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Huang MX, Angeles-Quinto A, Robb-Swan A, De-la-Garza BG, Huang CW, Cheng CK, Hesselink JR, Bigler ED, Wilde EA, Vaida F, Troyer EA, Max JE. Assessing Pediatric Mild Traumatic Brain Injury and Its Recovery Using Resting-State Magnetoencephalography Source Magnitude Imaging and Machine Learning. J Neurotrauma 2023; 40:1112-1129. [PMID: 36884305 PMCID: PMC10259613 DOI: 10.1089/neu.2022.0220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023] Open
Abstract
The objectives of this machine-learning (ML) resting-state magnetoencephalography (rs-MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury (OI) controls were to define a neural injury signature of mTBI and to delineate the pattern(s) of neural injury that determine behavioral recovery. Children ages 8-15 years with mTBI (n = 59) and OI (n = 39) from consecutive admissions to an emergency department were studied prospectively for parent-rated post-concussion symptoms (PCS) at: 1) baseline (average of 3 weeks post-injury) to measure pre-injury symptoms and also concurrent symptoms; and 2) at 3-months post-injury. rs-MEG was conducted at the baseline assessment. The ML algorithm predicted cases of mTBI versus OI with sensitivity of 95.5 ± 1.6% and specificity of 90.2 ± 2.7% at 3-weeks post-injury for the combined delta-gamma frequencies. The sensitivity and specificity were significantly better (p < 0.0001) for the combined delta-gamma frequencies compared with the delta-only and gamma-only frequencies. There were also spatial differences in rs-MEG activity between mTBI and OI groups in both delta and gamma bands in frontal and temporal lobe, as well as more widespread differences in the brain. The ML algorithm accounted for 84.5% of the variance in predicting recovery measured by PCS changes between 3 weeks and 3 months post-injury in the mTBI group, and this was significantly lower (p < 10-4) in the OI group (65.6%). Frontal lobe pole (higher) gamma activity was significantly (p < 0.001) associated with (worse) PCS recovery exclusively in the mTBI group. These findings demonstrate a neural injury signature of pediatric mTBI and patterns of mTBI-induced neural injury related to behavioral recovery.
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Affiliation(s)
- Ming-Xiong Huang
- Department of Radiology, University of California, San Diego, California, USA
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, California, USA
| | - Annemarie Angeles-Quinto
- Department of Radiology, University of California, San Diego, California, USA
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, California, USA
| | - Ashley Robb-Swan
- Department of Radiology, University of California, San Diego, California, USA
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, California, USA
| | | | - Charles W. Huang
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Chung-Kuan Cheng
- Department of Computer Science and Engineering, University of California, San Diego, California, USA
| | - John R. Hesselink
- Department of Radiology, University of California, San Diego, California, USA
| | - Erin D. Bigler
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | | | - Florin Vaida
- Herbert Wertheim School of Public Health, Division of Biostatistics and Bioinformatics, University of California, San Diego, California, USA
| | - Emily A. Troyer
- Department of Psychiatry, University of California, San Diego, California, USA
| | - Jeffrey E. Max
- Department of Psychiatry, University of California, San Diego, California, USA
- Department of Psychiatry, Rady Children's Hospital, San Diego, California, USA
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DeMaster D, Godlewska BR, Liang M, Vannucci M, Bockmann T, Cao B, Selvaraj S. Effective connectivity between resting-state networks in depression. J Affect Disord 2022; 307:79-86. [PMID: 35331822 DOI: 10.1016/j.jad.2022.03.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 03/12/2022] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
Abstract
RATIONALE Although depression has been widely researched, findings characterizing how brain regions influence each other remains scarce, yet this is critical for research on antidepressant treatments and individual responses to particular treatments. OBJECTIVES To identify pre-treatment resting state effective connectivity (rsEC) patterns in patients with major depressive disorder (MDD) and explore their relationship with treatment response. METHODS Thirty-four drug-free MDD patients had an MRI scan and were subsequently treated for 6 weeks with an SSRI escitalopram 10 mg daily; the response was defined as ≥50% decrease in Hamilton Depression Rating Scale (HAMD) score. RESULTS rsEC networks in default mode, central executive, and salience networks were identified for patients with depression. Exploratory analyses indicated higher connectivity strength related to baseline depression severity and response to treatment. CONCLUSIONS Preliminary analyses revealed widespread dysfunction of rsEC in depression. Functional rsEC may be useful as a predictive tool for antidepressant treatment response. A primary limitation of the current study was the small size; however, the group was carefully chosen, well-characterized, and included only medication-free patients. Further research in large samples of placebo-controlled studies would be required to confirm the results.
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Affiliation(s)
- Dana DeMaster
- Children's Learning Institute, Department of Pediatrics, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA.
| | - Beata R Godlewska
- Department of Psychiatry, Medical Sciences Division, University of Oxford, United Kingdom.; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Mingrui Liang
- Department of Statistics, Rice University, Houston, TX, USA
| | | | - Taya Bockmann
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Bo Cao
- University of Alberta, Department of Psychiatry, Edmonton, Canada
| | - Sudhakar Selvaraj
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
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