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Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Calibrated MRI techniques use the changes in cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) signal evoked by a respiratory manipulation to extrapolate the total BOLD signal attributable to deoxyhemoglobin at rest (M). This parameter can then be used to estimate changes in the cerebral metabolic rate of oxygen consumption (CMRO(2)) based on task-induced BOLD and CBF signals. Different approaches have been described previously, including addition of inspired CO(2) (hypercapnia) or supplemental O(2) (hyperoxia). We present here a generalized BOLD signal model that reduces under appropriate conditions to previous models derived for hypercapnia or hyperoxia alone, and is suitable for use during hybrid breathing manipulations including simultaneous hypercapnia and hyperoxia. This new approach yields robust and accurate M maps, in turn allowing more reliable estimation of CMRO(2) changes evoked during a visual task. The generalized model is valid for arbitrary flow changes during hyperoxia, thus benefiting from the larger total oxygenation changes produced by increased blood O(2) content from hyperoxia combined with increases in flow from hypercapnia. This in turn reduces the degree of extrapolation required to estimate M. The new procedure yielded M estimates that were generally higher (7.6 ± 2.6) than those obtained through hypercapnia (5.6 ± 1.8) or hyperoxia alone (4.5 ± 1.5) in visual areas. These M values and their spatial distribution represent a more accurate and robust depiction of the underlying distribution of tissue deoxyhemoglobin at rest, resulting in more accurate estimates of evoked CMRO(2) changes., (Copyright © 2012 Wiley Periodicals, Inc.)