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1,064 results for “human brain”
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 4. Name and role of the Chess Pieces
<p>In assigning the brain function to the computational processing units the strategy of the chess game will be pursued: 1 king – consciousness, mind, resolving undefined situations, undetermined risk analysis, feedback: 1 queen – implementation strategy, thinking, learning; 2 rooks – initial knowledge memory and learning memory; 2 bishops – good or updated, time or emergency decision; 2 knights – rules, open schemes, fixed processes, templates; 8 pawns – interfaces with own senses and actions. Double chess pieces will be assigned in the model with initial knowledge (‘ marked) that can be updated as a learning experience to a second set (“ marked).</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 3. Assigning ` the "SAH" Human Brain Model the role of the Chess Pieces
<p>The components are not topically subordinated to each other but in a strong interoperability and used for outputs reflected as result of thinking, actions to receiving information from the sensor of the interfaces, movement or speaking. </p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 5. Block Diagram of the SAH Human Brain Model
<p>Three vertical areas are defined in the field of activities: Processes units, Computational Intelligence Block, and Smart Interfaces Block (Details are presented in Figure 5). The Processes units fully communicate with the Computational Intelligence Block, Central Processing Unit and Smart Interfaces. Some specific links and functions are not specified here. Smart interfaces defined for “sight, sound, taste, touch and hearing senses” are bidirectional and completed by input-output interfaces that ensure the communication for output actions like “speech, sound, movements” and other commands resulting in the thinking process. An important issue is the ‘equilibrium’ that must be treated in either “decision or movement” framework.</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 1. Being Brain and Chess Game Strategy - similarities
<p>Finally, the following similar reactions between a chess player and a human being must be mentioned and considered. The power of reason for every being, human brain, or chess game player lies in similarities and has three main directions (see Figure 1)</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 2. Human body sizes for men/women.
<p>We propose an efficient, simple and robust human body feature extraction based on the front and side images of a human body. Description of anthropometric data - men/women: Dataset based on an experiment is used to test the system data describing the anthropometric features of men, includes 12 sizes of the human body, which are presented in figure 2. </p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 1. Science branches involved in translating product features to human needs
<p>Approach notwithstanding, designing product features involves basically a two-step process: (a) detecting and recognizing emotional information and (b) exhibiting a suitable reaction to the previously considered input. Since needs are reflected in the emotional impact, a method to measure the emotions is necessary (Abraham & Michie, 2008) to correctly assess the impact. Referring strictly to a software product, in order to implement the capability to sense the users’ emotional state, the first step should be developing an affective database (Tao & Tan, 2005), to allow correct identification of the affective status. This results in a translation between the affective status of the user and the computer, therefore allowing the program to process the user emotion just like any other input, successfully “digitizing” emotion. </p>
Complete numerical solutions for "Inference of ecological and social drivers of human brain-size evolution" by Mauricio González-Forero and Andy Gardner
