List of Topics:
Location Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification - 2022

using-computational-modeling-to-capture-schizophrenia-specific-reinforcement-learning-differences-and-their-implications-on-patient-classification.jpg

Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification | S-Logix

Research Area:  Machine Learning

Abstract:

Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error–driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex.

Keywords:  
Symptomatology
Bipolar disorder
Schizophrenia
Basal ganglia

Author(s) Name:  Andra Geana,Barch, Deanna M.,James M. Gold

Journal name:  Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.bpsc.2021.03.017

Volume Information:  Volume 7

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S2451902221001154

Related Papers