NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders

TLDR

Many mental illnesses exhibit overlapping symptoms, and while large neuroimaging datasets offer unprecedented opportunities to study them, reproducible and comparable imaging markers remain elusive, underscoring the need for standardized approaches. The authors propose NeuroMark, a priori‑driven ICA pipeline that estimates brain functional network measures from fMRI data to link network abnormalities across datasets, studies, and disorders. NeuroMark automatically derives subject‑specific features using reliable brain network templates from 1,828 healthy controls, and its validity was evaluated in four studies involving 2,442 subjects across six disorders, assessing replication, cross‑study comparison, subtle changes, and multi‑disorder classification. The pipeline replicated schizophrenia abnormalities across datasets, revealed overlapping and specific patterns between autism and schizophrenia, identified functional impairments in mild cognitive impairment and Alzheimer’s disease, and produced biomarkers that accurately classified bipolar disorder and major depressive disorder.

Abstract

Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer's disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.