Beyond Description and Deficits: How Computational Psychiatry Can Enhance an Understanding of Decision-Making in Anorexia Nervosa

Despite decades of research, anorexia nervosa (AN) remains poorly understood and clearly effective treatments continue to be elusive. Thus, novel theoretical frameworks are needed to advance mechanistic and treatment research for this disorder. Progress in research on AN may have been slowed by systematic biases historically affecting psychiatric research. In this manuscript, we describe the influence of two such biases: 1) Descriptive bias: the tendency to define mechanisms on the basis of surface or face-valid characteristics; and 2) Deficit bias: the tendency to search for mechanisms associated with under-functioning of decision-making abilities and related circuity, and to neglect problems of over-functioning, in psychiatric disorders. The theories and methods of computational psychiatry are well-suited to overcome these biases by emphasizing the role of computational misalignments (rather than absolute deficits or excesses) between decision-making strategies and environmental demands as the key factors promoting psychiatric illnesses. Informed by this approach, we present an account of AN as a disorder of excess goal pursuit, maintained by over-engagement of executive functioning strategies and circuits conventionally considered to be positive for supporting mental health. We provide evidence that this same computational imbalance may constitute neglected phenotype presenting transdiagnostically across psychiatric disorders. We highlight how traditional psychiatric treatments could be ineffective, or even iatrogenic, for this clinical group and suggest future directions for computational models that may be well suited for identifying more precise mechanistic targets for disorders of excess goal pursuit.

Computational Psychiatry Needs Time and Context

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Mental State of Inpatients with COVID-19: a Computational Psychiatry Approach

ABSTRACTThe overload of healthcare systems around the world and the danger of infection have limited the ability of researchers to obtain sufficient and reliable data on psychopathology in hospitalized patients with COVID-19. The relationship between severe SARS-CoV-2 infection and specific mental disturbances remains poorly understood.Aimto reveal the possibility of identifying the typology and frequency of psychiatric syndromes associated with acute COVID-19 using cluster analysis of discrete psychopathological phenomena.Materials and methodsDescriptive data on the mental state of 55 inpatients with COVID-19 were obtained by young-career physicians with psychiatric backgrounds. Classification of observed clinical phenomena was performed with k-means cluster analysis of variables codded from the main psychopathological symptoms. Dispersion analysis with p-level 0.05 was used to reveal the cluster’s differences in demography, parameters of inflammation and respiration function collected on the basis of the original medical records.ResultsThree resulting clusters of patients were identified: persons with anxiety, disorders of fluency and tempo of thinking, mood, attention, motor-volitional sphere, reduced insight, and pessimistic plans for the future (n=11); persons without psychopathology (n=37); persons with disorientation, disorders of memory, attention, fluency, and tempo of thinking, reduced insight (n=7). The development of a certain type of impaired mental state was specifically associated with: age, lung lesions according to computed tomography, saturation, respiratory rate, C-reactive protein level, platelet count.ConclusionThe prevalence and typology of psychiatric disorders in patients with acute COVID-19 were described using the computational psychiatry approach.

The Normative Modeling Framework for Computational Psychiatry

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus healthy control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of downstream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.