The Experts below are selected from a list of 2622 Experts worldwide ranked by ideXlab platform

Patric Hagmann - One of the best experts on this subject based on the ideXlab platform.

  • structural Connectomics in brain diseases
    NeuroImage, 2013
    Co-Authors: Alessandra Griffa, Patric Hagmann, Philipp S Baumann, Jeanphilippe Thiran
    Abstract:

    Imaging the connectome in vivo has become feasible through the integration of several rapidly developing fields of science and engineering, namely magnetic resonance imaging and in particular diffusion MRI on one side, image processing and network theory on the other side. This framework brings in vivo brain imaging closer to the real topology of the brain, contributing to narrow the existing gap between our understanding of brain structural organization on one side and of human behavior and cognition on the other side. Given the seminal technical progresses achieved in the last few years, it may be ready to tackle even greater challenges, namely exploring disease mechanisms. In this review we analyze the current situation from the technical and biological perspectives. First, we critically review the technical solutions proposed in the literature to perform clinical studies. We analyze for each step (i.e. MRI acquisition, network building and network statistical analysis) the advantages and potential limitations. In the second part we review the current literature available on a selected subset of diseases, namely, dementia, schizophrenia, multiple sclerosis and others, and try to extract for each disease the common findings and main differences between reports.

  • mr Connectomics principles and challenges
    Journal of Neuroscience Methods, 2010
    Co-Authors: Patric Hagmann, Leila Cammoun, Xavier Gigandet, Stephan Gerhard, Ellen P Grant, Van J Wedeen, Reto Meuli
    Abstract:

    MR Connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

  • FROM DIFFUSION MRI TO BRAIN Connectomics
    2005
    Co-Authors: Patric Hagmann
    Abstract:

    The success of diffusion MRI is deeply rooted in the fact that during their micrometric random displacements water molecules explore tissue microstructure. Hence by labeling magnetically spins of displaced water, diffusion MRI provides us with exquisite information about the sizes and orientations on the multiple compartments present inside an imaging voxel. Through the causal relation between on one hand the imaged restricted and oriented water mobility and on the other hand the axonal orientations in brain white matter, diffusion MRI has become a powerful method to infer fiber tract architecture and brain anatomic connectivity. This work is not only a journey that takes us from essential diffusion MRI physics to an investigation of the brain neuronal circuitry, but also a thesis aiming at demonstrating the power of large scale analysis of brain connectivity, where every technological component is essential. After a short introduction on molecular diffusion and diffusion NMR, we start by showing that diffusion contrast is positive. This key issue that was only postulated up to now, allows us to justify why diffusion can be computed accurately with the only signal modulus. Accordingly, various emerging MRI techniques that map non-Gaussian diffusion have found a sound justification. In particular it is precisely the result that allows us to map distribution of diffusion related spin displacements by Fourier transformation of the measured signal modulus, hence to do Diffusion Spectrum MRI. We show through multiples MR experiments how the shape of this distribution in a complicated multi-compartment biological system can be measured and how its characteristic local heterogeneity is a mirror of fiber architectures. We discuss in detail its interpretation and its relation to other diffusion MRI techniques. Tractography is the necessary link that from diffusion MRI provides us with nerve fiber trajectories and maps of brain axonal connectivity. In this thesis two algorithms are proposed, while the first is designed for diffusion tensor MRI, the second is shaped for high angular resolution diffusion MRI and specifically tested on Diffusion Spectrum MRI. After having considered the potentials and the limitations of these line generation algorithms, we investigate whether tractography can be formulated as a segmentation problem in a high dimensional non Euclidean space, i.e. position-orientation space. With the help of the developed tools we address some key neuro-scientific questions. Based on diffusion tensor MRI data of 32 healthy volunteers, language networks are investigated. It is shown that right-handed men are massively interconnected between the left-hemisphere speech areas whereas the homologous in the right hemisphere are sparse; furthermore interhemispheric connections between the speech areas and their contralatera1 homologues are relatively strong. Women and left-handed men have equally strong intrahemispheric connections in both hemispheres, but women have a higher density of interhemispheric connections. After this quantitative study, Diffusion Spectrum MRI data of a single subject is collected and tractography performed in order to analyze the global connectivity pattern of the human brain. For this purpose we propose to model this large scale architecture by an abstract graph. It is shown that the long-range axonal network exhibits a "small world" topology. This type of particular architecture is present in various large scale communication networks. They emerge usually from a growing process where optimal communication has to be developed under some resource constraints. Furthermore we show also that the architecture of axonal connectivity between cortical areas exhibits a hierarchical organization. These results, which confirm more indirect studies, provide essential material to discuss not only brain evolution and development but also information processing at the level of the brain.

