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Xu H, Xu C, Guo Y, Hu Y, Fang Q, Yang D, Niu X, Bai G. Abnormal longitudinal changes of structural covariance networks of cortical thickness in mild traumatic brain injury with posttraumatic headache. Prog Neuropsychopharmacol Biol Psychiatry 2024; 133:111012. [PMID: 38641235 DOI: 10.1016/j.pnpbp.2024.111012] [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] [Received: 01/11/2024] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 04/21/2024]
Abstract
BACKGROUND It is widely acknowledged that mild traumatic brain injury (MTBI) leads to either functionally or anatomically abnormal brain regions. Structural covariance networks (SCNs) that depict coordinated regional maturation patterns are commonly employed for investigating brain structural abnormalities. However, the dynamic nature of SCNs in individuals with MTBI who suffer from posttraumatic headache (PTH) and their potential as biomarkers have hitherto not been investigated. METHODS This study included 36 MTBI patients with PTH and 34 well-matched healthy controls (HCs). All participants underwent magnetic resonance imaging scans and were assessed with clinical measures during the acute and subacute phases. Structural covariance matrices of cortical thickness were generated for each group, and global as well as nodal network measures of SCNs were computed. RESULTS MTBI patients with PTH demonstrated reduced headache impact and improved cognitive function from the acute to subacute phase. In terms of global network metrics, MTBI patients exhibited an abnormal normalized clustering coefficient compared to HCs during the acute phase, although no significant difference in the normalized clustering coefficient was observed between the groups during the subacute phase. Regarding nodal network metrics, MTBI patients displayed alterations in various brain regions from the acute to subacute phase, primarily concentrated in the prefrontal cortex (PFC). CONCLUSIONS These findings indicate that the cortical thickness topography in the PFC determines the typical structural-covariance topology of the brain and may serve as an important biomarker for MTBI patients with PTH.
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Affiliation(s)
- Hui Xu
- School of Mental Health, Wenzhou Medical University, Wenzhou 325035, China; The Affiliated Kangning Hospital of Wenzhou Medical University, Zhejiang Provincial Clinical Research Center for Mental Disorder, Wenzhou 325007, China.
| | - Cheng Xu
- School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China
| | - Yunyu Guo
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
| | - Yike Hu
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
| | - Qiaofang Fang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
| | - Dandan Yang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang, China
| | - Xuan Niu
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Guanghui Bai
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, Zhejiang, China; Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou 325027, Zhejiang, China.
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2
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Kang X, Yoon BC, Grossner E, Adamson MM. Characteristics of the Structural Connectivity in Patients with Brain Injury and Chronic Health Symptoms: A Pilot Study. Neuroinformatics 2024:10.1007/s12021-024-09681-7. [PMID: 38990502 DOI: 10.1007/s12021-024-09681-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 07/12/2024]
Abstract
Diffusion properties from diffusion tensor imaging (DTI) are exquisitely sensitive to white matter abnormalities incurred during traumatic brain injury (TBI), especially for those patients with chronic post-TBI symptoms such as headaches, dizziness, fatigue, etc. The evaluation of structural and functional connectivity using DTI has become a promising method for identifying subtle alterations in brain connectivity associated with TBI that are otherwise not visible with conventional imaging. This study assessed whether TBI patients with (n = 17) or without (n = 16) chronic symptoms (TBIcs/TBIncs) exhibit any changes in structural connectivity (SC) and mean fractional anisotropy (mFA) of intra- and inter-hemispheric connections when compared to a control group (CG) (n = 13). Reductions in SC and mFA were observed for TBIcs compared to CG, but not for TBIncs. More connections were found to have mFA reductions than SC reductions. On the whole, SC is dominated by ipsilateral connections for all the groups after the comparison of contralateral and ipsilateral connections. More contra-ipsi reductions of mFA were found for TBIcs than TBIncs compared to CG. These findings suggest that TBI patients with chronic symptoms not only demonstrate decreased global and regional mFA but also reduced structural network connectivity.
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Affiliation(s)
- Xiaojian Kang
- WRIISC-Women, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA.
