Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 18, Issue 8, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1010401
Keywords
-
Funding
- numerous UC San Diego programs
- UCSD ECE Department's Medical Devices & Systems Initiative
- UCSD Center for Human Brain Activity Mapping (CHBAM)
- UCSD Center for Brain Activity Mapping (CBAM)
- Frontiers of Innovation Scholars Program
- Qualcomm Institute's Calit2 Strategic Research Opportunities (CSRO) program
- Hellman Fellowship
- Altman Clinical and Translational Research Institute
- UCSD Office of Research Affairs Center Launch Program
- Institute of Engineering in Medicine Graduate Student Fellowship
Ask authors/readers for more resources
In this study, the authors analyzed neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings. They found multiple neural signal characteristics that discriminate between unstructured and naturalistic behavioral states. The high gamma amplitude and Gaussian process factor analysis (GPFA) were used to demonstrate the discriminability of these features.
In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as engaging in dialogue and using electronics. Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity's covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available