<p>This zip file contains the complete numerical solutions across the parameter sweep over the P parameters for the six cases considered.</p> <p>/1RatioForm/ -> solutions for power competence.<br> /2DiffForm/ -> solutions for exponential competence.</p> <p>/1RatioForm/1BenchmarkFromSimpleInitialGuess -> solution for the step 1 of initialization (section 5 of the SI).<br> /1RatioForm/2Benchmark/ -> solution for the step 2 of the initialization (section 5 of the SI).<br> /1RatioForm/3AdditiveCoop/ -> solutions across the P parameter combinations for additive cooperation.<br> /1RatioForm/4MultCoop/ -> solutions across the P parameter combinations for multiplicative cooperation.<br> /1RatioForm/5SubMultCoop/ -> solutions across the P parameter combinations for submultiplicative cooperation.</p> <p>The contents of /2DiffForm/ are analogous.</p> <p>The P vector is written in these files in the order (etas,etac,etaC,etag), where<br> etas -> P1<br> etac -> P3<br> etaC -> P2<br> etag -> P4</p> <p>The files 1runNotesACMC.pdf and 2runNotesSC.pdf contain the tree structure of the parameter sweep, specifying which parameter combination was used as the resident and which combinations converged to an uninvadable strategy (those with a checkmark).</p> <p>The file 3runNotesMaternalCareOptimization.pdf contains the 10 parameter combinations that yielded the best adult fit, which then were subject to variation in the parameter phi to find the combination that yielded the best ontogenetic fit.</p> <p>The file 4runNotesDuplicates.pdf gives the parameter combinations that were not run because they are equivalent to other parameter combinations.</p> <p>Running [T,N1,run,Tshort,N1short,runShort]=etaCombinations in Matlab and typing run.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i (where i is a number 1,2,...) for PC-AC, EC-MC, PC-SC, and EC-SC. The meaning of "next" is explained in step 4 of the parameter sweep (section 5 of the SI). Typing runShort.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i for PC-MC and EC-AC.</p> <p>The terminal folders contain the solutions and have the following files:<br> brainNashDeep.m -> the master file launching the iteration of best responses.<br> brainMainDeep.m -> the file launching one iteration solving the optimal control problem to find best response.<br> brainMainTestRunDeep.m -> runs a test to check if there are infeasibility warnings.<br> brainContinuous.m -> specifies the dynamic constraints.<br> brainEndpoint.m -> specifies the terminal constraints.<br> parameters.m -> specifies the parameter values and rescales them to rescale units as specified in section 5 of the SI.<br> getSolution.m -> extracts solution.<br> plots.m -> plots solutions over best response iterations.<br> brainPlot.m -> plots solution of a given best response iteration.<br> guessDeep.mat -> initial guess and resident used.<br> solutionNashDeep.mat -> solutions over best response iterations.<br> solutionDeep.mat -> solution of last best response iteration.</p> <p> </p>
Functional Coactivation Map of the Human Brain - Figures
<p>These are author versions of the figures of the article "Functional coactivation map of the human brain", <a href="https://doi.org/10.1093/cercor/bhn014">https://doi.org/10.1093/cercor/bhn014</a></p> <p><strong>Fig. 1 (fig1v2.tif)</strong> Characterization of the experiments used in the coactivation map. Distribution of the different cognitive domains represented by the experiments after the BrainMap classification (A). Histogram of the number of locations per experiment (B). Experiments reported on average 8 locations, and a decreasing number of experiments reported large numbers of locations.</p> <p><strong>Fig. 2 (fig2v2.tif)</strong> Reproducibility of the coactivation map. Pairs of partial coactivation maps computed from disjoint random subsets of the total database of experiments were progressively more similar as the number of experiments increased. The plot shows the distribution of the correlation coefficient for 20 pairs of partial coactivation maps computed from independent sets of 500, 700, 900, 1100, 1300, 1500, and 1700 experiments.</p> <p><strong>Fig. 3 (fig-symm.tif)</strong> Symmetric interhemispheric coactivations. Coactivations of regions in the left hemisphere included most of the time the symmetric region in the right hemisphere, and vice versa. The figure shows 3-dimensional reconstructions (A, B) and stereotaxic slices of 4 networks corresponding to 4 seed-voxels in the axial plane z = 28 (C), and 4 networks in the coronal plane y = −6 (D). The network clusters are isosurfaces for P = 0.01, and the location of the seed-voxels is indicated by white squares in the stereotaxic slices.