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computational model psychiatry

James A Roberts, Karl J Friston, Michael Breakspear
Brain activity derives from intrinsic dynamics (due to neurophysiology and anatomical connectivity) in concert with stochastic effects that arise from sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random fluctuations can be studied with stochastic dynamic models (SDMs). This article, Part II of a two-part series, reviews applications of SDMs to large-scale neural systems in health and disease. Stochastic models have already elucidated a number of pathophysiological phenomena, such as epilepsy and hypoxic ischemic encephalopathy, although their use in biological psychiatry remains rather nascent...
April 2017: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
James A Roberts, Karl J Friston, Michael Breakspear
Biological phenomena arise through interactions between an organism's intrinsic dynamics and stochastic forces-random fluctuations due to external inputs, thermal energy, or other exogenous influences. Dynamic processes in the brain derive from neurophysiology and anatomical connectivity; stochastic effects arise through sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random effects can be studied with stochastic dynamic models (SDMs)...
April 2017: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Frederike H Petzschner
No abstract text is available yet for this article.
April 2017: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Marion Rouault, Tricia Seow, Claire M Gillan, Stephen M Fleming
BACKGROUND: Distortions in metacognition-the ability to reflect on and control other cognitive processes-are thought to be characteristic of poor mental health. However, it remains unknown whether such shifts in self-evaluation are due to specific alterations in metacognition and/or a downstream consequence of changes in decision-making processes. METHODS: Using perceptual decision making as a model system, we employed a computational psychiatry approach to relate parameters governing both decision formation and metacognitive evaluation to self-reported transdiagnostic symptom dimensions in a large general population sample (N = 995)...
January 11, 2018: Biological Psychiatry
O Hirsch, M Schulz, M Erhart, N Donner-Banzhoff
Objective: All health care systems in the world struggle with rising costs for drugs. We sought to explore factors impacting on prescribing costs in a nationwide database of ambulatory care in Germany. Factors identified by this research can be used for adjustment in future profiling efforts. Methods: We analysed nationwide prescription data of physicians having contractual relationships with statutory health insurance funds in 2014. Predictor and outcome variables were aggregated at the practice level...
2018: Cost Effectiveness and Resource Allocation: C/E
Dominik Spinczyk, Karolina Nabrdalik, Katarzyna Rojewska
BACKGROUND: Diagnosing and treating anorexia nervosa is an important challenge for modern psychiatry. Taking into account a connection between the mental state of a person and the characteristics of their language, this paper presents developed and tested method for analyzing the written statements of patients with anorexia nervosa and healthy individuals, including the identification of keywords. METHODS: Due to the short nature of the texts, which is related to the difficulty of expressing oneself about one's body when suffering from anorexia, the bag of words approach was used for documents' information representation...
February 2, 2018: Biomedical Engineering Online
Silvia Lopez-Guzman, Anna B Konova, Kenway Louie, Paul W Glimcher
Measuring temporal discounting through the use of intertemporal choice tasks is now the gold standard method for quantifying human choice impulsivity (impatience) in neuroscience, psychology, behavioral economics, public health and computational psychiatry. A recent area of growing interest is individual differences in discounting levels, as these may predispose to (or protect from) mental health disorders, addictive behaviors, and other diseases. At the same time, more and more studies have been dedicated to the quantification of individual attitudes towards risk, which have been measured in many clinical and non-clinical populations using closely related techniques...
2018: PloS One
Teresa A Victor, Sahib S Khalsa, W Kyle Simmons, Justin S Feinstein, Jonathan Savitz, Robin L Aupperle, Hung-Wen Yeh, Jerzy Bodurka, Martin P Paulus
INTRODUCTION: Although neuroscience has made tremendous progress towards understanding the basic neural circuitry underlying important processes such as attention, memory and emotion, little progress has been made in applying these insights to psychiatric populations to make clinically meaningful treatment predictions. The overall aim of the Tulsa 1000 (T-1000) study is to use the NIMH Research Domain Criteria framework in order to establish a robust and reliable dimensional set of variables that quantifies the positive and negative valence, cognition and arousal domains, including interoception, to generate clinically useful treatment predictions...
January 24, 2018: BMJ Open
Stefan Frässle, Yu Yao, Dario Schöbi, Eduardo A Aponte, Jakob Heinzle, Klaas E Stephan
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...
January 25, 2018: Wiley Interdisciplinary Reviews. Cognitive Science
Anne G E Collins
Learning to make rewarding choices in response to stimuli depends on a slow but steady process, reinforcement learning, and a fast and flexible, but capacity-limited process, working memory. Using both systems in parallel, with their contributions weighted based on performance, should allow us to leverage the best of each system: rapid early learning, supplemented by long-term robust acquisition. However, this assumes that using one process does not interfere with the other. We use computational modeling to investigate the interactions between the two processes in a behavioral experiment and show that working memory interferes with reinforcement learning...
January 18, 2018: Journal of Cognitive Neuroscience
Jie Sui, Xiaosi Gu
Self representation is fundamental to mental functions. While the self has mostly been studied in traditional psychophilosophical terms ('self as subject'), recent laboratory work suggests that the self can be measured quantitatively by assessing biases towards self-associated stimuli ('self as object'). Here, we summarize new quantitative paradigms for assessing the self, drawn from psychology, neuroeconomics, embodied cognition, and social neuroscience. We then propose a neural model of the self as an emerging property of interactions between a core 'self network' (e...
