Magnetoencephalography abnormalities in adult mild traumatic brain injury: A systematic review

Allen, Christopher; Halsey, Lloyd; Topcu, Gogem; Rier, Lukas; Gascoyne, Lauren E.; Scadding, John W; Furlong, Paul L.; Dunkley, Benjamin T.; Nair, Roshan das; Brookes, Matthew J.; Evangelou, Nikos

doi:10.1016/j.nicl.2021.102697

Cited by 19 publications

(14 citation statements)

References 60 publications

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“…From a theoretical perspective, oscillations have been argued to play a mechanistic role in the dynamic temporal and spatial organisation of neural activity (Bastos et al, 2015;Buzsaki, 2006;Fries, 2015). Furthermore, perturbations to oscillations are associated with several clinical conditions, including Autism spectrum disorder (Kessler et al, 2016;Seymour et al, 2019), schizophrenia (Kirihara et al, 2012;Thuné et al, 2016) and mild traumatic brain injury (Allen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Robust estimation of 1/f activity improves oscillatory burst detection

Seymour

Alexander

Maguire

2022

Eur J of Neuroscience

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Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain–behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC, which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non‐linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a ‘knee’ below ~.5–10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g., theta‐ and alpha‐bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta‐band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.

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Section: Introductionmentioning

confidence: 99%

Robust estimation of 1/f activity improves oscillatory burst detection

Seymour

Alexander

Maguire

2022

Eur J of Neuroscience

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“…Although deemed "mild", concussed patients remain symptomatic (characterized by physical, emotional, cognitive, and sleep disturbances) for more than 2 weeks in 50% of cases and up to several months in 5-20% of cases after the initial injury (2). Several neuropsychological studies have reported reduced cognitive efficiency in patients with mTBI, especially in processing speed, executive function, connectivity, attention, and memory (3,4). However, to date, the neurobiological mechanism of post-mTBI cognitive impairment remains poorly understood.…”

Section: Introductionmentioning

confidence: 99%

Rich-club reorganization of functional brain networks in acute mild traumatic brain injury with cognitive impairment

Li¹,

Liu²,

Lu³

et al. 2022

Quant Imaging Med Surg

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Background: Mild traumatic brain injury (mTBI) is typically characterized by temporally limited cognitive impairment and regarded as a brain connectome disorder. Recent findings have suggested that a higher level of organization named the "rich-club" may play a central role in enabling the integration of information and efficient communication across different systems of the brain. However, the alterations in rich-club organization and hub topology in mTBI and its relationship with cognitive impairment after mTBI have been scarcely elucidated.Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 88 patients with mTBI and 85 matched healthy controls (HCs). Large-scale functional brain networks were established for each participant. Rich-club organizations and network properties were assessed and analyzed between groups. Finally, we analyzed the correlations between the cognitive performance and changes in rich-club organization and network properties.Results: Both mTBI and HCs groups showed significant rich-club organization. Meanwhile, the rich-club organization was aberrant, with enhanced functional connectivity (FC) among rich-club nodes and peripheral regions in acute mTBI. In addition, significant differences in partial global and local network topological property measures were found between mTBI patients and HCs (P<0.01). In patients with mTBI, changes in rich-club organization and network properties were found to be related to early cognitive impairment after mTBI (P<0.05).Conclusions: Our findings suggest that such patterns of disruption and reorganization will provide the basic functional architecture for cognitive function, which may subsequently be used as an earlier biomarker for cognitive impairment after mTBI.

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“…For example, lower P3 is also seen in other disorders, i.e., depression (Bruder et al, 2012;Klawohn et al, 2020), schizophrenia (Qiu et al, 2014), bipolar (Wada et al, 2019), attention deficit/hyper-activity (Kaiser et al, 2020), autism (Cui et al, 2017), psychopathy (Gao and Raine, 2009), panic (Howe et al, 2014), and Alzheimer's (Hedges et al, 2016), suggesting that P3 may be a trans-diagnostic factor (i.e., not specific to SUD) related to deficits in P3-associated attentional, working memory, and behavioral updating processes. Similarly, there is evidence for beta as a transdiagnostic rather than SUD-specific factor, as suggested by higher beta in insomnia , psychosocial stress (Vanhollebeke et al, 2022), and Parkinson's (Cagnan et al, 2019), and dysregulated (or decreased) beta in externalizing disorder (Rudo-Hutt, 2015), traumatic brain injury (Allen et al, 2021) and epilepsy (Dharan et al, 2021). In addition, theta deserves more research attention, as it is (like beta) a heritable marker of AUD (Meyers et al, 2021), involved with working memory and executive control (Cavanagh and Frank, 2014), and a well-studied biomarker in depression (Jamieson et al, 2022) and schizophrenia (Hirano and Uhlhaas, 2021).…”

Section: Discussionmentioning

confidence: 99%

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

Bel-Bahar

Khan²,

Shaik³

et al. 2022

Front. Hum. Neurosci.

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Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

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Magnetoencephalography abnormalities in adult mild traumatic brain injury: A systematic review

Cited by 19 publications

References 60 publications

Robust estimation of 1/f activity improves oscillatory burst detection

Robust estimation of 1/f activity improves oscillatory burst detection

Rich-club reorganization of functional brain networks in acute mild traumatic brain injury with cognitive impairment

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

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