</p> <p><strong>Fig. 4 (fig-ipsl.tif)</strong> Fronto-parietal “attention” network. Three-dimensional reconstruction and axial (z = 48) and para-sagittal (x = 30) stereotaxic slices of the network recovered with a seed-voxel at the left intraparietal sulcus (IPS, x = −26, y = −58, z = 48). It includes the supplementary motor area (SMA) and preSMA, left and right anterior insula (aIns), frontal eye fields (FEF), dorsolateral prefrontal cortex (DLPFC), inferior precentral sulcus (iPCS), ventral occipital cortex (vOC), inferior parietal lobule (iPL), and the ventral IPS (vIPS). The network clusters are isosurfaces for P = 0.01, and the location of the seed-voxel is indicated in the axial slice by a white square.</p> <p><strong>Fig. 5 (fig-acc.tif) </strong>Cingulo-parietal “resting state” network. Three-dimensional reconstructions and sagittal stereotaxic slice (x = −2) of the network recovered with a seed-voxel at the anterior cingulate cortex (aCC, x = −2, y = 46, z = −4). It includes the posterior cingulate cortex (pCC), nucleus accumbens (NA), lateral parietal cortex (LPC), inferior temporal cortex (iTC), and the superior frontal cortex (SFC). The network clusters are isosurfaces for P = 0.01 (strong red), and P = 0.5 (in transparency). The location of the seed-voxel is indicated by a white square in the sagittal slice.</p> <p><strong>Fig. 6 (fig-motor.tif)</strong> Cortico-diencephalo-cerebellar “motor” network. Three-dimensional reconstructions and coronal (y = −26) and para-sagittal (x = −34) stereotaxic slices of the network recovered with a seed-voxel at the dorsal part of the left central sulcus (CS, x = −34, y = −26, z = 60). The network includes the right central sulcus, caudal cingulate motor area (CMA), ipsilateral putamen (Pu), thalamus (Th), and left cerebellum (Cb-L), and the contralateral anterior lobe of the cerebellum (aCb). The network clusters are isosurfaces for P = 0.01, and the seed-voxel is indicated by white squares in the coronal and sagittal slices.</p>
18X7 Human brain field maps for shim coil designs
<p>128 B0 field maps of head brains of 18 different healthy adult volunteers in seven different head positions.</p>
Source code and experimental data of human brain tissue (visual cortex, corona radiata) for poro-viscoelastic parameter identification
<p>Computer code and experimental data that we used for our inverse parameter identification of poro-viscoelastic material parameters for two different brain regions: visual cortex (gray matter) and corona radiata (white matter). The experimental data comprises large-strain cyclic loading and compression/tension relaxation. For details see the corresponding publication: "Model-driven exploration of poro-viscoelasticity in human brain tissue: Be careful with the parameters!".</p> <p>Further explanation regarding the specimen preparation, experimental setup, as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol (2023). <a href="https://doi.org/10.1007/s10237-023-01739-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10237-023-01739-w</a>.</p> <p>The file "<span>nonlinear-poro-viscoelasticity.cc</span>" contains our C++ Finite Element code based on the open source library deal.II. It is accompanied by an exemplary parameter file.</p> <p><strong>Funding:</strong> The support from the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the grants BU 3728/1-1, BU 3728/3-1, STE 544/70-1 as well as through project number 460333672 CRC1540 Exploring Brain Mechanics is gratefully acknowledged.</p>
Deep spatial profiling of human COVID-19 brains
<p>Imaging mass cytometry data (txt files and folders containing single tiffs + cell masks) generated for the analysis of postmortem brains of COVID-19 patients and control groups (multiple sclerosis and patients who received ECMO therapy).</p>
Two-photon fluorescence microscopy image stacks of human brain sections (grey and white matter)
<p>Two-photon fluorescence microscopy (TPFM) image stacks of human brain sections including grey matter (N<sub>g</sub>=10) and white matter (N<sub>w</sub>=10), considered in the validation of the 3D fiber orientation analysis pipeline proposed in: "<em>Fiber enhancement and 3D orientation analysis in label-free two-photon fluorescence microscopy</em>". <br> Human brain tissue was preliminarily treated for TPFM following the label-free MAGIC preparation technique, presented in (Costantini et al., <em>Scientific Reports</em> 2021).</p> <p>The PSF of the TPFM system has a FWHM of (0.692, 0.692, 2.612) μm along the x, y, and z axes, respectively, whereas the adopted voxel size is 0.88 μm x 0.88 μm x 1 μm.</p>
Ginkgo Chauvel's deep white matter atlas of the human brain
