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Articles by Jiook Cha
Polygenic risk-informed white matter integrity improves deep learning-based prediction of youth depression
Abstract Early detection of youth depression is crucial, given its rising prevalence and long-term consequences. Although genetic factors contribute significantly to youth depression, their integration with neuroimaging remains limited. This study aims to bridge the gap by predicting youth depression through a combined genetic and neuroimaging framework.
Polygenic Risk-Informed White Matter Integrity Improves Deep Learning-Based Prediction of Youth Depression
The authors have declared no competing interest.
Awe is characterized as an ambivalent experience in the human behavior and cortex: integrated virtual reality-electroencephalogram study
Abstract Understanding complex emotions, characterized by the co-occurrence of positive and negative feelings, is crucial for unlocking the full spectrum of human affective experiences. However, the behavior and neural representation of ambivalent feelings, particularly during awe, remain elusive. To address this gap, we combined awe-inducing virtual reality clips, electroencephalogram, and a deep learning-based dimensionality reduction technique (N = 43).
Structural neural markers of response to cognitive behavioral therapy in pediatric obsessive‐compulsive disorder
Original Article David Pagliaccio Corresponding Author david.pagliaccio@nyspi.columbia.edu orcid.org/https://orcid.org/0000-0002-1214-1965 Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA Correspondence David Pagliaccio, Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, Room 6106, 1051 Riverside Drive,...
Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data
Abstract Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data.
Structural neural markers of response to cognitive behavioral therapy in pediatric obsessive‐compulsive disorder
Original Article David Pagliaccio Corresponding Author E-mail address: david.pagliaccio@nyspi.columbia.edu https://orcid.org/0000-0002-1214-1965 Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA Correspondence David Pagliaccio, Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, Room 6106, 1051 Riverside...
Associations Between Brain Structure and Connectivity in Infants and Exposure to Selective Serotonin Reuptake Inhibitors During Pregnancy
Question Is prenatal exposure to selective serotonin reuptake inhibitors associated with fetal brain development? Findings In this cohort study including 98 infants, significant gray matter volume expansion was noted in the amygdala and insula, as well as an increase in white matter structural connectivity between these same regions in selective serotonin reuptake inhibitor–exposed infants, compared with infants exposed to untreated prenatal maternal depression and healthy controls.
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