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Hack L, Singh B, Binkofski F, Helmich I. Repetitive Subconcussive Head Impacts in Sports and Their Impact on Brain Anatomy and Function: A Systematic Review. Int J Sports Med 2024. [PMID: 38857880 DOI: 10.1055/a-2342-3604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Repetitive subconcussive head impacts occur regularly in sports. However, the exact relationship between their biomechanical properties and their consequences on brain structure and function has not been clarified yet. We therefore reviewed prospective cohort studies that objectively reported the biomechanical characteristics of repetitive subconcussive head impacts and their impact on brain anatomy and function. Only studies with a pre- to post-measurement design were included. Twenty-four studies met the inclusion criteria. Structural white matter alterations, such as reduced fractional anisotropy and an increase in mean diffusivity values, seem to be evident in athletes exposed to repetitive subconcussive head impacts exceeding 10 g. Such changes are observable after only one season of play. Furthermore, a dose-response relationship exists between white matter abnormalities and the total number of subconcussive head impacts. However, functional changes after repetitive subconcussive head impacts remain inconclusive. We therefore conclude that repetitive subconcussive head impacts induce structural changes, but thus far without overt functional changes.
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Affiliation(s)
- Lukas Hack
- Department of Motor Behavior in Sports, German Sport University Cologne, Koln, Germany
- Department of Human Movement Science, University of Hamburg, Hamburg, Germany
| | - Bhagyashree Singh
- Department of Motor Behavior in Sports, German Sport University Cologne, Koln, Germany
| | - Ferdinand Binkofski
- Clinical Cognitive Sciences, University Hospital RWTH Aachen, Aachen , Germany
| | - Ingo Helmich
- Department of Motor Behavior in Sports, German Sport University Cologne, Koln, Germany
- Department of Exercise and Sport Studies, Smith College, Northampton, United States
- Department of Neurology, Psychosomatic Medicine and Psychiatry, German Sport University Cologne, Koln, Germany
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2
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Chen JR, Lin CJ, Chang FC, Lee IH, Lu CF. Territory-Related Functional Connectivity Changes Associated with Verbal Memory Decline in Patients with Unilateral Asymptomatic Internal Carotid Stenosis. AJNR Am J Neuroradiol 2024; 45:934-942. [PMID: 38871370 DOI: 10.3174/ajnr.a8248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/12/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND AND PURPOSE Verbal memory decline is a common complaint of patients with severe asymptomatic stenosis of the internal carotid artery (aICS). Previous publications explored the associations between verbal memory decline and altered functional connectivity (FC) after aICS. Patients with severe aICS may show reduced perfusion in the ipsilateral territory and redistribution of cerebral blood flow to compensate for the deficient regions, including expansion of the posterior and contralateral ICA territories via the circle of Willis. However, aICS-related FC changes in anterior and posterior territories and the impact of the sides of stenosis were less explored. This study aims to investigate the altered FC in anterior and posterior circulation territories of patients with left or right unilateral aICS and its association with verbal memory decline. MATERIALS AND METHODS We enrolled 15 healthy controls (HCs), 22 patients with left aICS (aICSL), and 33 patients with right aICS (aICSR) to receive fMRI, Mini-Mental State Examination (MMSE), the Digit Span Test (DST), and the 12-item Chinese version of Verbal Learning Tests. We selected brain regions associated with verbal memory within anterior and posterior circulation territories. Territory-related FC alterations and verbal memory decline were identified by comparing the aICSL and aICSR groups with HC groups (P < .05, corrected for multiple comparisons), respectively. Furthermore, the association between altered FC and verbal memory decline was tested with the Pearson correlation analysis. RESULTS Compared with HCs, patients with aICSL or aICSR had significant impairment in delayed recall of verbal memory. Decline in delayed recall of verbal memory was significantly associated with altered FC between the right cerebellum and right middle temporal pole in the posterior circulation territory (r = 0.40, P = .03) in the aICSR group and was significantly associated with altered FC between the right superior medial frontal gyrus and left lingual gyrus in the anterior circulation territory (r = 0.56, P = .01) in the aICSL group. CONCLUSIONS Patients with aICSL and aICSR showed different patterns of FC alterations in both anterior and posterior circulation territories, which suggests that the side of aICS influences the compensatory mechanism for decline in delayed recall of verbal memory.
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Affiliation(s)
- Jyun-Ru Chen
- From the Department of Biomedical Imaging and Radiological Sciences (J.-R.C., C.-F.L.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Jen Lin
- School of Medicine (C.-J.L., F.-C.C., I.-H.L.), National Yang Ming Chiao Tung University, Taipei, Taiwan
- Neurological Institute (C.-J.L., I.-H.L.), Taipei Veterans General Hospital, Taipei, Taiwan
| | - Feng-Chi Chang
- School of Medicine (C.-J.L., F.-C.C., I.-H.L.), National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Radiology (F.-C.C.), Taipei Veterans General Hospital, Taipei, Taiwan
| | - I-Hui Lee
- School of Medicine (C.-J.L., F.-C.C., I.-H.L.), National Yang Ming Chiao Tung University, Taipei, Taiwan
- Neurological Institute (C.-J.L., I.-H.L.), Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science (I.-H.L.), National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chia-Feng Lu
- From the Department of Biomedical Imaging and Radiological Sciences (J.-R.C., C.-F.L.), National Yang Ming Chiao Tung University, Taipei, Taiwan
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Huang S, Han J, Zheng H, Li M, Huang C, Kui X, Liu J. Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury: predictors of patient prognosis. Neural Regen Res 2024; 19:1553-1558. [PMID: 38051899 PMCID: PMC10883483 DOI: 10.4103/1673-5374.387971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 09/04/2023] [Indexed: 12/07/2023] Open
Abstract
Abstract
JOURNAL/nrgr/04.03/01300535-202407000-00035/figure1/v/2023-11-20T171125Z/r/image-tiff
Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of follow-up data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.
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Affiliation(s)
- Sihong Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Jungong Han
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, UK
| | - Hairong Zheng
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China
| | - Mengjun Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Chuxin Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Xiaoyan Kui
- School of Computer Science and Engineering, Central South University, Changsha, Hunan Province, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
- Department of Radiology, Quality Control Center of Hunan Province, Changsha, Hunan Province, China
- Clinical Research Center for Medical Imaging of Hunan Province, Changsha, Hunan Province, China
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Dogra S, Arabshahi S, Wei J, Saidenberg L, Kang SK, Chung S, Laine A, Lui YW. Functional Connectivity Changes on Resting-State fMRI after Mild Traumatic Brain Injury: A Systematic Review. AJNR Am J Neuroradiol 2024; 45:795-801. [PMID: 38637022 DOI: 10.3174/ajnr.a8204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/22/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Mild traumatic brain injury is theorized to cause widespread functional changes to the brain. Resting-state fMRI may be able to measure functional connectivity changes after traumatic brain injury, but resting-state fMRI studies are heterogeneous, using numerous techniques to study ROIs across various resting-state networks. PURPOSE We systematically reviewed the literature to ascertain whether adult patients who have experienced mild traumatic brain injury show consistent functional connectivity changes on resting-state -fMRI, compared with healthy patients. DATA SOURCES We used 5 databases (PubMed, EMBASE, Cochrane Central, Scopus, Web of Science). STUDY SELECTION Five databases (PubMed, EMBASE, Cochrane Central, Scopus, and Web of Science) were searched for research published since 2010. Search strategies used keywords of "functional MR imaging" and "mild traumatic brain injury" as well as related terms. All results were screened at the abstract and title levels by 4 reviewers according to predefined inclusion and exclusion criteria. For full-text inclusion, each study was evaluated independently by 2 reviewers, with discordant screening settled by consensus. DATA ANALYSIS Data regarding article characteristics, cohort demographics, fMRI scan parameters, data analysis processing software, atlas used, data characteristics, and statistical analysis information were extracted. DATA SYNTHESIS Across 66 studies, 80 areas were analyzed 239 times for at least 1 time point, most commonly using independent component analysis. The most analyzed areas and networks were the whole brain, the default mode network, and the salience network. Reported functional connectivity changes varied, though there may be a slight trend toward decreased whole-brain functional connectivity within 1 month of traumatic brain injury and there may be differences based on the time since injury. LIMITATIONS Studies of military, sports-related traumatic brain injury, and pediatric patients were excluded. Due to the high number of relevant studies and data heterogeneity, we could not be as granular in the analysis as we would have liked. CONCLUSIONS Reported functional connectivity changes varied, even within the same region and network, at least partially reflecting differences in technical parameters, preprocessing software, and analysis methods as well as probable differences in individual injury. There is a need for novel rs-fMRI techniques that better capture subject-specific functional connectivity changes.
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Affiliation(s)
- Siddhant Dogra
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
| | - Soroush Arabshahi
- Department of Biomedical Engineering (S.A., A.L.), Department of Radiology, Columbia University, New York, New York
| | - Jason Wei
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
| | - Lucia Saidenberg
- Department of Neurology (L.S.), Department of Radiology. New York University Grossman School of Medicine, New York, New York
| | - Stella K Kang
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
| | - Sohae Chung
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
| | - Andrew Laine
- Department of Biomedical Engineering (S.A., A.L.), Department of Radiology, Columbia University, New York, New York
| | - Yvonne W Lui
- From the Department of Radiology (S.D., J.W., S.K.K., S.C., Y.L.), New York University Grossman School of Medicine, New York, New York
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Koochaki F, Najafizadeh L. A Siamese Convolutional Neural Network for Identifying Mild Traumatic Brain Injury and Predicting Recovery. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1779-1786. [PMID: 38635385 DOI: 10.1109/tnsre.2024.3391067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential in understanding how the diseases progresses/regresses over time for enhancing personalized patient care, a standardized approach for this purpose is not yet available. Recent functional neuroimaging studies have provided evidence of brain function alterations following mTBI, suggesting mTBI-detection models can be built based on these changes. Most of these models, however, rely on manual feature engineering, but the optimal set of features for detecting mTBI may be unknown. Data-driven approaches, on the other hand, may uncover hidden relationships in an automated manner, making them suitable for the problem of mTBI detection. This paper presents a data-driven framework based on Siamese Convolutional Neural Network (SCNN) to detect mTBI and to monitor the recovery state from mTBI over time. The proposed framework is tested on the cortical images of Thy1-GCaMP6s mice, obtained via widefield calcium imaging, acquired in a longitudinal study. Results show that the proposed model achieves a classification accuracy of 96.5%. To track the state of the injured brain over time, a reference distance map is constructed, which together with the SCNN model, are employed to assess the recovery state in subsequent sessions after injury, revealing that the recovery progress varies among subjects. The promising results of this work suggest that a similar approach could be potentially applicable for monitoring recovery from mTBI, in humans.
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van Rhijn S, Teixeira-Dias M, Medford N, Nicholson T, Okai D, Shotbolt P, Deeley Q. Predictive Utility of Diffusion MRI After Mild Traumatic Brain Injury in Civilian Populations: A Systematic Review. J Neuropsychiatry Clin Neurosci 2024; 36:187-196. [PMID: 38528807 DOI: 10.1176/appi.neuropsych.20230122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
OBJECTIVE A considerable number of people experience persisting symptoms and functional limitations after mild traumatic brain injury (mTBI). It is unclear whether subtle white matter changes contribute to this phenomenon. In this systematic review, the authors evaluated whether microstructural white matter indices on advanced MRI are related to clinical dysfunction among patients without abnormalities on standard brain computed tomography (CT) or MRI (uncomplicated mTBI). METHODS A search of multiple databases was performed. Studies with individuals who experienced blast-related, sports-related, or multiple mTBIs were excluded. Diffusion tensor imaging (DTI) and susceptibility-weighted imaging (SWI) metrics and cognitive, neuropsychiatric, or functional outcome measures were extracted from each study. RESULTS Thirteen studies were selected (participants with mTBI, N=553; healthy control group, N=438). Seven DTI studies evaluated cognitive function, with five reporting significant correlations between reduced white matter integrity and deficits in attention, processing speed, and executive function at 6-12 months after injury (three studies included only individuals with uncomplicated mTBI). Four studies found significant correlations between DTI metrics and persistent postconcussive symptoms after 3-12 months (one study included only individuals with uncomplicated mTBI). Two SWI studies reported conflicting findings regarding the relationship between the presence of microbleeds and postconcussive symptoms. CONCLUSIONS The results revealed that indices of microstructural white matter integrity may relate to clinical presentation 3-12 months after injury in uncomplicated mTBI. However, analysis methods and brain regions studied varied across studies. Further research is needed to identify relationships between white matter indices in specific brain regions and symptom persistence beyond 12 months.
