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  • Neuroinformatics
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  • National Institutes of Health
  • Mapping the Human Connectome: Structure, Function, and Heritability

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Csaba Kerepesi; Balázs Szalkai; Bálint Varga; Vince Kornél Grolmusz;

    Abstract The human braingraph, or connectome is a description of the connections of the brain: the nodes of the graph correspond to small areas of the gray matter, and two nodes are connected by an edge if a diffusion MRI-based workflow finds fibers between those brain areas. We have constructed 1015-vertex graphs from the diffusion MRI brain images of 392 human subjects and compared the individual graphs with respect to several different areas of the brain. The inter-individual variability of the graphs within different brain regions was discovered and described. We have found that the frontal and the limbic lobes are more conservative, while the edges in the temporal and occipital lobes are more diverse. Interestingly, a “hybrid” conservative and diverse distribution was found in the paracentral lobule and the fusiform gyrus. Smaller cortical areas were also evaluated: precentral gyri were found to be more conservative, and the postcentral and the superior temporal gyri to be very diverse. Similar studies concerning the human genome discovered more and less conservative sections of the DNA, opening up entirely new fields in genomics. We think that the present study is the first step in this direction in human connectomics. The clinical significance of the conservativity of a given cerebral area could be the higher sensitivity for traumas and developmental or neuro-degenerative events than the less conservative areas.

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    Neuroscience Letters
    Article . 2018 . Peer-reviewed
    License: Elsevier TDM
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Neuroscience Lettersarrow_drop_down
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Neuroscience Letters
      Article . 2018 . Peer-reviewed
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    Authors: Bertrand eThirion; Bertrand eThirion; Gael eVaroquaux; Gael eVaroquaux; +4 Authors

    International audience; Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subjects images, parcellation approaches use brain activity (e.g. found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and K-means clustering algorithms. We show that in general Ward's clustering performs better than alternative methods with regard to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.

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    Europe PubMed Central
    Article . 2014
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    Frontiers in Neuroscience
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    Frontiers in Neuroscience
    Article . 2014 . Peer-reviewed
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      Europe PubMed Central
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      Frontiers in Neuroscience
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      Frontiers in Neuroscience
      Article . 2014 . Peer-reviewed
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    Authors: Yashar Zeighami; Miguel Ulla; Yasser Iturria-Medina; Mahsa Dadar; +6 Authors

    eLife digest Although Parkinson's disease is the second most common neurodegenerative disorder, its cause is not known and there is no cure. The symptoms of Parkinson's disease, which include tremor and slowing of voluntary movements, get progressively worse over time. The numbers of neurons in certain brain regions also decrease, causing those parts of the brain to shrink; this is known as ‘atrophy’. However, no conclusive signs of atrophy have been found in the brains of people in the early stages of the disease. One theory suggests that Parkinson's disease is caused by a toxic protein that is able to spread from neuron to neuron. Recent advances in brain imaging have made it possible to map networks in the living human brain—the so-called brain connectome. These networks could form the ‘highways’ through which a disease-causing agent might spread. The Parkinson's Progression Markers Initiative (PPMI) is a large study that collects data from hundreds of people in an effort to identify the causes of Parkinson's disease. Zeighami et al. have now analyzed MRI scans that were collected as part of this initiative, which show the structure of the brains of 230 people in the early stages of Parkinson's disease. Comparing these scans to those from age-matched healthy individuals allowed Zeighami et al. to identify the set of brain regions that show atrophy in the early stages of Parkinson's disease. These regions correspond to a normal brain network, and the relative extent of atrophy in each brain region supports the theory that the disease spreads through the connectome. The patients who were enrolled in this study will continue to be evaluated on a yearly basis. Zeighami et al. plan to continue mapping how the disease progresses throughout the brain and to relate this to the development of new symptoms of Parkinson's disease. DOI: http://dx.doi.org/10.7554/eLife.08440.002 We mapped the distribution of atrophy in Parkinson's disease (PD) using magnetic resonance imaging (MRI) and clinical data from 232 PD patients and 117 controls from the Parkinson's Progression Markers Initiative. Deformation-based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation. DOI: http://dx.doi.org/10.7554/eLife.08440.001

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    Europe PubMed Central
    Article . 2015
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    eLife
    Article . 2015 . Peer-reviewed
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      Europe PubMed Central
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      eLife
      Article . 2015 . Peer-reviewed
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    Authors: Wu, Jianxiao; Eickhoff, Simon B; Hoffstaedter, Felix; Patil, Kaustubh R; +3 Authors

    Abstract The recent availability of population-based studies with standard neuroimaging measurements and extensive psychometric characterization opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of the prediction model based on connectivity within a network of brain regions severely limits the interpretation of the brain-behavior patterns from a cognitive neuroscience perspective. To address this issue, we here propose a connectivity-based psychometric prediction (CBPP) framework based on individual region’s connectivity profile. Preliminary to the development of this region-wise machine learning approach, we performed an extensive assessment of the general CBPP framework based on whole-brain connectivity information. Because a systematic evaluation of different parameters was lacking from previous literature, we evaluated several approaches pertaining to the different steps of a CBPP study. We hence tested 72 different approach combinations in a cohort of over 900 healthy adults across 98 psychometric variables. Overall, our extensive evaluation combined to an innovative region-wise machine learning approach, offering a framework that optimizes both prediction performance and neurobiological validity (and hence interpretability) to study brain-behavior relationships.

