A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies

• Published in: Human brain mapping Vol. 45; no. 17; pp. e70037 - n/a
• Main Authors: Silva, Rogers F., Damaraju, Eswar, Li, Xinhui, Kochunov, Peter, Ford, Judith M., Mathalon, Daniel H., Turner, Jessica A., Erp, Theo G. M., Adali, Tulay, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2024
• Subjects: Adult; Age; Age Factors; Aged; Biobanks; biomarker; Biomarkers; Bipolar disorder; Brain - diagnostic imaging; Brain research; Cognition; Cognition & reasoning; Cognition - physiology; Cognitive ability; Cognitive tasks; Data integration; Datasets; Demographics; Diffusion Magnetic Resonance Imaging - methods; Female; Functional magnetic resonance imaging; fusion; Humans; Image processing; independent vector analysis; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Mental disorders; Middle Aged; multimodal; Multimodal Imaging; Neuroimaging; Neuroimaging - methods; Patients; Psychosis; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenia - pathology; Schizophrenia - physiopathology; Sex; Structure-function relationships; Vector analysis; Young Adult; United Kingdom > UK; fusion; independent vector analysis; multimodal; schizophrenia; biomarker
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.70037

ABSTRACT With the increasing availability of large‐scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross‐modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross‐modal sources in multiple datasets. In this work, we utilized the multimodal independent vector analysis (MMIVA) model in MISA to directly identify meaningful linked features across three neuroimaging modalities—structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI—in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (sources) that capture age‐associated decline, schizophrenia‐related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality‐specific brain‐age deltas were evaluated for association with non‐imaging variables. In addition, each set of linked sources reveals a corresponding set of cross‐modal spatial patterns that can be studied jointly. We demonstrate that the MMIVA fusion model can identify linked sources across multiple modalities, and that at least one set of linked, age‐related sources replicates across two independent and separately analyzed datasets. The same set also presented age‐adjusted group differences, with schizophrenia patients indicating lower multimodal source levels. Linked sets associated with sex and cognition are also reported for the UK Biobank dataset. MMIVA fusion of GM, mALFF, and FA features by treating each modality as one of M datasets in an IVA model and without requiring linked expression levels to be identical for all modalities. Such flexibility permits even strongly linked sources to capture modality‐specific variability, as supported by the data.