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Affiliation(s)
- Sanne van Rhijn
- Department of Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London (all authors); Perinatal Mental Health Service, West London National Health Service Trust, London (van Rhijn)
| | - Maria Teixeira-Dias
- Department of Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London (all authors); Perinatal Mental Health Service, West London National Health Service Trust, London (van Rhijn)
| | - Nick Medford
- Department of Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London (all authors); Perinatal Mental Health Service, West London National Health Service Trust, London (van Rhijn)
| | - Timothy Nicholson
- Department of Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London (all authors); Perinatal Mental Health Service, West London National Health Service Trust, London (van Rhijn)
| | - David Okai
- Department of Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London (all authors); Perinatal Mental Health Service, West London National Health Service Trust, London (van Rhijn)
| | - Paul Shotbolt
- Department of Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London (all authors); Perinatal Mental Health Service, West London National Health Service Trust, London (van Rhijn)
| | - Quinton Deeley
- Department of Neuropsychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London (all authors); Perinatal Mental Health Service, West London National Health Service Trust, London (van Rhijn)
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7
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Caeyenberghs K, Imms P, Irimia A, Monti MM, Esopenko C, de Souza NL, Dominguez D JF, Newsome MR, Dobryakova E, Cwiek A, Mullin HAC, Kim NJ, Mayer AR, Adamson MM, Bickart K, Breedlove KM, Dennis EL, Disner SG, Haswell C, Hodges CB, Hoskinson KR, Johnson PK, Königs M, Li LM, Liebel SW, Livny A, Morey RA, Muir AM, Olsen A, Razi A, Su M, Tate DF, Velez C, Wilde EA, Zielinski BA, Thompson PM, Hillary FG. ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury. Neuroimage Clin 2024; 42:103585. [PMID: 38531165 PMCID: PMC10982609 DOI: 10.1016/j.nicl.2024.103585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 03/28/2024]
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.
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Affiliation(s)
- Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Alfred E. Mann Department of Biomedical Engineering, Andrew & Erna Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Martin M Monti
- Department of Psychology, UCLA, USA; Brain Injury Research Center (BIRC), Department of Neurosurgery, UCLA, USA.
| | - Carrie Esopenko
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, NY, USA.
| | - Nicola L de Souza
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, NY, USA.
| | - Juan F Dominguez D
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
| | - Mary R Newsome
- Michael E. DeBakey VA Medical Center, Houston, TX, USA; H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA; TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA.
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ, USA; Rutgers New Jersey Medical School, Newark, NJ, USA.
| | - Andrew Cwiek
- Department of Psychology, Penn State University, State College, PA, USA.
| | - Hollie A C Mullin
- Department of Psychology, Penn State University, State College, PA, USA.
| | - Nicholas J Kim
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Alfred E. Mann Department of Biomedical Engineering, Andrew & Erna Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
| | - Andrew R Mayer
- Mind Research Network, Albuquerque, NM, USA; Departments of Neurology and Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, USA.
| | - Maheen M Adamson
- Women's Operational Military Exposure Network (WOMEN) & Rehabilitation Department, VA Palo Alto, Palo Alto, CA, USA; Rehabilitation Service, VA Palo Alto, Palo Alto, CA, USA; Neurosurgery, Stanford School of Medicine, Stanford, CA, USA.
| | - Kevin Bickart
- UCLA Steve Tisch BrainSPORT Program, USA; Department of Neurology, David Geffen School of Medicine at UCLA, USA.
| | - Katherine M Breedlove
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Emily L Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Seth G Disner
- Minneapolis VA Health Care System, Minneapolis, MN, USA; Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Courtney Haswell
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
| | - Cooper B Hodges
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA; Department of Psychology, Brigham Young University, Provo, UT, USA.
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA; Department of Pediatrics, The Ohio State University College of Medicine, OH, USA.
| | - Paula K Johnson
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; Neuroscience Center, Brigham Young University, Provo, UT, USA.
| | - Marsh Königs
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, The Netherlands; Amsterdam Reproduction and Development, Amsterdam, The Netherlands.
| | - Lucia M Li
- C3NL, Imperial College London, United Kingdom; UK DRI Centre for Health Care and Technology, Imperial College London, United Kingdom.
| | - Spencer W Liebel
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Abigail Livny
- Division of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Israel; Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Rajendra A Morey
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, USA; VA Mid-Atlantic Mental Illness Research Education and Clinical Center, Durham, NC, USA.
| | - Alexandra M Muir
- Department of Psychology, Brigham Young University, Provo, UT, USA.
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; NorHEAD - Norwegian Centre for Headache Research, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia; Wellcome Centre for Human Neuroimaging, University College London, WC1N 3AR London, United Kingdom; CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, ON, Canada.
| | - Matthew Su
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA.
| | - David F Tate
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Carmen Velez
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Elisabeth A Wilde
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA; TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Brandon A Zielinski
- Departments of Pediatrics, Neurology, and Neuroscience, University of Florida, Gainesville, FL, USA; Departments of Pediatrics, Neurology, and Radiology, University of Utah, Salt Lake City, UT, USA.
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA.
| | - Frank G Hillary
- Department of Psychology, Penn State University, State College, PA, USA; Department of Neurology, Hershey Medical Center, PA, USA.
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8
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Wei YC, Chen CK, Lin C, Shyu YC, Chen PY. Life After Traumatic Brain Injury: Effects on the Lifestyle and Quality of Life of Community-Dwelling Patients. Neurotrauma Rep 2024; 5:159-171. [PMID: 38463415 PMCID: PMC10924056 DOI: 10.1089/neur.2023.0113] [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] [Indexed: 03/12/2024] Open
Abstract
Persons who have experienced traumatic brain injury (TBI) may encounter a range of changes in their physical, mental, and cognitive functions as well as high fatigue levels. To gain a comprehensive understanding of the challenges faced by persons after TBI, we conducted multi-domain assessments among community-dwelling persons with a history of TBI and compared them with age- and sex-matched controls from the Northeastern Taiwan Community Medicine Research Cohort between 2019 and 2021. A total of 168 persons with TBI and 672 non-TBI controls were not different in terms of demographics, comorbidities, and physiological features. However, compared with the non-TBI group, the TBI group had a distinct lifestyle that involved increased reliance on analgesics (6.9% vs. 15.0%, respectively; p = 0.001) and sleep aids (p = 0.008), which negatively affected their quality of life. Moreover, they consumed more coffee (p < 0.001), tea (p < 0.001), cigarettes (p = 0.002), and betel nuts (p = 0.032) than did the non-TBI group. Notably, the use of coffee had a positive effect on the quality of life of the TBI group (F = 4.034; p = 0.045). Further, compared with the non-TBI group, the TBI group had increased risks of sarcopenia (p = 0.003), malnutrition (p = 0.003), and anxiety (p = 0.029) and reduced blood levels of vitamin D (29.83 ± 10.39 vs. 24.20 ± 6.59 ng/mL, respectively; p < 0.001). Overall, the TBI group had a reduced health-related quality of life, with significant challenges related to physical health, mental well-being, social interactions, pain management, and fatigue levels. Moreover, the TBI group experienced poorer sleep quality and efficiency than did the non-TBI group. In conclusion, persons who have sustained brain injuries that require comprehensive and holistic care that includes lifestyle modification, mental and physical healthcare plans, and increased long-term support from their communities. ClinicalTrials.gov (identifier: NCT04839796).
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Affiliation(s)
- Yi-Chia Wei
- Department of Neurology, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
| | - Chih-Ken Chen
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
| | - Chemin Lin
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
- Department of Psychiatry, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
| | - Yu-Chiau Shyu
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
- Department of Nursing, Chang Gung University of Science and Technology, Taoyuan, Taiwan
| | - Pin-Yuan Chen
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung Branch, Keelung, Taiwan
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9
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Vedaei F, Newberg AB, Alizadeh M, Zabrecky G, Navarreto E, Hriso C, Wintering N, Mohamed FB, Monti D. Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study. Front Neurol 2024; 15:1282198. [PMID: 38299014 PMCID: PMC10829764 DOI: 10.3389/fneur.2024.1282198] [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: 08/23/2023] [Accepted: 01/03/2024] [Indexed: 02/02/2024] Open
Abstract
Mild traumatic brain injury (mTBI) is a significant public health concern, specially characterized by a complex pattern of abnormal neural activity and functional connectivity. It is often associated with a broad spectrum of short-term and long-term cognitive and behavioral symptoms including memory dysfunction, headache, and balance difficulties. Furthermore, there is evidence that oxidative stress significantly contributes to these symptoms and neurophysiological changes. The purpose of this study was to assess the effect of N-acetylcysteine (NAC) on brain function and chronic symptoms in mTBI patients. Fifty patients diagnosed with chronic mTBI participated in this study. They were categorized into two groups including controls (CN, n = 25), and patients receiving treatment with N-acetyl cysteine (NAC, n = 25). NAC group received 50 mg/kg intravenous (IV) medication once a day per week. In the rest of the week, they took one 500 mg NAC tablet twice per day. Each patient underwent rs-fMRI scanning at two timepoints including the baseline and 3 months later at follow-up, while the NAC group received a combination of oral and IV NAC over that time. Three rs-fMRI metrics were measured including fractional amplitude of low frequency fluctuations (fALFF), degree centrality (DC), and functional connectivity strength (FCS). Neuropsychological tests were also assessed at the same day of scanning for each patient. The alteration of rs-fMRI metrics and cognitive scores were measured over 3 months treatment with NAC. Then, the correlation analysis was executed to estimate the association of rs-fMRI measurements and cognitive performance over 3 months (p < 0.05). Two significant group-by-time effects demonstrated the changes of rs-fMRI metrics particularly in the regions located in the default mode network (DMN), sensorimotor network, and emotional circuits that were significantly correlated with cognitive function recovery over 3 months treatment with NAC (p < 0.05). NAC appears to modulate neural activity and functional connectivity in specific brain networks, and these changes could account for clinical improvement. This study confirmed the short-term therapeutic efficacy of NAC in chronic mTBI patients that may contribute to understanding of neurophysiological effects of NAC in mTBI. These findings encourage further research on long-term neurobehavioral assessment of NAC assisting development of therapeutic plans in mTBI.
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Andrew B. Newberg
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Mahdi Alizadeh
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - George Zabrecky
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel Monti
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
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10
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Xu Z, Li Y, Fan X, Xu W, Liu J, Li J. Disrupted functional connectivity of the striatum in patients with diffuse axonal injury: a resting-state functional MRI study. Neuroreport 2023; 34:792-800. [PMID: 37756204 PMCID: PMC10538614 DOI: 10.1097/wnr.0000000000001956] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023]
Abstract
Diffuse axonal injury (DAI) disrupts the integrity of white matter microstructure and affects brain functional connectivity, resulting in persistent cognitive, behavioral and affective deficits. Mounting evidence suggests that altered cortical-subcortical connectivity is a major contributor to cognitive dysfunction. The functional integrity of the striatum is particularly vulnerable to DAI, but has received less attention. This study aimed to investigate the alteration patterns of striatal subdivision functional connectivity. Twenty-six patients with DAI and 27 healthy controls underwent resting-state fMRI scans on a 3.0 T scanner. We assessed striatal subdivision functional connectivity using a seed-based analysis in DAI. Furthermore, a partial correlation was used to measure its clinical association. Compared to controls, patients with DAI showed decreased functional connectivity between the right inferior ventral striatum and right inferior frontal gyrus, as well as the right inferior parietal lobule, between the left inferior ventral striatum and right inferior frontal gyrus, between the right superior ventral striatum and bilateral cerebellar posterior lobe, between the bilateral dorsal caudal putamen and right anterior cingulate gyrus, and between the right dorsal caudal putamen and right inferior parietal lobule. Moreover, decreased functional connectivity was observed between the left dorsal caudate and the right cerebellar posterior lobe, while increased functional connectivity was found between the left dorsal caudate and right inferior parietal lobule. Correlation analyses showed that regions with functional connectivity differences in the DAI group correlated with multiple clinical scoring scales, including cognition, motor function, agitated behavior, and anxiety disorders. These findings suggest that abnormalities in cortico-striatal and cerebellar-striatal functional connectivity are observed in patients with DAI, enriching our understanding of the neuropathological mechanisms of post-injury cognitive disorders and providing potential neuroimaging markers for the diagnosis and treatment of DAI.
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Affiliation(s)
- Zhigang Xu
- Department of Radiology, Fifth Hospital of Fuzhou Jianqiang
| | - Ye Li
- Department of Radiology, First Affiliated Hospital of Nanchang University
| | - Xiaole Fan
- Department of Ultrasound, the First Affiliated Hospital, Jinan University
| | - Wenhua Xu
- Department of Radiology, Fifth Hospital of Fuzhou Jianqiang
| | - Jinliang Liu
- Department of Radiology, Fifth Hospital of Fuzhou Jianqiang
| | - Jian Li
- Department of Radiology, First Affiliated Hospital of Nanchang University
- Clinical Research Center For Medical Imaging In Jiangxi Province, Nanchang, China
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11
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Kim SY, Yeh PH, Ollinger JM, Morris HD, Hood MN, Ho VB, Choi KH. Military-related mild traumatic brain injury: clinical characteristics, advanced neuroimaging, and molecular mechanisms. Transl Psychiatry 2023; 13:289. [PMID: 37652994 PMCID: PMC10471788 DOI: 10.1038/s41398-023-02569-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 07/18/2023] [Accepted: 07/24/2023] [Indexed: 09/02/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is a significant health burden among military service members. Although mTBI was once considered relatively benign compared to more severe TBIs, a growing body of evidence has demonstrated the devastating neurological consequences of mTBI, including chronic post-concussion symptoms and deficits in cognition, memory, sleep, vision, and hearing. The discovery of reliable biomarkers for mTBI has been challenging due to under-reporting and heterogeneity of military-related mTBI, unpredictability of pathological changes, and delay of post-injury clinical evaluations. Moreover, compared to more severe TBI, mTBI is especially difficult to diagnose due to the lack of overt clinical neuroimaging findings. Yet, advanced neuroimaging techniques using magnetic resonance imaging (MRI) hold promise in detecting microstructural aberrations following mTBI. Using different pulse sequences, MRI enables the evaluation of different tissue characteristics without risks associated with ionizing radiation inherent to other imaging modalities, such as X-ray-based studies or computerized tomography (CT). Accordingly, considering the high morbidity of mTBI in military populations, debilitating post-injury symptoms, and lack of robust neuroimaging biomarkers, this review (1) summarizes the nature and mechanisms of mTBI in military settings, (2) describes clinical characteristics of military-related mTBI and associated comorbidities, such as post-traumatic stress disorder (PTSD), (3) highlights advanced neuroimaging techniques used to study mTBI and the molecular mechanisms that can be inferred, and (4) discusses emerging frontiers in advanced neuroimaging for mTBI. We encourage multi-modal approaches combining neuropsychiatric, blood-based, and genetic data as well as the discovery and employment of new imaging techniques with big data analytics that enable accurate detection of post-injury pathologic aberrations related to tissue microstructure, glymphatic function, and neurodegeneration. Ultimately, this review provides a foundational overview of military-related mTBI and advanced neuroimaging techniques that merit further study for mTBI diagnosis, prognosis, and treatment monitoring.
