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Raizman R, Itzhaki N, Sirkin J, Meningher I, Tsarfaty G, Keren O, Zibli Z, Silberg T, Pick CG, Livny A. Decreased homotopic functional connectivity in traumatic brain injury. Cereb Cortex 2023; 33:1207-1216. [PMID: 35353131 DOI: 10.1093/cercor/bhac130] [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: 12/29/2021] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION Homotopic functional connectivity (HoFC), the synchrony in activity patterns between homologous brain regions, is a fundamental characteristic of resting-state functional connectivity (RsFC). METHODS We examined the difference in HoFC, computed as the correlation between atlas-based regions and their counterpart on the opposite hemisphere, in 16 moderate-severe traumatic brain injury patients (msTBI) and 36 healthy controls. Regions of decreased HoFC in msTBI patients were further used as seeds for examining differences between groups in correlations with other brain regions. Finally, we computed logistic regression models of regional HoFC and fractional anisotropy (FA) of the corpus callosum (CC). RESULTS TBI patients exhibited decreased HoFC in the middle and posterior cingulate cortex, thalamus, superior temporal pole, and cerebellum III. Furthermore, decreased RsFC was found between left cerebellum III and right parahippocampal cortex and vermis, between superior temporal pole and left caudate and medial left and right frontal orbital gyri. Thalamic HoFC and FA of the CC discriminate patients as msTBI with a high accuracy of 96%. CONCLUSION TBI is associated with regionally decreased HoFC. Moreover, a multimodality model of interhemispheric connectivity allowed for a high degree of accuracy in disease discrimination and enabled a deeper understanding of TBI effects on brain interhemispheric reorganization post-TBI.
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Affiliation(s)
- Reut Raizman
- Division of Diagnostic Imaging, Sheba Medical Center, 5262000 Tel-Hashomer, Israel.,Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel-Aviv University, 69979 Tel-Aviv, Israel
| | - Nofar Itzhaki
- Division of Diagnostic Imaging, Sheba Medical Center, 5262000 Tel-Hashomer, Israel
| | - Johanna Sirkin
- Department of Psychology, Reichman University, Herzelia, Israel
| | - Inbar Meningher
- Division of Diagnostic Imaging, Sheba Medical Center, 5262000 Tel-Hashomer, Israel
| | - Galia Tsarfaty
- Division of Diagnostic Imaging, Sheba Medical Center, 5262000 Tel-Hashomer, Israel.,Department of imaging, Sackler Faculty of Medicine, Tel-Aviv University, 69979 Tel-Aviv, Israel
| | - Ofer Keren
- Department of Brain Injury Rehabilitation, Sheba Medical Center, 5262000 Tel-Hashomer, Israel
| | - Zion Zibli
- Department of Neurosurgery, Sheba Medical Center, Tel Hashomer, Sackler Faculty of Medicine, Tel Aviv University, 69979 Ramat Gan, Israel
| | - Tamar Silberg
- Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Ramat Gan, Israel.,Department of Psychology, Bar Ilan University, 5290002 Ramat Gan, Israel
| | - Chaim G Pick
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel-Aviv University, 69979 Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, 69979 Tel Aviv, Israel.,The Dr. Miriam and Sheldon G. Adelson, Chair and Center for the Biology of Addictive Diseases, Tel-Aviv University, 69979 Tel-Aviv, Israel.,Sylvan Adams Sports Institute, Tel Aviv University, 69979 Tel Aviv, Israel
| | - Abigail Livny
- Division of Diagnostic Imaging, Sheba Medical Center, 5262000 Tel-Hashomer, Israel.,Department of imaging, Sackler Faculty of Medicine, Tel-Aviv University, 69979 Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, 69979 Tel Aviv, Israel
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2
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Dennis EL, Caeyenberghs K, Asarnow RF, Babikian T, Bartnik-Olson B, Bigler ED, Figaji A, Giza CC, Goodrich-Hunsaker NJ, Hodges CB, Hoskinson KR, Königs M, Levin HS, Lindsey HM, Livny A, Max JE, Merkley TL, Newsome MR, Olsen A, Ryan NP, Spruiell MS, Suskauer SJ, Thomopoulos SI, Ware AL, Watson CG, Wheeler AL, Yeates KO, Zielinski BA, Thompson PM, Tate DF, Wilde EA. Challenges and opportunities for neuroimaging in young patients with traumatic brain injury: a coordinated effort towards advancing discovery from the ENIGMA pediatric moderate/severe TBI group. Brain Imaging Behav 2021; 15:555-575. [PMID: 32734437 PMCID: PMC7855317 DOI: 10.1007/s11682-020-00363-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Traumatic brain injury (TBI) is a major cause of death and disability in children in both developed and developing nations. Children and adolescents suffer from TBI at a higher rate than the general population, and specific developmental issues require a unique context since findings from adult research do not necessarily directly translate to children. Findings in pediatric cohorts tend to lag behind those in adult samples. This may be due, in part, both to the smaller number of investigators engaged in research with this population and may also be related to changes in safety laws and clinical practice that have altered length of hospital stays, treatment, and access to this population. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric Moderate/Severe TBI (msTBI) group aims to advance research in this area through global collaborative meta-analysis of neuroimaging data. In this paper, we discuss important challenges in pediatric TBI research and opportunities that we believe the ENIGMA Pediatric msTBI group can provide to address them. With the paucity of research studies examining neuroimaging biomarkers in pediatric patients with TBI and the challenges of recruiting large numbers of participants, collaborating to improve statistical power and to address technical challenges like lesions will significantly advance the field. We conclude with recommendations for future research in this field of study.
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Affiliation(s)
- Emily L Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA.
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, MA, USA.
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Robert F Asarnow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- Brain Research Institute, UCLA, Los Angeles, CA, USA
- Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Erin D Bigler
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Anthony Figaji
- Division of Neurosurgery, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christopher C Giza
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Naomi J Goodrich-Hunsaker
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Cooper B Hodges
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, 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, Columbus, OH, USA
| | - Marsh Königs
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, Amsterdam, The Netherlands
| | - Harvey S Levin
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Hannah M Lindsey
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Abigail Livny
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
- Joseph Sagol Neuroscience Center, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
| | - Jeffrey E Max
- Department of Psychiatry, University of California, La Jolla, San Diego, CA, USA
- Department of Psychiatry, Rady Children's Hospital, San Diego, CA, USA
| | - Tricia L Merkley
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Mary R Newsome
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Nicholas P Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
- Department of Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Matthew S Spruiell
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Stacy J Suskauer
- Kennedy Krieger Institute, Baltimore, MD, USA
- Departments of Physical Medicine & Rehabilitation and Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Ashley L Ware
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Christopher G Watson
- Department of Pediatrics, Children's Learning Institute, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anne L Wheeler
- Hospital for Sick Children, Neuroscience and Mental Health Program, Toronto, Canada
- Physiology Department, University of Toronto, Toronto, Canada
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Departments of Pediatrics and Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Brandon A Zielinski
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, USA
| | - David F Tate
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- Missouri Institute of Mental Health and University of Missouri, St Louis, MO, USA
| | - Elisabeth A Wilde
- TBI and Concussion Center, 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
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
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King DJ, Novak J, Shephard AJ, Beare R, Anderson VA, Wood AG. Lesion Induced Error on Automated Measures of Brain Volume: Data From a Pediatric Traumatic Brain Injury Cohort. Front Neurosci 2020; 14:491478. [PMID: 33424529 PMCID: PMC7793828 DOI: 10.3389/fnins.2020.491478] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 11/06/2020] [Indexed: 11/13/2022] Open
Abstract