- Rehabilitation Service, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA.
| | - Byung C Yoon
- Department of Radiology, Stanford University School of Medicine, VA Palo Alto Heath Care System, Palo Alto, CA, 94304, USA
| | - Emily Grossner
- Department of Psychology, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
| | - Maheen M Adamson
- WRIISC-Women, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
- Rehabilitation Service, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
- Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA, 94305, USA
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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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Ruiz T, Brown S, Farivar R. Graph Analysis of the Visual Cortical Network during Naturalistic Movie Viewing Reveals Increased Integration and Decreased Segregation Following Mild TBI. Vision (Basel) 2024; 8:33. [PMID: 38804354 PMCID: PMC11130927 DOI: 10.3390/vision8020033] [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: 02/02/2024] [Revised: 04/03/2024] [Accepted: 04/18/2024] [Indexed: 05/29/2024] Open
Abstract
Traditional neuroimaging methods have identified alterations in brain activity patterns following mild traumatic brain injury (mTBI), particularly during rest, complex tasks, and normal vision. However, studies using graph theory to examine brain network changes in mTBI have produced varied results, influenced by the specific networks and task demands analyzed. In our study, we employed functional MRI to observe 17 mTBI patients and 54 healthy individuals as they viewed a simple, non-narrative underwater film, simulating everyday visual tasks. This approach revealed significant mTBI-related changes in network connectivity, efficiency, and organization. Specifically, the mTBI group exhibited higher overall connectivity and local network specialization, suggesting enhanced information integration without overwhelming the brain's processing capabilities. Conversely, these patients showed reduced network segregation, indicating a less compartmentalized brain function compared to healthy controls. These patterns were consistent across various visual cortex subnetworks, except in primary visual areas. Our findings highlight the potential of using naturalistic stimuli in graph-based neuroimaging to understand brain network alterations in mTBI and possibly other conditions affecting brain integration.
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Affiliation(s)
- Tatiana Ruiz
- Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H4A 0A4, Canada (S.B.)
- Research Institute of the McGill University Health Center, Montreal, QC H3G 1A4, Canada
| | - Shael Brown
- Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H4A 0A4, Canada (S.B.)
- Research Institute of the McGill University Health Center, Montreal, QC H3G 1A4, Canada
| | - Reza Farivar
- Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H4A 0A4, Canada (S.B.)
- Research Institute of the McGill University Health Center, Montreal, QC H3G 1A4, Canada
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Xin H, Liang C, Fu Y, Feng M, Wang S, Gao Y, Sui C, Zhang N, Guo L, Wen H. Disrupted brain structural networks associated with depression and cognitive dysfunction in cerebral small vessel disease with microbleeds. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110944. [PMID: 38246218 DOI: 10.1016/j.pnpbp.2024.110944] [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] [Received: 09/03/2023] [Revised: 12/26/2023] [Accepted: 01/14/2024] [Indexed: 01/23/2024]
Abstract
Emerging evidence highlights cerebral microbleeds (CMBs) as hallmarks of cerebral small vessel disease (CSVD) underlying depression and cognitive dysfunction. This study aimed to reveal how depression and cognition-related white matter (WM) abnormalities are topologically presented, and the network-level structural disruptions associated with CMBs in CSVD. We used probabilistic diffusion tractography and graph theory to investigate brain WM network topology in CSVD patients with (n = 64, CSVD-c) and without (n = 138, CSVD-n) CMBs and 90 healthy controls. Then we evaluated the Pearson's correlations between disrupted network metrics and neuropsychological parameters. For global topology, the CSVD-c group exhibited significantly decreased global (Eglob) and local (Eloc) efficiency and increased shortest path length compared with the controls, while no significant difference was found between the CSVD-c and CSVD-n groups. For regional topology, although all groups showed highly similar hub distributions, compare with control group, the CSVD-c group exhibited significantly decreased nodal efficiency mainly in the bilateral supplementary motor area (SMA), median cingulate gyrus (DCG) and right orbital middle frontal gyrus, while the CSVD-n group showed significantly decreased nodal efficiency only in the right SMA. Notably, Eglob, Eloc and nodal efficiency of the right anterior cingulate gyrus, DCG, middle temporal gyrus and left insula showed significantly negative correlations with depression score, significantly positive correlations with Rey auditory verbal learning test and symbol digit modalities test scores in CSVD-n group, as well as significantly negative correlations with Stroop color-word test scores in CSVD-c group. The WM networks of CSVD patients are characterized by decreased global integration and local specialization, and decreased nodal efficiency highly related to depression and cognitive dysfunction in the attention, default mode network and sensorimotor regions. These findings provide new insight into the neurobiological mechanisms of CSVD and concomitant affective and cognitive disorders.
