Riemannian Regression and Classification Models of Brain Networks Applied to Autism

Wong, Eleanor; Anderson, Jeffrey S.; Zielinski, Brandon A.; Fletcher, P. Thomas

doi:10.1007/978-3-030-00755-3_9

Cited by 31 publications

(28 citation statements)

References 20 publications

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“…With this covariance structure, we study three different parametrizations of functional interactions: full correlation, partial correlation (Smith et al, 2011;Varoquaux and Craddock, 2013) and the tangent space of covariance matrices. The latter is less frequently used but has solid mathematical foundations and a variety of groups have reported good decoding per-formances with this framework (Varoquaux et al, 2010a;Barachant et al, 2013;Ng et al, 2014;Dodero et al, 2015;Qiu et al, 2015;Rahim et al, 2017;Wong et al, 2018). We compared two variants, using as a reference point the Euclidean mean (Varoquaux et al, 2010a) or the geometric mean (Ng et al, 2014); in both cases we rely on Nilearn implementation (Abraham et al, 2014b).…”

Section: Connectivity Parametrizationmentioning

confidence: 99%

Benchmarking functional connectome-based predictive models for resting-state fMRI

et al. 2019

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Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices. We systematically study the prediction performances of models in 6 different cohorts and a total of 2 000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence). The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning. For each step we benchmark typical choices: 8 different ways of defining regions-either pre-defined or generated from the rest-fMRI data-3 measures to build functional connectomes from the extracted time-series, and 10 classification models to compare functional interactions across subjects. Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations in the populations and sites. We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations and partial correlation; and that simple linear predictors such as a logistic regression give the best predictions. Our work is a step forward to establishing reproducible imaging-based biomarkers for clinical settings.

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Section: Connectivity Parametrizationmentioning

confidence: 99%

Benchmarking functional connectome-based predictive models for resting-state fMRI

et al. 2019

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“…However, in most of the cases, these features are not sufficient enough to diagnose ASD accurately. Machine learning classifiers can perform well when information from neuroimage data are used as features [8].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks

Mostafa

Tang

2019

IEEE Access

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Autism spectrum disorder (ASD) is a neuro dysfunction which causes the repetitive behavior and social instability of patients. Diagnosing ASD has been of great interest. However, due to the lack of discriminate differences between neuroimages of healthy persons and ASD patients, there has been no powerful diagnosis approach. In this study, we have designed brain network-based features for the diagnosis of ASD. Specifically, we have used the 264 regions based parcellation scheme to construct a brain network from a brain functional magnetic resonance imaging (fMRI). Then we have defined 264 raw brain features by the 264 eigenvalues of the Laplacian matrix of the brain network and another three features by network centralities. By applying a feature selection algorithm, we have obtained 64 discriminate features. Furthermore, we have trained several machine learning models for diagnosing ASD with our obtained features on ABIDE (Autism Brain Imaging Data Exchange) dataset. With our derived features, the linear discriminant analysis has achieved the classification accuracy of 77.7%, which is better than the state-ofthe-art results.

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“…Furthermore, some metrics may be more suitable than others for different applications. Indeed, we find published results that show how useful geometry can be with data on the SPD manifold (e.g [WAZF18], [NDV + 14]). We saw how to use the representation of points on the manifold as tangent vectors at a reference point to fit any machine learning algorithm, and we compared the effect of different metrics on the manifold of SPD matrices.…”

Section: Classifying Brain Connectomes In Geomstatsmentioning

confidence: 87%

Introduction to Geometric Learning in Python with Geomstats

Miolane¹,

Guigui²,

Zaatiti³

et al. 2020

Proceedings of the Python in Science Conference

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There is a growing interest in leveraging differential geometry in the machine learning community. Yet, the adoption of the associated geometric computations has been inhibited by the lack of a reference implementation. Such an implementation should typically allow its users: (i) to get intuition on concepts from differential geometry through a hands-on approach, often not provided by traditional textbooks; and (ii) to run geometric machine learning algorithms seamlessly, without delving into the mathematical details. To address this gap, we present the open-source Python package geomstats and introduce hands-on tutorials for differential geometry and geometric machine learning algorithms-Geometric Learning-that rely on it. Code and documentation: github.com/geomstats/geomstats and geomstats.ai.

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Riemannian Regression and Classification Models of Brain Networks Applied to Autism

Cited by 31 publications

References 20 publications

Benchmarking functional connectome-based predictive models for resting-state fMRI

Benchmarking functional connectome-based predictive models for resting-state fMRI

Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks

Introduction to Geometric Learning in Python with Geomstats

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