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Sattari S, Kenny R, Liu CC, Hajra SG, Dumont GA, Virji-Babul N. Blink-related EEG oscillations are neurophysiological indicators of subconcussive head impacts in female soccer players: a preliminary study. Front Hum Neurosci 2023; 17:1208498. [PMID: 37538402 PMCID: PMC10394644 DOI: 10.3389/fnhum.2023.1208498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/03/2023] [Indexed: 08/05/2023] Open
Abstract
Introduction Repetitive subconcussive head impacts can lead to subtle neural changes and functional consequences on brain health. However, the objective assessment of these changes remains limited. Resting state blink-related oscillations (BROs), recently discovered neurological responses following spontaneous blinking, are explored in this study to evaluate changes in BRO responses in subconcussive head impacts. Methods We collected 5-min resting-state electroencephalography (EEG) data from two cohorts of collegiate athletes who were engaged in contact sports (SC) or non-contact sports (HC). Video recordings of all on-field activities were conducted to determine the number of head impacts during games and practices in the SC group. Results In both groups, we were able to detect a BRO response. Following one season of games and practice, we found a strong association between the number of head impacts sustained by the SC group and increases in delta and beta spectral power post-blink. There was also a significant difference between the two groups in the morphology of BRO responses, including decreased peak-to-peak amplitude of response over left parietal channels and differences in spectral power in delta and alpha frequency range post-blink. Discussion Our preliminary results suggest that the BRO response may be a useful biomarker for detecting subtle neural changes resulting from repetitive head impacts. The clinical utility of this biomarker will need to be validated through further research with larger sample sizes, involving both male and female participants, using a longitudinal design.
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Affiliation(s)
- Sahar Sattari
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Rebecca Kenny
- Department of Rehabilitation Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Careesa Chang Liu
- Department of Biomedical Engineering and Science, Florida Institute of Technology, Melbourne, FL, United States
| | - Sujoy Ghosh Hajra
- Department of Biomedical Engineering and Science, Florida Institute of Technology, Melbourne, FL, United States
| | - Guy A. Dumont
- School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada
- Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Naznin Virji-Babul
- Department of Rehabilitation Sciences, The University of British Columbia, Vancouver, BC, Canada
- Department of Physical Therapy, Djavad Mowafaghian Centre for Brain Health, The University of British Columbia, Vancouver, BC, Canada
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Tabor JB, Brett BL, Nelson L, Meier T, Penner LC, Mayer AR, Echemendia RJ, McAllister T, Meehan WP, Patricios J, Makdissi M, Bressan S, Davis GA, Premji Z, Schneider KJ, Zetterberg H, McCrea M. Role of biomarkers and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion: a systematic review. Br J Sports Med 2023; 57:789-797. [PMID: 37316184 DOI: 10.1136/bjsports-2022-106680] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVE Determine the role of fluid-based biomarkers, advanced neuroimaging, genetic testing and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion (SRC). DESIGN Systematic review. DATA SOURCES Searches of seven databases from 1 January 2001 through 24 March 2022 using keywords and index terms relevant to concussion, sports and neurobiological recovery. Separate reviews were conducted for studies involving neuroimaging, fluid biomarkers, genetic testing and emerging technologies. A standardised method and data extraction tool was used to document the study design, population, methodology and results. Reviewers also rated the risk of bias and quality of each study. ELIGIBILITY CRITERIA FOR SELECTING STUDIES Studies were included if they: (1) were published in English; (2) represented original research; (3) involved human research; (4) pertained only to SRC; (5) included data involving neuroimaging (including electrophysiological testing), fluid biomarkers or genetic testing or other advanced technologies used to assess neurobiological recovery after SRC; (6) had a minimum of one data collection point within 6 months post-SRC; and (7) contained a minimum sample size of 10 participants. RESULTS A total of 205 studies met inclusion criteria, including 81 neuroimaging, 50 fluid biomarkers, 5 genetic testing, 73 advanced technologies studies (4 studies overlapped two separate domains). Numerous studies have demonstrated the ability of neuroimaging and fluid-based biomarkers to detect the acute effects of concussion and to track neurobiological recovery after injury. Recent studies have also reported on the diagnostic and prognostic performance of emerging technologies in the assessment of SRC. In sum, the available evidence reinforces the theory that physiological recovery may persist beyond clinical recovery after SRC. The potential role of genetic testing remains unclear based on limited research. CONCLUSIONS Advanced neuroimaging, fluid-based biomarkers, genetic testing and emerging technologies are valuable research tools for the study of SRC, but there is not sufficient evidence to recommend their use in clinical practice. PROSPERO REGISTRATION NUMBER CRD42020164558.