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Affiliation(s)
- Haotian Xin
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jing-wu Road No. 324, Jinan, Shandong 250021, China; Department of Radiology and Nuclear medicine, Xuanwu Hospital, Capital Medical University, No. 45 Chang-chun St, Xicheng District, Beijing, China
| | - Changhu Liang
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Yajie Fu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jing-wu Road No. 324, Jinan, Shandong 250021, China; Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, 16766 Jing-shi Road,Jinan 250014,China
| | - Mengmeng Feng
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jing-wu Road No. 324, Jinan, Shandong 250021, China; Department of Radiology and Nuclear medicine, Xuanwu Hospital, Capital Medical University, No. 45 Chang-chun St, Xicheng District, Beijing, China
| | - Shengpei Wang
- Research Center for Brain-inspired Intelligence Institute of Automation, Chinese Academy of Sciences, ZhongGuanCun East Rd. 95#, Beijing 100190, China
| | - Yian Gao
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Chaofan Sui
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Nan Zhang
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Lingfei Guo
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China.
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education), Faculty of Psychology, Southwest University, Chongqing 400715, China.
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6
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Bouchard HC, Higgins KL, Amadon GK, Laing-Young JM, Maerlender A, Al-Momani S, Neta M, Savage CR, Schultz DH. Concussion-Related Disruptions to Hub Connectivity in the Default Mode Network Are Related to Symptoms and Cognition. J Neurotrauma 2024; 41:571-586. [PMID: 37974423 DOI: 10.1089/neu.2023.0089] [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] [Indexed: 11/19/2023] Open
Abstract
Concussions present with a myriad of symptomatic and cognitive concerns; however, the relationship between these functional disruptions and the underlying changes in the brain are not yet well understood. Hubs, or brain regions that are connected to many different functional networks, may be specifically disrupted after concussion. Given the implications in concussion research, we quantified hub disruption within the default mode network (DMN) and between the DMN and other brain networks. We collected resting-state functional magnetic resonance imaging data from collegiate student-athletes (n = 44) at three time points: baseline (before beginning their athletic season), acute post-injury (approximately 48h after a diagnosed concussion), and recovery (after starting return-to-play progression, but before returning to contact). We used self-reported symptoms and computerized cognitive assessments collected across similar time points to link these functional connectivity changes to clinical outcomes. Concussion resulted in increased connectivity between regions within the DMN compared with baseline and recovery, and this post-injury connectivity was more positively related to symptoms and more negatively related to visual memory performance compared with baseline and recovery. Further, concussion led to decreased connectivity between DMN hubs and visual network non-hubs relative to baseline and recovery, and this post-injury connectivity was more negatively related to somatic symptoms and more positively related to visual memory performance compared with baseline and recovery. Relationships between functional connectivity, symptoms, and cognition were not significantly different at baseline versus recovery. These results highlight a unique relationship between self-reported symptoms, visual memory performance, and acute functional connectivity changes involving DMN hubs after concussion in athletes. This may provide evidence for a disrupted balance of within- and between-network communication highlighting possible network inefficiencies after concussion. These results aid in our understanding of the pathophysiological disruptions after concussion and inform our understanding of the associations between disruptions in brain connectivity and specific clinical presentations acutely post-injury.
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Affiliation(s)
- Heather C Bouchard
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Kate L Higgins
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Athletics, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Grace K Amadon
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Julia M Laing-Young
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Arthur Maerlender
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Seima Al-Momani
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Maital Neta
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Cary R Savage
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Douglas H Schultz
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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7
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Newlin NR, Kanakaraj P, Li T, Pechman K, Archer D, Jefferson A, Landman B, Moyer D. Learning site-invariant features of connectomes to harmonize complex network measures. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12930:129302E. [PMID: 39220624 PMCID: PMC11364372 DOI: 10.1117/12.3009645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, 2) learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value <0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | - Thomas Li
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Kimberly Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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Xu H, Newlin NR, Kim ME, Gao C, Kanakaraj P, Krishnan AR, Remedios LW, Khairi NM, Pechman K, Archer D, Hohman TJ, Jefferson AL, Isgum I, Huo Y, Moyer D, Schilling KG, Landman BA. Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12926:129261X. [PMID: 39310215 PMCID: PMC11415266 DOI: 10.1117/12.3005563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.