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Affiliation(s)
- Sharon Y Kim
- School of Medicine, Uniformed Services University, Bethesda, MD, USA
- Program in Neuroscience, Uniformed Services University, Bethesda, MD, USA
| | - Ping-Hong Yeh
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - John M Ollinger
- Program in Neuroscience, Uniformed Services University, Bethesda, MD, USA
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Herman D Morris
- Department of Radiology and Radiological Sciences, Uniformed Services University, Bethesda, MD, USA
- Department of Radiology, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Maureen N Hood
- Department of Radiology and Radiological Sciences, Uniformed Services University, Bethesda, MD, USA
- Department of Radiology, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Vincent B Ho
- Department of Radiology and Radiological Sciences, Uniformed Services University, Bethesda, MD, USA
- Department of Radiology, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Kwang H Choi
- Program in Neuroscience, Uniformed Services University, Bethesda, MD, USA.
- Center for the Study of Traumatic Stress, Uniformed Services University, Bethesda, MD, USA.
- Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA.
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12
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Chakraborty P, Saha S, Deco G, Banerjee A, Roy D. Structural-and-dynamical similarity predicts compensatory brain areas driving the post-lesion functional recovery mechanism. Cereb Cortex Commun 2023; 4:tgad012. [PMID: 37559936 PMCID: PMC10409568 DOI: 10.1093/texcom/tgad012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 06/30/2023] [Accepted: 07/05/2023] [Indexed: 08/11/2023] Open
Abstract
The focal lesion alters the excitation-inhibition (E-I) balance and healthy functional connectivity patterns, which may recover over time. One possible mechanism for the brain to counter the insult is global reshaping functional connectivity alterations. However, the operational principles by which this can be achieved remain unknown. We propose a novel equivalence principle based on structural and dynamic similarity analysis to predict whether specific compensatory areas initiate lost E-I regulation after lesion. We hypothesize that similar structural areas (SSAs) and dynamically similar areas (DSAs) corresponding to a lesioned site are the crucial dynamical units to restore lost homeostatic balance within the surviving cortical brain regions. SSAs and DSAs are independent measures, one based on structural similarity properties measured by Jaccard Index and the other based on post-lesion recovery time. We unravel the relationship between SSA and DSA by simulating a whole brain mean field model deployed on top of a virtually lesioned structural connectome from human neuroimaging data to characterize global brain dynamics and functional connectivity at the level of individual subjects. Our results suggest that wiring proximity and similarity are the 2 major guiding principles of compensation-related utilization of hemisphere in the post-lesion functional connectivity re-organization process.
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Affiliation(s)
- Priyanka Chakraborty
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH-8, Manesar, Haryana 122051, India
| | - Suman Saha
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH-8, Manesar, Haryana 122051, India
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH-8, Manesar, Haryana 122051, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, NH-8, Manesar, Haryana 122051, India
- School of AIDE, Center for Brain Research and Applications, IIT Jodhpur, NH-62, Surpura Bypass Rd, Karwar, Rajasthan 342030, India
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13
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Ryan D, Mirbagheri S, Yahyavi-Firouz-Abadi N. The Current State of Functional MR Imaging for Trauma Prognostication. Neuroimaging Clin N Am 2023; 33:299-313. [PMID: 36965947 DOI: 10.1016/j.nic.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Abstract
In this review, we discuss the basics of functional MRI (fMRI) techniques including task-based and resting state fMRI, and overview the major findings in patients with traumatic brain injury. We summarize the studies that have longitudinally evaluated the changes in brain connectivity and task-related activation in trauma patients during different phases of trauma. We discuss how these data may potentially be used for prognostication, treatment planning, or monitoring and management of trauma patients.
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Affiliation(s)
- Daniel Ryan
- Southern Illinois University School of Medicine, 401 East Carpenter Street, Springfield, IL, USA
| | - Saeedeh Mirbagheri
- University of Vermont Medical Center, 111 Colchester Avenue, Burlington, VT 05401, USA
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Verhulst MMLH, Glimmerveen AB, van Heugten CM, Helmich RCG, Hofmeijer J. MRI factors associated with cognitive functioning after acute onset brain injury: Systematic review and meta-analysis. Neuroimage Clin 2023; 38:103415. [PMID: 37119695 PMCID: PMC10165272 DOI: 10.1016/j.nicl.2023.103415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/22/2023] [Accepted: 04/19/2023] [Indexed: 05/01/2023]
Abstract
Impairments of memory, attention, and executive functioning are frequently reported after acute onset brain injury. MRI markers hold potential to contribute to identification of patients at risk for cognitive impairments and clarification of mechanisms. The aim of this systematic review was to summarize and value the evidence on MRI markers of memory, attention, and executive functioning after acute onset brain injury. We included ninety-eight studies, on six classes of MRI factors (location and severity of damage (n = 15), volume/atrophy (n = 36), signs of small vessel disease (n = 15), diffusion-weighted imaging measures (n = 36), resting-state functional MRI measures (n = 13), and arterial spin labeling measures (n = 1)). Three measures showed consistent results regarding their association with cognition. Smaller hippocampal volume was associated with worse memory in fourteen studies (pooled correlation 0.58 [95% CI: 0.46-0.68] for whole, 0.11 [95% CI: 0.04-0.19] for left, and 0.34 [95% CI: 0.17-0.49] for right hippocampus). Lower fractional anisotropy in cingulum and fornix was associated with worse memory in six and five studies (pooled correlation 0.20 [95% CI: 0.08-0.32] and 0.29 [95% CI: 0.20-0.37], respectively). Lower functional connectivity within the default-mode network was associated with worse cognition in four studies. In conclusion, hippocampal volume, fractional anisotropy in cingulum and fornix, and functional connectivity within the default-mode network showed consistent associations with cognitive performance in all types of acute onset brain injury. External validation and cut off values for predicting cognitive impairments are needed for clinical implementation.
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Affiliation(s)
- Marlous M L H Verhulst
- Clinical Neurophysiology, University of Twente, Enschede, The Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands.
| | - Astrid B Glimmerveen
- Clinical Neurophysiology, University of Twente, Enschede, The Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Caroline M van Heugten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Limburg Brain Injury Center, Maastricht University, Maastricht, The Netherlands; Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Rick C G Helmich
- Donders Institute for Brain, Cognition, and Behavior, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands; Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Jeannette Hofmeijer
- Clinical Neurophysiology, University of Twente, Enschede, The Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
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15
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Sahrizan NSA, Manan HA, Abdul Hamid H, Abdullah JM, Yahya N. Functional Alteration in the Brain Due to Tumour Invasion in Paediatric Patients: A Systematic Review. Cancers (Basel) 2023; 15:cancers15072168. [PMID: 37046828 PMCID: PMC10093754 DOI: 10.3390/cancers15072168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/14/2023] Open
Abstract
Working memory, language and speech abilities, motor skills, and visual abilities are often impaired in children with brain tumours. This is because tumours can invade the brain's functional areas and cause alterations to the neuronal networks. However, it is unclear what the mechanism of tumour invasion is and how various treatments can cause cognitive impairment. Therefore, this study aims to systematically evaluate the effects of tumour invasion on the cognitive, language, motor, and visual abilities of paediatric patients, as well as discuss the alterations and modifications in neuronal networks and anatomy. The electronic database, PubMed, was used to find relevant studies. The studies were systematically reviewed based on the type and location of brain tumours, cognitive assessment, and pre- and post-operative deficits experienced by patients. Sixteen studies were selected based on the inclusion and exclusion criteria following the guidelines from PRISMA. Most studies agree that tumour invasion in the brain causes cognitive dysfunction and alteration in patients. The effects of a tumour on cognition, language, motor, and visual abilities depend on the type of tumour and its location in the brain. The alteration to the neuronal networks is also dependent on the type and location of the tumour. However, the default mode network (DMN) is the most affected network, regardless of the tumour type and location.Furthermore, our findings suggest that different treatment types can also contribute to patients' cognitive function to improve or deteriorate. Deficits that persisted or were acquired after surgery could result from surgical manipulation or the progression of the tumour's growth. Meanwhile, recovery from the deficits indicated that the brain has the ability to recover and reorganise itself.
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Affiliation(s)
- Nur Shaheera Aidilla Sahrizan
- Department of Radiology, Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), University Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
- Department of Radiology and Intervency, Hospital Pakar Kanak-Kanak (Children Specialist Hospital), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Hanani Abdul Manan
- Department of Radiology, Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), University Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
- Department of Radiology and Intervency, Hospital Pakar Kanak-Kanak (Children Specialist Hospital), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Hamzaini Abdul Hamid
- Department of Radiology, Makmal Pemprosesan Imej Kefungsian (Functional Image Processing Laboratory), University Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia
- Department of Radiology and Intervency, Hospital Pakar Kanak-Kanak (Children Specialist Hospital), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Jafri Malin Abdullah
- Jabatan Neurosains, Pusat Pengajian Sains Perubatan, Jalan Hospital USM, Kampus Kesihatan, Universiti Sains Malaysia, Kota Bharu 16150, Malaysia
- Brain and Behaviour Cluster, Pusat Pengajian Sains Perubatan, Kampus Kesihatan, Universiti Sains Malaysia, Kota Bharu 16150, Malaysia
- Department of Neurosciences & Brain Behaviour Cluster, Hospital Universiti Sains Malaysia, Kampus Kesihatan, Universiti Sains Malaysia, Kota Bharu 16150, Malaysia
| | - Noorazrul Yahya
- Diagnostic Imaging & Radiotherapy Program, School of Diagnostic & Applied Health Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, Malaysia
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16
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Gugger JJ, Sinha N, Huang Y, Walter AE, Lynch C, Kalyani P, Smyk N, Sandsmark D, Diaz-Arrastia R, Davis KA. Structural brain network deviations predict recovery after traumatic brain injury. Neuroimage Clin 2023; 38:103392. [PMID: 37018913 PMCID: PMC10122019 DOI: 10.1016/j.nicl.2023.103392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/10/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023]
Abstract
OBJECTIVE Traumatic brain injury results in diffuse axonal injury and the ensuing maladaptive alterations in network function are associated with incomplete recovery and persistent disability. Despite the importance of axonal injury as an endophenotype in TBI, there is no biomarker that can measure the aggregate and region-specific burden of axonal injury. Normative modeling is an emerging quantitative case-control technique that can capture region-specific and aggregate deviations in brain networks at the individual patient level. Our objective was to apply normative modeling in TBI to study deviations in brain networks after primarily complicated mild TBI and study its relationship with other validated measures of injury severity, burden of post-TBI symptoms, and functional impairment. METHOD We analyzed 70 T1-weighted and diffusion-weighted MRIs longitudinally collected from 35 individuals with primarily complicated mild TBI during the subacute and chronic post-injury periods. Each individual underwent longitudinal blood sampling to characterize blood protein biomarkers of axonal and glial injury and assessment of post-injury recovery in the subacute and chronic periods. By comparing the MRI data of individual TBI participants with 35 uninjured controls, we estimated the longitudinal change in structural brain network deviations. We compared network deviation with independent measures of acute intracranial injury estimated from head CT and blood protein biomarkers. Using elastic net regression models, we identified brain regions in which deviations present in the subacute period predict chronic post-TBI symptoms and functional status. RESULTS Post-injury structural network deviation was significantly higher than controls in both subacute and chronic periods, associated with an acute CT lesion and subacute blood levels of glial fibrillary acid protein (r = 0.5, p = 0.008) and neurofilament light (r = 0.41, p = 0.02). Longitudinal change in network deviation associated with change in functional outcome status (r = -0.51, p = 0.003) and post-concussive symptoms (BSI: r = 0.46, p = 0.03; RPQ: r = 0.46, p = 0.02). The brain regions where the node deviation index measured in the subacute period predicted chronic TBI symptoms and functional status corresponded to areas known to be susceptible to neurotrauma. CONCLUSION Normative modeling can capture structural network deviations, which may be useful in estimating the aggregate and region-specific burden of network changes induced by TAI. If validated in larger studies, structural network deviation scores could be useful for enrichment of clinical trials of targeted TAI-directed therapies.
