The field of computational psychiatry is growing in prominence along with recent advances in computational neuroscience, machine learning, and the cumulative scientific understanding of psychiatric disorders. Computational approaches based on cutting-edge technologies and high-dimensional data are expected to provide an understanding of psychiatric disorders with integrating the notions of psychology and neuroscience, and to contribute to clinical practices. However, the multidisciplinary nature of this field seems to limit the development of computational psychiatry studies. Computational psychiatry combines knowledge from neuroscience, psychiatry, and computation; thus, there is an emerging need for a platform to integrate and coordinate these perspectives. In this study, we developed a new database for visualizing research papers as a two-dimensional "map" called the Computational Psychiatry Research Map (CPSYMAP). This map shows the distribution of papers along neuroscientific, psychiatric, and computational dimensions to enable anyone to find niche research and deepen their understanding ofthe field.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746554 | PMC |
| http://dx.doi.org/10.3389/fpsyt.2020.578706 | DOI Listing |
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Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses.
Br J Psychiatry
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Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, USA; Department of Human Genetics, University of California Los Angeles, USA; and Department of Computational Medicine, University of California Los Angeles, USA.
Background: Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).
Aims: We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.
Genome-wide association study unravels mechanisms of brain glymphatic activity.
Nat Commun
January 2025
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
Brain glymphatic activity, as indicated by diffusion-tensor imaging analysis along the perivascular space (ALPS) index, is involved in developmental neuropsychiatric and neurodegenerative diseases, but its genetic architecture is poorly understood. Here, we identified 17 unique genome-wide significant loci and 161 candidate genes linked to the ALPS-indexes in a discovery sample of 31,021 individuals from the UK Biobank. Seven loci were replicated in two independent datasets.
View Article and Find Full Text PDFDistinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy.
Biol Psychiatry Cogn Neurosci Neuroimaging
January 2025
Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA. Electronic address:
Background: Decision-making in uncertain environments can lead to varied outcomes, and how we process those outcomes may depend on our emotional state. Understanding how individuals interpret the sources of uncertainty is crucial for understanding adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity.
View Article and Find Full Text PDFAltered Neural Responses to Punishment Learning in Conduct Disorder.
Biol Psychiatry Cogn Neurosci Neuroimaging
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Department of Child and Adolescent Psychiatry, Faculty of Medicine, TUD Dresden University of Technology, German Center for Child and Adolescent Health (DZKJ), partner site Leipzig/Dresden, Dresden, Germany.
Objective: Conduct disorder (CD) is associated with deficits in the use of punishment for reinforcement learning (RL) and subsequent decision-making, contributing to reckless, antisocial, and aggressive behaviors. Here, we used functional magnetic resonance imaging (fMRI) to examine whether differences in behavioral learning rates derived from computational modeling, particularly for punishment, are reflected in aberrant neural responses in youths with CD compared to typically-developing controls (TDCs).
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Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI.
Eur Radiol Exp
January 2025
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK.
Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2-10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.
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