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Affiliation(s)
- Hanliang Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Michael E Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Chenyu Gao
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Aravind R Krishnan
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Lucas W Remedios
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nazirah Mohd Khairi
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kimberly Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ivana Isgum
- Department of Biomedical Engineering and Physics & Radiology and Nuclear Medicine, University Medical Center Amsterdam, University of Amsterdam, Amsterdam, the Netherlands
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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9
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Xu H, Newlin NR, Kim ME, Gao C, Kanakaraj P, Krishnan AR, Remedios LW, Khairi NM, Pechman K, Archer D, Hohman TJ, Jefferson AL, Isgum I, Huo Y, Moyer D, Schilling KG, Landman BA. Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites. ARXIV 2024:arXiv:2401.06798v2. [PMID: 38344221 PMCID: PMC10854272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.
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Affiliation(s)
- Hanliang Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Michael E Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Chenyu Gao
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Aravind R Krishnan
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Lucas W Remedios
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nazirah Mohd Khairi
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kimberly Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ivana Isgum
- Department of Biomedical Engineering and Physics & Radiology and Nuclear Medicine, University Medical Center Amsterdam, University of Amsterdam, Amsterdam, the Netherlands
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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10
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Symons GF, Gregg MC, Hicks AJ, Rowe CC, Shultz SR, Ponsford JL, Spitz G. Altered grey matter structural covariance in chronic moderate-severe traumatic brain injury. Sci Rep 2024; 14:1728. [PMID: 38242923 PMCID: PMC10799053 DOI: 10.1038/s41598-023-50396-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/19/2023] [Indexed: 01/21/2024] Open
Abstract
Traumatic brain injury (TBI) alters brain network connectivity. Structural covariance networks (SCNs) reflect morphological covariation between brain regions. SCNs may elucidate how altered brain network topology in TBI influences long-term outcomes. Here, we assessed whether SCN organisation is altered in individuals with chronic moderate-severe TBI (≥ 10 years post-injury) and associations with cognitive performance. This case-control study included fifty individuals with chronic moderate-severe TBI compared to 75 healthy controls recruited from an ongoing longitudinal head injury outcome study. SCNs were constructed using grey matter volume measurements from T1-weighted MRI images. Global and regional SCN organisation in relation to group membership and cognitive ability was examined using regression analyses. Globally, TBI participants had reduced small-worldness, longer characteristic path length, higher clustering, and higher modularity globally (p < 0.05). Regionally, TBI participants had greater betweenness centrality (p < 0.05) in frontal and central areas of the cortex. No significant associations were observed between global network measures and cognitive ability in participants with TBI (p > 0.05). Chronic moderate-severe TBI was associated with a shift towards a more segregated global network topology and altered organisation in frontal and central brain regions. There was no evidence that SCNs are associated with cognition.
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Affiliation(s)
- Georgia F Symons
- Department of Neuroscience, Monash University, 6th Floor, The Alfred Centre, 99 Commercial Road, Melbourne, VIC, 3004, Australia.
| | - Matthew C Gregg
- Monash-Epworth Rehabilitation Research Centre, Ground Floor, 185-187 Hoddle St, Richmond, 3121, Australia
| | - Amelia J Hicks
- Monash-Epworth Rehabilitation Research Centre, Ground Floor, 185-187 Hoddle St, Richmond, 3121, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging and Therapy, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia
| | - Sandy R Shultz
- Department of Neuroscience, Monash University, 6th Floor, The Alfred Centre, 99 Commercial Road, Melbourne, VIC, 3004, Australia
- Health Sciences, Vancouver Island University, 900 Fifth Street, Nanaimo, BC, V9R 5S5, Canada
| | - Jennie L Ponsford
- Monash-Epworth Rehabilitation Research Centre, Ground Floor, 185-187 Hoddle St, Richmond, 3121, Australia
| | - Gershon Spitz
- Department of Neuroscience, Monash University, 6th Floor, The Alfred Centre, 99 Commercial Road, Melbourne, VIC, 3004, Australia
- Monash-Epworth Rehabilitation Research Centre, Ground Floor, 185-187 Hoddle St, Richmond, 3121, Australia
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11
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Udayakumar P, Subhashini R. Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN). JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:1041-1059. [PMID: 38820060 DOI: 10.3233/xst-230426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
BACKGROUND Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. OBJECTIVE To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. METHOD By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. RESULT The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). CONCLUSION The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients.