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Affiliation(s)
- James J Gugger
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nishant Sinha
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Yiming Huang
- Interdisciplinary Computing and Complex BioSystems, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alexa E Walter
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Cillian Lynch
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Priyanka Kalyani
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nathan Smyk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle Sandsmark
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ramon Diaz-Arrastia
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
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17
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Li W, Ding S, Zhao G. Static and dynamic topological organization of brain functional connectome in acute mild traumatic brain injury. Acta Radiol 2023; 64:1175-1183. [PMID: 35765198 DOI: 10.1177/02841851221109897] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Prior studies have detected topological changes of brain functional networks in patients with acute mild traumatic brain injury (mTBI). However, the alterations of dynamic topological characteristics in mTBI have been scarcely elucidated. PURPOSE To evaluate static and dynamic functional connectivity topological networks in patients with acute mTBI using resting-state functional magnetic resonance imaging (fMRI). MATERIAL AND METHODS A total of 55 patients with acute mTBI and 55 age-, sex-, and education-matched healthy controls (HCs) were enrolled in this study. All participants underwent resting-state fMRI scans, and data were analyzed using graph-theory methods and a sliding window approach. Post-traumatic cognitive performance and resting-state fMRI data were collected within one week after injury. Static and dynamic functional connectivity patterns were determined by independent component analysis. Spearman's correlation analysis was further performed between fMRI changes and Montreal cognitive assessment (MoCA) scores. RESULTS Global efficiency was lower (P = 0.02), and local efficiency (P < 0.001) and mean Cp (P < 0.001) were higher in patients with acute mTBI than in HCs. Local efficiency was correlated with visuospatial/executive performance (r = -0.421; P = 0.002) in patients with acute mTBI. Significant differences in nodal efficiency and node degree centrality (P < 0.01) were found between the mTBI and HC groups. For dynamic properties, patients with mTBI showed higher variance (P = 0.016) in global efficiency than HCs. CONCLUSIONS The present study shows that patients with mTBI have abnormal brain functional connectome topology, especially the dynamic graph theory characteristics, which provide new insights into the role of topological network properties in patients with acute mTBI.
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Affiliation(s)
- Weigang Li
- Department of Radiology, Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
| | - Shaohua Ding
- Department of Radiology, Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
| | - Guoqian Zhao
- Department of Radiology, Chinese Traditional Medicine Hospital of Danyang, Danyang, Jiangsu, PR China
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18
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Vedaei F, Mashhadi N, Zabrecky G, Monti D, Navarreto E, Hriso C, Wintering N, Newberg AB, Mohamed FB. Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques. Front Neurosci 2023; 16:1099560. [PMID: 36699521 PMCID: PMC9869678 DOI: 10.3389/fnins.2022.1099560] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 12/21/2022] [Indexed: 01/11/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level. Clinical trial registration [clinicaltrials.gov], identifier [NCT03241732].
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Najmeh Mashhadi
- Department of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, United States
| | - George Zabrecky
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel Monti
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Emily Navarreto
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Andrew B. Newberg
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
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19
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Mustafi SM, Yang HC, Harezlak J, Meier TB, Brett BL, Giza CC, Goldman J, Guskiewicz KM, Mihalik JP, LaConte SM, Duma SM, Broglio SP, McCrea MA, McAllister TW, Wu YC. Effects of White-Matter Tract Length in Sport-Related Concussion: A Tractography Study from the NCAA-DoD CARE Consortium. J Neurotrauma 2022; 39:1495-1506. [PMID: 35730116 PMCID: PMC9689766 DOI: 10.1089/neu.2021.0239] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Sport-related concussion (SRC) is an important public health issue. White-matter alterations after SRC are widely studied by neuroimaging approaches, such as diffusion magnetic resonance imaging (MRI). Although the exact anatomical location of the alterations may differ, significant white-matter alterations are commonly observed in long fiber tracts, but are never proven. In the present study, we performed streamline tractography to characterize the association between tract length and white-matter microstructural alterations after SRC. Sixty-eight collegiate athletes diagnosed with acute concussion (24-48 h post-injury) and 64 matched contact-sport controls were included in this study. The athletes underwent diffusion tensor imaging (DTI) in 3.0 T MRI scanners across three study sites. DTI metrics were used for tract-based spatial statistics to map white-matter regions-of-interest (ROIs) with significant group differences. Whole-brain white-mater streamline tractography was performed to extract "affected" white-matter streamlines (i.e., streamlines passing through the identified ROIs). In the concussed athletes, streamline counts and DTI metrics of the affected white-matter fiber tracts were summarized and compared with unaffected white-matter tracts across tract length in the same participant. The affected white-matter tracts had a high streamline count at length of 80-100 mm and high length-adjusted affected ratio for streamline length longer than 80 mm. DTI mean diffusivity was higher in the affected streamlines longer than 100 mm with significant associations with the Brief Symptom Inventory score. Our findings suggest that long fibers in the brains of collegiate athletes are more vulnerable to acute SRC with higher mean diffusivity and a higher affected ratio compared with the whole distribution.
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Affiliation(s)
- Sourajit M. Mustafi
- Institute of Genetics, San Diego, California, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ho-Ching Yang
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, Indiana, USA
| | - Timothy B. Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Benjamin L. Brett
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Christopher C. Giza
- Department of Neurosurgery, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
- Division of Pediatric Neurology, Mattel Children's Hospital, University of California, Los Angeles, Los Angeles, California, USA
| | - Joshua Goldman
- Family Medicine, Ronald Reagan UCLA Medical Center, UCLA Health - Santa Monica Medical Center, Los Angeles, California, USA
| | - Kevin M. Guskiewicz
- Matthew Gfeller Sport-Related Traumatic Brain Injury Research Center, Department of Exercise and Sport Science, University of North Carolina, at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jason P. Mihalik
- Matthew Gfeller Sport-Related Traumatic Brain Injury Research Center, Department of Exercise and Sport Science, University of North Carolina, at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stephen M. LaConte
- School of Biomedical Engineering and Sciences, Wake-Forest and Virginia Tech University, Blacksburg, Virginia, USA
- Virginia Tech Carilion Research Institute, Roanoke, Virginia, USA
| | - Stefan M. Duma
- School of Biomedical Engineering and Sciences, Wake-Forest and Virginia Tech University, Blacksburg, Virginia, USA
| | - Steven P. Broglio
- Michigan Concussion Center, School of Kinesiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Michael A. McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Thomas W. McAllister
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
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20
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Rifkin JA, Wu T, Rayfield AC, Anderson ED, Panzer MB, Meaney DF. Brain architecture-based vulnerability to traumatic injury. Front Bioeng Biotechnol 2022; 10:936082. [PMID: 36091446 PMCID: PMC9448929 DOI: 10.3389/fbioe.2022.936082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/01/2022] [Indexed: 02/03/2023] Open
Abstract
The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups’ dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.
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Affiliation(s)
- Jared A. Rifkin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
| | - Taotao Wu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Adam C. Rayfield
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Erin D. Anderson
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Matthew B. Panzer
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
| | - David F. Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: David F. Meaney,
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21
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Huang W, Hu W, Zhang P, Wang J, Jiang Y, Ma L, Zheng Y, Zhang J. Early Changes in the White Matter Microstructure and Connectome Underlie Cognitive Deficit and Depression Symptoms After Mild Traumatic Brain Injury. Front Neurol 2022; 13:880902. [PMID: 35847204 PMCID: PMC9279564 DOI: 10.3389/fneur.2022.880902] [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: 02/22/2022] [Accepted: 06/01/2022] [Indexed: 11/18/2022] Open
Abstract
Cognitive and emotional impairments are frequent among patients with mild traumatic brain injury (mTBI) and may reflect alterations in the brain structural properties. The relationship between microstructural changes and cognitive and emotional deficits remains unclear in patients with mTBI at the acute stage. The purpose of this study was to analyze the alterations in white matter microstructure and connectome of patients with mTBI within 7 days after injury and investigate whether they are related to the clinical questionnaires. A total of 79 subjects (42 mTBI and 37 healthy controls) underwent neuropsychological assessment and diffusion-tensor MRI scan. The microstructure and connectome of white matter were characterized by tract-based spatial statistics (TBSSs) and graph theory approaches, respectively. Mini-mental state examination (MMSE) and self-rating depression scale (SDS) were used to evaluate the cognitive function and depressive symptoms of all the subjects. Patients with mTBI revealed early increases of fractional anisotropy in most areas compared with the healthy controls. Graph theory analyses showed that patients with mTBI had increased nodal shortest path length, along with decreased nodal degree centrality and nodal efficiency, mainly located in the bilateral temporal lobe and right middle occipital gyrus. Moreover, lower nodal shortest path length and higher nodal efficiency of the right middle occipital gyrus were associated with higher SDS scores. Significantly, the strength of the rich club connection in the mTBI group decreased and was associated with the MMSE. Our study demonstrated that the neuroanatomical alterations of mTBI in the acute stage might be an initial step of damage leading to cognitive deficits and depression symptoms, and arguably, these occur due to distinct mechanisms.
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Affiliation(s)
- Wenjing Huang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Wanjun Hu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Pengfei Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jun Wang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Yanli Jiang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Laiyang Ma
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Yu Zheng
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
- *Correspondence: Jing Zhang
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22
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Abdul Rahman MR, Abd Hamid AI, Noh NA, Omar H, Chai WJ, Idris Z, Ahmad AH, Fitzrol DN, Ab. Ghani ARIG, Wan Mohamad WNA, Mohamed Mustafar MF, Hanafi MH, Reza MF, Umar H, Mohd Zulkifly MF, Ang SY, Zakaria Z, Musa KI, Othman A, Embong Z, Sapiai NA, Kandasamy R, Ibrahim H, Abdullah MZ, Amaruchkul K, Valdes-Sosa P, Luisa-Bringas M, Biswal B, Songsiri J, Yaacob HS, Sumari P, Jamir Singh PS, Azman A, Abdullah JM. Alteration in the Functional Organization of the Default Mode Network Following Closed Non-severe Traumatic Brain Injury. Front Neurosci 2022; 16:833320. [PMID: 35418832 PMCID: PMC8995774 DOI: 10.3389/fnins.2022.833320] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
The debilitating effect of traumatic brain injury (TBI) extends years after the initial injury and hampers the recovery process and quality of life. In this study, we explore the functional reorganization of the default mode network (DMN) of those affected with non-severe TBI. Traumatic brain injury (TBI) is a wide-spectrum disease that has heterogeneous effects on its victims and impacts everyday functioning. The functional disruption of the default mode network (DMN) after TBI has been established, but its link to causal effective connectivity remains to be explored. This study investigated the differences in the DMN between healthy participants and mild and moderate TBI, in terms of functional and effective connectivity using resting-state functional magnetic resonance imaging (fMRI). Nineteen non-severe TBI (mean age 30.84 ± 14.56) and twenty-two healthy (HC; mean age 27.23 ± 6.32) participants were recruited for this study. Resting-state fMRI data were obtained at the subacute phase (mean days 40.63 ± 10.14) and analyzed for functional activation and connectivity, independent component analysis, and effective connectivity within and between the DMN. Neuropsychological tests were also performed to assess the cognitive and memory domains. Compared to the HC, the TBI group exhibited lower activation in the thalamus, as well as significant functional hypoconnectivity between DMN and LN. Within the DMN nodes, decreased activations were detected in the left inferior parietal lobule, precuneus, and right superior frontal gyrus. Altered effective connectivities were also observed in the TBI group and were linked to the diminished activation in the left parietal region and precuneus. With regard to intra-DMN connectivity within the TBI group, positive correlations were found in verbal and visual memory with the language network, while a negative correlation was found in the cognitive domain with the visual network. Our results suggested that aberrant activities and functional connectivities within the DMN and with other RSNs were accompanied by the altered effective connectivities in the TBI group. These alterations were associated with impaired cognitive and memory domains in the TBI group, in particular within the language domain. These findings may provide insight for future TBI observational and interventional research.
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Affiliation(s)
- Muhammad Riddha Abdul Rahman
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Nerus, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Aini Ismafairus Abd Hamid
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
- *Correspondence: Aini Ismafairus Abd Hamid,
| | - Nor Azila Noh
- Faculty of Medicine and Health Sciences, Universiti Sains Islam Malaysia, Nilai, Malaysia
| | - Hazim Omar
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Wen Jia Chai
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Zamzuri Idris
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Asma Hayati Ahmad
- Department of Physiology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Diana Noma Fitzrol
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Ab. Rahman Izaini Ghani Ab. Ghani
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Wan Nor Azlen Wan Mohamad
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Mohamed Faiz Mohamed Mustafar
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Muhammad Hafiz Hanafi
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Mohamed Faruque Reza
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Hafidah Umar
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Mohd Faizal Mohd Zulkifly
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Song Yee Ang
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Zaitun Zakaria
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Azizah Othman
- Department of Paediatrics, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Zunaina Embong
- Department of Ophthalmology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Nur Asma Sapiai
- Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | | | - Haidi Ibrahim
- School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
| | - Mohd Zaid Abdullah
- School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
| | - Kannapha Amaruchkul
- Graduate School of Applied Statistics, National Institute of Development Administration (NIDA), Bangkok, Thailand
| | - Pedro Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- The Cuban Neurosciences Center, Havana, Cuba
| | - Maria Luisa-Bringas
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- The Cuban Neurosciences Center, Havana, Cuba
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Jitkomut Songsiri
- EE410 Control Systems Laboratory, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Hamwira Sakti Yaacob
- Department of Computer Science, Kulliyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
| | - Putra Sumari
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor, Malaysia
| | | | - Azlinda Azman
- School of Social Sciences, Universiti Sains Malaysia, Gelugor, Malaysia
| | - Jafri Malin Abdullah
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Brain and Behavior Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
- Jafri Malin Abdullah,
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23
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Chai WJ, Abd Hamid AI, Omar H, Abdul Rahman MR, Fitzrol DN, Idris Z, Ghani ARI, Wan Mohamad WNA, Mustafar F, Hanafi MH, Kandasamy R, Abdullah MZ, Amaruchkul K, Valdes-Sosa PA, Bringas-Vega ML, Biswal B, Songsiri J, Yaacob H, Ibrahim H, Sumari P, Noh NA, Musa KI, Ahmad AH, Azman A, Jamir Singh PS, Othman A, Abdullah JM. Neural alterations in working memory of mild-moderate TBI: An fMRI study in Malaysia. J Neurosci Res 2022; 100:915-932. [PMID: 35194817 DOI: 10.1002/jnr.25023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 10/10/2021] [Accepted: 12/31/2021] [Indexed: 02/05/2023]
Abstract
Working memory (WM) encompasses crucial cognitive processes or abilities to retain and manipulate temporary information for immediate execution of complex cognitive tasks in daily functioning such as reasoning and decision-making. The WM of individuals sustaining traumatic brain injury (TBI) was commonly compromised, especially in the domain of WM. The current study investigated the brain responses of WM in a group of participants with mild-moderate TBI compared to their healthy counterparts employing functional magnetic resonance imaging. All consented participants (healthy: n = 26 and TBI: n = 15) performed two variations of the n-back WM task with four load conditions (0-, 1-, 2-, and 3-back). The respective within-group effects showed a right hemisphere-dominance activation and slower reaction in performance for the TBI group. Random-effects analysis revealed activation difference between the two groups in the right occipital lobe in the guided n-back with cues, and in the bilateral occipital lobe, superior parietal region, and cingulate cortices in the n-back without cues. The left middle frontal gyrus was implicated in the load-dependent processing of WM in both groups. Further group analysis identified that the notable activation changes in the frontal gyri and anterior cingulate cortex are according to low and high loads. Though relatively smaller in scale, this study was eminent as it clarified the neural alterations in WM in the mild-moderate TBI group compared to healthy controls. It confirmed the robustness of the phenomenon in TBI with the reproducibility of the results in a heterogeneous non-Western sample.
