There is a rise in the study of functional connectivity among various cortical regions and investigations to uncover causal links between a stimulus and the corresponding neural dynamics through electrophysiological imaging of the human brain. Animal model that exhibit simplistic representations of such networks open a doorway for such investigations and are gaining rapid popularity. In this study, we investigate and compare resting state network and auditory stimulus related activity with minimal invasive technology along computational spectral analysis on a C57/BL6 based mouse model. Somatosensory, motor and visual cortex are observed to be highly active and significantly correlated (p-value<0.05). Moreover, given the spatial limitation due to small size of the mouse head, we also describe a low-cost and effective fabrication process for the mouse EEG Polyimide Based Microelectrodes (PBM) array. The easy-to-implement fabrication process involves transfer of the pattern on a copper layer of the Kapton film followed by gold electroplating and application of insulation paint. Acoustic stimulation is done by using tube extensions for avoiding electrical coupling to EEG signals. Unlike multi-electrode array type of invasive methods that are local to a cortical region, the methods established in this study can be used for examining functional connectivity analysis, neural dynamics and cortical response at a global level.
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Neural silences can be localized rapidly using noninvasive scalp EEG
A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.
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- Award ID(s):
- 1763561
- NSF-PAR ID:
- 10252508
- Date Published:
- Journal Name:
- Nature communications biology selections
- ISSN:
- 2188-5028
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Objective. Reorienting is central to how humans direct attention to different stimuli in their environment. Previous studies typically employ well-controlled paradigms with limited eye and head movements to study the neural and physiological processes underlying attention reorienting. Here, we aim to better understand the relationship between gaze and attention reorienting using a naturalistic virtual reality (VR)-based target detection paradigm.Approach. Subjects were navigated through a city and instructed to count the number of targets that appeared on the street. Subjects performed the task in a fixed condition with no head movement and in a free condition where head movements were allowed. Electroencephalography (EEG), gaze and pupil data were collected. To investigate how neural and physiological reorienting signals are distributed across different gaze events, we used hierarchical discriminant component analysis (HDCA) to identify EEG and pupil-based discriminating components. Mixed-effects general linear models (GLM) were used to determine the correlation between these discriminating components and the different gaze events time. HDCA was also used to combine EEG, pupil and dwell time signals to classify reorienting events.Main results. In both EEG and pupil, dwell time contributes most significantly to the reorienting signals. However, when dwell times were orthogonalized against other gaze events, the distributions of the reorienting signals were different across the two modalities, with EEG reorienting signals leading that of the pupil reorienting signals. We also found that the hybrid classifier that integrates EEG, pupil and dwell time features detects the reorienting signals in both the fixed (AUC = 0.79) and the free (AUC = 0.77) condition.Significance. We show that the neural and ocular reorienting signals are distributed differently across gaze events when a subject is immersed in VR, but nevertheless can be captured and integrated to classify target vs. distractor objects to which the human subject orients. -
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