Transport on Riemannian Manifold for Functional Connectivity-Based Classification

Ng, Bernard; Dressler, Martin; Varoquaux, Gaël; Poline, Jean‐Baptiste; Greicius, Michael D.; Thirion, Bertrand

doi:10.1007/978-3-319-10470-6_51

Cited by 29 publications

(42 citation statements)

References 15 publications

(35 reference statements)

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“…Inverse covariance (SGGM) the studies involved 51 healthy subjects lying at rest and were scanned twice, with a 24 min memory task between the two scans [8]. The accuracy achieved in classifying whether a connectivity pattern corresponds to before or after the memory task was 98% with our approach, whereas using Pearson's correlation as features obtained an accuracy of 76%.…”

Section: Inverse Covariance (Oas)mentioning

confidence: 98%

“…To bring the covariance estimates of different brain states of all subjects to a common tangent space, we propose a matrix whitening transport 1 1 A preliminary conference version of this work appeared in [8].…”

Section: Transport On Riemannian Manifold For Connectivity-based Braimentioning

confidence: 99%

“…In particular, a number of longitudinal studies have explored how interventions, e.g. motor [7] and memory training [8], between acquisitions alter resting state activity. The classification task under this setting involves separating resting state connectivity patterns before and after interventions [8].…”

Section: Introductionmentioning

confidence: 99%

“…motor [7] and memory training [8], between acquisitions alter resting state activity. The classification task under this setting involves separating resting state connectivity patterns before and after interventions [8]. Under these experimental frameworks where each time instance is no longer associated with a clear class label, the set of brain volumes over the entire task duration is usually taken as a single sample and estimates of functional connectivity between brain regions, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Transport on Riemannian Manifold for Connectivity-Based Brain Decoding

Varoquaux

Poline

et al. 2016

IEEE Trans. Med. Imaging

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Abstract-There is a recent interest in using functional magnetic resonance imaging (fMRI) for decoding more naturalistic, cognitive states, in which subjects perform various tasks in a continuous, self-directed manner. In this setting, the set of brain volumes over the entire task duration is usually taken as a single sample with connectivity estimates, such as Pearson's correlation, employed as features. Since covariance matrices live on the positive semidefinite cone, their elements are inherently inter-related. The assumption of uncorrelated features implicit in most classifier learning algorithms is thus violated. Coupled with the usual small sample sizes, the generalizability of the learned classifiers is limited, and the identification of significant brain connections from the classifier weights is nontrivial. In this paper, we present a Riemannian approach for connectivity-based brain decoding. The core idea is to project the covariance estimates onto a common tangent space to reduce the statistical dependencies between their elements. For this, we propose a matrix whitening transport, and compare it against parallel transport implemented via the Schild's ladder algorithm. To validate our classification approach, we apply it to fMRI data acquired from twenty four subjects during four continuous, selfdriven tasks. We show that our approach provides significantly higher classification accuracy than directly using Pearson's correlation and its regularized variants as features. To facilitate result interpretation, we further propose a non-parametric scheme that combines bootstrapping and permutation testing for identifying significantly discriminative brain connections from the classifier weights. Using this scheme, a number of neuroanatomically meaningful connections are detected, whereas no significant connections are found with pure permutation testing.

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Section: Inverse Covariance (Oas)mentioning

confidence: 98%

Section: Transport On Riemannian Manifold For Connectivity-based Braimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transport on Riemannian Manifold for Connectivity-Based Brain Decoding

Varoquaux

Poline

et al. 2016

IEEE Trans. Med. Imaging

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“…We refer to this technique as bootstrapped permutation test (BPT). BPT is originally proposed for inferring significant features from classifier weights [10], but as discussed here and in the next section, BPT is in fact applicable to arbitrary models with a number of properties that makes it advantageous over PT and SS. Bootstrapping is traditionally used for assessing variability in model parameters.…”

Section: Introductionmentioning

confidence: 99%

Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations

Poline

Thirion

et al. 2015

Lecture Notes in Computer Science

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Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence

Yamin

Valsasina

Tessadori

et al. 2023

Human Brain Mapping

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Multiple sclerosis (MS) is a neurological condition characterized by severe structural brain damage and by functional reorganization of the main brain networks that try to limit the clinical consequences of structural burden. Resting‐state (RS) functional connectivity (FC) abnormalities found in this condition were shown to be variable across different MS phases, according to the severity of clinical manifestations. The article describes a system exploiting machine learning on RS FC matrices to discriminate different MS phenotypes and to identify relevant functional connections for MS stage characterization. To this end, the system exploits some mathematical properties of covariance‐based RS FC representation, which can be described by a Riemannian manifold. The classification performance of the proposed framework was significantly above the chance level for all MS phenotypes. Moreover, the proposed system was successful in identifying relevant RS FC alterations contributing to an accurate phenotype classification.

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Transport on Riemannian Manifold for Functional Connectivity-Based Classification

Cited by 29 publications

References 15 publications

Transport on Riemannian Manifold for Connectivity-Based Brain Decoding

Transport on Riemannian Manifold for Connectivity-Based Brain Decoding

Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations

Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence

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