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Generative Embedding for Model-Based Classification of fMRI Data

Brodersen, KH; Schofield, TM; Leff, AP; Ong, CS; Lomakina, EI; Buhmann, JM; Stephan, KE; (2011) Generative Embedding for Model-Based Classification of fMRI Data. PLOS COMPUT BIOL , 7 (6) , Article e1002079. 10.1371/journal.pcbi.1002079. Green open access

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Abstract

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in 'hidden' physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups.

Type: Article
Title: Generative Embedding for Model-Based Classification of fMRI Data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pcbi.1002079
Publisher version: http://dx.doi.org/10.1371/journal.pcbi.1002079
Language: English
Additional information: © 2011 Brodersen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was funded by the University Research Priority Program ‘Foundations of Human Social Behaviour’ at the University of Zurich (KHB, KES), the SystemsX.ch project NEUROCHOICE (KHB, KES; http://www.systemsx.ch/), the NCCR ‘Neural Plasticity’ (KES; http://www.nccr-neuro.uzh.ch/), the Wellcome Trust (APL; grant ME033459MES), and the NIHR CBRC at University College Hospitals London (APL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Keywords: DYNAMIC CAUSAL-MODELS, DIMENSIONAL PATTERN-CLASSIFICATION, HUMAN VISUAL-CORTEX, STATE FUNCTIONAL CONNECTIVITY, PRODROMAL ALZHEIMERS-DISEASE, MACHINE LEARNING-METHODS, HIDDEN MARKOV-MODELS, HUMAN BRAIN ACTIVITY, EVOKED-RESPONSES, VECTOR MACHINE
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
URI: https://discovery.ucl.ac.uk/id/eprint/1316954
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