Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP)

Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP)

Blog Article

Abstract Traditional diagnostic formulations of psychotic disorders have low correspondence with underlying disease neurobiology.This has led to a growing interest in using brain-based biomarkers to capture Conditioner biologically-informed psychosis constructs.Building upon our prior work on the B-SNIP Psychosis Biotypes, we aimed to examine whether structural MRI (an independent biomarker not used in the Biotype development) can effectively classify the Biotypes.

Whole brain voxel-wise grey matter density (GMD) maps from T1-weighted images were used to train and test (using repeated randomized train/test splits) binary L2-penalized logistic regression models to discriminate psychosis cases (n = 557) from healthy controls (CON, n = 251).A total of six models were evaluated across two psychosis categorization schemes: (i) three Biotypes (B1, B2, B3) and (ii) three DSM diagnoses (schizophrenia (SZ), schizoaffective (SAD) and bipolar (BD) disorders).Above-chance classification accuracies were observed in all Biotype (B1 = 0.

70, B2 = 0.65, and B3 = 0.56) and diagnosis (SZ = 0.

64, SAD = 0.64, and BD = 0.59) models.

However, the only model that showed evidence of specificity was B1, i.e., the model Ball - Equipment was able to discriminate B1 vs.

CON and did not misclassify other psychosis cases (B2 or B3) as B1 at rates above nominal chance.The GMD-based classifier evidence for B1 showed a negative association with an estimate of premorbid general intellectual ability, regardless of group membership, i.e.

psychosis or CON.Our findings indicate that, complimentary to clinical diagnoses, the B-SNIP Psychosis Biotypes may offer a promising approach to capture specific aspects of psychosis neurobiology.