RT Journal Article
SR Electronic
T1 Highly Predictive Transdiagnostic Features Shared across Schizophrenia, Bipolar Disorder, and ADHD Identified Using a Machine Learning Based Approach
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 453951
DO 10.1101/453951
A1 Yuelu Liu
A1 Monika S. Mellem
A1 Humberto Gonzalez
A1 Matthew Kollada
A1 Atul R. Mahableshwarkar
A1 Annette Madrid
A1 William J. Martin
A1 Parvez Ahammad
YR 2018
UL http://biorxiv.org/content/early/2018/12/18/453951.abstract
AB The Diagnostic and Statistical Manual of Mental Disorders (DSM) is the standard for diagnosing psychiatric disorders in the United States. However, evidence has suggested that symptoms in psychiatric disorders are not restricted to the boundaries between DSM categories, implying an underlying latent transdiagnostic structure of psychopathology. Here, we applied an importance-guided machine learning technique for model selection to item-level data from self-reported instruments contained within the Consortium for Neuropsychiatric Phenomics dataset. From 578 questionnaire items, we identified a set of features which consisted of 85 items that were shared across diagnoses of schizophrenia (SCZ), bipolar disorder (BD), and attention deficit/hyperactivity disorder (ADHD). A classifier trained on the transdiagnostic features reliably distinguished the patient group as a whole from healthy controls (classification AUC = 0.95) and only 10 items were needed to attain the performance level of AUC being 0.90. A sum score created from the items produced high separability between patients and healthy controls (Cohen’s d = 2.85), and it outperformed predefined sum scores and sub-scores within the instruments (Cohen’s d ranging between 0.13 and 1.21). The transdiagnostic features comprised both symptom domains (e.g. dysregulated mood, attention deficit, and anhedonia) and personality traits (e.g. neuroticism, impulsivity, and extraversion). Moreover, by comparing the features that were common across the three patient groups with those that were most predictive of a single patient category, we can describe the unique features for each patient group superimposed on the transdiagnostic feature structure. Overall, our results reveal a latent transdiagnostic symptom/behavioral phenotypic structure shared across SCZ, BD, and ADHD and present a new perspective to understand insights offered by self-report psychiatric instruments.