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Affiliation(s)
- Wen Jia Chai
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Aini Ismafairus Abd Hamid
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Hazim Omar
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Muhammad Riddha Abdul Rahman
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Nerus, Malaysia
| | - Diana Noma Fitzrol
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Zamzuri Idris
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Abdul Rahman Izaini Ghani
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Wan Nor Azlen Wan Mohamad
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Faiz Mustafar
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Muhammad Hafiz Hanafi
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | | | - Mohd Zaid Abdullah
- School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
| | - Kannapha Amaruchkul
- Graduate School of Applied Statistics, National Institute of Development Administration (NIDA), Bangkok, Thailand
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,The Cuban Neurosciences Center, La Habana, Cuba
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,The Cuban Neurosciences Center, La Habana, Cuba
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Jitkomut Songsiri
- EE410 Control Systems Laboratory, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Hamwira Yaacob
- Department of Computer Science, Kulliyyah of Information and Communication Technology, Kuala Lumpur, International Islamic University Malaysia, Kuala Lumpur, Malaysia
| | - Haidi Ibrahim
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
| | - Putra Sumari
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
| | - Nor Azila Noh
- Department of Medical Science 1, Faculty of Medicine and Health Sciences, Universiti Sains Islam Malaysia, Nilai, Malaysia
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Asma Hayati Ahmad
- Department of Physiology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Azlinda Azman
- School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Nerus, Malaysia.,School of Social Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia
| | | | - Azizah Othman
- Department of Psychiatry, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia
| | - Jafri Malin Abdullah
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Malaysia.,Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Kota Bharu, Malaysia
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24
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Zhang D, Zhu P, Yin B, Zhao P, Wang S, Ye L, Bai L, Yan Z, Bai G. Frontal White Matter Hyperintensities Effect on Default Mode Network Connectivity in Acute Mild Traumatic Brain Injury. Front Aging Neurosci 2022; 13:793491. [PMID: 35250532 PMCID: PMC8890121 DOI: 10.3389/fnagi.2021.793491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
The functional connectivity of the brain depends not only on the structural integrity of the cortex but also on the white matter pathways between cortical areas. White matter hyperintensities (WMH), caused by chronic hypoperfusion in the white matter, play a role in the outcome of traumatic brain injury (TBI) and other neurodegenerative disorders. Herein, we investigate how the location and volume of WMH affect the default-mode network (DMN) connectivity in acute mild TBI (mTBI) patients. Forty-six patients with acute mTBI and 46 matched healthy controls were enrolled in the study. All participants underwent T2-weighted fluid-attenuated inversion recovery magnetic resonance imaging (MRI), resting-state functional MRI (fMRI),and neuropsychological assessments. The volume and location of WMH were recorded. The relationships between the WMH volume and clinical assessments were evaluated using Spearman’s correlation. Patients with higher frontal lobe WMH volume had more severe post-concussion symptoms and poorer information processing speed. Moreover, these patients had significantly lower functional connectivity in the right middle temporal gyrus, left middle frontal gyrus, right superior frontal gyrus, and left anterior cingulate cortex, compared with patients with low frontal lobe WMH volume. Compared to the controls, the patients with high frontal WMH volume exhibited significantly lower functional connectivity in the right inferior temporal gyrus, left anterior cingulate cortex, and right superior frontal gyrus. These findings suggest that frontal lobe WMH volume may modulate the functional connectivity within the DMN. Therefore, the WMH volume in specific regions of the brain, particularly the frontal and parietal lobes, may accelerate the process of aging and cognitive impairment may be a useful biomarker for the diagnosis and prognosis of acute mTBI.
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Affiliation(s)
- Danbin Zhang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Pingyi Zhu
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, China
| | - Bo Yin
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, China
| | - Pinghui Zhao
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, China
| | - Shan Wang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Limei Ye
- Department of Radiology, Jinhua Municipal Central Hospital and Jinhua Hospital of Zhejiang University, Jinhua, China
| | - Lijun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, China
- *Correspondence: Zhihan Yan,
| | - Guanghui Bai
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, China
- Wenzhou Key Laboratory of Basic Science and Translational Research of Radiation Oncology, Wenzhou, China
- Guanghui Bai,
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25
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An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks. Neuroimage 2022; 251:119002. [PMID: 35176490 DOI: 10.1016/j.neuroimage.2022.119002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 01/19/2022] [Accepted: 02/12/2022] [Indexed: 11/22/2022] Open
Abstract
The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.
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26
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Youn DH, Jung H, Tran NM, Jeon JP, Yoo H. The Therapeutic Role of Nanoparticle Shape in Traumatic Brain Injury : An in vitro Comparative Study. J Korean Neurosurg Soc 2022; 65:196-203. [PMID: 35108773 PMCID: PMC8918243 DOI: 10.3340/jkns.2021.0185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/23/2021] [Indexed: 11/27/2022] Open
Abstract
Objective To perform a comparative analysis of therapeutic effects associated with two different shapes of ceria nanoparticles, ceria nanorods (Ceria NRs) and ceria nanospheres (Ceria NSs), in an in vitro model of traumatic brain injury (TBI). Methods In vitro TBI was induced using six-well confluent plates by manually scratching with a sterile pipette tip in a 6×6-square grid. The cells were then incubated and classified into cells with scratch injury without nanoparticles and cells with scratch injury, which were treated separately with 1.16 mM of Ceria NSs and Ceria NRs. Antioxidant activities and anti-inflammatory effects were analyzed. Results Ceria NRs and Ceria NSs significantly reduced the level of reactive oxygen species compared with the control group of SH-SY5Y cells treated with Dulbecco's phosphate-buffered saline. The mRNA expression of superoxide dismutases was also reduced in nanoparticle-treated SH-SY5Y cells, but apparently the degree of mRNA expression decrease was not dependent on the nanoparticle shape. Exposure to ceria nanoparticles also decreased the cyclooxygenase-2 expression, especially prominent in Ceria NR-treated group than that in Ceria NS-treated group. Conclusion Ceria nanoparticles exhibit antioxidant and anti-inflammatory effects in TBI models in vitro. Ceria NRs had better antiinflammatory effect than Ceria NSs, but showed similar antioxidant activity.
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Affiliation(s)
- Dong Hyuk Youn
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea
| | - Harry Jung
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea
| | - Ngoc Minh Tran
- Department of Materials Science and Chemical Engineering, Hanyang University, Ansan, Korea
| | - Jin Pyeong Jeon
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea.,Department of Neurosurgery, Hallym University College of Medicine, Chuncheon, Korea
| | - Hyojong Yoo
- Department of Materials Science and Chemical Engineering, Hanyang University, Ansan, Korea
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27
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Mollica A, Dey A, Cairncross M, Silverberg N, Burke MJ. Neuropsychiatric Treatment for Mild Traumatic Brain Injury: Nonpharmacological Approaches. Semin Neurol 2022; 42:168-181. [PMID: 35114694 DOI: 10.1055/s-0041-1742143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Postconcussive symptoms following mild traumatic brain injury (mTBI)/concussion are common, disabling, and challenging to manage. Patients can experience a range of symptoms (e.g., mood disturbance, headaches, insomnia, vestibular symptoms, and cognitive dysfunction), and neuropsychiatric management relies heavily on nonpharmacological and multidisciplinary approaches. This article presents an overview of current nonpharmacological strategies for postconcussive symptoms including psychoeducation; psychotherapy; vestibular, visual, and physical therapies; cognitive rehabilitation; as well as more novel approaches, such as neuromodulation. Ultimately, treatment and management of mTBI should begin early with appropriate psychoeducation/counseling, and be tailored based on core symptoms and individual goals.
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Affiliation(s)
- Adriano Mollica
- Neuropsychiatry Program, Department of Psychiatry, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.,Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Ayan Dey
- Neuropsychiatry Program, Department of Psychiatry, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.,Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Molly Cairncross
- Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada.,Rehabilitation Research Program, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Noah Silverberg
- Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada.,Rehabilitation Research Program, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Matthew J Burke
- Neuropsychiatry Program, Department of Psychiatry, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.,Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
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28
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Vedaei F, Newberg AB, Alizadeh M, Muller J, Shahrampour S, Middleton D, Zabrecky G, Wintering N, Bazzan AJ, Monti DA, Mohamed FB. Resting-State Functional MRI Metrics in Patients With Chronic Mild Traumatic Brain Injury and Their Association With Clinical Cognitive Performance. Front Hum Neurosci 2022; 15:768485. [PMID: 35027887 PMCID: PMC8751629 DOI: 10.3389/fnhum.2021.768485] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/29/2021] [Indexed: 12/27/2022] Open
Abstract
Mild traumatic brain injury (mTBI) accounts for more than 80% of people experiencing brain injuries. Symptoms of mTBI include short-term and long-term adverse clinical outcomes. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was conducted to measure voxel-based indices including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) in patients suffering from chronic mTBI; 64 patients with chronic mTBI at least 3 months post injury and 40 healthy controls underwent rs-fMRI scanning. Partial correlation analysis controlling for age and gender was performed within mTBI cohort to explore the association between rs-fMRI metrics and neuropsychological scores. Compared with controls, chronic mTBI patients showed increased fALFF in the left middle occipital cortex (MOC), right middle temporal cortex (MTC), and right angular gyrus (AG), and increased ReHo in the left MOC and left posterior cingulate cortex (PCC). Enhanced FC was observed from left MOC to right precuneus; from right MTC to right superior temporal cortex (STC), right supramarginal, and left inferior parietal cortex (IPC); and from the seed located at right AG to left precuneus, left superior medial frontal cortex (SMFC), left MTC, left superior temporal cortex (STC), and left MOC. Furthermore, the correlation analysis revealed a significant correlation between neuropsychological scores and fALFF, ReHo, and seed-based FC measured from the regions with significant group differences. Our results demonstrated that alterations of low-frequency oscillations in chronic mTBI could be representative of disruption in emotional circuits, cognitive performance, and recovery in this cohort.
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Andrew B Newberg
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States.,Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Mahdi Alizadeh
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jennifer Muller
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Shiva Shahrampour
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Devon Middleton
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - George Zabrecky
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy Wintering
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Anthony J Bazzan
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Daniel A Monti
- Department of Integrative Medicine and Nutritional Sciences, Marcus Institute of Integrative Health, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
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29
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Roine T, Mohammadian M, Hirvonen J, Kurki T, Posti JP, Takala RS, Newcombe V, Tallus J, Katila AJ, Maanpää HR, Frantzen J, Menon D, Tenovuo O. Structural brain connectivity correlates with outcome in mild traumatic brain injury. J Neurotrauma 2022; 39:336-347. [PMID: 35018829 DOI: 10.1089/neu.2021.0093] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
We investigated the topology of structural brain connectivity networks and its association to outcome following mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent MRI twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. However, we found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared to control subjects. Furthermore, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage, and therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.