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Affiliation(s)
- P Udayakumar
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
| | - R Subhashini
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
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12
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De Benedictis A, Rossi-Espagnet MC, de Palma L, Sarubbo S, Marras CE. Structural networking of the developing brain: from maturation to neurosurgical implications. Front Neuroanat 2023; 17:1242757. [PMID: 38099209 PMCID: PMC10719860 DOI: 10.3389/fnana.2023.1242757] [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: 06/19/2023] [Accepted: 11/09/2023] [Indexed: 12/17/2023] Open
Abstract
Modern neuroscience agrees that neurological processing emerges from the multimodal interaction among multiple cortical and subcortical neuronal hubs, connected at short and long distance by white matter, to form a largely integrated and dynamic network, called the brain "connectome." The final architecture of these circuits results from a complex, continuous, and highly protracted development process of several axonal pathways that constitute the anatomical substrate of neuronal interactions. Awareness of the network organization of the central nervous system is crucial not only to understand the basis of children's neurological development, but also it may be of special interest to improve the quality of neurosurgical treatments of many pediatric diseases. Although there are a flourishing number of neuroimaging studies of the connectome, a comprehensive vision linking this research to neurosurgical practice is still lacking in the current pediatric literature. The goal of this review is to contribute to bridging this gap. In the first part, we summarize the main current knowledge concerning brain network maturation and its involvement in different aspects of normal neurocognitive development as well as in the pathophysiology of specific diseases. The final section is devoted to identifying possible implications of this knowledge in the neurosurgical field, especially in epilepsy and tumor surgery, and to discuss promising perspectives for future investigations.
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Affiliation(s)
| | | | - Luca de Palma
- Clinical and Experimental Neurology, Bambino Gesù Children’s Hospital, Rome, Italy
| | - Silvio Sarubbo
- Department of Neurosurgery, Santa Chiara Hospital, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
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13
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Newlin NR, Rheault F, Schilling KG, Landman BA. Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes. J Magn Reson Imaging 2023; 58:1211-1220. [PMID: 36840398 PMCID: PMC10447626 DOI: 10.1002/jmri.28631] [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: 10/20/2022] [Revised: 01/21/2023] [Accepted: 01/23/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes. PURPOSE The purpose was to define the relationship between streamline count and graph-measure value, reproducibility, and repeatability. STUDY TYPE Retrospective analysis of previously prospective study. POPULATION Ten healthy subjects, 70% female, aged 25.3 ± 5.9 years. FIELD STRENGTH/SEQUENCE A 3-T, T1-weighted sequences and diffusion-weighted imaging (DWI) with two gradient strengths (b-values = 1200 and 3000 sec/mm2 , echo time [TE] = 68 msec, repetition time [TR] = 5.4 seconds, 120 slices, field of view = 188 mm2 ). ASSESSMENT A total of 13 graph-theory measures were derived for each subject by generating probabilistic whole-brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived. STATISTICAL TESTS Paired t-test with P value <0.05 was used to compare graph-measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility. RESULTS Modularity and global efficiency converged to their reference mean with ICC > 0.90 and CCC > 0.99. Edge count, small-worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC > 0.90 and CCC > 0.95. Assortativity and average participation coefficient converged with ICC > 0.75 and CCC > 0.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC > 0.90 and CCC > 0.99. For these measures, alternate definitions that converge a reference mean are provided. DATA CONCLUSION Modularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count. EVIDENCE LEVEL 1. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Nancy R. Newlin
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - François Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Bennett A. Landman
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
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14
