Faculty Bibliography

This paper extends the frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. ii) We also show that the multinational harmonized Riemannian norms produce z-scores with increased diagnostic accuracy to predict brain dysfunction at school-age produced by malnutrition only in the first year of life. iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.

Chronic pain represents a significant unmet health need, affecting over 35% of the US adult population. Currently, there are no objective means for identifying of quantifying the presence of pain. Brain mechanisms mediating the painful state were imaged in this study, using source localization of the EEG. Using conventional functional neuroimaging tools, regions of the "Pain Matrix" have been reported in the literature. In this study, a population of 77 neuropathic chronic pain patients was studied using EEG source localization. Significant frequency specific overactivation in brain regions of the Pain Matrix were found in regions including the anterior cingulate, anterior and posterior insula, parietal lobule, thalamus, S1. and dorsolateral prefrontal cortex (DLPFC), consistent with those reported with conventional functional imaging, and extended to include the mid and posterior cingulate, most probably reflecting the increased temporal resolution of electrophysiological measures allowing more precise identification of the pain pathway. Significant differences between self-reported high- A nd lowpain subjects were found for some of the regions if interest (ROIs), suggesting encoding of pain intensity occurs in a subset of Pain Matrix ROIs. Findings support the potential to derive a quantitative measure of the severity of pain using information extracted from a multivariate descriptor of the abnormal overactivation. Furthermore, the frequency specific (theta band) overactivation in the regions including the thalamus, supports a model of thalamocortical dysrhythmia (TCD) as the underlying mechanism of the neuropathic painful condition

BACKGROUND:Prompt, accurate, objective assessment of concussion is crucial, particularly for children/adolescents and young adults. While there is currently no gold standard for the diagnosis of concussion, the importance of multidimensional/multimodal assessments has recently been emphasized. METHODS:Concussed subjects (N = 177), matched controls (N = 187) and healthy volunteers (N = 204) represented a convenience sample of male and female subjects between the ages of 13 and 25 years, enrolled at 29 Colleges and 19 High Schools in the US. Subjects were tested at time of injury and at multiple time points during recovery. Assessments included EEG, neurocognitive tests and standard concussion assessment tools. Multimodal classifiers to maximally separate controls from concussed subjects with prolonged recovery (≥14 days) were derived using quantitative EEG, neurocognitive and vestibular measures, informed feature reduction and a Genetic Algorithm methodology for classifier derivation. The methodology protected against overtraining using an internal cross-validation framework. An enhanced multimodal Brain Function Index (eBFI) was derived from the classifier output and mapped to a percentile scale which expressed the index relative to non-injured controls. RESULTS:At time of injury eBFIs were significantly different between controls and concussed subjects with prolonged recovery, showing return to non-concussed levels at return-to-play plus 45 days. For the combined concussed population, and for the short recovery subjects, a more rapid recovery was seen. CONCLUSIONS:This multivariate, multimodal, objective index of brain function impairment can potentially be used, along with other tools, to aid in diagnosis, assessment, and tracking of recovery from concussion.

Chronic pain affects more than 35% of the US adult population representing a major public health imperative. Currently, there are no objective means for identifying the presence of pain, nor for quantifying pain severity. Through a better understanding of the pathophysiology of pain, objective indicators of pain might be forthcoming. Brain mechanisms mediating the painful state were imaged in this study, using source localization of the EEG. In a population of 77 chronic pain patients, significant overactivation of the "Pain Matrix" or pain network, was found in brain regions including, the anterior cingulate, anterior and posterior insula, parietal lobule, thalamus, S1, and dorsolateral prefrontal cortex (DLPFC), consistent with those reported with conventional functional imaging, and extended to include the mid and posterior cingulate, suggesting that the increased temporal resolution of electrophysiological measures may allow a more precise identification of the pain network. Significant differences between those who self-report high and low pain were reported for some of the regions of interest (ROIs), maximally on left hemisphere in the DLPFC, suggesting encoding of pain intensity occurs in a subset of pain network ROIs. Furthermore, a preliminary multivariate logistic regression analysis was used to select quantitative-EEG features which demonstrated a highly significant predictive relationship of self-reported pain scores. Findings support the potential to derive a quantitative measure of the severity of pain using information extracted from a multivariate descriptor of the abnormal overactivation. Furthermore, the frequency specific (theta/low alpha band) overactivation in the regions reported, while not providing direct evidence, are consistent with a model of thalamocortical dysrhythmia as the potential mechanism of the neuropathic painful condition.

