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864 results for “Brain function”
The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity
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Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
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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>
CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS
<p>Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI).</p> <p>We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.</p>
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>
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>
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-Brain-Like Artificial Intelligence for Automation-Figure 7. Function Principle of Neuro-Symbols
<p>In Figure 7, the basic function principle of neuro-symbols is illustrated. One characteristic of neuro-symbols is that they represent symbolic information. In the case of perception, this symbolic inforamtion are perceptual images like for instance a face or a voice (see Section 4.2.1.2 for more details). Furthermore, neuro-symbols show a number of analogies to biological neurons. They have an activation degree (AD), which indicates if the perceptual image that each neuro-symbol respresents is currently perceived in the environment. Each neuro-symbol has a certain number of inputs and one output. Via the inputs, information about the activation degree of other neurosymbols is collected. Like illustrated in the example of Figure 7, a neuro-symbol representing a face could for instance receive information from neuro-symbols representing a head, eyes, and a mouth.</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 10b. The control loop's information displayed by the controller-Designing a Growing Functional Modules "Artificial Brain"
<p>Once performed the design and configuration, the controller and the local application are<br> executed by pressing the corresponding button. In case of a local simulation, two terminal windows<br> are generated. The first one, localized on the left side of the screen (figure 10.a), displays the<br> behavior of the simulation; the second one, localized on the right side of the screen (figure 10.b),<br> displays the behavior of the controller. Both application run concurrently and their respective<br> contents allow the user to observe and monitor the control session. The last text line of figure 10.b<br> displays the set of commands at cycle 201.</p>
Figure 10.a. Simulation's display: The characters "\__/" represents the front part of the car that should pass through the string "....==..=.==..==" representing the range of obstacles-Designing a Growing Functional Modules "Artificial Brain"
<p>Once performed the design and configuration, the controller and the local application are<br> executed by pressing the corresponding button. In case of a local simulation, two terminal windows<br> are generated. The first one, localized on the left side of the screen (figure 10.a), displays the<br> behavior of the simulation; the second one, localized on the right side of the screen (figure 10.b),<br> displays the behavior of the controller. Both application run concurrently and their respective<br> contents allow the user to observe and monitor the control session. The last text line of figure 10.b<br> displays the set of commands at cycle 201.</p>
Figure 8. Configuration of the controller-Designing a Growing Functional Modules "Artificial Brain"
<p>Before running the controller, it is necessary to specify the communication's configuration.<br> When pressing the “configure” button, two text windows appear (figure 8). The upper one allows to<br> specify the application's localization in order to run it previously to the controller. Presently, the<br> corresponding simulation will run on the same machine, thus the name of the corresponding<br> executable file must be specified. Otherwise, in the same field, the user should specify the IP<br> address of the server where the controlled system is running.</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 6. Adding a Global Goal with its required type-Designing a Growing Functional Modules "Artificial Brain"
<p>The next step consists of adding a Global Goal expressing a motivation required by the<br> controller. The goal is to keep the vehicle's front free of obstacles, thus the Sensation “free” should<br> stay equal to “1”. After adding a new Global Goal, its assigned type should be “Cst” corresponding<br> to a constant output request (see figure 6). In the parameter field, its specified value is “1”.</p>
Figure 4. Adding a Sensing Module and selecting its type-Designing a Growing Functional Modules "Artificial Brain"
<p>GFM controllers learn to satisfy some predefined goals<br> while interacting with the environment and thus should be considered as artificial brains. An<br> example of the design process of a simple controller is provided herein to explain the inherent<br> methodology, to exhibit the components' interconnections and to demonstrate the control process.</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>
Figure 2. The editor's components from left to right: a) Sensation, b) Sensing Module, c) Global Goal, d) Acting Module.-Designing a Growing Functional Modules "Artificial Brain"
<p>Each “Sensation” corresponds to an integer value corresponding to a specific system's<br> sensor. Sensations are symbolized by a green rectangle on the editor's canvas (figure 2.a). Each<br> newly created sensation is assigned an identifier previously incremented. Its unique field, initially<br> filled with question-marks, allows it to associate a mnemonic in order to facilitate its interpretation.</p>
Figure 1. The GFM controller and its control loop-Designing a Growing Functional Modules "Artificial Brain"
<p>The GFM control loop has many similarities to a standard one as shown in figure 1. The<br> controller, delimited by a dotted line, sends during each cycle an output command in order to trigger<br> some mechanical or virtual actuators and in return, receives a feedback composed of a sequence of<br> sensors' values. However, the concept of reference value is replaced by “Global Goals” which are<br> integrated to the controller.</p>
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 1. Factors that Affect Thyroid Function (The Institute for Functional Medicine, 2014)
<p> In Figure 1 are presented the main factors that affect the thyroid function. It is obvious that factors such as stress, infection, toxins, trauma and certain medication are directly responsible for the improper production of thyroid hormones. Symptoms identification and the early detection of abnormal values of thyroid hormones after clinical investigation will help in establishing the proper diagnostic and to prescribe the right medication. The patient must periodically evaluate his clinical state in order to receive the treatment as long as he needs it. </p>
Method to assess the functional role of noisy brain signals by mining envelope dynamics
<p>Preprocessed envelope EEG features based on a spatial filter approach. The features were computed across multiple within-trial SVIPT events for a large hyperparameter space on data of an exemplary subject.</p> <p>The file "components.bsv" contains the preprocessed envelope features of all investigated configurations and provides underlying parameters as well as a relative path for the key ``record_dir'' to additional component information. Specifically, for each configuration the spatial filter, spatial activity pattern and the time-resolved within-trial envelope signal is provided under "records/". </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.