Longitudinal studies can provide more precise measure of brain development, as they focus on within-subject variability, as opposed to cross-sectional studies. In this study, we track longitudinal changes in resting state fMRI data using spectrum of time-courses generated via group independent component analysis (gICA), in a multi time point dataset containing healthy children 8-18 years old, collected on both eyes open and eyes closed resting state conditions. Clinical Relevance - Tracking normal brain development and identifying biomarkers of healthy brain development are critically important to diagnose mental disorders at early ages. We found increased spectral power in low frequencies and decreased spectral power in high frequencies in children with typical development in both the eyes open and eyes closed conditions though the eyes closed condition showed greater changes with development mostly in the visual networks. Results are also replicated on an independent dataset.