MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability
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 activat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/1/84 |
| _version_ | 1797494749939105792 |
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| author | Don Krieger Paul Shepard Ryan Soose Ava Puccio Sue Beers Walter Schneider Anthony P. Kontos Michael W. Collins David O. Okonkwo |
| author_facet | Don Krieger Paul Shepard Ryan Soose Ava Puccio Sue Beers Walter Schneider Anthony P. Kontos Michael W. Collins David O. Okonkwo |
| author_sort | Don Krieger |
| collection | DOAJ |
| description | 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 (CamCAN, baseline: <i>n</i> = 613; <i>mean</i> 16-month follow-up: <i>n</i> = 245) and a chronic symptomatic TBI cohort (TEAM-TBI, baseline: <i>n</i> = 62; <i>mean</i> 6-month follow-up: <i>n</i> = 40). The MEG-derived neuroelectric measures were corrected for the empty-room contribution using a random forest classifier. The <i>mean</i> 16-month correlation between baseline and 16-month follow-up CamCAN measures was 0.67; test-retest reliability was markedly improved in this study compared with previous work. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index and was assessed via adjudication for six clinical syndromes: chronic pain, psychological health, and oculomotor, vestibular, cognitive, and sleep dysfunction. Linear classifiers constructed from the 136 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, <i>p</i> < 0.0003 for each, i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms and clinical syndromes. 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. |
| first_indexed | 2024-03-10T01:38:51Z |
| format | Article |
| id | doaj.art-d0594d94df0148988f46b5158a4e80d0 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T01:38:51Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-d0594d94df0148988f46b5158a4e80d02023-11-23T13:27:58ZengMDPI AGDiagnostics2075-44182021-12-011218410.3390/diagnostics12010084MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest ReliabilityDon Krieger0Paul Shepard1Ryan Soose2Ava Puccio3Sue Beers4Walter Schneider5Anthony P. Kontos6Michael W. Collins7David O. Okonkwo8Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15217, USADepartment of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15217, USADepartment of Otolaryngology, University of Pittsburgh, Pittsburgh, PA 15217, USADepartment of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15217, USADepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15217, USADepartment of Psychology, University of Pittsburgh, Pittsburgh, PA 15217, USADepartment of Sports Medicine, University of Pittsburgh, Pittsburgh, PA 15217, USADepartment of Sports Medicine, University of Pittsburgh, Pittsburgh, PA 15217, USADepartment of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15217, USANeuroelectric 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 (CamCAN, baseline: <i>n</i> = 613; <i>mean</i> 16-month follow-up: <i>n</i> = 245) and a chronic symptomatic TBI cohort (TEAM-TBI, baseline: <i>n</i> = 62; <i>mean</i> 6-month follow-up: <i>n</i> = 40). The MEG-derived neuroelectric measures were corrected for the empty-room contribution using a random forest classifier. The <i>mean</i> 16-month correlation between baseline and 16-month follow-up CamCAN measures was 0.67; test-retest reliability was markedly improved in this study compared with previous work. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index and was assessed via adjudication for six clinical syndromes: chronic pain, psychological health, and oculomotor, vestibular, cognitive, and sleep dysfunction. Linear classifiers constructed from the 136 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, <i>p</i> < 0.0003 for each, i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms and clinical syndromes. 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.https://www.mdpi.com/2075-4418/12/1/84CamCANTEAM-TBIpost-concussion syndrometest-retest reliabilityinsomniadepression |
| spellingShingle | Don Krieger Paul Shepard Ryan Soose Ava Puccio Sue Beers Walter Schneider Anthony P. Kontos Michael W. Collins David O. Okonkwo MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability Diagnostics CamCAN TEAM-TBI post-concussion syndrome test-retest reliability insomnia depression |
| title | MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability |
| title_full | MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability |
| title_fullStr | MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability |
| title_full_unstemmed | MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability |
| title_short | MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability |
| title_sort | meg derived symptom sensitive biomarkers with long term test retest reliability |
| topic | CamCAN TEAM-TBI post-concussion syndrome test-retest reliability insomnia depression |
| url | https://www.mdpi.com/2075-4418/12/1/84 |
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