Oktay Agcaoglu1, Tony W Wilson2, Yu-Ping Wang3, Julia M Stephen4, Zening Fu5, Vince D Calhoun6. 1. Tri-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. Electronic address: oagcaoglu@gsu.edu. 2. Institute for Human Neuroscience, Boys Town National Research Hospital, 378 Bucher, Boys Town, NE 68010, USA. Electronic address: tony.w.wilson@gmail.com. 3. Department of Biomedical Engineering, Tulane University, 6823 St. Charles Avenue, New Orleans, LA 70118, USA; Department of Global Biostatistics and Data Science, Tulane University, 1440 Canal Street, New Orleans, LA 70112, USA. Electronic address: wyp@tulane.edu. 4. The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106, USA. Electronic address: jstephen@mrn.org. 5. Tri-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. Electronic address: zfu@gsu.edu. 6. Tri-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; The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106, USA. Electronic address: vcalhoun@gsu.edu.
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. Published by Elsevier B.V.
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. Published by Elsevier B.V.
Entities:
Keywords:
Amplitude of low frequency fluctuations; Frequency spectrum analysis; Independent component analysis; Longitudinal analysis - brain development; Resting state - eyes open - eyes closed
Authors: Abigail G Garrity; Godfrey D Pearlson; Kristen McKiernan; Dan Lloyd; Kent A Kiehl; Vince D Calhoun Journal: Am J Psychiatry Date: 2007-03 Impact factor: 18.112
Authors: Zening Fu; Yiheng Tu; Xin Di; Yuhui Du; G D Pearlson; J A Turner; Bharat B Biswal; Zhiguo Zhang; V D Calhoun Journal: Neuroimage Date: 2017-09-20 Impact factor: 6.556
Authors: Anna C K van Duijvenvoorde; Bianca Westhoff; Frank de Vos; Lara M Wierenga; Eveline A Crone Journal: Hum Brain Mapp Date: 2019-05-17 Impact factor: 5.038
Authors: Jessica A Turner; Eswar Damaraju; Theo G M van Erp; Daniel H Mathalon; Judith M Ford; James Voyvodic; Bryon A Mueller; Aysenil Belger; Juan Bustillo; Sarah McEwen; Steven G Potkin; Vince D Calhoun Journal: Front Neurosci Date: 2013-08-08 Impact factor: 4.677