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Affiliation(s)
- Timo Roine
- University of Turku, 8058, Turku Brain and Mind Center, Turku, Finland.,Aalto University School of Science, 313201, Department of Neuroscience and Biomedical Engineering, Espoo, Finland;
| | - Mehrbod Mohammadian
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland;
| | - Jussi Hirvonen
- TYKS Turku University Hospital, 60652, Department of Radiology, Turku, Varsinais-Suomi, Finland;
| | - Timo Kurki
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland.,TYKS Turku University Hospital, 60652, Department of Radiology, Turku, Varsinais-Suomi, Finland;
| | - Jussi P Posti
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland.,TYKS Turku University Hospital, 60652, Department of Neurosurgery. Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Riikka Sk Takala
- Turku University Hospital, Perioperative Services, Intensive Care Medicine and Pain Management, Turku, Finland.,University of Turku, 8058, Anaesthesiology, Intensive Care, Emergency Care and Pain Medicine, Turku, Varsinais-Suomi, Finland;
| | - Virginia Newcombe
- University of Cambridge, Division of Anaesthesia, Addenbrooke's Hospital, Cambridge, United Kingdom of Great Britain and Northern Ireland;
| | - Jussi Tallus
- Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Ari J Katila
- Turku University Hospital, Perioperative Services, Intensive Care Medicine and Pain Management, Turku, Varsinais-Suomi, Finland;
| | - Henna-Riikka Maanpää
- Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland.,Turku University Hospital, Department of Neurosurgery, Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Janek Frantzen
- Turku University Hospital, Turku Brain Injury Center, Neurocenter, Turku, Finland.,Turku University Hospital, Department of Neurosurgery, Neurocenter, Turku, Varsinais-Suomi, Finland.,University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland;
| | - David Menon
- University of Cambridge, Division of Anaesthesia, Addenbrooke's Hospital, Cambridge, United Kingdom of Great Britain and Northern Ireland;
| | - Olli Tenovuo
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland;
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30
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Gumus M, Santos A, Tartaglia MC. Diffusion and functional MRI findings and their relationship to behaviour in postconcussion syndrome: a scoping review. J Neurol Neurosurg Psychiatry 2021; 92:1259-1270. [PMID: 34635568 DOI: 10.1136/jnnp-2021-326604] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 09/22/2021] [Indexed: 11/04/2022]
Abstract
Postconcussion syndrome (PCS) is a term attributed to the constellation of symptoms that fail to recover after a concussion. PCS is associated with a variety of symptoms such as headaches, concentration deficits, fatigue, depression and anxiety that have an enormous impact on patients' lives. There is currently no diagnostic biomarker for PCS. There have been attempts at identifying structural and functional brain changes in patients with PCS, using diffusion tensor imaging (DTI) and functional MRI (fMRI), respectively, and relate them to specific PCS symptoms. In this scoping review, we appraised, synthesised and summarised all empirical studies that (1) investigated structural or functional brain changes in PCS using DTI or fMRI, respectively, and (2) assessed behavioural alterations in patients with PCS. We performed a literature search in MEDLINE (Ovid), Embase (Ovid) and PsycINFO (Ovid) for primary research articles published up to February 2020. We identified 8306 articles and included 45 articles that investigated the relationship between DTI and fMRI parameters and behavioural changes in patients with PCS: 20 diffusion, 20 fMRI studies and 5 papers with both modalities. Most frequently studied structures were the corpus callosum, superior longitudinal fasciculus in diffusion and the dorsolateral prefrontal cortex and default mode network in the fMRI literature. Although some white matter and fMRI changes were correlated with cognitive or neuropsychiatric symptoms, there were no consistent, converging findings on the relationship between neuroimaging abnormalities and behavioural changes which could be largely due to the complex and heterogeneous presentation of PCS. Furthermore, the heterogeneity of symptoms in PCS may preclude discovery of one biomarker for all patients. Further research should take advantage of multimodal neuroimaging to better understand the brain-behaviour relationship, with a focus on individual differences rather than on group comparisons.
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Affiliation(s)
- Melisa Gumus
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Alexandra Santos
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Maria Carmela Tartaglia
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada .,Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada.,Canadian Concussion Centre, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
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31
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de Souza NL, Buckman JF, Dennis EL, Parrott JS, Velez C, Wilde EA, Tate DF, Esopenko C. Association between white matter organization and cognitive performance in athletes with a history of sport-related concussion. J Clin Exp Neuropsychol 2021; 43:704-715. [PMID: 34779351 DOI: 10.1080/13803395.2021.1991893] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Impairments in cognitive performance after sport-related concussion (SRC) typically resolve within weeks of the injury, whereas alterations to white matter (WM) organization have been found to persist longer into the chronic injury stage. However, longer-term associations between cognition and WM organization following SRC have not been studied. The objective of this study was to compare WM organization and cognitive performance in collegiate athletes an average of almost 4 years post-SRC to athletes with no history of SRC. METHOD National Collegiate Athletic Association Division III athletes (n = 71, age = 19.3 ± 1.2; 14 with self-reported SRC) completed a neurocognitive assessment and diffusion tensor imaging (DTI). WM organization was assessed by extracting measures of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) from 20 WM regions of interest (ROIs). Multivariate partial least squares analyses were used to compare athletes with and without a history of SRC and assess relationships between DTI-derived metrics of WM organization and cognitive measures. RESULTS Cognitive performance and ROI metrics did not differ between athletes with and without prior SRC. However, among athletes with a history of SRC, better executive function, processing speed, and memory but worse choice reaction time were associated with higher FA and lower MD and RD in several WM tracts. CONCLUSION Athletes with a history of SRC demonstrated greater associations between cognitive performance and WM organization, but also variability in the domains showing associations. Taken together, the findings demonstrate the importance of examining brain-behavior relationships several years after SRC to better gauge how WM organization supports cognition.
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Affiliation(s)
- Nicola L de Souza
- School of Graduate Studies, Biomedical Sciences, Rutgers Biomedical and Health Sciences, Newark, NJ, USA
| | - Jennifer F Buckman
- Department of Kinesiology and Health, Rutgers University - New Brunswick, Piscataway, NJ, USA
| | - Emily L Dennis
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.,George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | | | - Carmen Velez
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.,Missouri Institute of Mental Health, University of Missouri, St. Louis, MO, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.,George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - David F Tate
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.,George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA.,Missouri Institute of Mental Health, University of Missouri, St. Louis, MO, USA
| | - Carrie Esopenko
- Department of Rehabilitation & Movement Sciences, School of Health Professions, Rutgers Biomedical and Health Sciences, Newark, NJ, USA
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32
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Jia X, Chang X, Bai L, Wang Y, Dong D, Gan S, Wang S, Li X, Yang X, Sun Y, Li T, Xiong F, Niu X, Yan H. A Longitudinal Study of White Matter Functional Network in Mild Traumatic Brain Injury. J Neurotrauma 2021; 38:2686-2697. [PMID: 33906419 DOI: 10.1089/neu.2021.0017] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Some patients after mild traumatic brain injury (mTBI) experience microstructural damages in the long-distance white matter (WM) connections, which disrupts the functional connectome of large-scale brain networks that support cognitive function. Patterns of WM structural damage following mTBI were well documented using diffusion tensor imaging (DTI). However, the functional organization of WM and its association with gray matter functional networks (GM-FNs) and its DTI metrics remain unknown. The present study adopted resting-state functional magnetic resonance imaging to explore WM functional properties in mTBI patients (108 acute patients, 48 chronic patients, 46 healthy controls [HCs]). Eleven large-scale WM functional networks (WM-FNs) were constructed by the k-means clustering algorithm of voxel-wise WM functional connectivity (FC). Compared with HCs, acute mTBI patients observed enhanced FC between inferior fronto-occipital fasciculus (IFOF) WM-FN and primary sensorimotor WM-FNs, and cortical primary sensorimotor GM-FNs. Further, acute mTBI patients showed increased DTI metrics (mean diffusivity, axial diffusivity, and radial diffusivity) in deep WM-FNs and higher-order cognitive WM-FNs. Moreover, mTBI patients demonstrated full recovery of FC and partial recovery of DTI metrics in the chronic stage. Additionally, enhanced FC between IFOF WM-FN and anterior cerebellar GM-FN was correlated with impaired information processing speed. Our findings provide novel evidence for functional and structural alteration of WM-FNs in mTBI patients. Importantly, the convergent damage of the IFOF network might imply its crucial role in our understanding of the pathophysiology mechanism of mTBI patients.
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Affiliation(s)
- Xiaoyan Jia
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xuebin Chang
- School of Life Science and Technology, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Lijun Bai
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Yulin Wang
- Department of Experimental and Applied Psychology, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Debo Dong
- School of Life Science and Technology, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Shuoqiu Gan
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Shan Wang
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xuan Li
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xuefei Yang
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Yinxiang Sun
- Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Tianhui Li
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Feng Xiong
- Department of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xuan Niu
- Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hao Yan
- Key Laboratory for Artificial Intelligence and Cognitive Neuroscience of Language, Xi'an International Studies University, Xi'an, China
- Department of Linguistics, Xidian University, Xi'an, China
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Ueda R, Hara H, Hata J, Senoo A. White matter degeneration in diffuse axonal injury and mild traumatic brain injury observed with automatic tractography. Neuroreport 2021; 32:936-941. [PMID: 34132707 DOI: 10.1097/wnr.0000000000001688] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
A better understanding of white matter tract damage in patients with diffuse axonal injury (DAI) and mild traumatic brain injury (MTBI) is important to obtain an objective basis for sequelae. The purpose of this study was to clarify the characteristics of white matter tract degeneration in DAI and MTBI using automated tractography. T1-weighted and diffusion tensor imaging (DTI) was performed on seven DAI and seven MTBI patients as well as on nine healthy subjects. Automated probabilistic tractography analysis was performed using FreeSurfer and TRACULA (tracts constrained by underlying anatomy) for the reconstruction of major nerve fibers. We investigated the difference between DTI quantitative values in each white matter nerve fiber between groups and attempted to evaluate the classification accuracy of DAI and MTBI using receiver operator curve analysis. Both DAI and MTBI appeared to exhibit axonal degeneration along the nerve fiber tract in a scattered manner. The mean diffusivity of the ampulla of the corpus callosum was significantly higher in DAI than that in MTBI patients, suggesting axonal degeneration of the corpus callosum in DAI patients. Using mean diffusivity of the right cingulum-angular bundle, DAI and MTBI could be discriminated with an area under the curve of 94%. Both DAI and MTBI exhibited scattered axonal degeneration; however, DAI appeared to exhibit more pronounced axonal degeneration in the ampulla of the corpus callosum than MTBI. Our results suggest that DAI and MTBI can be accurately distinguished using DTI.
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Affiliation(s)
- Ryo Ueda
- Office of Radiation Technology, Keio University Hospital, Tokyo
| | - Hiroyoshi Hara
- Neurorehabilitation Center, Ainomiyako Neurosurgery Hospital, Osaka
| | - Junichi Hata
- Division of Regenerative Medicine, Jikei University Graduate School of Medicine
| | - Atsushi Senoo
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
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34
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Teasing apart trauma: neural oscillations differentiate individual cases of mild traumatic brain injury from post-traumatic stress disorder even when symptoms overlap. Transl Psychiatry 2021; 11:345. [PMID: 34088901 PMCID: PMC8178364 DOI: 10.1038/s41398-021-01467-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 05/08/2021] [Accepted: 05/19/2021] [Indexed: 01/21/2023] Open
Abstract
Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are highly prevalent and closely related disorders. Affected individuals often exhibit substantially overlapping symptomatology - a major challenge for differential diagnosis in both military and civilian contexts. According to our symptom assessment, the PTSD group exhibited comparable levels of concussion symptoms and severity to the mTBI group. An objective and reliable system to uncover the key neural signatures differentiating these disorders would be an important step towards translational and applied clinical use. Here we explore use of MEG (magnetoencephalography)-multivariate statistical learning analysis in identifying the neural features for differential PTSD/mTBI characterisation. Resting state MEG-derived regional neural activity and coherence (or functional connectivity) across seven canonical neural oscillation frequencies (delta to high gamma) were used. The selected features were consistent and largely confirmatory with previously established neurophysiological markers for the two disorders. For regional power from theta, alpha and high gamma bands, the amygdala, hippocampus and temporal areas were identified. In line with regional activity, additional connections within the occipital, parietal and temporal regions were selected across a number of frequency bands. This study is the first to employ MEG-derived neural features to reliably and differentially stratify the two disorders in a multi-group context. The features from alpha and beta bands exhibited the best classification performance, even in cases where distinction by concussion symptom profiles alone were extremely difficult. We demonstrate the potential of using 'invisible' neural indices of brain functioning to understand and differentiate these debilitating conditions.
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35
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Cao M, Luo Y, Wu Z, Mazzola CA, Catania L, Alvarez TL, Halperin JM, Biswal B, Li X. Topological Aberrance of Structural Brain Network Provides Quantitative Substrates of Post-Traumatic Brain Injury Attention Deficits in Children. Brain Connect 2021; 11:651-662. [PMID: 33765837 DOI: 10.1089/brain.2020.0866] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Traumatic brain injury (TBI)-induced attention deficits are among the most common long-term cognitive consequences in children. Most of the existing studies attempting to understand the neuropathological underpinnings of cognitive and behavioral impairments in TBI have utilized heterogeneous samples and resulted in inconsistent findings. The current research proposed to investigate topological properties of the structural brain network in children with TBI and their relationship with post-TBI attention problems in a more homogeneous subgroup of children who had severe post-TBI attention deficits (TBI-A). Materials and Methods: A total of 31 children with TBI-A and 35 group-matched controls were involved in the study. Diffusion tensor imaging-based probabilistic tractography and graph theoretical techniques were used to construct the structural brain network in each subject. Network topological properties were calculated in both global level and regional (nodal) level. Between-group comparisons among the topological network measures and analyses for searching brain-behavioral were all corrected for multiple comparisons using Bonferroni method. Results: Compared with controls, the TBI-A group showed significantly higher nodal local efficiency and nodal clustering coefficient in left inferior frontal gyrus and right transverse temporal gyrus, whereas significantly lower nodal clustering coefficient in left supramarginal gyrus and lower nodal local efficiency in left parahippocampal gyrus. The temporal lobe topological alterations were significantly associated with the post-TBI inattentive and hyperactive symptoms in the TBI-A group. Conclusion: The results suggest that TBI-related structural re-modularity in the white matter subnetworks associated with temporal lobe may play a critical role in the onset of severe post-TBI attention deficits in children. These findings provide valuable input for understanding the neurobiological substrates of post-TBI attention deficits, and have the potential to serve as quantitatively measurable criteria guiding the development of more timely and tailored strategies for diagnoses and treatments to the affected individuals. Impact statement This study provides a new insight into the neurobiological substrates associated with post-traumatic brain injury attention deficits (TBI-A) in children, by evaluating topological alterations of the structural brain network. The results demonstrated that relative to group-matched controls, the children with TBI-A had significantly altered nodal local efficiency and nodal clustering coefficient in temporal lobe, which strongly linked to elevated inattentive and hyperactive symptoms in the TBI-A group. These findings suggested that white matter structural re-modularity in subnetworks associated with temporal lobe may serve as quantitatively measurable biomarkers for early prediction and diagnosis of post-TBI attention deficits in children.
