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Abstract #3129

Detection of High-Frequency Resting-State Connectivity using Spectrally and Temporally Segmented Regression of High-Speed fMRI Data

Bruno Sa de La Rocque Guimaraes1, Khaled Talaat1, and Stefan Posse2,3
1Nuclear Engineering, U New Mexico, Albuquerque, NM, United States, 2Neurology, U New Mexico, Albuquerque, NM, United States, 3Physics and Astronomy, U New Mexico, Albuquerque, NM, United States

This study investigates resting-state signal fluctuations at high-frequencies (>0.3Hz) using a novel regression method for high-speed fMRI data. Respiration and cardiac related signal changes and motion parameters were regressed using a spectral and temporal segmentation approach. This novel approach was shown to substantially remove physiological noise and motion effects. It reduces artificial high-frequency correlations compared with a recently developed sliding window regression approach. High frequency connectivity maps showed comparable localization to low frequency connectivity maps.

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