The potential clinical utility of a novel quantitative EEG-based Brain Function Index (BFI) as a measure of the presence and severity of functional brain injury was studied as part of an independent prospective validation trial. The BFI was derived using EEG features associated with functional brain impairment reflecting current consensus on the physiology of concussive injury. 720 adult patients (18-85 years) evaluated within 72 hours of sustaining a closed head injury were enrolled at 11 US Emergency Departments. Glasgow Coma Scale was 15 in 97%. Standard clinical evaluations were conducted and 5-10 minutes of EEG acquired from frontal locations. Clinical utility of the BFI was assessed for raw scores and percentile values. A multinomial logistic regression analysis demonstrated that both the odds ratio of mild functionally impaired group compared to uninjured as well as the odds ratio of the moderate functionally impaired group compared to uninjured, were significantly different from the CT+ (structural injury visible on CT) TBI group (p=0.0009 and p=0.0026, respectively). However, no significance differences in odds (ratios compared to uninjured) of the mild and moderately functionally impaired groups were obtained. ANOVAs demonstrated significant differences in BFI among normal (16.8%), mTBI/concussed (61.3%), and CT+ (21.9%) patients (p<0.0001). Regression slopes of the odds ratios for likelihood of group membership suggest a relationship between the BFI and severity of impairment. Findings support the BFI as a quantitative marker of brain function impairment, which scaled with severity of functional impairment in mTBI patients. When integrated into the clinical assessment, the BFI has the potential to aid in the early diagnosis and thereby potential to impact the sequelae of TBI by providing an objective marker available at the point of care, hand-held, non-invasive and rapid to obtain.

OBJECTIVE: To evaluate the effectiveness of the electroencephalographic (EEG) Brain Function Index (BFI) for characterizing sports-related concussive injury and recovery. PARTICIPANTS: Three hundred fifty-four (354) male contact sport high school and college athletes were prospectively recruited from multiple locations over 6 academic years of play (244 control baseline athletes and 110 athletes with a concussion). METHODS: Using 5 to 10 minutes of eyes closed resting EEG collected from frontal and frontotemporal regions, a BFI was computed for all subjects and sessions. Group comparisons were performed to test for the significance of the difference in the BFI score between the controls at baseline and athletes with a concussion at several time points. RESULTS: There was no significant difference in BFI between athletes with a concussion at baseline (ie, prior to injury) and controls at baseline (P = .4634). Athletes with a concussion, tested within 72 hours of injury, exhibited significant differences in BFI compared with controls (P = .0036). The significant differences in BFI were no longer observed at 45 days following injury (P = .19). CONCLUSION: Controls and athletes with a concussion exhibited equivalent BFI scores at preseason baseline. The concussive injury (measured within 72 hours) significantly affected brain function reflected in the BFI in the athletes with a concussion. The BFI of the athletes with a concussion returned to levels seen in controls by day 45, suggesting recovery. The BFI may provide an important objective marker of concussive injury and recovery.

The goal of this study is to identify the quantitative electroencephalographic (qEEG) signature of early childhood malnutrition [protein-energy malnutrition (PEM)]. To this end, archival digital EEG recordings of 108 participants in the Barbados Nutrition Study (BNS) were recovered and cleaned of artifacts (46 children who suffered an episode of PEM limited to the first year of life) and 62 healthy controls). The participants of the still ongoing BNS were initially enrolled in 1973, and EEGs for both groups were recorded in 1977-1978 (at 5-11 years). Scalp and source EEG Z-spectra (to correct for age effects) were obtained by comparison with the normative Cuban Human Brain Mapping database. Differences between both groups in the z spectra (for all electrode locations and frequency bins) were assessed by t-tests with thresholds corrected for multiple comparisons by permutation tests. Four clusters of differences were found: (a) increased theta activity (3.91-5.86 Hz) in electrodes T4, O2, Pz and in the sources of the supplementary motor area (SMA); b) decreased alpha1 (8.59-8.98 Hz) in Fronto-central electrodes and sources of widespread bilateral prefrontal are; (c) increased alpha2 (11.33-12.50 Hz) in Temporo-parietal electrodes as well as in sources in Central-parietal areas of the right hemisphere; and (d) increased beta (13.67-18.36 Hz), in T4, T5 and P4 electrodes and decreased in the sources of bilateral occipital-temporal areas. Multivariate Item Response Theory of EEGs scored visually by experts revealed a neurophysiological latent variable which indicated excessive paroxysmal and focal abnormality activity in the PEM group. A robust biomarker construction procedure based on elastic-net regressions and 1000-cross-validations was used to: (i) select stable variables and (ii) calculate the area under ROC curves (AUC). Thus, qEEG differentiate between the two nutrition groups (PEM vs Control) performing as well as visual inspection of the EEG scored by experts (AUC = 0.83). Since PEM is a global public health problem with lifelong neurodevelopmental consequences, our finding of consistent differences between PEM and controls, both in qualitative and quantitative EEG analysis, suggest that this technology may be a source of scalable and affordable biomarkers for assessing the long-term brain impact of early PEM.