Edward T Bullmore - One of the best experts on this subject based on the ideXlab platform.

  • prefrontal Connectomics from anatomy to human imaging
    Neuropsychopharmacology, 2021
    Co-Authors: Hesheng Liu, Suzanne N Haber, Jakob Seidlitz, Edward T Bullmore
    Abstract:

    The fundamental importance of prefrontal cortical connectivity to information processing and, therefore, disorders of cognition, emotion, and behavior has been recognized for decades. Anatomic tracing studies in animals have formed the basis for delineating the direct monosynaptic connectivity, from cells of origin, through axon trajectories, to synaptic terminals. Advances in neuroimaging combined with network science have taken the lead in developing complex wiring diagrams or connectomes of the human brain. A key question is how well these magnetic resonance imaging (MRI)-derived networks and hubs reflect the anatomic "hard wiring" first proposed to underlie the distribution of information for large-scale network interactions. In this review, we address this challenge by focusing on what is known about monosynaptic prefrontal cortical connections in non-human primates and how this compares to MRI-derived measurements of network organization in humans. First, we outline the anatomic cortical connections and pathways for each prefrontal cortex (PFC) region. We then review the available MRI-based techniques for indirectly measuring structural and functional connectivity, and introduce graph theoretical methods for analysis of hubs, modules, and topologically integrative features of the connectome. Finally, we bring these two approaches together, using specific examples, to demonstrate how monosynaptic connections, demonstrated by tract-tracing studies, can directly inform understanding of the composition of PFC nodes and hubs, and the edges or pathways that connect PFC to cortical and subcortical areas.

  • micro Connectomics probing the organization of neuronal networks at the cellular scale
    Nature Reviews Neuroscience, 2017
    Co-Authors: Edward T Bullmore, Manuel Schroter, Ole Paulsen
    Abstract:

    Micro-Connectomics involves determining the principles of how neuronal networks are organized at the cellular level. In this Review, Schroter, Paulsen and Bullmore examine studies that have provided insight into the network organization of relatively small, as well as more complex, nervous systems.

  • meta Connectomics human brain network and connectivity meta analyses
    Psychological Medicine, 2016
    Co-Authors: Nicolas Crossley, Peter T Fox, Edward T Bullmore
    Abstract:

    Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as 'meta-Connectomics'. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-Connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI 'lesions' to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-Connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.

  • Connectomics a new paradigm for understanding brain disease
    European Neuropsychopharmacology, 2015
    Co-Authors: Alex Fornito, Edward T Bullmore
    Abstract:

    In recent years, pathophysiological models of brain disorders have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disturbances of interconnected neural systems. This shift has paralleled rapid advances in Connectomics, a field concerned with comprehensively mapping the neural elements and inter-connections that constitute the brain. Magnetic resonance imaging (MRI) has played a central role in these efforts, as it allows relatively cost-effective in vivo assessment of the macro-scale architecture of brain network connectivity. In this paper, we provide a brief introduction to some of the basic concepts in the field and review how recent developments in imaging Connectomics are yielding new insights into brain disease, with a particular focus on Alzheimer's disease and schizophrenia. Specifically, we consider how research into circuit-level, connectome-wide and topological changes is stimulating the development of new aetiopathological theories and biomarkers with potential for clinical translation. The findings highlight the advantage of conceptualizing brain disease as a result of disturbances in an interconnected complex system, rather than discrete pathology in isolated sub-sets of brain regions.

  • schizophrenia neuroimaging and Connectomics
    NeuroImage, 2012
    Co-Authors: Alex Fornito, Andrew Zalesky, Christos Pantelis, Edward T Bullmore
    Abstract:

    Schizophrenia is frequently characterized as a disorder of brain connectivity. Neuroimaging has played a central role in supporting this view, with nearly two decades of research providing abundant evidence of structural and functional connectivity abnormalities in the disorder. In recent years, our understanding of how schizophrenia affects brain networks has been greatly advanced by attempts to map the complete set of inter-regional interactions comprising the brain's intricate web of connectivity; i.e., the human connectome. Imaging Connectomics refers to the use of neuroimaging techniques to generate these maps which, combined with the application of graph theoretic methods, has enabled relatively comprehensive mapping of brain network connectivity and topology in unprecedented detail. Here, we review the application of these techniques to the study of schizophrenia, focusing principally on magnetic resonance imaging (MRI) research, while drawing attention to key methodological issues in the field. The published findings suggest that schizophrenia is associated with a widespread and possibly context-independent functional connectivity deficit, upon which are superimposed more circumscribed, context-dependent alterations associated with transient states of hyper- and/or hypo-connectivity. In some cases, these changes in inter-regional functional coupling dynamics can be related to measures of intra-regional dysfunction. Topological disturbances of functional brain networks in schizophrenia point to reduced local network connectivity and modular structure, as well as increased global integration and network robustness. Some, but not all, of these functional abnormalities appear to have an anatomical basis, though the relationship between the two is complex. By comprehensively mapping connectomic disturbances in patients with schizophrenia across the entire brain, this work has provided important insights into the highly distributed character of neural abnormalities in the disorder, and the potential functional consequences that these disturbances entail.