Structural segmentation of T1-weighted (T1w) MRI has shown morphometric differences, both compared to controls and longitudinally, following a traumatic brain injury (TBI). While many patients with TBI present with abnormalities on structural MRI images, most neuroimaging software packages have not been systematically evaluated for accuracy in the presence of these pathology-related MRI abnormalities. The current study aimed to assess whether acute MRI lesions (MRI acquired 7–71 days post-injury) cause error in the estimates of brain volume produced by the semi-automated segmentation tool, Freesurfer. More specifically, to investigate whether this error was global, the presence of lesion-induced error in the contralesional hemisphere, where no abnormal signal was present, was measured. A dataset of 176 simulated lesion cases was generated using actual lesions from 16 pediatric TBI (pTBI) cases recruited from the emergency department and 11 typically-developing controls. Simulated lesion cases were compared to the “ground truth” of the non-lesion control-case T1w images. Using linear mixed-effects models, results showed that hemispheric measures of cortex volume were significantly lower in the contralesional-hemisphere compared to the ground truth. Interestingly, however, cortex volume (and cerebral white matter volume) were not significantly different in the lesioned hemisphere. However, percent volume difference (PVD) between the simulated lesion and ground truth showed that the magnitude of difference of cortex volume in the contralesional-hemisphere (mean PVD = 0.37%) was significantly smaller than that in the lesioned hemisphere (mean PVD = 0.47%), suggesting a small, but systematic lesion-induced error. Lesion characteristics that could explain variance in the PVD for each hemisphere were investigated. Taken together, these results suggest that the lesion-induced error caused by simulated lesions was not focal, but globally distributed. Previous post-processing approaches to adjust for lesions in structural analyses address the focal region where the lesion was located however, our results suggest that focal correction approaches are insufficient for the global error in morphometric measures of the injured brain.
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Affiliation(s)
- Daniel J King
- College of Health and Life Sciences, Aston Institute of Health and Neurodevelopment, Aston University, Birmingham, United Kingdom
| | - Jan Novak
- College of Health and Life Sciences, Aston Institute of Health and Neurodevelopment, Aston University, Birmingham, United Kingdom
| | - Adam J Shephard
- College of Health and Life Sciences, Aston Institute of Health and Neurodevelopment, Aston University, Birmingham, United Kingdom
| | - Richard Beare
- Developmental Imaging, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Medicine, Peninsula Clinical School, Monash University, Melbourne, VIC, Australia
| | - Vicki A Anderson
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, VIC, Australia.,Department of Psychology, Royal Children's Hospital, Melbourne, VIC, Australia
| | - Amanda G Wood
- College of Health and Life Sciences, Aston Institute of Health and Neurodevelopment, Aston University, Birmingham, United Kingdom.,Faculty of Health, School of Psychology, Deakin University Melbourne Burwood Campus, Geelong, VIC, Australia
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4
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King DJ, Ellis KR, Seri S, Wood AG. A systematic review of cross-sectional differences and longitudinal changes to the morphometry of the brain following paediatric traumatic brain injury. NEUROIMAGE-CLINICAL 2019; 23:101844. [PMID: 31075554 PMCID: PMC6510969 DOI: 10.1016/j.nicl.2019.101844] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 04/26/2019] [Accepted: 04/29/2019] [Indexed: 01/27/2023]
Abstract
Paediatric traumatic brain injury (pTBI) is a leading cause of disability for children and young adults. Children are a uniquely vulnerable group with the disease process that occurs following a pTBI interacting with the trajectory of normal brain development. Quantitative MRI post-injury has suggested a long-term, neurodegenerative effect of TBI on the morphometry of the brain, in both adult and childhood TBI. Changes to the brain beyond that of anticipated, age-dependant differences may allow us to estimate the state of the brain post-injury and produce clinically relevant predictions for long-term outcome. The current review synthesises the existing literature to assess whether, following pTBI, the morphology of the brain exhibits either i) longitudinal change and/or ii) differences compared to healthy controls and outcomes. The current literature suggests that morphometric differences from controls are apparent cross-sectionally at both acute and late-chronic timepoints post-injury, thus suggesting a non-transient effect of injury. Developmental trajectories of morphometry are altered in TBI groups compared to patients, and it is unlikely that typical maturation overcomes damage post-injury, or even 'catches up' with that of typically-developing peers. However, there is limited evidence for diverted developmental trajectories being associated with cognitive impairment post-injury. The current review also highlights the apparent challenges to the existing literature and potential methods by which these can be addressed.
