Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure

Multi-task learning for joint diagnosis of CNVs and psychiatric conditions from rs-fMRI

L'imagerie par résonance magnétique fonctionnelle à l'état de repos (IRMf-R) s'est imposée comme une technologie diagnostique prometteuse. Toutefois, l'application dans la pratique clinique des biomarqueurs de l'IRMf-R visant à capturer les mécanismes biologiques sous-jacents aux troubles psychiatriques a été entravée par le manque de généralisation. Le diagnostic de ces troubles repose entièrement sur des évaluations comportementales et les taux élevés de comorbidités et de chevauchement génétique et symptomatique confirment l'existence de facteurs latents communs à toutes les pathologies. De grandes mutations génétiques rares, appelées variants du nombre de copies (CNV), ont été associées à une série de troubles psychiatriques et ont des effets beaucoup plus importants sur la structure et la fonction du cerveau, ce qui en fait une voie prometteuse pour démêler la génétique des catégories diagnostiques actuelles. L'apprentissage multitâche est une approche prometteuse pour extraire des représentations communes à des tâches connexes, qui permet de mieux utiliser les données en tirant parti des informations partagées et en améliorant la généralisabilité. Nous avons recueilli un ensemble de données sans précédent composé de 19 CNV et de troubles psychiatriques et nous avons cherché à évaluer systématiquement les avantages potentiels de l'apprentissage multitâche pour la précision de la prédiction, afin d'effectuer un diagnostic conjoint de ces conditions interdépendantes. Nous avons estimé les tailles d'effet pour chaque condition, comparé la précision du diagnostic en utilisant des méthodes courantes d'apprentissage automatique, puis en utilisant l'apprentissage multitâches. Nous avons tenté de contrôler les multiples facteurs confondants tout au long des analyses et discutons des différentes approches permettant de le faire dans le contexte de la modélisation prédictive. L'hypothèse selon laquelle les facteurs latents partagés entre les CNV et les troubles psychiatriques les rendraient suffisamment liés en tant que tâches de prédiction pour bénéficier d'un apprentissage conjoint n'a pas été confirmée. Cependant, nous avons également appliqué l'apprentissage multitâche entre les sites pour prédire une cible commune et nous avons montré que la prédiction peut être améliorée lorsque les tâches sont très étroitement liées. Nous avons mis en œuvre un modèle léger de partage des paramètres durs, mais nos résultats et la littérature montrent que ce cadre n'est pas bien adapté aux tâches hétérogènes ou, de manière contre-intuitive, aux échantillons de petite taille. Nous pensons qu'il est possible d'exploiter les similitudes entre les CNV et les troubles psychiatriques en utilisant des méthodes qui modélisent les relations entre les tâches, mais la petite taille des échantillons pour les CNV rares constitue une limitation majeure pour l'application de l'apprentissage multitâche.Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising diagnostic technology, however translation into clinical practice of rs-fMRI biomarkers that aim to capture the biological mechanisms underlying psychiatric disorders has been hindered by lack of generalizability. The diagnosis of these disorders is completely based on behavioural assessments and high rates of comorbidities and genetic and symptom overlap supports the existence of latent factors shared across conditions. Rare large genetic mutations, called copy number variants (CNVs), have been associated with a range of psychiatric conditions and have much larger effect sizes on brain structure and function, which makes them a promising avenue for untangling the genetics of the current diagnostic categories. Multi-task learning is a promising approach to extract common representations across related tasks that makes better use of data by leveraging shared information and improves generalizability. We collected an unprecedented dataset consisting of 19 CNVs and psychiatric disorders and aimed to systematically assess the potential benefits for prediction accuracy of using multi-task learning to perform joint diagnosis of these interlinked conditions. We estimated effect sizes for each condition, benchmarked diagnostic accuracy using common machine learning methods, and then using multi-task learning. We attempted to control for multiple confounding factors throughout the analyses, and discuss different approaches to do so in the predictive modelling context. The hypothesis that latent factors shared between CNVs and psychiatric conditions would make them sufficiently related as prediction tasks to benefit from being learned jointly was not supported. However, we also applied multi-task learning across sites to predict a common target and showed that prediction can be improved when tasks are very tightly related. We implemented a lightweight hard parameter sharing model, but evidence from our results and the literature shows this framework is not well suited to heterogeneous tasks or, counterintuitively, to small sample sizes. While we believe there is potential to exploit the similarities between CNVs and psychiatric conditions using methods that model relationships between tasks, small sample sizes for rare CNVs are a major limitation for the application of multi-task learning

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition

Separate lanes for adding and reading in the white matter highways of the human brain

Published: 15 August 2019 Es OAMath and reading involve distributed brain networks and have both shared (e.g. encoding of visual stimuli) and dissociated (e.g. quantity processing) cognitive components. Yet, to date, the shared vs. dissociated gray and white matter substrates of the math and reading networks are unknown. Here, we define these networks and evaluate the structural properties of their fascicles using functional MRI, diffusion MRI, and quantitative MRI. Our results reveal that there are distinct gray matter regions which are preferentially engaged in either math (adding) or reading, and that the superior longitudinal and arcuate fascicles are shared across the math and reading networks. Strikingly, within these fascicles, reading- and math-related tracts are segregated into parallel sub-bundles and show structural differences related to myelination. These findings open a new avenue of research that examines the contribution of sub-bundles within fascicles to specific behaviors.This research was supported by the National Institute of Health (NIH; 1R01EY023915), by the Deutsche Forschungsgemeinschaft (DFG; GR 4850/1–1) and by an Innovation Grant from the Stanford Center for Cognitive and Neurobiological Imaging (CNI)

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer\u27s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, \u27shape connections\u27 between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus