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The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state. •Functional parcel boundaries reconfigure with cognitive state.•Parcel reconfigurations are robust and reliable across sessions and subjects.•Parcel sizes can significantly predict task condition and task performance.•State-dependent functional atlases have implications for functional connectivity.

The recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using the AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field. [Display omitted]

The structural and functional organization of brain networks subserving basic daily activities (i.e. language, visuo-spatial cognition, movement, semantics, etc.) are not completely understood to date. Here, we report the first probabilistic cortical and subcortical atlas of critical structures mediating human brain functions based on direct electrical stimulation (DES), a well-validated tool for the exploration of cerebral processing and for performing safe surgical interventions in eloquent areas. We collected 1162 cortical and 659 subcortical DES responses during testing of 16 functional domains in 256 patients undergoing awake surgery. Spatial coordinates for each functional response were calculated, and probability distributions for the entire patient cohort were mapped onto a standardized three-dimensional brain template using a multinomial statistical analysis. In addition, matching analyses were performed against prior established anatomy-based cortical and white matter (WM) atlases. The probabilistic maps for each functional domain were provided. The topographical analysis demonstrated a wide spatial distribution of cortical functional responses, while subcortical responses were more restricted, localizing to known WM pathways. These DES-derived data showed reliable matching with existing cortical and WM atlases as well as recent neuroimaging and neurophysiological data. We present the first integrated and comprehensive cortical-subcortical atlas of structures essential for humans’ neural functions based on highly-specific DES mapping during real-time neuropsychological testing. This novel atlas can serve as a complementary tool for neuroscientists, along with data obtained from other modalities, to improve and refine our understanding of the functional anatomy of critical brain networks. •1821 direct electrical stimulation sites from 256 awake brain surgeries analyzed.•Probability maps of 16 key human brain functions generated.•Cortical stimulation points widely distributed over the brain surface.•Subcortical responses more restricted, localize to known major white matter tracts.

In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages. [Display omitted] •Resting-state connectivity data parcellated using graph theory approach.•Yields groupwise whole-brain parcellation of the order of 300 nodes•Uniform timecourses within nodes•Ideal for network analysis of functional connectivity•Functional atlas available online

Adolescent changes in human brain function are not entirely understood. Here, we used multiecho functional MRI (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in 298 healthy adolescents scanned 520 times. Participants were aged 14 to 26 y and were scanned on 1 to 3 occasions at least 6 mo apart. We found 2 distinct modes of age-related change in FC: “conservative” and “disruptive.” Conservative development was characteristic of primary cortex, which was strongly connected at 14 y and became even more connected in the period from 14 to 26 y. Disruptive development was characteristic of association cortex and subcortical regions, where connectivity was remodeled: connections that were weak at 14 y became stronger during adolescence, and connections that were strong at 14 y became weaker. These modes of development were quantified using the maturational index (MI), estimated as Spearman’s correlation between edgewise baseline FC (at 14 y, FC14) and adolescent change in FC (ΔFC14−26), at each region. Disruptive systems (with negative MI) were activated by social cognition and autobiographical memory tasks in prior fMRI data and significantly colocated with prior maps of aerobic glycolysis (AG), AG-related gene expression, postnatal cortical surface expansion, and adolescent shrinkage of cortical thickness. The presence of these 2 modes of development was robust to numerous sensitivity analyses. We conclude that human brain organization is disrupted during adolescence by remodeling of FC between association cortical and subcortical areas.

Brain areas show specific cellular, molecular, and gene expression patterns that are linked to function, but their precise relationships are largely unknown. To unravel these structure-function relationships, a combined analysis of 53 neurotransmitter receptor genes, receptor densities of six transmitter systems and cytoarchitectonic data of the auditory, somatosensory, visual, motor systems was conducted. Besides covariation of areal gene expression with receptor density, the study reveals specific gene expression patterns in functional systems, which are most prominent for the inhibitory GABAA and excitatory glutamatergic NMDA receptors. Furthermore, gene expression-receptor relationships changed in a systematic manner according to information flow from primary to higher associative areas. The findings shed new light on the relationship of anatomical, functional, and molecular and transcriptomic principles of cortical segregation towards a more comprehensive understanding of human brain organization.