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Affiliation(s)
- D J King
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - K R Ellis
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - S Seri
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - A G Wood
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK; Child Neuropsychology, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.
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5
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Ghosh N, Holshouser B, Oyoyo U, Barnes S, Tong K, Ashwal S. Combined Diffusion Tensor and Magnetic Resonance Spectroscopic Imaging Methodology for Automated Regional Brain Analysis: Application in a Normal Pediatric Population. Dev Neurosci 2017. [PMID: 28651252 DOI: 10.1159/000475545] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
During human brain development, anatomic regions mature at different rates. Quantitative anatomy-specific analysis of longitudinal diffusion tensor imaging (DTI) and magnetic resonance spectroscopic imaging (MRSI) data may improve our ability to quantify and categorize these maturational changes. Computational tools designed to quickly fuse and analyze imaging information from multiple, technically different datasets would facilitate research on changes during normal brain maturation and for comparison to disease states. In the current study, we developed a complete battery of computational tools to execute such data analyses that include data preprocessing, tract-based statistical analysis from DTI data, automated brain anatomy parsing from T1-weighted MR images, assignment of metabolite information from MRSI data, and co-alignment of these multimodality data streams for reporting of region-specific indices. We present statistical analyses of regional DTI and MRSI data in a cohort of normal pediatric subjects (n = 72; age range: 5-18 years; mean 12.7 ± 3.3 years) to establish normative data and evaluate maturational trends. Several regions showed significant maturational changes for several DTI parameters and MRSI ratios, but the percent change over the age range tended to be small. In the subcortical region (combined basal ganglia [BG], thalami [TH], and corpus callosum [CC]), the largest combined percent change was a 10% increase in fractional anisotropy (FA) primarily due to increases in the BG (12.7%) and TH (9%). The largest significant percent increase in N-acetylaspartate (NAA)/creatine (Cr) ratio was seen in the brain stem (BS) (18.8%) followed by the subcortical regions in the BG (11.9%), CC (8.9%), and TH (6.0%). We found consistent, significant (p < 0.01), but weakly positive correlations (r = 0.228-0.329) between NAA/Cr ratios and mean FA in the BS, BG, and CC regions. Age- and region-specific normative MR diffusion and spectroscopic metabolite ranges show brain maturation changes and are requisite for detecting abnormalities in an injured or diseased population.
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Affiliation(s)
- Nirmalya Ghosh
- Department of Pediatrics, Loma Linda University School of Medicine, Loma Linda, CA, USA
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6
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Mitra J, Shen KK, Ghose S, Bourgeat P, Fripp J, Salvado O, Pannek K, Taylor DJ, Mathias JL, Rose S. Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. Neuroimage 2016; 129:247-259. [DOI: 10.1016/j.neuroimage.2016.01.056] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Revised: 12/21/2015] [Accepted: 01/24/2016] [Indexed: 12/13/2022] Open
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Abstract
Traumatic brain injury survivors often experience cognitive deficits and neuropsychiatric symptoms. However, the neurobiological mechanisms underlying specific impairments are not fully understood. Advances in neuroimaging techniques (such as diffusion tensor imaging and functional MRI) have given us new insights on structural and functional connectivity patterns of the human brain in both health and disease. The connectome derived from connectivity maps reflects the entire constellation of distributed brain networks. Using these powerful neuroimaging approaches, changes at the microstructural level can be detected through regional and global properties of neuronal networks. Here we will review recent developments in the study of brain network abnormalities in traumatic brain injury, mainly focusing on structural and functional connectivity. Some connectomic studies have provided interesting insights into the neurological dysfunction that occurs following traumatic brain injury. These techniques could eventually be helpful in developing imaging biomarkers of cognitive and neurobehavioral sequelae, as well as predicting outcome and prognosis.
