Slow-wave modulation analysis during states of unconsciousness using the novel tau-modulation method
Year: 2023
Type: article
Abstract: Objective. Slow-wave modulation occurs during states of unconsciousness and is a large-scale indicator of underlying brain states. Conventional methods typically characterize these large-scale dynamics by assuming that slow-wave activity is sinusoidal with a stationary frequency. However, slow-wave activity typically has an irregular waveform shape with a non-stationary frequency, causing these methods to be highly unpredictable and inaccurate. To address these limitations, we developed a novel method using tau-modulation, which is more robust than conventional methods in estimating the modulation of slow-wave activity and does not require assumptions on the shape or stationarity of the underlying waveform. Approach. We propose a novel method to estimate modulatory effects on slow-wave activity. Tau-modulation curves are constructed from cross-correlation between slow-wave and high-frequency activity. The resultant curves capture several aspects of modulation, including attenuation or enhancement of slow-wave activity, the temporal synchrony between slow-wave and high-frequency activity, and the rate at which the overall brain activity oscillates between states. Main results. The method’s performance was tested on an open electrocorticographic dataset from two monkeys that were recorded during propofol-induced anesthesia, with electrodes implanted over the left hemispheres. We found a robust propagation of slow-wave modulation along the anterior–posterior axis of the lateral aspect of the cortex. This propagation preferentially originated from the anterior superior temporal cortex and anterior cingulate gyrus. We also found the modulation frequency and polarity to track the stages of anesthesia. The algorithm performed well, even with non-sinusoidal activity and in the presence of real-world noise. Significance. The novel method provides new insight into several aspects of slow-wave modulation that have been previously difficult to evaluate across several brain states. This ability to better characterize slow-wave modulation, without spurious correlations induced by non-sinusoidal signals, may lead to robust and physiologically-plausible diagnostic tools for monitoring brain functions during states of unconsciousness.
Source: Journal of Neural Engineering
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,075
Funders: National Natural Science Foundation of China, National Institutes of Health, McDonnell Center for Systems Neuroscience, National Institute of Mental Health, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering
Awards: G6154971310, Mechanisms of Amygdala-Mediated Memory Enhancement in Humans, Dynamics and Causal Functions of Large-Scale Cortical and Subcortical Networks, BCI2000+: A Software Platform for Adaptive Neurotechnologies, BCI2000: Software Resource for Adaptive Neurotechnology Research, Center for Adaptive Neurotechnologies, Development of a Micro-ECoG Neuroprosthesis for Motor Rehabilitation in a Chronic Corticospinal Stroke Injury, Optimizing Pharmacotherapy with Noninvasive Wearable Sensors and Subscalp EEG, Postdoctoral Training Program in Genomic Medicine, An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research in Humans, Advancing Neurosurgical Neuronavigation Using Resting State MRI and Machine Learning
OpenAlex ID: W4383746565