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1. Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy

   https://doi.org/10.1111/epi.17257  

   Varatharajah, Yogatheesan ; Joseph, Boney ; Brinkmann, Benjamin ; Morita‐Sherman, Marcia ; Fitzgerald, Zachary ; Vegh, Deborah ; Nair, Dileep ; Burgess, Richard ; Cendes, Fernando ; Jehi, Lara ; et al ( April 2022 , Epilepsia)

   Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.

   We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities.

   We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score).

   These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

    

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2. Neural silences can be localized rapidly using noninvasive scalp EEG

   Zar, Alireza Chaman ; Behrmann, Marlene ; Grover, Pulkit ( March 2021 , Nature communications biology selections)

   null (Ed.)

   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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3. Investigating the Role of Neck Muscle Activation and Neck Damping Characteristics in Brain Injury Mechanism

   https://doi.org/10.1101/2023.11.15.567289  

   Bahreinizad, Hossein ; Chowdhury, Suman K ( November 2023 , bioRxiv)

   This study aimed to investigate the role of neck muscle activity and neck damping characteristics in traumatic brain injury (TBI) mechanisms.

   We used a previously validated head-neck finite element (FE) model that incorporates various components such as scalp, skull, cerebrospinal fluid, brain, muscles, ligaments, cervical vertebrae, and intervertebral discs. Impact scenarios included a Golf ball impact, NBDL linear acceleration, and Zhang’s linear and rotational accelerations. Three muscle activation strategies (no-activation, low-to-medium, and high activation levels) and two neck damping levels by perturbing intervertebral disc properties (high: hyper-viscoelastic and low: hyper-elastic) strategies were examined. We employed Head Injury Criterion (HIC), Brain Injury Criterion (BrIC), and maximum principal strain (MPS) as TBI measures.

   Increased neck muscle activation consistently reduced the values of all TBI measures in Golf ball impact (HIC: 4%-7%, BrIC: 11%-25%, and MPS (occipital): 27%-50%) and NBDL study (HIC: 64%-69%, BrIC: 3%-9%, and MPS (occipital): 6%-19%) simulations. In Zhang’s study, TBI metric values decreased with the increased muscle activation from no-activation to low-to-medium (HIC: 74%-83%, BrIC: 27%-27%, and MPS (occipital): 60%-90%) and then drastically increased with further increases to the high activation level (HIC: 288%-507%, BrIC: 1%-25%, and MPS (occipital): 23%-305%). Neck damping changes from low to high decreased all values of TBI metrics, particularly in Zhang’s study (up to 40% reductions).

   Our results underscore the pivotal role of neck muscle activation and neck damping in TBI mitigation and holds promise to advance effective TBI prevention and protection strategies for diverse applications.

    

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4. A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram

   https://doi.org/10.3390/s21082779  

   Dhillon, Navjodh ; Sutandi, Agustinus ; Vishwanath, Manoj ; Lim, Miranda ; Cao, Hung ; Si, Dong ( April 2021 , Sensors)

   Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16–64 s epochs for TBI vs. control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems. 

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5. Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial

   https://doi.org/10.2196/24698  

   Ehsani, Sina ; Reddy, Chandan K ; Foreman, Brandon ; Ratcliff, Jonathan ; Subbian, Vignesh ( January 2021 , JMIR Biomedical Engineering)

   null (Ed.)

   Background With advances in digital health technologies and proliferation of biomedical data in recent years, applications of machine learning in health care and medicine have gained considerable attention. While inpatient settings are equipped to generate rich clinical data from patients, there is a dearth of actionable information that can be used for pursuing secondary research for specific clinical conditions. Objective This study focused on applying unsupervised machine learning techniques for traumatic brain injury (TBI), which is the leading cause of death and disability among children and adults aged less than 44 years. Specifically, we present a case study to demonstrate the feasibility and applicability of subspace clustering techniques for extracting patterns from data collected from TBI patients. Methods Data for this study were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment–Phase III (PROTECT III) trial, which included a cohort of 882 TBI patients. We applied subspace-clustering methods (density-based, cell-based, and clustering-oriented methods) to this data set and compared the performance of the different clustering methods. Results The analyses showed the following three clusters of laboratory physiological data: (1) international normalized ratio (INR), (2) INR, chloride, and creatinine, and (3) hemoglobin and hematocrit. While all subclustering algorithms had a reasonable accuracy in classifying patients by mortality status, the density-based algorithm had a higher F1 score and coverage. Conclusions Clustering approaches serve as an important step for phenotype definition and validation in clinical domains such as TBI, where patient and injury heterogeneity are among the major reasons for failure of clinical trials. The results from this study provide a foundation to develop scalable clustering algorithms for further research and validation. 

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