Journal
NEUROIMAGE
Volume 83, Issue -, Pages 826-836Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2013.07.036
Keywords
Functional connectivity; Neural basis; Sliding window; Dynamic; Time varying; Global signal
Funding
- National Institute of Health [1R21NS072810-01A1, 1R21NS057718-01]
- Scholarly Inquiry and Research at Emory graduate fellowship
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Functional connectivity between brain regions, measured with resting state functional magnetic resonance imaging, holds great potential for understanding the basis of behavior and neuropsychiatric diseases. Recently it has become clear that correlations between the blood oxygenation level dependent (BOLD) signals from different areas vary over the course of a typical scan (6-10 min in length), though the changes are obscured by standard methods of analysis that assume the relationships are stationary. Unfortunately, because similar variability is observed in signals that share no temporal information, it is unclear which dynamic changes are related to underlying neural events. To examine this question, BOLD data were recorded simultaneously with local field potentials (LFP) from interhemispheric primary somatosensory cortex (SI) in anesthetized rats. LP signals were converted into band-limited power (BLP) signals including delta, theta, alpha, beta and gamma. Correlation between signals from interhemispheric SI was performed in sliding windows to produce signals of con-elation over time for BOLD and each BLP band. Both BOLD and BLP signals showed large changes in correlation over time and the changes in BOLD were significantly correlated to the changes in BLP. The strongest relationship was seen when using the theta, beta and gamma bands. Interestingly, while steady-state BOLD and BLP correlate with the global fMRI signal, dynamic BOLD becomes more like dynamic BLP after the global signal is regressed. As BOLD sliding window connectivity is partially reflecting underlying LP changes, the present study suggests it may be a valuable method of studying dynamic changes in brain states. (C) 2013 Elsevier Inc. All rights reserved.
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