state functional magnetic resonance imaging (fMRI) and photometry-based neuronal calcium recordings in the anesthetized rat, we identified blood oxygenation level-dependent (BOLD) responses directly related to slow calcium waves, revealing a cortex-wide and spatially organized correlate of locally recorded neuronal activity (<a href="#_ENREF_68" title="Schwalm, 2017 #1"><span>Schwalm et al., 2017</span></a>). Here, using the same techniques, we investigate two distinct cortical activity states: persistent activity, in which compartmentalized network dynamics were observed; and slow wave activity, dominated by a cortex-wide BOLD component, suggesting a strong functional coupling of inter-cortical activity. During slow wave activity we find a correlation between the occurring slow wave events and the strength of functional connectivity between different cortical areas. These findings suggest that down-up transitions of neuronal excitability can drive cortex-wide functional connectivity. This study provides further evidence that changes in functional connectivity are dependent on the brain's current state, directly linked to the generation of slow waves.</p> <p> </p>
The brains of elite soccer players are subject to experience-dependent alterations in white matter connectivity
<p>Soccer is the only major sport with voluntary unprotected head-to-ball contact. It is crucial to determine if head impact through regular soccer sports training is manifested in brain structure and connectivity, and whether such alterations are due to sustained training per se. Using diffusion tensor imaging, we documented a comprehensive view of soccer players' brains in a sample of twenty-five right-handed male elite soccer players aged from 18 to 22 years and twenty-five non-athletic controls aged 19 to 24 years. Importantly, none had recalled a history of concussion. We performed a whole-brain tract-based spatial statistical analysis, and a tract-specific probabilistic tractography method to measure differences of white matter properties between groups. Whole-brain integrity analysis showed increased microstructural integrity within the corpus callosum tract in soccer players compared to controls. Further, tract-specific probabilistic tractography revealed that the anterior part of corpus callosum may be the brain structure most relevant to training experience, which may put into perspective prior evidence showing corpus callosum alteration in retired or concussed athletes practicing contact sports. Intriguingly, experience-related alterations showed left hemispheric lateralization of potential early signs of concussion-like effects. This is the first study to-date providing a definitive characterization of soccer-specific experience-related structural alterations. In sum, we concluded that the observed gains and losses may be due to a consequence of engagement in protracted soccer training that incurs prognostic hallmarks associated with minor injury-induced neural inflammation. Soccer is the only major sport with voluntary unprotected head-to-ball contact. It is crucial to determine if head impact through regular soccer sports training is manifested in brain structure and connectivity, and whether such alterations are due to sustained training per se. Using diffusion tensor imaging, we documented a comprehensive view of soccer players' brains in a sample of twenty-five right-handed male elite soccer players aged from 18 to 22 years and twenty-five non-athletic controls aged 19 to 24 years. Importantly, none had recalled a history of concussion. We performed a whole-brain tract-based spatial statistical analysis, and a tract-specific probabilistic tractography method to measure differences of white matter properties between groups. Whole-brain integrity analysis showed increased microstructural integrity within the corpus callosum tract in soccer players compared to controls. Further, tract-specific probabilistic tractography revealed that the anterior part of corpus callosum may be the brain structure most relevant to training experience, which may put into perspective prior evidence showing corpus callosum alteration in retired or concussed athletes practicing contact sports. Intriguingly, experience-related alterations showed left hemispheric lateralization of potential early signs of concussion-like effects. This is the first study to-date providing a definitive characterization of soccer-specific experience-related structural alterations. In sum, we concluded that the observed gains and losses may be due to a consequence of engagement in protracted soccer training that incurs prognostic hallmarks associated with minor injury-induced neural inflammation. Soccer is the only major sport with voluntary unprotected head-to-ball contact. It is crucial to determine if head impact through regular soccer sports training is manifested in brain structure and connectivity, and whether such alterations are due to sustained training per se. Using diffusion tensor imaging, we documented a comprehensive view of soccer players' brains in a sample of twenty-five right-handed male elite soccer players aged from 18 to 22 years and twenty-five non-athletic controls aged 19 to 24 years. Importantly, none had recalled a history of concussion. We performed a whole-brain tract-based spatial statistical analysis, and a tract-specific probabilistic tractography method to measure differences of white matter properties between groups. Whole-brain integrity analysis showed increased microstructural integrity within the corpus callosum tract in soccer players compared to controls. Further, tract-specific probabilistic tractography revealed that the anterior part of corpus callosum may be the brain structure most relevant to training experience, which may put into perspective prior evidence showing corpus callosum alteration in retired or concussed athletes practicing contact sports. Intriguingly, experience-related alterations showed left hemispheric lateralization of potential early signs of concussion-like effects. This is the first study to-date providing a definitive characterization of soccer-specific experience-related structural alterations. In sum, we concluded that the observed gains and losses may be due to a consequence of engagement in protracted soccer training that incurs prognostic hallmarks associated with minor injury-induced neural inflammation.</p>
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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.