Spectral Changes in Cortical Surface Potentials during Motor Movement

By Kai J. Miller, Eric C. Leuthardt, Gerwin Schalk, Rajesh P. N. Rao, Nicholas R. Anderson, Daniel W. Moran, John W. Miller, Jeffrey G. Ojemann. Journal of Neuroscience, 2007

772 citations recorded by the source corpus.

In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5–7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76–100 Hz) and a low-frequency band (LFB) (8–32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/71bbf0187426835663a410486c4fc73957af8793

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In Cognitive Neuroscience

Citations in the corpus

High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations (2013, background): “Interestingly, in iEEG recordings of primary motor regions, the bandwidth of movement-related increases in power extends up to ∼180 Hz (Crone et al., 2006; Miller et al., 2007, 2009), and these power increases can be broadband (50–200 Hz).”
Neural correlates of high-gamma oscillations (60–200 Hz) in macaque local field potentials and their potential implications in electrocorticography (2008, background): “These “high-gamma” responses have been observed in a variety of functional domains, including motor (Crone et al., 1998; Miller et al., 2007), auditory (Crone et al.”
“Roles for the pre-supplementary motor area and the right inferior frontal gyrus in stopping action: electrophysiological responses and functional and structural connectivity” (2011, background): “We note that high gamma band activity likely indicates local neuronal activity in each region (Conner et al., in press; Crone et al., 1998; Edwards et al., 2005; Logothetis et al., 2001; Miller et al., 2007a; Miller et al., 2007b; Ray et al., 2008; Scheeringa et al., 2011).”
Power-Law Scaling in the Brain Surface Electric Potential (2009, background): “In a previous paper [22], we hypothesized that observed high frequency changes called ‘‘high-c’’ [53] or x [31] were reflective of Figure 4.”
Negative Covariation between Task-related Responses in Alpha/Beta-Band Activity and BOLD in Human Sensorimotor Cortex: an EEG and fMRI Study of Motor Imagery and Movements (2009, background): “It is worth noting that an increase in the high frequency gamma range accompanying a decrease in low frequency (alpha/beta) activity was found in the contralateral sensorimotor cortex during ECoG recordings (Crone et al., 1998a; Miller et al., 2007).”
Decoding the tradeoff between encoding and retrieval to predict memory for overlapping events (2018, positive): “Notably, the HFAi/ LFAd pattern has also been observed across other cognitive tasks and domains (Crone et al., 1998a,b; Crone et al., 2001; Miller et al., 2007; Jensen et al., 2007), suggesting that this pattern is not memory specific.”
Review of the BCI Competition IV (2012, background): “In addition, frequency-domain features have been shown to robustly capture shifts in behavioral state (Crone et al., 1998a,b; Miller et al., 2007).”
Brain Computer Interfaces, a Review (2012, background): “Nevertheless, this range is attracting growing attention in BCI research because, compared to traditional beta and alpha signals, gamma activity may increase the information transfer rate and offer higher spatial specifity [44,45].”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, positive): “Notably, the gamma-band SMR tends to be more spatially focused than the low-frequency activities [34], [44].”
Complex oscillatory waves emerging from cortical organoids model early human brain network development (2019, background): “In the LFP and other mesoscopic brain signals, this manifests as a phenomenon known as cross-frequency phase-amplitude coupling (PAC) (Voytek and Knight, 2015), wherein the high-frequency content of the LFP is entrained to the phase of slow oscillations (Manning et al., 2009; Miller et al., 2007; Mu”
Two-dimensional movement control using electrocorticographic signals in humans (2008, methodology): “The results were consistent with current understanding of the effects of different tasks on ECoG [23– 28, 31, 32].”