Generative embedding for model-based classification of fMRI data
Kay H. Brodersen,Thomas M. Schofield,Alexander P. Leff,Cheng Soon Ong,Ekaterina I. Lomakina,Ekaterina I. Lomakina,Joachim M. Buhmann,Klaas E. Stephan,Klaas E. Stephan +8 more
TLDR
Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods and is envisaged that future applications of generativeembedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups.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.read more
Citations
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Journal ArticleDOI
Functional and effective connectivity: a review.
TL;DR: The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses.
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Building better biomarkers: brain models in translational neuroimaging
TL;DR: The state of translational neuroimaging is reviewed, an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings is outlined and a program of broad exploration followed by increasingly rigorous assessment of generalizability is outlined.
Journal ArticleDOI
The Computational Anatomy of Psychosis
Rick A. Adams,Klaas E. Stephan,Klaas E. Stephan,Harriet R. Brown,Chris D. Frith,Karl J. Friston +5 more
TL;DR: It is suggested that both pervasive trait abnormalities and florid failures of inference in the psychotic state can be linked to factors controlling post-synaptic gain – such as NMDA receptor function and (dopaminergic) neuromodulation.
Journal ArticleDOI
Bayesian model reduction and empirical Bayes for group (DCM) studies
Karl J. Friston,Vladimir Litvak,Ashwini Oswal,Adeel Razi,Klaas E. Stephan,Bernadette C.M. van Wijk,Gabriel Ziegler,Peter Zeidman +7 more
TL;DR: The robustness of Bayesian model reduction to violations of the Laplace assumption in dynamic causal modelling is illustrated and how its recursive application can facilitate both classical and Bayesian inference about group differences is considered.
Journal ArticleDOI
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
TL;DR: A new SNN architecture, called NeuCube, is introduced for the creation of concrete models to map, learn and understand STBD and can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events.
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