Generative models for clinical applications in computational psychiatry

Abstract

Despite the success of modern neuroimaging techniques in furthering our understanding of cognitive and pathophysiological processes, translation of these advances into clinically relevant tools has been virtually absent until now. Neuromodeling represents a powerful framework for overcoming this translational deadlock, and the development of computational models to solve clinical problems has become a major scientific goal over the last decade, as reflected by the emergence of clinically oriented neuromodeling fields like Computational Psychiatry, Computational Neurology, and Computational Psychosomatics. Generative models of brain physiology and connectivity in the human brain play a key role in this endeavor, striving for computational assays that can be applied to neuroimaging data from individual patients for differential diagnosis and treatment prediction. In this review, we focus on dynamic causal modeling (DCM) and its use for Computational Psychiatry. DCM is a widely used generative modeling framework for functional magnetic resonance imaging (fMRI) and magneto-/electroencephalography (M/EEG) data. This article reviews the basic concepts of DCM, revisits examples where it has proven valuable for addressing clinically relevant questions, and critically discusses methodological challenges and recent methodological advances. We conclude this review with a more general discussion of the promises and pitfalls of generative models in Computational Psychiatry and highlight the path that lies ahead of us. This article is categorized under: Neuroscience > Computation Neuroscience > Clinical Neuroscience.

Regression dynamic causal modeling for resting-state fMRI

“…The combination of computational efficiency and applicability to simple conditions like the "resting state" renders rDCM particularly promising for application in the fields of Computational Psychiatry and Computational Neurology. Here, computational readouts of directed connectivity in whole-brain networks are of major relevance because global dysconnectivity has been postulated as a hallmark of most psychiatric and neurological disorders (Deco and Kringelbach, 2014;Fornito et al, 2015;Frässle et al, 2018b;Menon, 2011;Stephan et al, 2015). The ability to infer whole-brain effective connectivity patterns from rs-fMRI measurements is appealing from a clinical perspective as it puts minimal burden on the patient in the MR scanner.…”

Theoretical Modeling of Cognitive Dysfunction in Schizophrenia by Means of Errors and Corresponding Brain Networks

“…The basic model of dynamic causal modeling (DCM) was enriched of modeling of neuronal fluctuations, called the stochastic modeling or spectral models. Thus, DCM could be used also for resting state fMRI data (Frässle et al, 2018 ). The debate is whether model involves all possible biological knowledge to model neuronal function.…”

“…Some authors suggest that for example activity dependent plasticity or back-propagation is neglected in standard DCM (Daunizeau et al, 2011 ) But, Daunizeau also questioned if these specific “fine grained” mechanisms could be captured in BOLD signal. The other limitation of DCM is that the model is limited to maximum 10 regions, though new regression DCM method could possibly extend to whole-brain connectome analysis (Frässle et al, 2018 ).…”

Breathlessness and the brain: the role of expectation

“…The closer the model fits the real data, the more confident we can be that the model may be capturing some important aspects of the data. We can then test whether the model is capturing some important aspects of the data, and therefore potentially behaviour [80 ▪ ]. Existing neuroimaging work typically describes correlations between brain and behaviour, whereas computational modelling moves beyond correlations and allows us to generate and test hypotheses regarding the possible underlying mechanisms of breathlessness originating in the brain.…”