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Affiliation(s)
- Hui Xiao
- Center of Medical Imaging, Fuzhou General Hospital of Nanjing Military Command, Fuzhou, Fujian Province, China; Department of Medical Imaging, Dongfang Hospital, Xiamen University, Fuzhou, Fujian Province, China
| | - Yang Yang
- Department of Emergency, Fuzhou General Hospital of Nanjing Military Command, Fuzhou, Fujian Province, China
| | - Ji-Hui Xi
- Department of Medical Imaging, Dongfang Hospital, Xiamen University, Fuzhou, Fujian Province, China
| | - Zi-Qian Chen
- Center of Medical Imaging, Fuzhou General Hospital of Nanjing Military Command, Fuzhou, Fujian Province, China
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Irimia A, Van Horn JD. Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks. Brain Connect 2016; 6:152-63. [PMID: 26596775 DOI: 10.1089/brain.2015.0360] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Studying brain connectivity is important due to potential differences in brain circuitry between health and disease. One drawback of graph-theoretic approaches to this is that their results are dependent on the spatial scale at which brain circuitry is examined and explicitly on how vertices and edges are defined in network models. To investigate this, magnetic resonance and diffusion tensor images were acquired from 136 healthy adults, and each subject's cortex was parceled into as many as 50,000 regions. Regions were represented as nodes in a reconstructed network representation, and interregional connectivity was inferred via deterministic tractography. Network model behavior was explored as a function of nodal number and connectivity weighing. Three distinct regimes of quantitative behavior assumed by network models as a function of spatial scale are identified, and their existence may be modulated by the spatial folding scale of the cortex. The maximum number of network nodes used to model human brain circuitry in this study (∼50,000) is larger than in previous macroscale neuroimaging studies. Results suggest that network model properties vary appreciably as a function of vertex assignment convention and edge weighing scheme and that graph-theoretic analysis results should not be compared across spatial scales without appropriate understanding of how spatial scale and model topology modulate network model properties. These findings have implications for comparing macro- to mesoscale studies of brain network models and understanding how choosing network-theoretic parameters affects the interpretation of brain connectivity studies.
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Affiliation(s)
- Andrei Irimia
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California , Los Angeles, California
| | - John Darrell Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California , Los Angeles, California
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Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques. Neuropsychol Rev 2015; 25:224-49. [PMID: 26280751 DOI: 10.1007/s11065-015-9290-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 07/16/2015] [Indexed: 12/11/2022]
Abstract
Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.
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Cheng X, Marcus D, Van Horn JD, Luo Q, Mattay VS, Weinberger DR. Going beyond the current neuroinformatics infrastructure. Front Neuroinform 2015; 9:15. [PMID: 26136680 PMCID: PMC4468376 DOI: 10.3389/fninf.2015.00015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 05/27/2015] [Indexed: 11/19/2022] Open
Affiliation(s)
- Xi Cheng
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus Baltimore, MD, USA
| | - Daniel Marcus
- Department of Radiology, Washington University School of Medicine St. Louis, MO, USA
| | - John D Van Horn
- Laboratory of Neuro Imaging, Department of Neurology, Institute of Neuroimaging and Informatics, University of Southern California Los Angeles, CA, USA
| | - Qian Luo
- Center for Military Psychiatry and Neuroscience Research, Walter Reed Army Research Institute Silver Spring, MD, USA †
| | - Venkata S Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus Baltimore, MD, USA
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11
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Rapp PE, Keyser DO, Albano A, Hernandez R, Gibson DB, Zambon RA, Hairston WD, Hughes JD, Krystal A, Nichols AS. Traumatic brain injury detection using electrophysiological methods. Front Hum Neurosci 2015; 9:11. [PMID: 25698950 PMCID: PMC4316720 DOI: 10.3389/fnhum.2015.00011] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Accepted: 01/07/2015] [Indexed: 11/20/2022] Open
Abstract
Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.
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Affiliation(s)
- Paul E. Rapp
- Uniformed Services University of the Health Sciences School of Medicine, Bethesda, MD, USA
| | - David O. Keyser
- Uniformed Services University of the Health Sciences School of Medicine, Bethesda, MD, USA
| | | | - Rene Hernandez
- US Navy Bureau of Medicine and Surgery, Frederick, MD, USA
| | | | | | - W. David Hairston
- U. S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, USA
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