Abstract
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.
Highlights
Functional magnetic resonance imaging can noninvasively reveal the functional organization of the human brain, even in the absence of explicit tasks
The primary goal of this study was to investigate whether intra-subject inter-session resting state networks (RSNs) outcome measures were reproducible over an even longer period that is more relevant for rehabilitation studies, and this is confirmed by the results
The unique longitudinal dataset of the study enabled us to investigate whether seasonal patterns exists in RSN outcome measures, and the results show that such seasonal patterns exist in the weekly η2 and temporal fluctuation magnitude measures of relevant RSNs (Fig 7 MIDDLE and Table 6)
Summary
Functional magnetic resonance imaging (fMRI) can noninvasively reveal the functional organization of the human brain, even in the absence of explicit tasks. The ability to study the brain’s functional networks without requiring participants to perform explicit tasks has clinical appeal, as it allows use of an identical protocol for all patients, regardless of cognitive or physical limitations. This is especially important in chronic conditions that affect motor function, and the need for non-invasive and reproducible biomarkers is enhanced by advances in long-term therapeutic interventions for such chronic conditions [2,3,4,5]. The dataset is exceptional in its length and frequency of acquisition and provides a unique opportunity to gain insight into two different aspects of rsfMRI derived measures that were previously not accessible: 1) the reproducibility of the RSN outcome measures over an extended time period relevant for long-term clinical trials, and 2) inter-session temporal characteristics of the multi-year time courses of the rs-fMRI based outcome measures, provided through time series analysis
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
