BACKGROUND AND PURPOSE: In subjects performing no specific cognitive task ("resting state"), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients ("functional connectivity"). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. METHODS: In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the "resting state" with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). RESULTS: For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies (<0.1 Hz). For all cortical regions studied, low-frequency fluctuations contributed more than 90% of the correlation coefficient. Physiological (respiratory and cardiac) noise sources contributed less than 10% to any functional connectivity MR imaging map. In blood vessels and cerebrospinal fluid, physiological noise contributed more to the correlation coefficient. CONCLUSION: Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.
BACKGROUND AND PURPOSE: In subjects performing no specific cognitive task ("resting state"), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients ("functional connectivity"). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. METHODS: In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the "resting state" with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). RESULTS: For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies (<0.1 Hz). For all cortical regions studied, low-frequency fluctuations contributed more than 90% of the correlation coefficient. Physiological (respiratory and cardiac) noise sources contributed less than 10% to any functional connectivity MR imaging map. In blood vessels and cerebrospinal fluid, physiological noise contributed more to the correlation coefficient. CONCLUSION: Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.
Authors: D Cordes; V M Haughton; K Arfanakis; G J Wendt; P A Turski; C H Moritz; M A Quigley; M E Meyerand Journal: AJNR Am J Neuroradiol Date: 2000-10 Impact factor: 3.825
Authors: K J Friston; A P Holmes; J B Poline; P J Grasby; S C Williams; R S Frackowiak; R Turner Journal: Neuroimage Date: 1995-03 Impact factor: 6.556
Authors: Kathleen A Williams; Matthew Magnuson; Waqas Majeed; Stephen M LaConte; Scott J Peltier; Xiaoping Hu; Shella D Keilholz Journal: Magn Reson Imaging Date: 2010-04-24 Impact factor: 2.546
Authors: Christopher Lee Keown; Patricia Shih; Aarti Nair; Nick Peterson; Mark Edward Mulvey; Ralph-Axel Müller Journal: Cell Rep Date: 2013-11-07 Impact factor: 9.423