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    bioRxiv
    Preprint . 2020
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    Cerebral Cortex
    Article . 2021 . Peer-reviewed
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    Other literature type . 2021
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      Cerebral Cortex
      Article . 2021 . Peer-reviewed
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      Europe PubMed Central
      Other literature type . 2021
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      Cerebral Cortex
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    Authors: Thiago Junqueira Ribeiro de Rezende; Brunno Machado de Campos; Johnny Hsu; Yue Li; +6 Authors

    Abstract Introduction The increasing use of large sample sizes for population and personalized medicine requires high‐throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification. Exploring the reproducibility of these tools reveals the specific strengths and weaknesses that heavily influence the interpretation of results, contributing to transparence in science. Methods We tested–retested the reproducibility of MRICloud, a free automated method for whole‐brain, multimodal MRI segmentation and quantification, on two public, independent datasets of healthy adults. Results The reproducibility was extremely high for T1‐volumetric analysis, high for diffusion tensor images (DTI) (however, regionally variable), and low for resting‐state fMRI. Conclusion In general, the reproducibility of the different modalities was slightly superior to that of widely used software. This analysis serves as a normative reference for planning samples and for the interpretation of structure‐based MRI studies. Addressing the methodological reproducibility of imaging postprocessing is essential for planning and data interpretation. MRICloud showed reproducible results for whole‐brain, multimodal, structure‐based quantification. Structural analyses (volumetric and DTI) show higher reproducibility than rsfMRI analysis.

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    Europe PubMed Central
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    Brain and Behavior
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      Brain and Behavior
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    Authors: Balázs Szalkai; Csaba Kerepesi; Bálint Varga; Vince Grolmusz;

    Here we show a method of directing the edges of the connectomes, prepared from diffusion tensor imaging (DTI) datasets from the human brain. Before the present work, no high-definition directed braingraphs (or connectomes) were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the "Consensus Connectome Dynamics" (CCD), described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed connectomes publicly available at the site \url{http://braingraph.org}. We also show the robustness of our edge directing method in four independently chosen connectome datasets: we have found that 86\% of the edges, which were present in all four datasets, get the very same directions in all datasets; therefore the direction method is robust, it does not depend on the particular choice of the dataset. We think that our present contribution opens up new possibilities in the analysis of the high-definition human connectome: from now on we can work with a robust assignment of directions of the connections of the human brain.

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    PLoS ONE
    Article . 2018
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    PLoS ONE
    Article . 2019 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2016
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      https://doi.org/10.48550/arxiv...
      Article . 2016
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    Authors: Sybren Van Hoornweder; Marten Nuyts; Joana Frieske; Stefanie Verstraelen; +2 Authors

    AbstractBackgroundElectric field (E-field) modeling is a potent tool to examine the cortical effects of transcranial magnetic and electrical stimulation (TMS and tES, respectively) and to address the high variability in efficacy observed in the literature. However, outcome measures used to report E-field magnitude vary considerably and have not yet been compared in detail.ObjectivesThe goal of this two-part study, encompassing a systematic review and modeling experiment, was to provide an overview of the different outcome measures used to report the magnitude of tES and TMS E-fields, and to conduct a direct comparison of these measures across different stimulation montages.MethodsThree electronic databases were searched for tES and/or TMS studies reporting E-field magnitude. We extracted and discussed outcome measures in studies meeting the inclusion criteria. Additionally, outcome measures were compared via models of four common tES and two TMS modalities in 100 healthy younger adults.ResultsIn the systematic review, we included 118 studies using 151 outcome measures related to E-field magnitude. Structural and spherical regions of interest (ROI) analyses and percentile-based whole-brain analyses were used most often. In the modeling analyses, we found that there was an average of only 6% overlap between ROI and percentile-based whole-brain analyses in the investigated volumes within the same person. The overlap between ROI and whole-brain percentiles was montage- and person-specific, with more focal montages such as 4×1 and APPS-tES, and figure-of-eight TMS showing up to 73%, 60%, and 52% overlap between ROI and percentile approaches respectively. However, even in these cases, 27% or more of the analyzed volume still differed between outcome measures in every analyses.ConclusionsThe choice of outcome measures meaningfully alters the interpretation of tES and TMS E-field models. Well-considered outcome measure selection is imperative for accurate interpretation of results, valid between-study comparisons, and depends on stimulation focality and study goals. We formulated four recommendations to increase the quality and rigor of E-field modeling outcome measures. With these data and recommendations, we hope to guide future studies towards informed outcome measure selection, and improve the comparability of studies.