Islem Rekik - One of the best experts on this subject based on the ideXlab platform.

  • neuropsychiatric disease classification using functional Connectomics results of the Connectomics in neuroimaging transfer learning challenge
    arXiv: Neurons and Cognition, 2020
    Co-Authors: Islem Rekik, Markus D Schirmer, Archana Venkataraman, Minjeong Kim, Stewart Mostofsky, Mary Beth Nebel, Keri S Rosch
    Abstract:

    Large, open-source consortium datasets have spurred the development of new and increasingly powerful machine learning approaches in brain Connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided demographic information of age, sex, IQ, and handedness. A second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Models were submitted in a standardized format as Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 different metrics. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each method. Five participants submitted their model for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are needed to reach the clinical translation of functional Connectomics. We are keeping the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of Connectomics.

  • joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to autism
    Medical Image Analysis, 2020
    Co-Authors: Islem Mhiri, Islem Rekik
    Abstract:

    Abstract Image-based brain maps, generally coined as ‘intensity or image atlases’, have led the field of brain mapping in health and disease for decades, while investigating a wide spectrum of neurological disorders. Estimating representative brain atlases constitute a fundamental step in several MRI-based neurological disorder mapping, diagnosis, and prognosis. However, these are strikingly lacking in the field of brain Connectomics, where connectional brain atlases derived from functional MRI (fRMI) or diffusion MRI (dMRI) are almost absent. On the other hand, conventional connectomic-based classification methods traditionally resort to feature selection methods to decrease the high-dimensionality of connectomic data for learning how to diagnose new patients. However, these are generally limited by high computational cost and a large variability in performance across different datasets, which might hinder the identification of reproducible biomarkers. To address both limitations, we unprecedentedly propose a brain network atlas-guided feature selection (NAG-FS) method to disentangle the healthy from the disordered connectome. To this aim, given a population of brain connectomes, we propose to learn how estimate a centered and representative functional brain network atlas (i.e., a population center) to reliably map the functional connectome and its variability across training individuals, thereby capturing their shared traits (i.e., connectional fingerprint of a population). Essentially, we first learn the pairwise similarities between connectomes in the population to map them into different subspaces. Next, we non-linearly diffuse and fuse connectomes living in each subspace, respectively. By integrating the produced subspace-specific network atlases we ultimately estimate the population network atlas. Last, we compute the difference between healthy and disordered network atlases to identify the most discriminative features, which are then used to train a predictive learner. Our method boosted the classification performance by 6% in comparison to state-of-the-art FS methods when classifying autistic and healthy subjects.

Andrew Zalesky - One of the best experts on this subject based on the ideXlab platform.

  • high resolution connectomic fingerprints mapping neural identity and behavior
    NeuroImage, 2021
    Co-Authors: Sina Mansour L, Ye Tian, B Thomas T Yeo, Vanessa Cropley, Andrew Zalesky
    Abstract:

    Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution Connectomics.

  • rich club and reward network connectivity as endophenotypes for alcohol dependence a diffusion tensor imaging study
    Addiction Biology, 2019
    Co-Authors: Nabi Zorlu, Andrew Zalesky, Necip Capraz, Esra Oztekin, Basak Bagci, Maria A Di Biase, Fazil Gelal, Emre Bora, Ercan Durmaz
    Abstract:

    We aimed to examine the whole-brain white matter connectivity and local topology of reward system nodes in patients with alcohol use disorder (AUD) and unaffected siblings, relative to healthy comparison individuals. Diffusion-weighted magnetic resonance imaging scans were acquired from 18 patients with AUD, 15 unaffected siblings of AUD patients and 15 healthy controls. Structural networks were examined using network-based statistic and connectomic analysis. Connectomic analysis showed a significant ordered difference in normalized rich club organization (AUD   Sibling > AUD) for both nodal clustering coefficient and nodal local efficiency in reward system nodes, particularly left caudate, right putamen and left hippocampus. Network-based statistic analyses showed that AUD group had significantly weaker connectivity than controls in the right hemisphere, mostly in the edges connecting putamen and hippocampus with other brain regions. Our results suggest that reward system network abnormalities, especially in subcortical structures, and impairments in rich-club organization might be related to the familial predisposition for AUD.