Nonlinear registration of individual brain MRI scans to standard brain templates is common practice in neuroimaging and multiple registration algorithms have been developed and refined over the last 20 years. However, little has been done to quantitatively compare the available algorithms and much of that work has exclusively focused on cortical structures given their importance in the fMRI literature. In contrast, for clinical applications such as functional neurosurgery and deep brain stimulation (DBS), proper alignment of subcortical structures between template and individual space is important. This allows for atlas-based segmentations of anatomical DBS targets such as the subthalamic nucleus (STN) and internal pallidum (GPi). Here, we systematically evaluated the performance of six modern and established algorithms on subcortical normalization and segmentation results by calculating over 11,000 nonlinear warps in over 100 subjects. For each algorithm, we evaluated its performance using T1-or T2-weighted acquisitions alone or a combination of T1-, T2-and PD-weighted acquisitions in parallel. Furthermore, we present optimized parameters for the best performing algorithms. We tested each algorithm on two datasets, a state-of-the-art MRI cohort of young subjects and a cohort of subjects age- and MR-quality-matched to a typical DBS Parkinson's Disease cohort. Our final pipeline is able to segment DBS targets with precision comparable to manual expert segmentations in both cohorts. Although the present study focuses on the two prominent DBS targets, STN and GPi, these methods may extend to other small subcortical structures like thalamic nuclei or the nucleus accumbens. •We compared six nonlinear deformation algorithms for atlas-based segmentation of two common DBS targets, the STN and GPi.•Parameters of the best performing algorithms were optimized to maximize precision of subcortical deformations.•These optimized pipelines are able to segment DBS targets with precision that is comparable to manual expert segmentations.•The optimized pipelines have been made available to the scientific community through the open source toolbox Lead-DBS.

Despite recent advances in non-invasive brain mapping imaging, the resectability of a given area in a patient harboring a WHO grade II glioma cannot be predicted preoperatively with high reliability, due to mechanisms of functional reorganization. Therefore, intraoperative mapping by direct electrical stimulation remains the gold standard for detection and preservation of eloquent areas during glioma surgery, because it enables to perform on-line anatomo-functional correlations. To study potentials and limitations of brain plasticity, we gathered 58 postoperative MRI of patients operated on for a WHO grade II glioma under direct electrical cortico-subcortical stimulation. Postoperative images were registered on the MNI template to construct an atlas of functional resectability for which each voxel represents the probability to observe residual non-resectable tumor, that is, non-compensable area. The resulting atlas offers a rigorous framework to identify areas with high plastic potential (i.e. with probabilities of residual tumor close to 0), with low compensatory capabilities (i.e. probabilities of residual tumor close to 1) and with intermediate level of resectability (probability around 0.5). The resulting atlas highlights the utmost importance of preserving a core of connectivity through the main associative pathways, namely, it supports the existence of a “minimal common brain” among patients. ►Resectability of brain areas is unpredictable before tumor surgery due to reshaping. ►Electrical mapping allows the study of potentials and limitations of plasticity. ►We build an atlas of functional resectability based on postoperative residue. ►Each voxel represents the probability to observe non-compensable area. ►Our atlas highlights the existence of a “minimal common brain” among patients.

Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD and control subjects based on the patterns of functional connectivity. Our experimental outcomes indicate that the proposed model is able to detect ASD correctly with an accuracy of 70.22% using the ABIDE I dataset and the CC400 functional parcellation atlas of the brain. Also, the CNN model developed used fewer parameters than the state-of-art techniques and is hence computationally less intensive. Our developed model is ready to be tested with more data and can be used to prescreen ASD patients.

The purpose of this study is to create a white matter atlas of the human brain using diffusion tensor imaging (DTI) tractography and to describe the constant and variable features of the major pathways. DTI was acquired from 40 healthy right-handed adults and reconstructed tracts mapped within a common reference space (MNI). Group effect maps of each tract defined constant anatomical features while overlap maps were generated to study inter-subject variability and to compare DTI derived anatomy with a histological atlas. Two patients were studied to assess the localizing validity of the atlas. The DTI-derived maps are overall consistent with a previously published histological atlas. A statistically significant leftward asymmetry was found for the volume and number of streamlines of the cortico-spinal tract and the direct connections between Broca's and Wernicke's territories (long segment). A statistically significant rightward asymmetry was found for the inferior fronto-occipital fasciculus and the fronto-parietal connections (anterior segment) of the arcuate fasciculus. Furthermore, males showed a left lateralization of the fronto-temporal segment of the arcuate fasciculus (long segment), while females had a more bilateral distribution. In two patients with brain lesions, DTI was acquired and tractography used to show that the tracts affected by the lesions were correctly identified by the atlas. This study suggests that DTI-derived maps can be used together with a previous histological atlas to establish the relationship of focal lesions with nearby tracts and improve clinico-anatomical correlation. ►The anterior segment of the arcuate fasciculus and the inferior fronto-occipital fasciculus (IFOF) are asymmetric; with the right side larger than the left. ►Variability maps of the white matter produced with DTI tractography are consistent with the variability maps generated by prior postmortem histological studies. ►Our DTI-derived atlas is a valuable tool for learning the neuroanatomy of white matter, and establishing the relationship of focal lesions with nearby tracts.