64 research outputs found

    Bayesian pathway analysis over brain network mediators for survival data

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    Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity and time to disease onset, we propose an integrative Bayesian framework to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural modeling framework that includes a symmetric matrix-variate accelerated failure time model and a symmetric matrix response regression to characterize the effect paths. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Extensive simulations confirm the superiority of our method compared with existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies

    AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

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    BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

    Lead-DBS v3.0: Mapping Deep Brain Stimulation Effects to Local Anatomy and Global Networks.

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    Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics

    Virtual Fly Brain—An interactive atlas of the Drosophila nervous system

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    As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates

    How worms move in 3D

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    Animals that live in the sky, underwater or underground display unique three dimensional behaviours made possible by their ability to generate movement in all directions. As animals explore their environment, they constantly adapt their locomotion strategies to balance factors such as distance travelled, speed, and energy expenditure. While exploration strategies have been widely studied across a variety of species, how animals explore 3D space remains an open problem. The nematode Caenorhabditis elegans presents an ideal candidate for the study of 3D exploration as it is naturally found in complex fluid and granular environments and is well sized (~1mm long) for the simultaneous capture of individual postures and long term trajectories using a fixed imaging setup. However, until recently C. elegans has been studied almost exclusively in planar environments and in 3D neither its modes of locomotion nor its exploration strategies are known. Here we present methods for reconstructing microscopic postures and tracking macroscopic trajectories from a large corpus of triaxial recordings of worms freely exploring complex gelatinous fluids. To account for the constantly changing optical properties of these gels we develop a novel differentiable renderer to construct images from 3D postures for direct comparison with the recorded images. The method is robust to interference such as air bubbles and dirt trapped in the gel, stays consistent through complex sequences of postures and recovers reliable estimates from low-resolution, blurry images. Using this approach we generate a large dataset of 3D exploratory trajectories (over 6 hours) and midline postures (over 4 hours). We find that C. elegans explore 3D space through the composition of quasi-planar regions separated by turns and variable-length runs. To achieve this, C. elegans use locomotion gaits and complex manoeuvres that differ from those previously observed on an agar surface. We show that the associated costs of locomotion increase with non-planarity and we develop a mathematical model to probe the implications of this connection. We find that quasi-planar strategies (such as we find in the data) yield the largest volumes explored as they provide a balance between 3D coverage and trajectory distance. Taken together, our results link locomotion primitives with exploration strategies in the context of short term volumetric foraging to provide a first integrated study into how worms move in 3D

    Dense 4D nanoscale reconstruction of living brain tissue

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    Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue

    Immersive Virtual Reality Tool for Connectome Visualization and Analysis

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    The human brain is a complex organ made up of billions of neurons that are interconnected through a vast network of synapses. This network of connections enables the brain to perform a wide range of cognitive and motor functions. Studying and analyzing these brain networks is important for understanding how different regions of the brain communicate and work together to carry out specific tasks and how neurological disorders such as Alzheimer’s disease, Parkinson’s disease, or schizophrenia impact brain connectivity contributing to the development of these disorders. Virtual reality technology has proven to be a versatile tool for learning, exploration, and analysis. It can expand the user’s senses, provide a more detailed and immersive view of the subject matter, encourage active learning and exploration, and facilitate global analysis of complex data. In this dissertation, we present VRNConnect, a virtual reality system for interactively exploring brain connectivity data. VRNConnect enables users to analyze brain networks using either structural or functional connectivity matrices. By visualizing the 3D brain connectome network as a graph, users can interact with various regions using hand gestures or controllers to access network analysis metrics and information about Regions of Interest (ROIs). The system includes features such as colour coding of nodes and edges, thresholding, and shortest path calculation to enhance usability. Moreover, VRNConnect has the ability to be tailored to specific needs, allowing for the importation of connectivity data from various modalities. Our platform was designed with flexibility in mind, making it easy to incorporate additional features as needed. In order to evaluate the usability and cognitive workload associated with using our system, we conducted a study with 16 participants. Our findings suggest that VRNConnect could serve as an effective academic and analytical tool

