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Recent improvements in the speed and sensitivity of fMRI acquisition techniques suggest that fast fMRI can be used to detect and precisely localize sub-second neural dynamics. This enhanced temporal resolution has enormous potential for neuroscientists. However, physiological noise poses a major challenge for the analysis of fast fMRI data. Physiological noise scales with sensitivity, and its autocorrelation structure is altered in rapidly sampled data, suggesting that new approaches are needed for physiological noise removal in fast fMRI. Existing strategies either rely on external physiological recordings, which can be noisy or difficult to collect, or employ data-driven approaches which make assumptions that may not hold true in fast fMRI. We created a statistical model of harmonic regression with autoregressive noise (HRAN) to estimate and remove cardiac and respiratory noise from the fMRI signal directly. This technique exploits the fact that cardiac and respiratory noise signals are fully sampled (rather than aliasing) when imaging at fast rates, allowing us to track and model physiology over time without requiring external physiological measurements. We then created a joint model of neural hemodynamics, and physiological and autocorrelated noise to more accurately remove noise. We first verified that HRAN accurately estimates cardiac and respiratory dynamics and that our model demonstrates goodness-of-fit in fast fMRI data. In task-driven data, we then demonstrated that HRAN is able to remove physiological noise while leaving the neural signal intact, thereby increasing detection of task-driven voxels. Finally, we established that in both simulations and fast fMRI data HRAN is able to improve statistical inferences as compared with gold-standard physiological noise removal techniques. In conclusion, we created a tool that harnesses the novel information in fast fMRI to remove physiological noise, enabling broader use of the technology to study human brain function.

BackgroundDiscontinuous electroencephalographic activity in children is thought to reflect brain inactivation. Discontinuity has been observed in states of pathology, where it is predictive of adverse neurological outcome, as well as under general anesthesia. Though in preterm-infants discontinuity reflects normal brain development, less is known regarding its role in term children, particularly in the setting of general anesthesia. Here, we conduct a post-hoc exploratory analysis to investigate the spectral features of discontinuous activity in children under general anesthesia.MethodsWe previously recorded electroencephalography in children less than forty months of age under general anesthesia (n = 65). We characterized the relationship between age, anesthetic depth, and discontinuous activity, and used multitaper spectral methods to compare the power spectra of subjects with (n = 35) and without (n = 30) discontinuous activity. In the subjects with discontinuous activity, we examined the amplitude and power spectra associated with the discontinuities and analyzed how these variables varied with age.ResultsCumulative time of discontinuity was associated with increased anesthetic depth and younger age. In particular, age-matched children with discontinuity received higher doses of propofol during induction as compared with children without discontinuity. In the tens of seconds preceding the onset of discontinuous activity, there was a decrease in high-frequency power in children four months and older that could be visually observed with spectrograms. During discontinuous activity, there were distinctive patterns of amplitude, spectral edge, and power in canonical frequency bands that varied with age. Notably, there was a decline in spectral edge in the seconds immediately following each discontinuity.ConclusionDiscontinuous activity in children reflects a state of a younger or more deeply anesthetized brain, and characteristic features of discontinuous activity evolve with age and may reflect neurodevelopment.

The (8–12 Hz) neocortical alpha rhythm is associated with shifts in attention across sensory systems, and is thought to represent a sensory gating mechanism for the inhibitory control of cortical processing. The present preliminary study sought to explore whether alpha frequency transcranial alternating current stimulation (tACS) could modulate endogenous alpha power in the somatosensory system, and whether the hypothesized modulation would causally impact perception of tactile stimuli at perceptual threshold. We combined electroencephalography (EEG) with simultaneous brief and intermittent tACS applied over primary somatosensory cortex at individuals’ endogenous alpha frequency during a tactile detection task (n = 12 for EEG, n = 20 for behavior). EEG-measured pre-stimulus alpha power was higher on non-perceived than perceived trials, and analogous perceptual correlates emerged in early components of the tactile evoked response. Further, baseline normalized tactile detection performance was significantly lower during alpha than sham tACS, but the effect did not last into the post-tACS time period. Pre- to post-tACS changes in alpha power were linearly dependent upon baseline state, such that alpha power tended to increase when pre-tACS alpha power was low, and decrease when it was high. However, these observations were comparable in both groups, and not associated with evidence of tACS-induced alpha power modulation. Nevertheless, the tactile stimulus evoked response potential (ERP) revealed a potentially lasting impact of alpha tACS on circuit dynamics. The post-tACS ERP was marked by the emergence of a prominent peak ∼70 ms post-stimulus, which was not discernible post-sham, or in either pre-stimulation condition. Computational neural modeling designed to simulate macroscale EEG signals supported the hypothesis that the emergence of this peak could reflect synaptic plasticity mechanisms induced by tACS. The primary lesson learned in this study, which commanded a small sample size, was that while our experimental paradigm provided some evidence of an influence of tACS on behavior and circuit dynamics, it was not sufficient to induce observable causal effects of tACS on EEG-measured alpha oscillations. We discuss limitations and suggest improvements that may help further delineate a causal influence of tACS on cortical dynamics and perception in future studies.