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Newlin NR, Kim ME, Kanakaraj P, Yao T, Hohman T, Pechman KR, Beason-Held LL, Resnick SM, Archer D, Jefferson A, Landman BA, Moyer D. MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.12.553099. [PMID: 37645973 PMCID: PMC10462069 DOI: 10.1101/2023.08.12.553099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Objective Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. Methods We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. Conclusion MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Significance Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Michael E Kim
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | | | - Tianyuan Yao
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Timothy Hohman
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | | | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Derek Archer
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | - Angela Jefferson
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
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15
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Ware AL, Onicas AI, Abdeen N, Beauchamp MH, Beaulieu C, Bjornson BH, Craig W, Dehaes M, Deschenes S, Doan Q, Freedman SB, Goodyear BG, Gravel J, Ledoux AA, Zemek R, Yeates KO, Lebel C. Altered longitudinal structural connectome in paediatric mild traumatic brain injury: an Advancing Concussion Assessment in Paediatrics study. Brain Commun 2023; 5:fcad173. [PMID: 37324241 PMCID: PMC10265725 DOI: 10.1093/braincomms/fcad173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/18/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023] Open
Abstract
Advanced diffusion-weighted imaging techniques have increased understanding of the neuropathology of paediatric mild traumatic brain injury (i.e. concussion). Most studies have examined discrete white-matter pathways, which may not capture the characteristically subtle, diffuse and heterogenous effects of paediatric concussion on brain microstructure. This study compared the structural connectome of children with concussion to those with mild orthopaedic injury to determine whether network metrics and their trajectories across time post-injury differentiate paediatric concussion from mild traumatic injury more generally. Data were drawn from of a large study of outcomes in paediatric concussion. Children aged 8-16.99 years were recruited from five paediatric emergency departments within 48 h of sustaining a concussion (n = 360; 56% male) or mild orthopaedic injury (n = 196; 62% male). A reliable change score was used to classify children with concussion into two groups: concussion with or without persistent symptoms. Children completed 3 T MRI at post-acute (2-33 days) and/or chronic (3 or 6 months, via random assignment) post-injury follow-ups. Diffusion-weighted images were used to calculate the diffusion tensor, conduct deterministic whole-brain fibre tractography and compute connectivity matrices in native (diffusion) space for 90 supratentorial regions. Weighted adjacency matrices were constructed using average fractional anisotropy and used to calculate global and local (regional) graph theory metrics. Linear mixed effects modelling was performed to compare groups, correcting for multiple comparisons. Groups did not differ in global network metrics. However, the clustering coefficient, betweenness centrality and efficiency of the insula, cingulate, parietal, occipital and subcortical regions differed among groups, with differences moderated by time (days) post-injury, biological sex and age at time of injury. Post-acute differences were minimal, whereas more robust alterations emerged at 3 and especially 6 months in children with concussion with persistent symptoms, albeit differently by sex and age. In the largest neuroimaging study to date, post-acute regional network metrics distinguished concussion from mild orthopaedic injury and predicted symptom recovery 1-month post-injury. Regional network parameters alterations were more robust and widespread at chronic timepoints than post-acutely after concussion. Results suggest that increased regional and local subnetwork segregation (modularity) and inefficiency occurs across time after concussion, emerging after post-concussive symptom resolve in most children. These differences persist up to 6 months after concussion, especially in children who showed persistent symptoms. While prognostic, the small to modest effect size of group differences and the moderating effects of sex likely would preclude effective clinical application in individual patients.