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Affiliation(s)
- Meng Cao
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Yuyang Luo
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Ziyan Wu
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | | | - Lori Catania
- North Jersey Neurodevelopmental Center, North Haledon, New Jersey, USA
| | - Tara L Alvarez
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Jeffrey M Halperin
- Department of Psychology, Queens College, City University of New York, New York, New York, USA
| | - Bharat Biswal
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Xiaobo Li
- Department of Biomedical Engineering and New Jersey Institute of Technology, Newark, New Jersey, USA.,Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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Boroda E, Armstrong M, Gilmore CS, Gentz C, Fenske A, Fiecas M, Hendrickson T, Roediger D, Mueller B, Kardon R, Lim K. Network topology changes in chronic mild traumatic brain injury (mTBI). Neuroimage Clin 2021; 31:102691. [PMID: 34023667 PMCID: PMC8163989 DOI: 10.1016/j.nicl.2021.102691] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/14/2021] [Accepted: 05/01/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND In mild traumatic brain injury (mTBI), diffuse axonal injury results in disruption of functional networks in the brain and is thought to be a major contributor to cognitive dysfunction even years after trauma. OBJECTIVE Few studies have assessed longitudinal changes in network topology in chronic mTBI. We utilized a graph theoretical approach to investigate alterations in global network topology based on resting-state functional connectivity in veterans with chronic mTBI. METHODS 50 veterans with chronic mTBI (mean of 20.7 yrs. from trauma) and 40 age-matched controls underwent two functional magnetic resonance imaging scans 18 months apart. Graph theory analysis was used to quantify network topology measures (density, clustering coefficient, global efficiency, and modularity). Hierarchical linear mixed models were used to examine longitudinal change in network topology. RESULTS With all network measures, we found a significant group × time interaction. At baseline, brain networks of individuals with mTBI were less clustered (p = 0.03) and more modular (p = 0.02) than those of HC. Over time, the mTBI networks became more densely connected (p = 0.002), with increased clustering (p = 0.001) and reduced modularity (p < 0.001). Network topology did not change across time in HC. CONCLUSION These findings demonstrate that brain networks of individuals with mTBI remain plastic decades after injury and undergo significant changes in network topology even at the later phase of the disease.
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Affiliation(s)
- Elias Boroda
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
| | | | | | - Carrie Gentz
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Alicia Fenske
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Mark Fiecas
- Center for the Prevention and Treatment of Visual Loss, Iowa City VA Healthcare System, Iowa City, IA, USA
| | - Tim Hendrickson
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Donovan Roediger
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Bryon Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Randy Kardon
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA
| | - Kelvin Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA; Minneapolis VA Health Care System, Minneapolis, MN, USA; School of Public Health, Department of Biostatistics, University of Minnesota, Minneapolis, MN, USA
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37
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Wu SCJ, Jenkins LM, Apple AC, Petersen J, Xiao F, Wang L, Yang FPG. Longitudinal fMRI task reveals neural plasticity in default mode network with disrupted executive-default coupling and selective attention after traumatic brain injury. Brain Imaging Behav 2021; 14:1638-1650. [PMID: 30937828 DOI: 10.1007/s11682-019-00094-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Executive dysfunctions are common in individuals with Traumatic Brain Injury (TBI). However, change in functional neural coupling of default and executive networks in the post-acute phase (≥ 1 month after injury) patients over time has yet to be understood. During a 5-week observation period, we examined changes in the goal-oriented executive function networks in 20 TBI participants, using a face/scene matching 1-back fMRI task (Chen et al. 2011). We conducted multivariate pattern analysis to assess working memory and visual selective attention, followed by a repeat-measures ANOVA to examine longitudinal changes, with a cluster FDR at p = .001. Results showed that task accuracy significantly improved after follow-up. Significantly increased activity patterns over time were observed in the right dorsolateral prefrontal cortex and right insula. Decreased activity patterns were seen in the left posterior cingulate cortex (PCC), bilateral precuneus, right inferior occipital gyrus and right temporo-occipital junction. Improvement in task accuracy correlated with decreased activity patterns in the PCC (r = -0.478, p = 0.031) and temporo-occipital junction (r = -0.592, p = 0.006), which were interpreted as neural plastic changes. However, we did not observe the default mode network (DMN)-executive network decoupling during task performance that is found in other studies. These results suggest that fMRI of attentional task performance could serve as a potential biomarker for neural plasticity of selective attention in TBI patients in the post-acute phase.
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Affiliation(s)
- Shun-Chin Jim Wu
- National Defense Medical Center, School of Medicine, Taipei, Taiwan
| | - Lisanne M Jenkins
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alexandra C Apple
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Julie Petersen
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Furen Xiao
- National Taiwan University Hospital, Taipei, Taiwan
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Fan-Pei Gloria Yang
- Center for Cognition & Mind Science, National Tsinghua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, Taiwan, 30013.
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38
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Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer's disease. GeroScience 2021; 43:2015-2039. [PMID: 33900530 DOI: 10.1007/s11357-021-00355-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/10/2021] [Indexed: 10/21/2022] Open
Abstract
Traumatic brain injuries (TBIs) are often followed by persistent structural brain alterations and by cognitive sequalae, including memory deficits, reduced neural processing speed, impaired social function, and decision-making difficulties. Although mild TBI (mTBI) is a risk factor for Alzheimer's disease (AD), the extent to which these conditions share patterns of macroscale neurodegeneration has not been quantified. Comparing such patterns can not only reveal how the neurodegenerative trajectories of TBI and AD are similar, but may also identify brain atrophy features which can be leveraged to prognosticate AD risk after TBI. The primary aim of this study is to systematically map how TBI affects white matter (WM) and gray matter (GM) properties in AD-analogous patterns. Our findings identify substantial similarities in the regional macroscale neurodegeneration patterns associated with mTBI and AD. In cerebral GM, such similarities are most extensive in brain areas involved in memory and executive function, such as the temporal poles and orbitofrontal cortices, respectively. Our results indicate that the spatial pattern of cerebral WM degradation observed in AD is broadly similar to the pattern of diffuse axonal injury observed in TBI, which frequently affects WM structures like the fornix, corpus callosum, and corona radiata. Using machine learning, we find that the severity of AD-like brain changes observed during the chronic stage of mTBI can be accurately prognosticated based on acute assessments of post-traumatic mild cognitive impairment. These findings suggest that acute post-traumatic cognitive impairment predicts the magnitude of AD-like brain atrophy, which is itself associated with AD risk.
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39
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Maleki N, Finkel A, Cai G, Ross A, Moore RD, Feng X, Androulakis XM. Post-traumatic Headache and Mild Traumatic Brain Injury: Brain Networks and Connectivity. Curr Pain Headache Rep 2021; 25:20. [PMID: 33674899 DOI: 10.1007/s11916-020-00935-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/29/2020] [Indexed: 01/06/2023]
Abstract
PURPOSE OF REVIEW Post-traumatic headache (PTH) consequent to mild traumatic brain injury (mTBI) is a complex, multidimensional, chronic neurological disorder. The purpose of this review is to evaluate the current neuroimaging studies on mTBI and PTH with a specific focus on brain networks and connectivity patterns. RECENT FINDINGS We present findings on PTH incidence and prevalence, as well as the latest neuroimaging research findings on mTBI and PTH. Additionally, we propose a new strategy in studying PTH following mTBI. The diversity and heterogeneity of pathophysiological mechanisms underlying mild traumatic brain injury pose unique challenges on how we interpret neuroimaging findings in PTH. Evaluating alterations in the intrinsic brain network connectivity patterns using novel imaging and analytical techniques may provide additional insights into PTH disease state and therefore inform effective treatment strategies.
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Affiliation(s)
- Nasim Maleki
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
| | - Alan Finkel
- Carolina Headache Institute, 6114 Fayetteville Rd, Suite 109, Durham, NC, USA
| | - Guoshuai Cai
- Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Alexandra Ross
- University of South Carolina School of Medicine, Columbia, SC, 29209, USA
| | - R Davis Moore
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Xuesheng Feng
- Navy Region Mid-Atlantic, Reserve Component Command, 1683 Gilbert Street, Norfolk, VA, 23511, USA
| | - X Michelle Androulakis
- University of South Carolina School of Medicine, Columbia, SC, 29209, USA. .,Columbia VA Health Care System, Columbia, SC, 20208, USA.
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40
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de Souza NL, Parker R, Gonzalez CS, Ryan JD, Esopenko C. Effect of age at time of injury on long-term changes in intrinsic functional connectivity in traumatic brain injury. Brain Inj 2020; 34:1646-1654. [PMID: 33090913 DOI: 10.1080/02699052.2020.1832257] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
BACKGROUND Alterations in resting-state functional connectivity (rsFC) occur in the acute and chronic phases following traumatic brain injury (TBI); however, few studies have assessed long-term (>1 year) changes in rsFC. METHODS Resting-state functional magnetic resonance imaging (rsfMRI) scans were obtained from the Federal Interagency Traumatic Brain Injury Research Informatics Systems. Patients with primarily mild TBI (n = 39) completed rsfMRI scans at the sub-acute (~10 days) and long-term (~18 months) phases. We examined changes in voxel-based rsFC from anterior medial prefrontal cortex (aMPFC) and posterior cingulate cortex (PCC) seeds in the default mode network (DMN) between both phases. The effect of age at the time of injury on long-term rsFC was also examined. RESULTS Increased rsFC from the aMPFC and the PCC to frontal and temporal regions was shown at ~18-months post-injury. Widespread increases in rsFC from the aMPFC and between the PCC and frontal regions were shown for younger patients at time of injury, but limited increases of rsFC were noted at ~18 months in older patients. CONCLUSION Long-term increases in rsFC were found following TBI, but age at the time of injury was associated with distinct rsFC profiles suggesting that younger patients show greater increases in rsFC over time.
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Affiliation(s)
- Nicola L de Souza
- School of Graduate Studies, Biomedical Sciences, Rutgers Biomedical and Health Sciences , Newark, New Jersey, USA
| | - Rachel Parker
- Rotman Research Institute at Baycrest , Toronto, Ontario, Canada
| | - Christie S Gonzalez
- Department of Rehabilitation and Movement Sciences, School of Health Professions, Rutgers Biomedical and Health Sciences , Newark, New Jersey, USA
| | - Jennifer D Ryan
- Rotman Research Institute at Baycrest , Toronto, Ontario, Canada.,Department of Psychology, University of Toronto , Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto , Toronto, Ontario, Canada
| | - Carrie Esopenko
- Department of Rehabilitation and Movement Sciences, School of Health Professions, Rutgers Biomedical and Health Sciences , Newark, New Jersey, USA
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41
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Gabrieli D, Schumm SN, Vigilante NF, Parvesse B, Meaney DF. Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks. PLoS One 2020; 15:e0234749. [PMID: 32966291 PMCID: PMC7510994 DOI: 10.1371/journal.pone.0234749] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/01/2020] [Indexed: 12/26/2022] Open
Abstract
Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.
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Affiliation(s)
- David Gabrieli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Samantha N. Schumm
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Nicholas F. Vigilante
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Brandon Parvesse
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - David F. Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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42
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Sheth C, Rogowska J, Legarreta M, McGlade E, Yurgelun-Todd D. Functional connectivity of the anterior cingulate cortex in Veterans with mild traumatic brain injury. Behav Brain Res 2020; 396:112882. [PMID: 32853657 DOI: 10.1016/j.bbr.2020.112882] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 07/30/2020] [Accepted: 08/20/2020] [Indexed: 01/04/2023]
Abstract
BACKGROUND Traumatic brain injury (TBI) is one of the most prevalent injuries in the military with mild traumatic brain injury (mTBI) accounting for approximately 70-80 % of all TBI. TBI has been associated with diffuse and focal brain changes to structures and networks underlying cognitive-emotional processing. Although the anterior cingulate cortex (ACC) plays a critical role in emotion regulation and executive function and is susceptible to mTBI, studies focusing on ACC resting state functional connectivity (rs-fc) in Veterans are limited. METHODS Veterans with mTBI (n = 49) and with no history of TBI (n = 25), ages 20-54 completed clinical assessments and an 8-minute resting state functional magnetic resonance imaging (rs-fMRI) on a 3 T Siemens scanner. Imaging results were analyzed with left and right ACC as seed regions using SPM8. Regression analyses were performed with time since injury. RESULTS Seed-based analysis showed increased connectivity of the left and right ACC with brain regions including middle and posterior cingulate regions, preceneus, and occipital regions in the mTBI compared to the non-TBI group. CONCLUSIONS The rs-fMRI results indicate hyperconnectivity in Veterans with mTBI. These results are consistent with previous studies of recently concussed athletes showing ACC hyperconnectivity. Enhanced top-down control of attention networks necessary to compensate for the microstructural damage following mTBI may explain ACC hyperconnectivity post-mTBI.
