Source-domain spectral EEG analysis of sports-related concussion via Measure Projection Analysis

Balkan, Ozgur; Virji‐Babul, Naznin; Miyakoshi, Makoto; Makeig, Scott; Garudadri, Harinath

doi:10.1109/embc.2015.7319284

Cited by 11 publications

(7 citation statements)

References 18 publications

(28 reference statements)

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“…In a cross-sectional study utilizing resting-state EEG, the British Columbia group (Balkan et al, 2015; Virji-Babul et al, 2014) reported increased beta, reduced theta, and reduced delta power across several frontal sources for SRC patients relative to uninjured control athletes, as well as abnormal connectivity metrics. In a study with overlapping samples, the Belgium group (van Beek et al, 2015a) utilized EEG to demonstrate that pmTBI patients exhibited lower amplitude in a late positivity component (posited to reflect attentional failure) during cognitive tasks, but were similar across more basic early sensory components.…”

Section: Literature Review Resultsmentioning

confidence: 99%

Advanced biomarkers of pediatric mild traumatic brain injury: Progress and perils

Mayer

Kaushal

Dodd

et al. 2018

Neuroscience & Biobehavioral Reviews

130

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There is growing public concern about neurodegenerative changes (e.g., Chronic Traumatic Encephalopathy) that may occur chronically following clinically apparent and clinically silent (i.e., sub-concussive blows) pediatric mild traumatic brain injury (pmTBI). However, there are currently no biomarkers that clinicians can use to objectively diagnose patients or predict those who may struggle to recover. Non-invasive neuroimaging, electrophysiological and neuromodulation biomarkers have promise for providing evidence of the so-called "invisible wounds" of pmTBI. Our systematic review, however, belies that notion, identifying a relative paucity of high-quality, clinically impactful, diagnostic or prognostic biomarker studies in the sub-acute injury phase (36 studies on unique samples in 28 years), with the majority focusing on adolescent pmTBI. Ultimately, well-powered longitudinal studies with appropriate control groups, as well as standardized and clearly-defined inclusion criteria (time post-injury, injury severity and past history) are needed to truly understand the complex pathophysiology that is hypothesized (i.e., still needs to be determined) to exist during the acute and sub-acute stages of pmTBI and may underlie post-concussive symptoms.

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Section: Literature Review Resultsmentioning

confidence: 99%

Advanced biomarkers of pediatric mild traumatic brain injury: Progress and perils

Mayer

Kaushal

Dodd

et al. 2018

Neuroscience & Biobehavioral Reviews

130

View full text Add to dashboard Cite

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“…EEG spectral differences have been used to differentiate matched controls from those with pathological conditions like dyslexia (Galin et al, 1992;Papagiannopoulou and Lagopoulos, 2016), insomnia (Buysse et al, 2008), fibromyalgia (Gonzalez-Roldan et al, 2016), epilepsy (Adebimpe et al, 2015), adolescents with sports related concussions (Balkan et al, 2015), and Parkinson’s disease (Caviness et al, 2016). In the current study, μ-β amplitudes did not correlate with a behavioral measure of stuttering severity making it unclear how well μ-β amplitude defines the disorder.…”

Section: Discussionmentioning

confidence: 99%

EEG Mu ( µ ) rhythm spectra and oscillatory activity differentiate stuttering from non-stuttering adults

et al. 2017

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Stuttering is linked to sensorimotor deficits related to internal modeling mechanisms. This study compared spectral power and oscillatory activity of EEG mu (μ) rhythms between persons who stutter (PWS) and controls in listening and auditory discrimination tasks. EEG data were analyzed from passive listening in noise and accurate (same/different) discrimination of tones or syllables in quiet and noisy backgrounds. Independent component analysis identified left and/or right μ rhythms with characteristic alpha (α) and beta (β) peaks localized to premotor/motor regions in 23 of 27 people who stutter (PWS) and 24 of 27 controls. PWS produced μ spectra with reduced β amplitudes across conditions, suggesting reduced forward modeling capacity. Group time-frequency differences were associated with noisy conditions only. PWS showed increased μ-β desynchronization when listening to noise and early in discrimination events, suggesting evidence of heightened motor activity that might be related to forward modeling deficits. PWS also showed reduced μ-α synchronization in discrimination conditions, indicating reduced sensory gating. Together these findings indicate spectral and oscillatory analyses of μ rhythms are sensitive to stuttering. More specifically, these analyses can reveal stuttering-related sensorimotor processing differences in passive listening and auditory discrimination tasks which may be influenced by basal ganglia deficits.

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“…oscillations with frequencies down to ∼ 0.01 − 0.02 Hz (also known as infraslow oscillations or ISO). The latter was motivated by two considerations: (a) EEG studies find that, compared to healthy controls, concussed athletes exhibit altered activity in the delta band (0.5 − 4 Hz) [19,33,89], with increasing divergences towards to lowest frequencies. (b) Among adolescents, one of the resting state networks strongly impacted by concussion is the default mode network (DMN) [20,48].…”

Section: Data Segmentation and Augmentationmentioning

confidence: 99%

“…At the group-level, structural changes in the integrity of white matter in adolescents has been observed using diffusion tensor imaging (DTI) [20,[27][28][29][30][31][32]. Similarly, studies of youths during "resting state" reveal significant mTBI-induced alterations in the functional organization of the brain with respect to their healthy counterparts, with key features being (i) a shift in spectral profile toward higher frequencies in the frontal brain regions, [33] (ii) an increase in functional connectivity (hyperconnectivity) [17,34,35], and (iii) disrupted information flow patterns [24], again particularly in the frontal regions. All of these studies rely on summary measures to characterize functional changes in the brain.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-based Acute Concussion Classifier using Raw Resting State EEG Data

Thanjavur

Babul

Foran

et al. 2020

Preprint

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ABSTRACTConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and recovering individuals are more prone to suffer additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, concussion management faces two significant challenges: there are no objective, clinically accepted, brain-based approaches for determining (i) whether an athlete has suffered a concussion, and (ii) when the athlete has recovered. Diagnosis is based on clinical testing and self-reporting of symptoms and their severity. Self-reporting is highly subjective and symptoms only indirectly reflect the underlying brain injury. Here, we introduce a deep learning Long Short Term Memory (LSTM)-based recurrent neural network that is able to distinguish between healthy and acute post-concussed adolescent athletes using only a short (i.e. 90 seconds long) sample of resting state EEG data as input. The athletes were neither required to perform a specific task nor subjected to a stimulus during data collection, and the acquired EEG data was neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and tested on data from 27 male, adolescent athletes with sports related concussion, bench marked against 35 healthy, adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of >90% and its ensemble-median Area Under the Curve (AUC) corresponds to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state EEG data. It represents a key step towards the development of an easy-to-use, brain-based, automatic classification of concussion at an individual level.

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Source-domain spectral EEG analysis of sports-related concussion via Measure Projection Analysis

Cited by 11 publications

References 18 publications

Advanced biomarkers of pediatric mild traumatic brain injury: Progress and perils

Advanced biomarkers of pediatric mild traumatic brain injury: Progress and perils

EEG Mu ( µ ) rhythm spectra and oscillatory activity differentiate stuttering from non-stuttering adults

Recurrent Neural Network-based Acute Concussion Classifier using Raw Resting State EEG Data

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