Advanced Mapping Techniques with ECoG in Awake Craniotomies and Consciousness State Assessment
Poster No:
31
Submission Type:
Abstract Submission
Authors:
Priscella Asman1, Mathew Hall1, Israt Tasnim2, Giuseppe Pellizzer3, Shreyas Bhavsar1, Sudhakar Tummala1, Firat Ince2, Sujit Prabhu4
Institutions:
1Md Anderson, Houston, TX, 2University of Houston, Houston, TX, 3University of Minnesota, Minneapolis, MN, 4MD Anderson, Houston, TX
First Author:
Co-Author(s):
Introduction:
In the treatment of gliomas located in the peri Rolandic areas of the brain, accurately mapping the sensorimotor regions is crucial to preserve both motor and sensory functions. Typically, techniques like electrical cortical stimulation (ECS) and the median nerve somatosensory evoked potential (SSEPs) phase reversal technique (MSSEP-PRT) are employed [1-2]. However, ECS can be time-consuming and carries the risk of inducing seizures, while MSSEP-PRT may be challenging to interpret [3-6]. This study aims to investigate the use of high-density electrocorticography (ECoG) for passive functional mapping, with the spatial-temporal, and spectral features of SSEPs in real time. Additionally, we demonstrate that besides central sulcus (CS) delineation, the spectral patterns of the SSEPs can differentiate between different consciousness states.
Methods:
During surgery, SSEPs were recorded using high-density ECoG grids placed on the sensorimotor cortex of fourteen patients, both in an anesthetized and awake state. Neural data from 0.6Hz median nerve stimulation were captured at 2.4kHz and processed in real-time using MATLAB Simulink. The system displays SSEPs' peak activations as a 2D heat map on a screen, particularly around the 20ms time point (N20), and generates the spectral power in the gamma range using Stockwell transform. This was then projected on the 3D rendering of each patient's brain generated from Magnetic Resonance Imaging (MRI). We used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve to determine the accuracy in distinguishing the anterior and posterior channels based on the peak amplitude at N20 and gamma power. We also applied paired t-tests to compare the gamma oscillations in each state.
Results:
Consistently across patients, the 20ms time point showed a clear discrimination between anterior (motor) and posterior (sensory) channels with a high separation accuracy of 93.6±14.9%. The color contrast revealed the delineated CS correlating with the sulcus in the 3D rendering, Figure 1A. We also observed late gamma (60–250 Hz) modulation in all subjects approximately 50 ms after stimulation onset, extending up to 250 ms in each state in the primary somatosensory area (S1), Figure 1B. The late gamma activity was suppressed in the anesthetized state (independent t-test t (13) = -3.2519, p = 0.0140) and increased significantly relative to baseline in the awake state (independent t-test t (13) = 6.0072, p < 0.01; Figure 3A). The late gamma had a delineation accuracy of 81±10.3% in the anesthetized and 91±13.4% in the awake state.
·Figure 1: A: Real time mapping with the spatial temporal features of SSEPs shown delineating the central sulcus for Patient 5 and Patient 8 at around 20ms on the 2D grid and 3D rendering.
Conclusions:
These results show that both spatial-temporal mapping from SSEPs and long-latency gamma modulations can individually delineate sensorimotor areas and the spectral profile can assess consciousness during neurosurgery. These findings have significant implications for operative planning in neurosurgical procedures
Brain Stimulation:
Direct Electrical/Optogenetic Stimulation 1
Learning and Memory:
Neural Plasticity and Recovery of Function
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 2
Neuroinformatics and Data Sharing:
Brain Atlases
Keywords:
Cerebellum
Cognition
ELECTROCORTICOGRAPHY
Somatosensory
1|2Indicates the priority used for review
Provide references using author date format
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