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Affiliation(s)
- Ashley L Ware
- Correspondence to: Ashley L. Ware, PhD Department of Psychology, Georgia State University 140 Decatur Street SE, Atlanta, GA 30303, USA E-mail:
| | - Adrian I Onicas
- Department of Psychology, University of Calgary, Calgary, AB T2N 0V2, Canada
- Computer Vision Group, Sano Centre for Computational Medicine, Kraków 30-054, Poland
| | - Nishard Abdeen
- Department of Radiology, Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa,Ottawa, ON, Canada K1H 8L1
| | - Miriam H Beauchamp
- Department of Psychology, University of Montreal and CHU Sainte-Justine Hospital Research Center, Montréal, QC, Canada H3C 3J7
| | - Christian Beaulieu
- Department of Biomedical Engineering, 1098 Research Transition Facility, University of Alberta, Edmonton, AB, Canada T6G 2V2
| | - Bruce H Bjornson
- Division of Neurology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada V6H 3V4
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada V6H 3V4
| | - William Craig
- University of Alberta and Stollery Children’s Hospital, Edmonton, AB, Canada T6G 1C9
| | - Mathieu Dehaes
- Department of Radiology, Radio-oncology and Nuclear Medicine, Institute of Biomedical Engineering, University of Montreal, Montréal, QC, Canada H3T1J4
- CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
| | - Sylvain Deschenes
- CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
- Department of Radiology, Radio-oncology and Nuclear Medicine, University of Montreal, Montréal, QC, CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
| | - Quynh Doan
- Department of Pediatrics University of British Columbia, BC Children’s Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Stephen B Freedman
- Departments of Pediatric and Emergency Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada T3B 6A8
| | - Bradley G Goodyear
- Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, AB T2N 0V2, Canada
- Department of Radiology, University of Calgary, Calgary, AB T2N 0V2, Canada
| | - Jocelyn Gravel
- Pediatric Emergency Department, CHU Sainte-Justine, Montréal, QC H3T1C5, Canada
- Department of Pediatric, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Andrée-Anne Ledoux
- Department of Cellular Molecular Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada K1H8L1
| | - Roger Zemek
- Department of Pediatrics and Emergency Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada K1H8L1
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16
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Parsons N, Irimia A, Amgalan A, Ugon J, Morgan K, Shelyag S, Hocking A, Poudel G, Caeyenberghs K. Structural-functional connectivity bandwidth predicts processing speed in mild traumatic brain Injury: A multiplex network analysis. Neuroimage Clin 2023; 38:103428. [PMID: 37167841 PMCID: PMC10196722 DOI: 10.1016/j.nicl.2023.103428] [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: 01/10/2023] [Revised: 04/17/2023] [Accepted: 05/01/2023] [Indexed: 05/13/2023]
Abstract
An emerging body of work has revealed alterations in structural (SC) and functional (FC) brain connectivity following mild TBI (mTBI), with mixed findings. However, these studies seldom integrate complimentary neuroimaging modalities within a unified framework. Multilayer network analysis is an emerging technique to uncover how white matter organization enables functional communication. Using our novel graph metric (SC-FC Bandwidth), we quantified the information capacity of synchronous brain regions in 53 mild TBI patients (46 females; age mean = 40.2 years (y), σ = 16.7 (y), range: 18-79 (y). Diffusion MRI and resting state fMRI were administered at the acute and chronic post-injury intervals. Moreover, participants completed a cognitive task to measure processing speed (30 Seconds and Counting Task; 30-SACT). Processing speed was significantly increased at the chronic, relative to the acute post-injury intervals (p = <0.001). Nonlinear principal components of direct (t = -1.84, p = 0.06) and indirect SC-FC Bandwidth (t = 3.86, p = <0.001) predicted processing speed with a moderate effect size (R2 = 0.43, p < 0.001), while controlling for age. A subnetwork of interhemispheric edges with increased SC-FC Bandwidth was identified at the chronic, relative to the acute mTBI post-injury interval (pFDR = 0.05). Increased interhemispheric SC-FC Bandwidth of this network corresponded with improved processing speed at the chronic post-injury interval (partial r = 0.32, p = 0.02). Our findings revealed that mild TBI results in complex reorganization of brain connectivity optimized for maximum information flow, supporting improved cognitive performance as a compensatory mechanism. Moving forward, this measurement may complement clinical assessment as an objective marker of mTBI recovery.
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Affiliation(s)
- Nicholas Parsons
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia; BrainCast Neurotechnologies, Australia; School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Julien Ugon
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Kerri Morgan
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Sergiy Shelyag
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Alex Hocking
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Govinda Poudel
- BrainCast Neurotechnologies, Australia; Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia
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17
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Diffusion-Weighted Imaging in Mild Traumatic Brain Injury: A Systematic Review of the Literature. Neuropsychol Rev 2023; 33:42-121. [PMID: 33721207 DOI: 10.1007/s11065-021-09485-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
There is evidence that diffusion-weighted imaging (DWI) is able to detect tissue alterations following mild traumatic brain injury (mTBI) that may not be observed on conventional neuroimaging; however, findings are often inconsistent between studies. This systematic review assesses patterns of differences in DWI metrics between those with and without a history of mTBI. A PubMed literature search was performed using relevant indexing terms for articles published prior to May 14, 2020. Findings were limited to human studies using DWI in mTBI. