Altered resting fMRI spectral power in data-driven brain networks during development: A longitudinal study
Oktay Agcaoglu1, Tony W Wilson2, Yu-Ping Wang3
1Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Place, NE, 18th floor, Atlanta, GA 30303, USA.
Longitudinal brain development in children shows decreasing high-frequency spectral power, a potential marker for typical development. This study used a novel non-binning approach to analyze resting-state fMRI data, revealing age-related spectral changes.
Area of Science:
- Neuroscience
- Developmental Neuroscience
- Brain Imaging
Background:
- Longitudinal studies offer precise measures of brain development by tracking within-subject variability.
- This is crucial in children due to rapid brain changes and high inter-subject variability.
- Identifying markers of typical development aids early diagnosis of mental disorders.
Purpose of the Study:
- To track longitudinal changes in spectral power of resting-state functional magnetic resonance imaging (fMRI) time-courses in children.
- To investigate the role of eyes open (EO) and eyes closed (EC) resting states in age-related spectral differences.
- To identify potential markers of typical brain development using a novel analytical approach.
Main Methods:
- Utilized a unique non-binning approach with group independent component analysis (ICA) on a large multi-time-point resting-state fMRI dataset (N=124) of healthy children (ages 8.2-17.6).
- Examined spectral power changes in both EO and EC conditions.
- Validated findings using the Adolescent Brain Cognitive Development (ABCD) dataset (N=3371).
Main Results:
- Typical brain development is characterized by increased low-frequency and decreased high-frequency spectral power in both EO and EC states.
- Significant differences in power spectra were observed between EO and EC conditions, and between sexes (females showing higher power in mid/high frequencies).
- Replication analysis confirmed general trends of increasing low-frequency and decreasing high-frequency power with age.
Conclusions:
- Decreased high-frequency spectral power with development may serve as a general marker of typical brain maturation.
- The non-binning approach provides enhanced frequency resolution for detecting developmental changes compared to traditional binning methods.
- Further research is needed to confirm the utility of high-frequency spectral power as a developmental marker.
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