Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years

• Published in: PloS one Vol. 10; no. 10; p. e0140134
• Main Authors: Choe, Ann S., Jones, Craig K., Joel, Suresh E., Muschelli, John, Belegu, Visar, Caffo, Brian S., Lindquist, Martin A., van Zijl, Peter C. M., Pekar, James J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.10.2015; Public Library of Science (PLoS)
• Subjects: Adult; Aging; Alzheimer's disease; Biomarkers; Brain; Brain - physiology; Brain mapping; Brain Mapping - methods; Brain research; Care and treatment; Chronic conditions; Chronic illnesses; Circadian rhythm; Clinical trials; Data processing; Datasets; Diagnosis; Female; Functional magnetic resonance imaging; Health aspects; Health care; Humans; Independent component analysis; Longitudinal Studies; Magnetic resonance imaging; Magnetic Resonance Imaging - instrumentation; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medical research; Medicine; Middle Aged; Neural networks; NMR; Nuclear magnetic resonance; Patient outcomes; Periodicity; Public health; Radiology; Rehabilitation; Reproducibility; Reproducibility of Results; Rest - physiology; Risk factors; Science; Spinal cord injuries; Therapeutic applications; Time series; Young Adult; United States; United States > US; Baltimore Maryland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0140134

Resting-state functional MRI (rs-fMRI) permits study of the brain's functional networks without requiring participants to perform tasks. Robust changes in such resting state networks (RSNs) have been observed in neurologic disorders, and rs-fMRI outcome measures are candidate biomarkers for monitoring clinical trials, including trials of extended therapeutic interventions for rehabilitation of patients with chronic conditions. In this study, we aim to present a unique longitudinal dataset reporting on a healthy adult subject scanned weekly over 3.5 years and identify rs-fMRI outcome measures appropriate for clinical trials. Accordingly, we assessed the reproducibility, and characterized the temporal structure of, rs-fMRI outcome measures derived using independent component analysis (ICA). Data was compared to a 21-person dataset acquired on the same scanner in order to confirm that the values of the single-subject RSN measures were within the expected range as assessed from the multi-participant dataset. Fourteen RSNs were identified, and the inter-session reproducibility of outcome measures-network spatial map, temporal signal fluctuation magnitude, and between-network connectivity (BNC)-was high, with executive RSNs showing the highest reproducibility. Analysis of the weekly outcome measures also showed that many rs-fMRI outcome measures had a significant linear trend, annual periodicity, and persistence. Such temporal structure was most prominent in spatial map similarity, and least prominent in BNC. High reproducibility supports the candidacy of rs-fMRI outcome measures as biomarkers, but the presence of significant temporal structure needs to be taken into account when such outcome measures are considered as biomarkers for rehabilitation-style therapeutic interventions in chronic conditions.