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    Europe PubMed Central
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    Lirias
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    NeuroImage
    Article . 2023 . Peer-reviewed
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      NeuroImage
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    Authors: Bastiani, M; Cottaar, M; Dikranian, K; Ghosh, A; +5 Authors

    Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and –x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity. Highlights • A new comprehensive framework for both asymmetric fod estimation and tractography. • Extension of classical CSD approaches applicable to single and multi-shell data. • Validation using anatomically relevant fibre patterns derived from histology. • Correct reconstruction of sub-voxel fanning polarities and sharp bends.

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    Other literature type . 2018
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    Authors: Diedrichsen, Jörn; Zotow, Ewa;

    The paper presents a flat representation of the human cerebellum, useful for visualizing functional imaging data after volume-based normalization and averaging across subjects. Instead of reconstructing individual cerebellar surfaces, the method uses a white- and greymatter surface defined on volume-averaged anatomical data. Functional data can be projected along the lines of corresponding vertices on the two surfaces. The flat representation is optimized to yield a roughly proportional relationship between the surface area of the 2Drepresentation and the volume of the underlying cerebellar grey matter. The map allows users to visualize the activation state of the complete cerebellar grey matter in one concise view, equally revealing both the anterior-posterior (lobular) and medial-lateral organization. As examples, published data on resting-state networks and task-related activity are presented on the flatmap. The software and maps are freely available and compatible with most major neuroimaging packages. Copyright:

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    Article . 2015
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    PLoS ONE
    Article . 2015 . Peer-reviewed
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    PLoS ONE
    Article . 2015
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    Authors: Roberta, Riccelli; Nicola, Toschi; Salvatore, Nigro; Antonio, Terracciano; +1 Authors

    Abstract The five-factor model (FFM) is a widely used taxonomy of human personality; yet its neuro anatomical basis remains unclear. This is partly because past associations between gray-matter volume and FFM were driven by different surface-based morphometry (SBM) indices (i.e. cortical thickness, surface area, cortical folding or any combination of them). To overcome this limitation, we used Free-Surfer to study how variability in SBM measures was related to the FFM in n = 507 participants from the Human Connectome Project. Neuroticism was associated with thicker cortex and smaller area and folding in prefrontal–temporal regions. Extraversion was linked to thicker pre-cuneus and smaller superior temporal cortex area. Openness was linked to thinner cortex and greater area and folding in prefrontal–parietal regions. Agreeableness was correlated to thinner prefrontal cortex and smaller fusiform gyrus area. Conscientiousness was associated with thicker cortex and smaller area and folding in prefrontal regions. These findings demonstrate that anatomical variability in prefrontal cortices is linked to individual differences in the socio-cognitive dispositions described by the FFM. Cortical thickness and surface area/folding were inversely related each others as a function of different FFM traits (neuroticism, extraversion and consciousness vs openness), which may reflect brain maturational effects that predispose or protect against psychiatric disorders.

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    Europe PubMed Central
    Article . 2017
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    Authors: Csaba Kerepesi; Balázs Szalkai; Bálint Varga; Vince Kornél Grolmusz;

    Abstract The human braingraph, or connectome is a description of the connections of the brain: the nodes of the graph correspond to small areas of the gray matter, and two nodes are connected by an edge if a diffusion MRI-based workflow finds fibers between those brain areas. We have constructed 1015-vertex graphs from the diffusion MRI brain images of 392 human subjects and compared the individual graphs with respect to several different areas of the brain. The inter-individual variability of the graphs within different brain regions was discovered and described. We have found that the frontal and the limbic lobes are more conservative, while the edges in the temporal and occipital lobes are more diverse. Interestingly, a “hybrid” conservative and diverse distribution was found in the paracentral lobule and the fusiform gyrus. Smaller cortical areas were also evaluated: precentral gyri were found to be more conservative, and the postcentral and the superior temporal gyri to be very diverse. Similar studies concerning the human genome discovered more and less conservative sections of the DNA, opening up entirely new fields in genomics. We think that the present study is the first step in this direction in human connectomics. The clinical significance of the conservativity of a given cerebral area could be the higher sensitivity for traumas and developmental or neuro-degenerative events than the less conservative areas.

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    Neuroscience Letters
    Article . 2018 . Peer-reviewed
    License: Elsevier TDM
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      Neuroscience Letters
      Article . 2018 . Peer-reviewed
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    Authors: Bertrand eThirion; Bertrand eThirion; Gael eVaroquaux; Gael eVaroquaux; +4 Authors

    International audience; Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subjects images, parcellation approaches use brain activity (e.g. found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and K-means clustering algorithms. We show that in general Ward's clustering performs better than alternative methods with regard to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.

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