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Affiliation(s)
- Jason B Tabor
- Sport Injury Prevention Research Centre, Faculty of Kinesiology; University of Calgary, Calgary, Alberta, Canada
| | - Benjamin L Brett
- Department of Neurosurgery and Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Lindsay Nelson
- Department of Neurosurgery and Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Timothy Meier
- Department of Neurosurgery and Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Linden C Penner
- Sport Injury Prevention Research Centre, Faculty of Kinesiology; University of Calgary, Calgary, Alberta, Canada
| | - Andrew R Mayer
- The Mind Research Network, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
| | - Ruben J Echemendia
- Psychology, University of Missouri Kansas City, Kansas City, Missouri, USA
- Psychological and Neurobehavioral Associates, Inc, State College, PA, USA
| | - Thomas McAllister
- Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - William P Meehan
- Micheli Center for Sports Injury Prevention, Boston Children's Hospital, Boston, Massachusetts, USA
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jon Patricios
- Wits Sport and Health (WiSH), School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand South, Johannesburg, South Africa
| | - Michael Makdissi
- Florey Institute of Neuroscience and Mental Health - Austin Campus, Heidelberg, Victoria, Australia
- Australian Football League, Melbourne, Victoria, Australia
| | - Silvia Bressan
- Department of Women's and Children's Health, University of Padova, Padova, Italy
| | - Gavin A Davis
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Zahra Premji
- Libraries, University of Victoria, Victoria, British Columbia, Canada
| | - Kathryn J Schneider
- Sport Injury Prevention Research Centre, Faculty of Kinesiology; University of Calgary, Calgary, Alberta, Canada
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, Molndal, Sweden
| | - Michael McCrea
- Department of Neurosurgery and Center for Neurotrauma Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Avberšek LK, Repovš G. Deep learning in neuroimaging data analysis: Applications, challenges, and solutions. FRONTIERS IN NEUROIMAGING 2022; 1:981642. [PMID: 37555142 PMCID: PMC10406264 DOI: 10.3389/fnimg.2022.981642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/10/2022] [Indexed: 08/10/2023]
Abstract
Methods for the analysis of neuroimaging data have advanced significantly since the beginning of neuroscience as a scientific discipline. Today, sophisticated statistical procedures allow us to examine complex multivariate patterns, however most of them are still constrained by assuming inherent linearity of neural processes. Here, we discuss a group of machine learning methods, called deep learning, which have drawn much attention in and outside the field of neuroscience in recent years and hold the potential to surpass the mentioned limitations. Firstly, we describe and explain the essential concepts in deep learning: the structure and the computational operations that allow deep models to learn. After that, we move to the most common applications of deep learning in neuroimaging data analysis: prediction of outcome, interpretation of internal representations, generation of synthetic data and segmentation. In the next section we present issues that deep learning poses, which concerns multidimensionality and multimodality of data, overfitting and computational cost, and propose possible solutions. Lastly, we discuss the current reach of DL usage in all the common applications in neuroimaging data analysis, where we consider the promise of multimodality, capability of processing raw data, and advanced visualization strategies. We identify research gaps, such as focusing on a limited number of criterion variables and the lack of a well-defined strategy for choosing architecture and hyperparameters. Furthermore, we talk about the possibility of conducting research with constructs that have been ignored so far or/and moving toward frameworks, such as RDoC, the potential of transfer learning and generation of synthetic data.
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Affiliation(s)
- Lev Kiar Avberšek
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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Holmes S, Mar'i J, Simons LE, Zurakowski D, LeBel AA, O'Brien M, Borsook D. Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls. FRONTIERS IN PAIN RESEARCH 2022; 3:859881. [PMID: 35655747 PMCID: PMC9152124 DOI: 10.3389/fpain.2022.859881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/09/2022] [Indexed: 11/14/2022] Open
Abstract
Post-traumatic headache (PTH) is a challenging clinical condition to identify and treat as it integrates multiple subjectively defined symptoms with underlying physiological processes. The precise mechanisms underlying PTH are unclear, and it remains to be understood how to integrate the patient experience with underlying biology when attempting to classify persons with PTH, particularly in the pediatric setting where patient self-report may be highly variable. The objective of this investigation was to evaluate the use of different machine learning (ML) classifiers to differentiate pediatric and young adult subjects with PTH from healthy controls using behavioral data from self-report questionnaires that reflect concussion symptoms, mental health, pain experience of the participants, and structural brain imaging from cortical and sub-cortical locations. Behavioral data, alongside brain imaging, survived data reduction methods and both contributed toward final models. Behavioral data that contributed towards the final model included both the child and parent perspective of the pain-experience. Brain imaging features produced two unique clusters that reflect regions that were previously found in mild traumatic brain injury (mTBI) and PTH. Affinity-based propagation analysis demonstrated that behavioral data remained independent relative to neuroimaging data that suggest there is a role for both behavioral and brain imaging data when attempting to classify children with PTH.
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Affiliation(s)
- Scott Holmes
- Pediatric Pain Pathway Lab, Department of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital – Harvard Medical School, Boston, MA, United States
- Pain and Affective Neuroscience Center, Boston Children's Hospital, Boston, MA, United States
- *Correspondence: Scott Holmes
| | - Joud Mar'i
- Pediatric Pain Pathway Lab, Department of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital – Harvard Medical School, Boston, MA, United States
| | - Laura E. Simons
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - David Zurakowski
- Department of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Alyssa Ann LeBel
- Department of Anesthesia, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Michael O'Brien
- Sports Medicine Division, Sports Concussion Clinic, Orthopedic Surgery, Harvard Medical School, Boston, MA, United States
| | - David Borsook
- Departments of Psychiatry ad Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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