Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry

Wiecki, Thomas V.; Poland, Jeffrey; Frank, Michael J.

doi:10.1177/2167702614565359

Cited by 161 publications

(161 citation statements)

References 84 publications

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“…Moreover, our prior work showed that two of the key parameters characterizing WM and RL systems are related to genetic variations in prefrontal versus striatal function, respectively. The use of computational modeling for teasing apart the purported neural and psychological processes governing motivational deficits in learning and decision making may be particularly useful with disorders, such as schizophrenia, that involve multiple cognitive deficits (Montague et al, 2012;Wiecki et al, 2014). Indeed, it is difficult to isolate the role of specific processes in a quantifiable fashion using behavioral or imaging methods alone.…”

Section: Discussionmentioning

confidence: 99%

Working Memory Contributions to Reinforcement Learning Impairments in Schizophrenia

et al. 2014

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Previous research has shown that patients with schizophrenia are impaired in reinforcement learning tasks. However, behavioral learning curves in such tasks originate from the interaction of multiple neural processes, including the basal ganglia-and dopaminedependent reinforcement learning (RL) system, but also prefrontal cortex-dependent cognitive strategies involving working memory (WM). Thus, it is unclear which specific system induces impairments in schizophrenia. We recently developed a task and computational model allowing us to separately assess the roles of RL (slow, cumulative learning) mechanisms versus WM (fast but capacity-limited) mechanisms in healthy adult human subjects. Here, we used this task to assess patients' specific sources of impairments in learning. In 15 separate blocks, subjects learned to pick one of three actions for stimuli. The number of stimuli to learn in each block varied from two to six, allowing us to separate influences of capacity-limited WM from the incremental RL system. As expected, both patients (n ϭ 49) and healthy controls (n ϭ 36) showed effects of set size and delay between stimulus repetitions, confirming the presence of working memory effects. Patients performed significantly worse than controls overall, but computational model fits and behavioral analyses indicate that these deficits could be entirely accounted for by changes in WM parameters (capacity and reliability), whereas RL processes were spared. These results suggest that the working memory system contributes strongly to learning impairments in schizophrenia.

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Section: Discussionmentioning

confidence: 99%

Working Memory Contributions to Reinforcement Learning Impairments in Schizophrenia

et al. 2014

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“…Hyett et al, 2015). Finally, it is possible to disregard physiological data altogether and define subgroups based on generative modeling of behavioral data alone (Wiecki et al, 2015).…”

Section: Strategies For Identifying Patient Subgroupsmentioning

confidence: 99%

Translational Perspectives for Computational Neuroimaging

Stephan

Iglesias

Heinzle

et al. 2015

Neuron

161

148

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Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches-biophysical network models, generative models, and model-based fMRI analyses of neuromodulation-strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.

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“…The study at hand is one step in this direction. The adopted computational psychiatry approach enables estimation of specific parameters that provide mechanistic accounts of functioning in one or another cognitive domain (Wiecki et al, 2015) and informs the modeling-based fMRI analysis of neural learning signatures (Stephan et al, 2015).…”

Section: Relevance To Addiction Theoriesmentioning

confidence: 99%

Impaired Flexible Reward-Based Decision-Making in Binge Eating Disorder: Evidence from Computational Modeling and Functional Neuroimaging

Reiter

Heinze

Schlagenhauf

et al. 2016

Neuropsychopharmacol

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Despite its clinical relevance and the recent recognition as a diagnostic category in the DSM-5, binge eating disorder (BED) has rarely been investigated from a cognitive neuroscientific perspective targeting a more precise neurocognitive profiling of the disorder. BED patients suffer from a lack of behavioral control during recurrent binge eating episodes and thus fail to adapt their behavior in the face of negative consequences, eg, high risk for obesity. To examine impairments in flexible reward-based decision-making, we exposed BED patients (n = 22) and matched healthy individuals (n = 22) to a reward-guided decision-making task during functional resonance imaging (fMRI). Performing fMRI analysis informed via computational modeling of choice behavior, we were able to identify specific signatures of altered decision-making in BED. On the behavioral level, we observed impaired behavioral adaptation in BED, which was due to enhanced switching behavior, a putative deficit in striking a balance between exploration and exploitation appropriately. This was accompanied by diminished activation related to exploratory decisions in the anterior insula/ventro-lateral prefrontal cortex. Moreover, although so-called model-free reward prediction errors remained intact, representation of ventro-medial prefrontal learning signatures, incorporating inference on unchosen options, was reduced in BED, which was associated with successful decision-making in the task. On the basis of a computational psychiatry account, the presented findings contribute to defining a neurocognitive phenotype of BED.

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Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry

Cited by 161 publications

References 84 publications

Working Memory Contributions to Reinforcement Learning Impairments in Schizophrenia

Working Memory Contributions to Reinforcement Learning Impairments in Schizophrenia

Translational Perspectives for Computational Neuroimaging

Impaired Flexible Reward-Based Decision-Making in Binge Eating Disorder: Evidence from Computational Modeling and Functional Neuroimaging

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