Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches

Lanxin Ji, Shashwath A. Meda, Carol A. Tamminga, Brett A. Clementz, Matcheri S. Keshavan, John A. Sweeney, Elliot S. Gershon, Godfrey D. Pearlson

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Background: Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiological markers. Here, we characterized a local functional-connectivity measure, regional homogeneity (ReHo), as a biomarker across Biotypes and conventional DSM diagnoses. Methods: Whole-brain ReHo measures of resting-state functional MRI were examined in psychosis patients and healthy controls organized by Biotype and by DSM-IV-TR diagnosis (n = 737). Group-level ANOVA and individual-level prediction models using support vector machines (SVM) were employed to evaluate the discriminative characteristics in comparisons of 1) DSM diagnostic groups, 2) Biotypes, to controls, and 3) within-proband subgroups with each other. Results: Probands grouped by Biotype versus controls showed a unique abnormality pattern: Biotype-1 displayed bidirectional ReHo differences in more widespread areas, with higher ReHo in para-hippocampus, fusiform, inferior temporal, cerebellum, thalamus and caudate, plus lower ReHo in the postcentral gyrus, middle temporal, cuneus, and middle occipital cortex; Biotype-2 and Biotype-3 showed lesser and unidirectional ReHo changes. Among diagnostic groups, only schizophrenia showed higher ReHo versus control values in the inferior/middle temporal area and fusiform gyrus. For within-patient comparisons, Biotype-1 showed characteristic ReHo when compared to Biotype-2 and Biotype-3. SVM results more accurately identified Biotypes than DSM diagnoses. Conclusion: We characterized patterns of ReHo abnormalities across both Biotypes and DSM sub-groups. Both group-level statistical and machine-learning methods were more sensitive in capturing ReHo deficits in Biotypes than DSM. Overall ReHo is a robust psychosis biomarker.

Original languageEnglish (US)
Pages (from-to)430-438
Number of pages9
JournalSchizophrenia Research
Volume215
DOIs
StatePublished - Jan 2020

Keywords

  • Biological marker
  • Biotypes
  • Machine learning
  • Psychosis
  • ReHo
  • Resting-state

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry

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