Computational psychiatry as a bridge from neuroscience to clinical applications

Nature Neuroscience, Journal Year: 2016, Volume and Issue: 19(3), P. 404 - 413

Published: Feb. 23, 2016

Language: Английский

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The Predictive Coding Account of Psychosis

Biological Psychiatry, Journal Year: 2018, Volume and Issue: 84(9), P. 634 - 643

Published: May 25, 2018

Fueled by developments in computational neuroscience, there has been increasing interest the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding current state world are made combining prior beliefs with incoming sensory signals. Mismatches between signals constitute prediction errors that drive new learning. Psychosis suggested to result from a decreased precision encoding relative data, thereby garnering maladaptive inferences. we review evidence for aberrant discuss challenges this canonical account For example, hallucinations delusions may relate distinct alterations coding, despite their common co-occurrence. More broadly, some studies implicate weakened psychosis, others find stronger priors. These might be answered more nuanced view coding. Different priors specified different modalities integration, deficits each modality need not uniform. Furthermore, hierarchical organization critical. Altered processes at lower levels hierarchy linearly related higher (and vice versa). Finally, theories do highlight active inference—the process through which effects our actions on sensations anticipated minimized. It is possible conflicting findings reconciled considering these complexities, portending framework psychosis equipped deal its many manifestations.

Language: Английский

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Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders

Neuron, Journal Year: 2014, Volume and Issue: 84(5), P. 892 - 905

Published: Dec. 1, 2014

Language: Английский

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The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

NeuroImage, Journal Year: 2016, Volume and Issue: 145, P. 377 - 388

Published: July 29, 2016

Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization healthcare options to individual patient should accordingly improve results. We propose a novel approach brain interventions based on personalized network models derived from non-invasive structural data patients. Along example with bitemporal epilepsy, we show step by how develop Virtual Epileptic Patient (VEP) model integrate patient-specific information such as connectivity, epileptogenic zone MRI lesions. Using high-performance computing, systematically carry out parameter space explorations, fit validate against patient's empirical stereotactic EEG (SEEG) demonstrate strategies towards therapy intervention.

Language: Английский

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Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression

Frontiers in Human Neuroscience, Journal Year: 2016, Volume and Issue: 10

Published: Nov. 15, 2016

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as inversion generative model viscerosensory inputs allows formal definition dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence brain’s states) allostasis change in prior beliefs or predictions which define setpoints homeostatic reflex arcs). Critically, we propose performance interoceptive-allostatic circuitry is monitored by metacognitive layer updates capacity successfully regulate states (allostatic self-efficacy). In this framework, can be understood sequential responses interoceptive ensuing diagnosis allostatic self-efficacy. While might represent an early response with adaptive value (cf. sickness behaviour), may trigger generalised belief self-efficacy lack control learned helplessness), resulting depression. perspective implies alternative pathophysiological mechanisms are reflected differential abnormalities effective connectivity circuits allostasis. We discuss suitably extended models could distinguish these patterns individual patients help inform future.

Language: Английский

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Human cognitive aging: Corriger la fortune?

Science, Journal Year: 2014, Volume and Issue: 346(6209), P. 572 - 578

Published: Oct. 30, 2014

Human cognitive aging differs between and is malleable within individuals. In the absence of a strong genetic program, it open to host hazards, such as vascular conditions, metabolic syndrome, chronic stress, but also protective enhancing factors, experience-dependent plasticity. Longitudinal studies suggest that leading an intellectually challenging, physically active, socially engaged life may mitigate losses consolidate gains. Interventions help identify contexts mechanisms successful give science society hint about what would be possible if conditions were different.

Language: Английский

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Psychiatric Symptom Dimensions Are Associated With Dissociable Shifts in Metacognition but Not Task Performance

Biological Psychiatry, Journal Year: 2018, Volume and Issue: 84(6), P. 443 - 451

Published: Jan. 11, 2018

Language: Английский

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Computational Psychiatry: towards a mathematically informed understanding of mental illness

Journal of Neurology Neurosurgery & Psychiatry, Journal Year: 2015, Volume and Issue: unknown, P. jnnp - 310737

Published: July 8, 2015

Computational Psychiatry aims to describe the relationship between brain9s neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification diagnosis treatment of illness. It can unite many levels description a mechanistic rigorous fashion, while avoiding biological reductionism artificial categorisation. We how models cognition infer current state weigh up future actions, these provide new perspectives on two example disorders, depression schizophrenia. Reinforcement learning describes brain choose value courses actions according their long-term value. Some depressive result from aberrant valuations, which could arise prior beliefs about loss agency (‘helplessness’), or an inability inhibit exploration aversive events. Predictive coding explains might perform Bayesian inference by combining sensory data with beliefs, each weighted certainty (or precision). Several cortical abnormalities schizophrenia reduce precision at higher inferential hierarchy, biasing towards away beliefs. discuss whether striatal hyperdopaminergia have adaptive function this context, also reinforcement incentive salience shed light disorder. Finally, we review some Psychiatry9s applications neurological such as Parkinson9s disease, pitfalls avoid when applying methods.

Language: Английский

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Searching for Cross-Diagnostic Convergence: Neural Mechanisms Governing Excitation and Inhibition Balance in Schizophrenia and Autism Spectrum Disorders

Biological Psychiatry, Journal Year: 2017, Volume and Issue: 81(10), P. 848 - 861

Published: March 15, 2017

Language: Английский

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Reinforcement learning in depression: A review of computational research

Neuroscience & Biobehavioral Reviews, Journal Year: 2015, Volume and Issue: 55, P. 247 - 267

Published: May 12, 2015

Language: Английский

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