Symptom-Dependent Changes in MEG-Derived Neuroelectric Brain Activity in Traumatic Brain Injury Patients with Chronic Symptoms

Abstract

Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort, Cambridge Centre for Ageing and Neuroscience (CamCAN), baseline: n = 619; mean 16-month follow-up: n = 253) and a chronic symptomatic TBI cohort, Targeted Evaluation, Action and Monitoring of Traumatic Brain Injury (TEAM-TBI), baseline: n = 64; mean 6-month follow-up: n = 39). For the CamCAN cohort, MEG-derived neuroelectric measures showed good long-term test-retest reliability for most of the 103 automatically identified stereotypic regions. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index. Linear classifiers constructed from the 103 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, with p < 0.01 for each—i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful—i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.

MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability

“…The referee consensus solver falls short of this ideal—there is a significant contribution of falsely validated currents, i.e., “dark count”. The normalized current densities extracted from empty room recordings consistently demonstrate significant correlations to the densities extracted from human resting recordings obtained the same day ([ 10 ], Figure 1 ). We previously used an approximate correction to each ρ region.…”

“…The current study is a natural follow-on to our most recent report [ 10 ] with two notable additions. (1) The MEG-derived measures used there were corrected for empty-room contributions using a weaker method.…”

“…The work described here utilized more than 70,000,000 processor-hours on the OSG. The solver is detailed in [ 8 , 9 , 10 ].…”

“…Note that data segments contaminated by movement or other artifacts were not manually identified for removal. Instead, artifact rejection relied upon the referee consensus solver’s inherent failure to validate neuroelectric currents when presented with noisy data [ 10 , 19 ].…”

“…The referee consensus solver utilizes a method which extracts profuse validated and localized (1 mm resolution) neuroelectric current waveforms ( p < 10 −12 for each), from cortical, subcortical and white matter volumes [ 8 , 9 , 10 ]. Recordings from healthy volunteers (CamCAN, n = 621) were processed to generate a normative database, including metrics of test-retest reliability ( n = 253) across MEG studies repeated 16 months apart.…”