Altered resting fMRI spectral power in data-driven brain networks during development: A longitudinal study

• Authors: Oktay Agcaoglu, Emory University; Tony W Wilson, Boys Town National Research Hospital; Yu-Ping Wang, Tulane University; Julia M Stephen, The Mind Research Network; Zening Fu, Emory University; Vince Calhoun, Emory University
• Journal Title: JOURNAL OF NEUROSCIENCE METHODS
• Volume: Volume 372
• Publisher: ELSEVIER | 2022-03-03, Pages 109537-109537
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.jneumeth.2022.109537
• Subjects: Engineering, Biomedical
• Keywords: Science & Technology; Life Sciences & Biomedicine; Biochemical Research Methods; Neurosciences; Biochemistry & Molecular Biology; Neurosciences & Neurology; Longitudinal analysis-brain development; Independent component analysis; Amplitude of low frequency fluctuations; Frequency spectrum analysis; Resting state-eyes open-eyes closed; LOW-FREQUENCY FLUCTUATIONS; FUNCTIONAL CONNECTIVITY; STATE FMRI; BASE-LINE; AMPLITUDE; CHILDREN; VOLUME; ICA; AGE

Abstract

Background: Longitudinal studies provide a more precise measure of brain development over time, as they focus on within subject variability, as opposed to cross-sectional studies. This is especially important in children, where rapid brain development occurs, and inter-subject variability can be large. Tracking healthy brain development and identifying markers of typical development are also critically important to diagnose mental disorders at early ages. New method: We track longitudinal changes in spectral power of time-courses using a unique non-binning approach assessed with group independent component analysis, in a large multi time-point resting state functional magnetic resonance imaging dataset (N = 124) containing healthy children from 8.2 to 17.6 years old (m=12.6) called the Developmental Chronnecto-Genomics study. We examined how eyes open (EO) and eyes closed (EC) resting states play a role in age-related spectral differences, as several studies have reported differences in these conditions. Results: Typical brain development shows increased spectral power in low frequencies and decreased spectral power in high frequencies in as children grow and develop, for both the EO and EC conditions. In addition, we observed significant differences in power spectra between EO and EC and between sexes, mainly suggesting higher spectral power in females at middle and high frequencies. A replication analysis using the Adolescent Brain Cognitive Development data (N = 3371, mean age 9.9 years old) further supported this result, also showing general increases in low frequencies and decreases in higher frequencies, though some network level differences are present comparing to the main dataset. Comparison with existing method: Our results indicate that spectral power changes significantly with typical development and our non-binning approach shows these changes with more detailed frequency resolution comparing to binning approaches. This is important as many studies reported an association of higher frequency power with brain disorders. Conclusion: Our findings of decreased spectral power in the high frequencies with development may be a general marker of typical development., though this needs further investigation.