November 2017: Trends in Neurosciences
Jacqueline Scholl, Miriam Klein-Flügge
Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision-making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms...
September 28, 2017: Behavioural Brain Research
Pierre Bourdillon, Caroline Apra, Marc Lévêque, Fabien Vinckier
Contrary to common psychosurgical practice in the 1950s, Dr. Jean Talairach had the intuition, based on clinical experience, that the brain connectome and neuroplasticity had a role to play in psychosurgery. Due to the remarkable progress of pharmacology at that time and to the technical limits of neurosurgery, these concepts were not put into practice. Currently, these concepts are being confirmed by modern techniques such as neuroimaging and computational neurosciences, and could pave the way for therapeutic innovation in psychiatry...
September 2017: Neurosurgical Focus
Quentin J M Huys, Daniel Renz
Computational psychiatry aims to apply mathematical and computational techniques to help improve psychiatric care. To achieve this, the phenomena under scrutiny should be within the scope of formal methods. As emotions play an important role across many psychiatric disorders, such computational methods must encompass emotions. Here, we consider formal valuation accounts of emotions. We focus on the fact that the flexibility of emotional responses and the nature of appraisals suggest the need for a model-based valuation framework for emotions...
September 15, 2017: Biological Psychiatry
Vaughan Bell, Caryl Marshall, Zara Kanji, Sam Wilkinson, Peter Halligan, Quinton Deeley
BACKGROUND: Capgras delusion is scientifically important but most commonly reported as single case studies. Studies analysing large clinical records databases focus on common disorders but none have investigated rare syndromes. AIMS: Identify cases of Capgras delusion and associated psychopathology, demographics, cognitive function and neuropathology in light of existing models. METHOD: Combined computational data extraction and qualitative classification using 250 000 case records from South London and Maudsley Clinical Record Interactive Search (CRIS) database...
July 2017: BJPsych Open
Robb B Rutledge, Michael Moutoussis, Peter Smittenaar, Peter Zeidman, Tanja Taylor, Louise Hrynkiewicz, Jordan Lam, Nikolina Skandali, Jenifer Z Siegel, Olga T Ousdal, Gita Prabhu, Peter Dayan, Peter Fonagy, Raymond J Dolan
Importance: Major depressive disorder (MDD) is associated with deficits in representing reward prediction errors (RPEs), which are the difference between experienced and predicted reward. Reward prediction errors underlie learning of values in reinforcement learning models, are represented by phasic dopamine release, and are known to affect momentary mood. Objective: To combine functional neuroimaging, computational modeling, and smartphone-based large-scale data collection to test, in the absence of learning-related concerns, the hypothesis that depression attenuates the impact of RPEs...
August 1, 2017: JAMA Psychiatry
Frederike H Petzschner, Lilian A E Weber, Tim Gard, Klaas E Stephan
This article outlines how a core concept from theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differential diagnosis in computational psychiatry and computational psychosomatics. In particular, we discuss 1) how conceptualizing perception and action as inference-control loops yields a joint computational perspective on brain-world and brain-body interactions and 2) how the concrete formulation of this loop as a hierarchical Bayesian model points to key computational quantities that inform a taxonomy of potential disease mechanisms...
September 15, 2017: Biological Psychiatry
Anne Katrine Pagsberg, Pia Jeppesen, Dea Gowers Klauber, Karsten Gjessing Jensen, Ditte Rudå, Marie Stentebjerg-Olesen, Peter Jantzen, Simone Rasmussen, Eva Ann-Sofie Saldeen, Maj-Britt Glenn Lauritsen, Niels Bilenberg, Anne Dorte Stenstrøm, Louise Nyvang, Sarah Madsen, Thomas M Werge, Theis Lange, Christian Gluud, Maria Skoog, Per Winkel, Jens Richardt M Jepsen, Birgitte Fagerlund, Christoph U Correll, Anders Fink-Jensen
BACKGROUND: Head-to-head trials to guide antipsychotic treatment choices for paediatric psychosis are urgently needed because extrapolations from adult studies might not be implementable. In this superiority trial with two-sided significance testing, we aimed to compare the efficacy and safety of quetiapine-extended release (quetiapine-ER) versus aripiprazole in children and adolescents with first-episode psychosis, to determine whether differences between the two treatments were sufficient to guide clinicians in their choice of one drug over the other...
August 2017: Lancet Psychiatry
Petr Dluhoš, Daniel Schwarz, Wiepke Cahn, Neeltje van Haren, René Kahn, Filip Španiel, Jiří Horáček, Tomáš Kašpárek, Hugo Schnack
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues...
July 15, 2017: NeuroImage
John H Krystal, John D Murray, Adam M Chekroud, Philip R Corlett, Genevieve Yang, Xiao-Jing Wang, Alan Anticevic
Schizophrenia research is plagued by enormous challenges in integrating and analyzing large datasets and difficulties developing formal theories related to the etiology, pathophysiology, and treatment of this disorder. Computational psychiatry provides a path to enhance analyses of these large and complex datasets and to promote the development and refinement of formal models for features of this disorder. This presentation introduces the reader to the notion of computational psychiatry and describes discovery-oriented and theory-driven applications to schizophrenia involving machine learning, reinforcement learning theory, and biophysically-informed neural circuit models...
May 1, 2017: Schizophrenia Bulletin
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