  • The Connectomics of brain disorders
    Nature Reviews Neuroscience, 2015
    Co-Authors: Alex Fornito, Andrew Zalesky, Michael Breakspear
    Abstract:

    Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

  • schizophrenia neuroimaging and Connectomics
    NeuroImage, 2012
    Co-Authors: Alex Fornito, Andrew Zalesky, Christos Pantelis, Edward T Bullmore
    Abstract:

    Schizophrenia is frequently characterized as a disorder of brain connectivity. Neuroimaging has played a central role in supporting this view, with nearly two decades of research providing abundant evidence of structural and functional connectivity abnormalities in the disorder. In recent years, our understanding of how schizophrenia affects brain networks has been greatly advanced by attempts to map the complete set of inter-regional interactions comprising the brain's intricate web of connectivity; i.e., the human connectome. Imaging Connectomics refers to the use of neuroimaging techniques to generate these maps which, combined with the application of graph theoretic methods, has enabled relatively comprehensive mapping of brain network connectivity and topology in unprecedented detail. Here, we review the application of these techniques to the study of schizophrenia, focusing principally on magnetic resonance imaging (MRI) research, while drawing attention to key methodological issues in the field. The published findings suggest that schizophrenia is associated with a widespread and possibly context-independent functional connectivity deficit, upon which are superimposed more circumscribed, context-dependent alterations associated with transient states of hyper- and/or hypo-connectivity. In some cases, these changes in inter-regional functional coupling dynamics can be related to measures of intra-regional dysfunction. Topological disturbances of functional brain networks in schizophrenia point to reduced local network connectivity and modular structure, as well as increased global integration and network robustness. Some, but not all, of these functional abnormalities appear to have an anatomical basis, though the relationship between the two is complex. By comprehensively mapping connectomic disturbances in patients with schizophrenia across the entire brain, this work has provided important insights into the highly distributed character of neural abnormalities in the disorder, and the potential functional consequences that these disturbances entail.

Gaspar Jekely - One of the best experts on this subject based on the ideXlab platform.

  • a serial multiplex immunogold labeling method for identifying peptidergic neurons in connectomes
    eLife, 2015
    Co-Authors: Reza Shahidi, E Williams, Markus Conzelmann, Albina Asadulina, Csaba Veraszto, Sanja Jasek, Luis A Bezarescalderon, Gaspar Jekely
    Abstract:

    Electron microscopy-based Connectomics aims to comprehensively map synaptic connections in neural tissue. However, current approaches are limited in their capacity to directly assign molecular identities to neurons. Here, we use serial multiplex immunogold labeling (siGOLD) and serial-section transmission electron microscopy (ssTEM) to identify multiple peptidergic neurons in a connectome. The high immunogenicity of neuropeptides and their broad distribution along axons, allowed us to identify distinct neurons by immunolabeling small subsets of sections within larger series. We demonstrate the scalability of siGOLD by using 11 neuropeptide antibodies on a full-body larval ssTEM dataset of the annelid Platynereis. We also reconstruct a peptidergic circuitry comprising the sensory nuchal organs, found by siGOLD to express pigment-dispersing factor, a circadian neuropeptide. Our approach enables the direct overlaying of chemical neuromodulatory maps onto synaptic connectomic maps in the study of nervous systems.

  • sigold a serial multiplex immunogold labeling method for identifying peptidergic neurons in connectomes
    bioRxiv, 2015
    Co-Authors: Reza Shahidi, E Williams, Markus Conzelmann, Albina Asadulina, Csaba Veraszto, Sanja Jasek, Luis A Bezarescalderon, Gaspar Jekely
    Abstract:

    Connectomics aims to comprehensively map synaptic connections in blocks of neural tissue. However, current approaches are limited in directly assigning molecular identities to neurons in a connectome. Here, we use serial multiplex immunogold labeling (siGOLD) combined with serial-section transmission electron microscopy (ssTEM) to reveal the identity of multiple peptidergic neurons in a connectome. The unique antigenicity of neuropeptides, combined with their uniform distribution along axons, allowed us to identify distinct neurons by immunolabeling a small subset of sections within larger series. We demonstrate the scalability of siGOLD by identifying several peptidergic neurons using 11 neuropeptide antibodies on a full-body larval ssTEM dataset of the annelid Platynereis. We also reconstruct the circuitry of the sensory nuchal organs that were found by siGOLD to express the circadian neuropeptide pigment-dispersing factor. Our approach opens up the possibility for directly overlaying chemical neuromodulatory maps onto synaptic connectomic maps in the study of nervous systems.