<p><strong>Deep Chauvel's human white matter atlas.</strong></p> <p>The deep white matter atlas of the human brain was built upon a cohort of 39 in vivo human magnetic resonance imaging (MRI) scans shared by the Human Connectome Project (HCP), registered on a template space (the MNI ICBM152 2009c non-linear asymmetric template). The construction of this atlas is based on the analysis of the anatomical and diffusion MRI dataset using the tractography and fiber clustering tools available from the Ginkgo toolbox (CEA, NeuroSpin, BAOBAB, GAIA, Ginkgo Team, <a href="https://framagit.org/cpoupon/gkg">https://framagit.org/cpoupon/gkg</a>). The atlas can be visualized using the BrainVISA/Anatomist viewer available at <a href="https://brainvisa.info/web/download.html">https://brainvisa.info/web/download.html</a>.</p> <p><br> This atlas is composed of 39 white matter bundles including :<br> - symmetrically on both hemispheres, the anterior, superior and posterior thalamic radiations (optic radiations), the arcuate, dorsal and ventral cingulum, the cortico-spinal tract, the fornix, the frontal aslants, the inferior fronto-occipital fascicle, the inferior longitudinal fasciculus, the middle longitudinal fascicle, the optic radiations, the uncinate fascicle and the visual occipito-temporal fibers (also called ventral visual stream),<br> - interhemispheric bundles such as the anterior commissure and the Witelson's subdivisions of the corpus callosum (I, II, III, IV, V, VI, VII),<br> - cerebellar bundles, such as the hypothamic-subthalamic fibers and the cortico-ponto-cerebellar fibers,<br> The atlas is provided using the Anatomist *.bundles/*.bundlesdata format for which meta-information can be found in the *.bundles file among which:<br> - the labels of the different white matter bundles ('labels' entry),<br> - the number of streamlines populating each white matter bundle ('curve3d_counts' entry), in the same order as the 'labels' key,<br> - the total number of white matter bundles ('item_count' entry),<br> - the total number of streamlines ('curves_count' entry)</p>
Ginkgo Chauvel's left and right superficial white matter atlas of the human brain
<p><strong>Superficial Chauvel's human white matter atlas.</strong></p> <p>The left and right superficial white matter atlases of the human brain were built upon a cohort of 39 in vivo human magnetic resonance imaging (MRI) scans from the Human Connectome Project (HCP), registered on a template space (the MNI ICBM152 2009c non-linear<br> asymmetric template). The construction of this atlas is based on the analysis of the anatomical and diffusion MRI dataset using the tractography and fiber clustering tools available from the Ginkgo toolbox (CEA, NeuroSpin, BAOBAB, GAIA, Ginkgo Team, <a href="https://framagit.org/cpoupon/gkg">https://framagit.org/cpoupon/gkg</a>). The atlas can be visualized using the BrainVISA/Anatomist viewer available at <a href="https://brainvisa.info/web/download.html">https://brainvisa.info/web/download.html</a>.<br> The left hemisphere atlas is composed of 733 superficial white matter bundles and the right hemisphere atlas of 632 superficial white matter bundles.</p> <p><br> <br> The 38 cortical regions considered for the left and right hemispheres were : the anterior/middle/posterior superior frontal gyrus (aSFG/mSFG/pSFG) ; the anterior and posterior middle frontal gyrus (aMFG/pMFG) ; the anterior/middle/posterior inferior frontal gyrus (aIFG/mIFG/pIFG) ; the medial lateral orbitofrontal cortex (mOFC/ lOFC) ; the superior, middle, inferior precentral gyrus (sPrCG / mPrCG / iPrCG) ; the Paracentral Lobule (PCL) ; the anterior and posterior insula (alns / plns) ; the anterior, posterior superior temporal gyrus (aSTG/pSTG) ; the anterior, posterior middle temporal gyrus (aMTG/pMTG) ; the anterior and posterior inferior temporal gyrus (aITG, pITG) ; the anterior and posterior fusiform gyrus (aFFG/pFFG) ; the superior, middle and inferior postcentral gyrus (sPoCG/mPoCG/iPoCG) ; the superior parietal lobule (SPL) ; the supramarginal gyrus (SMG) ; the angular gyrus (AnG) ; the Precuneus (PCun) ; the cuneus (Cun) ; the Lingual Gyrus (LG) ; the superior, middle, inferior occipital gyrus (sOG / mOG/ iOG) ; the entorhinal Cortex (EnC) ; the parahippocampal gyrus (PHC).<br> <br> The atlas is provided using the Anatomist *.bundles/*.bundlesdata format for which metainformation can be found in the *.bundles file among which:<br> - the labels of the different white matter bundles ('labels' entry) following the syntactic rule "<right/left>_<roi1>_<roi2>_<clusterId>",<br> - the number of streamlines populating each white matter bundle ('curve3d_counts' entry), in the same order as the 'labels' key,<br> - the total number of white matter bundles ('item_count' entry),<br> - the total number of streamlines ('curves_count' entry)</p> <p> </p> <p> </p>
