OBJECT: Heightened recognition of the prevalence and significance of head injury in sports and in combat veterans has brought increased attention to the physiological and behavioral consequences of concussion. Current clinical practice is in part dependent on patient self-report as the basis for medical decisions and treatment. Magnetoencephalography (MEG) shows promise in the assessment of the pathophysiological derangements in concussion. The authors have developed a novel MEG-based neuroimaging strategy to provide objective, noninvasive, diagnostic information in neurological disorders. In the current study the authors demonstrate a novel task protocol and then assess MEG virtual recordings obtained during task performance as a diagnostic tool for concussion. METHODS: Ten individuals (5 control volunteers and 5 patients with a history of concussion) were enrolled in this pilot study. All participants underwent an MEG evaluation during performance of a language/spatial task. Each individual produced 960 responses to 320 sentence stimuli; 0.3 sec of MEG data from each word presentation and each response were analyzed: the data from each participant were classified using a rule constructed from the data obtained from the other 9 participants. RESULTS: Analysis of response times showed significant differences (p < 10(-4)) between concussed and normal groups, demonstrating the sensitivity of the task. The MEG measures enabled the correct classification of 8 of 10 individuals as concussed versus nonconcussed (p = 0.055). Analysis of single-trial data classified 70% of trials correctly (p < 10(-10)). Concussed patients showed increased activation in the occipitoparietal and temporal regions during evaluation. CONCLUSIONS: These pilot findings are the first evidence of the utility of MEG virtual recording in diagnosing concussion. With further refinements, MEG virtual recordings may represent a noninvasive test to diagnose concussion and monitor its resolution.
OBJECT: Heightened recognition of the prevalence and significance of head injury in sports and in combat veterans has brought increased attention to the physiological and behavioral consequences of concussion. Current clinical practice is in part dependent on patient self-report as the basis for medical decisions and treatment. Magnetoencephalography (MEG) shows promise in the assessment of the pathophysiological derangements in concussion. The authors have developed a novel MEG-based neuroimaging strategy to provide objective, noninvasive, diagnostic information in neurological disorders. In the current study the authors demonstrate a novel task protocol and then assess MEG virtual recordings obtained during task performance as a diagnostic tool for concussion. METHODS: Ten individuals (5 control volunteers and 5 patients with a history of concussion) were enrolled in this pilot study. All participants underwent an MEG evaluation during performance of a language/spatial task. Each individual produced 960 responses to 320 sentence stimuli; 0.3 sec of MEG data from each word presentation and each response were analyzed: the data from each participant were classified using a rule constructed from the data obtained from the other 9 participants. RESULTS: Analysis of response times showed significant differences (p < 10(-4)) between concussed and normal groups, demonstrating the sensitivity of the task. The MEG measures enabled the correct classification of 8 of 10 individuals as concussed versus nonconcussed (p = 0.055). Analysis of single-trial data classified 70% of trials correctly (p < 10(-10)). Concussed patients showed increased activation in the occipitoparietal and temporal regions during evaluation. CONCLUSIONS: These pilot findings are the first evidence of the utility of MEG virtual recording in diagnosing concussion. With further refinements, MEG virtual recordings may represent a noninvasive test to diagnose concussion and monitor its resolution.
Authors: Elisabeth A Wilde; Sylvain Bouix; David F Tate; Alexander P Lin; Mary R Newsome; Brian A Taylor; James R Stone; James Montier; Samuel E Gandy; Brian Biekman; Martha E Shenton; Gerald York Journal: Brain Imaging Behav Date: 2015-09 Impact factor: 3.978
Authors: Franck Amyot; David B Arciniegas; Michael P Brazaitis; Kenneth C Curley; Ramon Diaz-Arrastia; Amir Gandjbakhche; Peter Herscovitch; Sidney R Hinds; Geoffrey T Manley; Anthony Pacifico; Alexander Razumovsky; Jason Riley; Wanda Salzer; Robert Shih; James G Smirniotopoulos; Derek Stocker Journal: J Neurotrauma Date: 2015-09-30 Impact factor: 5.269
Authors: D D Reddy; E M Davenport; F F Yu; B Wagner; J E Urban; C T Whitlow; J D Stitzel; J A Maldjian Journal: AJNR Am J Neuroradiol Date: 2021-09-09 Impact factor: 4.966
Authors: W Alan C Mutch; Michael J Ellis; M Ruth Graham; Vincent Wourms; Roshan Raban; Joseph A Fisher; David Mikulis; Jeffrey Leiter; Lawrence Ryner Journal: PLoS One Date: 2014-07-17 Impact factor: 3.240