Cortical surface electrical potentials are composed of multiple bandlimited frequency components, including high-gamma

Abstract

A common challenge in neuroscience is how to decompose noisy, multi-source signals measured in experiments into biophysically interpretable components. Analysis of cortical surface electrical potentials (CSEPs) measured using electrocorticography arrays (ECoG) typifies this problem. We hypothesized that high frequency (70-1,000 Hz) CSEPs are composed of broadband (i.e., power-law) and bandlimited components with potentially differing biophysical origins. In particular, the high-gamma band (70-150 Hz) has been shown to be highly predictive for encoding and decoding behaviors and stimuli. Despite its demonstrated importance, whether high-gamma is composed of a bandlimited signal is poorly understood. To address this gap, we recorded CSEPs from rat auditory cortex and demonstrate that the evoked CSEPs are composed of multiple distinct frequency components, including high-gamma. We then show, using a novel robust regression method, that at fast timescales and on single trials during speech production, human high-gamma amplitude cannot be explained by a modulating power-law component; thus, high-gamma is band-limited. Furthermore, we show that the power-law component is less predictive of produced speech compared to the raw high-gamma amplitude. Finally, we show that the largest variance component of human ECoG signals is low-frequency and band-limited, not broadband. Together these results demonstrate that there are multiple, band-limited components of high frequency power in cortical surface electrical potentials, including the high-gamma band, which may have different biophysical origins.

Decoding depression during subcallosal cingulate deep brain stimulation

“…Second, the testing-set performance achieves an R 2 of 0.11, reflecting approximately 11% of the depression signal being accounted for by the entire decoder. Several factors may explain this, including limited recording of the complete depression network, the use of linear regression models, and the technical limitations of the PC+S™ in the γ range [27, 43, 44, 45]. Efforts were taken to rule out stimulation-artifact as a driver of DR-SCC performance (Figure S8) but it remains possible that artifacts contribute to the DR-SCC.…”