Example meshes for 'Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation'
<p>Example meshes for 'Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation'</p> <p>The meshes contain DTI. To read the mesh to FEniCS, see e.g., <a href="https://github.com/bzapf/braintransport/blob/24ba4e37a3d6fadb37c249ca7f717a7355f48f30/optimal_velocity/postprocess_phi.py#L12">here </a>.</p> <p>Find the simulation codes for the manuscript here:</p> <p><a href="https://github.com/bzapf/braintransport">https://github.com/bzapf/braintransport</a></p>
Hyperelastic Human Brain 1-7
<p>This dataset contains experimental data from the mechanical testing of human brain specimens.<br> The data have been filtered (moving average and linear interpolation) and the load/unload curves averaged to approximate the hyperelastic material response for infinitely slow strain rates.<br> Every specimen folder contains data for the three loading modes tested: cyclic compression/tension up to 15% strain as well as cyclic torsional shear experiments up to an amount of shear of 0.15 (l1) and 0.3 (l2), respectively. Furthermore, there are separate files for the first cycle (c1) and the third cycle (c3). The files for the compression/tension mode columns contain displacement [m] and normal force [N] while the torsional shear mode columns contain angular displacement [rad] and torque [Nm]. The data are also split into negative (_neg) and positive (_pos) parts for the torsional shear.<br> The specimens are named HBE_<brain_id>_<specimen_id> and their brain region can be looked up in the sample_lookup.xlsx file.</p> <p>The specimens were cylindrical with a diameter of ~8mm. Their height was determined from the test data and is stored in the geometry.yaml file (measurements given in m) located in each specimen's directory.</p> <p>Further explanation regarding the specimen preparation, experimental setup as well as the assignment of regions and governing regions can be found in Hinrichsen, J., Reiter, N., Bräuer, L. <em>et al.</em> Inverse identification of region-specific hyperelastic material parameters for human brain tissue. <em>Biomech Model Mechanobiol</em> (2023). https://doi.org/10.1007/s10237-023-01739-w.</p>
Data from: Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces
Open the record for dataset details and reuse information.
Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders
Open the record for dataset details and reuse information.
Plastic changes in the brain after human hand allotransplantation
<p><span><span><span><span><span><span><span><span><span><span><span>The physiological mechanism after hand transplant was investigated using magnetic resonance imaging and transcranial magnetic stimulation. Somatosensory and motor representations of the upper arm proximal to amputation occupied the hand area before surgery and moved back toward normal position after the surgery. The absent cortical inhibition with amputation increased gradually after surgery. The cortical plastic changes preceded functional recovery and can be used to monitor functional restoration after hand transplant.</span></span></span></span></span></span></span></span></span></span></span></p>
Source Data and Scripts - Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols
<p>Source data and scripts associated with the manuscript "<em>Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols</em>" (Naas et al. 2024, <em>bioRxiv</em>, <a href="https://doi.org/10.1101/2024.11.15.623576" rel="nofollow">DOI: 10.1101/2024.11.15.623576</a>). Corresponding scripts to reproduce all figures and tables presented in the manuscript are also available on <a href="https://github.com/jn-goe/brain_organoids_four_protocols">https://github.com/jn-goe/brain_organoids_four_protocols</a>.</p> <div> <div> <p>The therein introduced NEST-Score is available as R package on <a href="https://github.com/jn-goe/NESTScore">https://github.com/jn-goe/NESTScore</a>.</p> <p>The interactive Shiny App data explorer is available on <a href="https://vienna-brain-organoid-explorer.vbc.ac.at/">https://vienna-brain-organoid-explorer.vbc.ac.at</a>.</p> </div> </div>
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.