    The relationship between white matter microstructure, attentional capabilities, and impulsive behavior in school-aged children

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    L’attention et l’impulsivité sont des construits cognitifs complexes subissant l’influence de plusieurs autres fonctions cognitives. Dans le cadre clinique, des déficits en termes d’attention ou d’impulsivité sont souvent évalués à l’intérieur du cadre diagnostique tel que le trouble d’attention avec ou sans hyperactivité (TDAH). Ce trouble psychiatrique touche près de 8% des enfants d’âge scolaire. La méthode de diagnostic repose entièrement sur des critères subjectifs basés sur l’observation des parents, enseignants et/ou cliniciens. Ceci génère une grande variabilité à l’intérieur du TDAH. L’attention et l’impulsivité font partie d’un des six grands domaines cognitifs élaborés par le Research Domain Criteria (RDoC) du National Institute of Mental Health (NIMH) et sont aussi impliqués dans le trouble de personnalité limite et le syndrome de la Tourette. L’identification de marqueurs biologiques est donc nécessaire afin d’aider les cliniciens dans leur prise de décision lors d’un diagnostic. Une des méthodes permettant d’évaluer la microstructure des fibres de matière blanche sous- jacente aux fonctions cognitives est l’imagerie par résonance magnétique (IRM) de diffusion. Bien que certains modèles dont l’imagerie par tenseur de diffusion, présentent plusieurs limitations importantes, de nouveaux modèles permettent une représentation plus juste de la situation biologique sous-jacente. En combinaison avec ces nouveaux modèles, l’approche connectomique et de réduction de dimension permet d’élargir les horizons de la circuiterie de matière blanche et mieux interpréter sa microstructure. Les travaux effectués dans le cadre de ce mémoire visent à identifier, caractériser et localiser les altérations de la microstructure de la matière blanche associées avec les capacités attentionnelles et le comportement impulsif chez une population d’enfants neurotypiques d’âge scolaire (N=171). Les résultats présentent une association négative entre la microstructure de la matière blanche et l’impulsivité sur des circuits reliant les régions frontales, pariétales, occipitales et des ganglions de base. De plus, les anomalies de matière blanche sont localisées très près des régions corticales sur ces circuits. Ces résultats suggèrent l’inclusion de d’autres modalités afin d’avoir un portrait plus global de la neurophysiologie des capacités attentionnelles et du comportement impulsif.Abstract : Attention and impulsivity are complex cognitive constructs influenced by many other cognitive functions. In the clinical world, attention deficits and impulsive behavior are mostly evaluated under the attention deficit-hyperactivity disorder (ADHD) diagnosis. This psychiatric disorder affects close to 8% of school-aged children. Since always, the diagnosis method lies solely on subjective criteria based on observations reported by the parents, teachers and/or clinicians. As a result, variability can be observed inside the ADHD diagnosis. Attention and impulsivity are part of one of the six cognitive domain established by the Research Domain Criteria (RDoC) by the National Institute of Mental Health (NIMH). They are also implicated in other psychiatric disorders such as the borderline personality disorder and the Tourette syndrome. Identifying biologicals markers is highly needed to provide a tool for a better clinical evaluation of children. One method that allows evaluation of the brain’s white matter (WM) fibers underneath the cognitive functions is the diffusion magnetic resonance imaging (MRI). Even though some model such as diffusion tensor imaging presents major limitations, new models can provide a better conceptualization of the complex WM microstructure found in the human brain. Combining this new model with the connectomics approach and dimensionality reduction of diffusion measures allows for a broader search of the brain’s WM circuitry and a better representation of the microstructural complexity. The work presented in this thesis aims to identify, characterize, and localize WM microstructural alterations associated with attentional capabilities and impulsive behavior in school-aged children (N=171). Results showed a negative association between WM microstructure and impulsivity on multiple connections in the frontal, parietal, occipital and basal ganglia regions. In addition, WM abnormalities were identified closer to the cortical regions on these connections. These results suggest the need for multimodal studies in order to obtain a more complete neurophysiological model of attentional capabilities and impulsive behavior
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