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Affiliation(s)
- Chandni Sheth
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA; Diagnostic Neuroimaging, University of Utah, Salt Lake City, UT, USA.
| | - Jadwiga Rogowska
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA.
| | - Margaret Legarreta
- Diagnostic Neuroimaging, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Department of Veterans Affairs Medical Center, VA VISN 19 Mental Illness Research, Education and Clinical Center (MIRECC), Salt Lake City, UT, USA.
| | - Erin McGlade
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA; Diagnostic Neuroimaging, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Department of Veterans Affairs Medical Center, VA VISN 19 Mental Illness Research, Education and Clinical Center (MIRECC), Salt Lake City, UT, USA.
| | - Deborah Yurgelun-Todd
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA; Diagnostic Neuroimaging, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Department of Veterans Affairs Medical Center, VA VISN 19 Mental Illness Research, Education and Clinical Center (MIRECC), Salt Lake City, UT, USA.
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Puig J, Ellis MJ, Kornelsen J, Figley TD, Figley CR, Daunis-i-Estadella P, Mutch WAC, Essig M. Magnetic Resonance Imaging Biomarkers of Brain Connectivity in Predicting Outcome after Mild Traumatic Brain Injury: A Systematic Review. J Neurotrauma 2020; 37:1761-1776. [DOI: 10.1089/neu.2019.6623] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Josep Puig
- Department of Radiology, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Radiology (IDI), Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr. Josep Trueta, Girona, Spain
| | - Michael J. Ellis
- Canada North Concussion Network, Winnipeg, Manitoba, Canada
- Department of Surgery and Pediatrics and Child Health, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
- Section of Neurosurgery, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
- Pan Am Concussion Program, Winnipeg, Manitoba, Canada
- Childrens Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
| | - Jennifer Kornelsen
- Department of Radiology, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Center, Winnipeg, Manitoba, Canada
- Department of Physiology and Pathophysiology, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Teresa D. Figley
- Department of Radiology, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Center, Winnipeg, Manitoba, Canada
| | - Chase R. Figley
- Department of Radiology, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Center, Winnipeg, Manitoba, Canada
- Department of Physiology and Pathophysiology, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Pepus Daunis-i-Estadella
- Department of Computer Science, Applied Mathematics and Statistics, Universitat de Girona, Girona, Spain
| | - W. Alan C. Mutch
- Canada North Concussion Network, Winnipeg, Manitoba, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Center, Winnipeg, Manitoba, Canada
- Department of Anesthesiology, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Marco Essig
- Department of Radiology, Perioperative and Pain Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
- Canada North Concussion Network, Winnipeg, Manitoba, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg Health Sciences Center, Winnipeg, Manitoba, Canada
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Osmanlıoğlu Y, Alappatt JA, Parker D, Verma R. Connectomic consistency: a systematic stability analysis of structural and functional connectivity. J Neural Eng 2020; 17:045004. [PMID: 32428883 PMCID: PMC7584380 DOI: 10.1088/1741-2552/ab947b] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVE Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain's functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans. APPROACH In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes. MAIN RESULTS Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated. SIGNIFICANCE Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.
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Affiliation(s)
- Yusuf Osmanlıoğlu
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
| | - Jacob A Alappatt
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
| | - Drew Parker
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
| | - Ragini Verma
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
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Calvillo M, Irimia A. Neuroimaging and Psychometric Assessment of Mild Cognitive Impairment After Traumatic Brain Injury. Front Psychol 2020; 11:1423. [PMID: 32733322 PMCID: PMC7358255 DOI: 10.3389/fpsyg.2020.01423] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 05/27/2020] [Indexed: 12/13/2022] Open
Abstract
Traumatic brain injury (TBI) can be serious partly due to the challenges of assessing and treating its neurocognitive and affective sequelae. The effects of a single TBI may persist for years and can limit patients’ activities due to somatic complaints (headaches, vertigo, sleep disturbances, nausea, light or sound sensitivity), affective sequelae (post-traumatic depressive symptoms, anxiety, irritability, emotional instability) and mild cognitive impairment (MCI, including social cognition disturbances, attention deficits, information processing speed decreases, memory degradation and executive dysfunction). Despite a growing amount of research, study comparison and knowledge synthesis in this field are problematic due to TBI heterogeneity and factors like injury mechanism, age at or time since injury. The relative lack of standardization in neuropsychological assessment strategies for quantifying sequelae adds to these challenges, and the proper administration of neuropsychological testing relative to the relationship between TBI, MCI and neuroimaging has not been reviewed satisfactorily. Social cognition impairments after TBI (e.g., disturbed emotion recognition, theory of mind impairment, altered self-awareness) and their neuroimaging correlates have not been explored thoroughly. This review consolidates recent findings on the cognitive and affective consequences of TBI in relation to neuropsychological testing strategies, to neurobiological and neuroimaging correlates, and to patient age at and assessment time after injury. All cognitive domains recognized by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) are reviewed, including social cognition, complex attention, learning and memory, executive function, language and perceptual-motor function. Affect and effort are additionally discussed owing to their relationships to cognition and to their potentially confounding effects. Our findings highlight non-negligible cognitive and affective impairments following TBI, their gravity often increasing with injury severity. Future research should study (A) language, executive and perceptual-motor function (whose evolution post-TBI remains under-explored), (B) the effects of age at and time since injury, and (C) cognitive impairment severity as a function of injury severity. Such efforts should aim to develop and standardize batteries for cognitive subdomains—rather than only domains—with high ecological validity. Additionally, they should utilize multivariate techniques like factor analysis and related methods to clarify which cognitive subdomains or components are indeed measured by standardized tests.
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Affiliation(s)
- Maria Calvillo
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States.,Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
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46
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Moreira da Silva N, Cowie CJA, Blamire AM, Forsyth R, Taylor PN. Investigating Brain Network Changes and Their Association With Cognitive Recovery After Traumatic Brain Injury: A Longitudinal Analysis. Front Neurol 2020; 11:369. [PMID: 32581989 PMCID: PMC7296134 DOI: 10.3389/fneur.2020.00369] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 04/14/2020] [Indexed: 01/25/2023] Open
Abstract
Traumatic brain injury (TBI) can result in acute cognitive deficits and diffuse axonal injury reflected in white matter brain network alterations, which may, or may not, later recover. Our objective is to first characterize the ways in which brain networks change after TBI and, second, investigate if those changes are associated with recovery of cognitive deficits. We aim to make initial progress in discerning the relationships between brain network changes, and their (dys)functional correlates. We analyze longitudinally acquired MRI from 23 TBI patients (two time points: 6 days, 12 months post-injury) and cross-sectional data from 28 controls to construct white matter brain networks. Cognitive assessment was also performed. Graph theory and regression analysis were applied to identify changed brain network metrics after injury that are associated with subsequent improvements in cognitive function. Sixteen brain network metrics were found to be discriminative of different post-injury phases. Eleven of those explain 90% (adjusted R 2) of the variability observed in cognitive recovery following TBI. Brain network metrics that had a high contribution to the explained variance were found in frontal and temporal cortex, additional to the anterior cingulate cortex. Our preliminary study suggests that network reorganization may be related to recovery of impaired cognitive function in the first year after a TBI.
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Affiliation(s)
- Nádia Moreira da Silva
- CNNP Lab, Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Christopher J. A. Cowie
- Faculty of Medical Sciences, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Neurosurgery, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Andrew M. Blamire
- Institute of Cellular Medicine, Newcastle MR Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rob Forsyth
- Institute of Cellular Medicine, Newcastle MR Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Peter Neal Taylor
- CNNP Lab, Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
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47
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Anderson ED, Giudice JS, Wu T, Panzer MB, Meaney DF. Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis. Front Bioeng Biotechnol 2020; 8:309. [PMID: 32351948 PMCID: PMC7174699 DOI: 10.3389/fbioe.2020.00309] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 03/23/2020] [Indexed: 12/11/2022] Open
Abstract
Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.
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Affiliation(s)
- Erin D. Anderson
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - J. Sebastian Giudice
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
| | - Taotao Wu
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
| | - Matthew B. Panzer
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
| | - David F. Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
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Champagne AA, Coverdale NS, Ross A, Chen Y, Murray CI, Dubowitz D, Cook DJ. Multi-modal normalization of resting-state using local physiology reduces changes in functional connectivity patterns observed in mTBI patients. Neuroimage Clin 2020; 26:102204. [PMID: 32058317 PMCID: PMC7013121 DOI: 10.1016/j.nicl.2020.102204] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 02/02/2020] [Accepted: 02/03/2020] [Indexed: 12/25/2022]
Abstract
Blood oxygenation level dependent (BOLD) resting-state functional magnetic resonance imaging (rs-fMRI) may serve as a sensitive marker to identify possible changes in the architecture of large-scale networks following mild traumatic brain injury (mTBI). Differences in functional connectivity (FC) measurements derived from BOLD rs-fMRI may however be confounded by changes in local cerebrovascular physiology and neurovascular coupling mechanisms, without changes in the underlying neuronally driven connectivity of networks. In this study, multi-modal neuroimaging data including BOLD rs-fMRI, baseline cerebral blood flow (CBF0) and cerebrovascular reactivity (CVR; acquired using a hypercapnic gas breathing challenge) were collected in 23 subjects with reported mTBI (14.6±14.9 months post-injury) and 27 age-matched healthy controls. Despite no group differences in CVR within the networks of interest (P > 0.05, corrected), significantly higher CBF0 was documented in the mTBI subjects (P < 0.05, corrected), relative to the controls. A normalization method designed to account for differences in CBF0 post-mTBI was introduced to evaluate the effects of such an approach on reported group differences in network connectivity. Inclusion of regional perfusion measurements in the computation of correlation coefficients within and across large-scale networks narrowed the differences in FC between the groups, suggesting that this approach may elucidate unique changes in connectivity post-mTBI while accounting for shared variance with CBF0. Altogether, our results provide a strong paradigm supporting the need to account for changes in physiological modulators of BOLD in order to expand our understanding of the effects of brain injury on large-scale FC of cortical networks.
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Affiliation(s)
- Allen A Champagne
- Centre for Neuroscience Studies, Room 260, Queen's University, Kingston ON K7L 3N6 Canada.
| | - Nicole S Coverdale
- Centre for Neuroscience Studies, Room 260, Queen's University, Kingston ON K7L 3N6 Canada.
| | - Andrew Ross
- Performance Phenomics, 180 John St., Toronto ON M5T 1 × 5 Canada.
| | - Yining Chen
- Centre for Neuroscience Studies, Room 260, Queen's University, Kingston ON K7L 3N6 Canada.
| | | | - David Dubowitz
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
| | - Douglas J Cook
- Centre for Neuroscience Studies, Room 260, Queen's University, Kingston ON K7L 3N6 Canada; Department of Surgery, Queen's University, Kingston, ON, Canada.
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49
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The Recovery of GABAergic Function in the Hippocampus CA1 Region After mTBI. Mol Neurobiol 2019; 57:23-31. [DOI: 10.1007/s12035-019-01753-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 08/29/2019] [Indexed: 10/26/2022]
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50
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Santhanam P, Wilson SH, Mulatya C, Oakes TR, Weaver LK. Age-Accelerated Reduction in Cortical Surface Area in United States Service Members and Veterans with Mild Traumatic Brain Injury and Post-Traumatic Stress Disorder. J Neurotrauma 2019; 36:2922-2929. [PMID: 31094282 DOI: 10.1089/neu.2018.6242] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Despite the prevalence of combat-related mild traumatic brain injury (mTBI) and relatively high incidence of concurrent post-traumatic stress disorder (PTSD), the joint effect of these conditions on the brain is not well understood. Further, few studies in the mTBI or PTSD populations focus on cortical surface area measures, despite known disruptions to cytoarchitecture of the cortex. This study examines the effects of comorbid mTBI and PTSD on age-related surface area changes across the cortex, as compared with a group with mTBI only. While a direct comparison of PTSD versus non-PTSD groups showed little difference on surface area measures, several regions showed a decline in surface area, with increasing age and a significant PTSD-by-age interaction effect, indicating an age-dependent decrease in surface area in those with both mTBI and PTSD. The findings suggest an apparent age-accelerated shrinking of the cortical surface area in some regions when mTBI and PTSD are present, a pattern that was not consistently found in those with mTBI only. Among the several cortical regions with significant age-by-group interactions were bilateral posterior cingulate cortex (left: p = 0.03; right: p = 0.02), isthmus of the cingulate (left: p = 0.016; right: p = 0.001), and lateral orbitofrontal cortex (left: p = 0.038; right: p = 0.02). It is possible that these findings are related to a larger pattern of premature neurodegeneration and age-acceleration noted in those with long-term PTSD.
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Affiliation(s)
- Priya Santhanam
- Lovelace Respiratory Research Institute, Albuquerque, New Mexico
| | | | | | - Terrence R Oakes
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lindell K Weaver
- Division of Hyperbaric, Medicine Intermountain Medical Center, Murray, UT and Intermountain LDS Hospital, Salt Lake City, Utah.,Department of Medicine, University of Utah, Salt Lake City, Utah
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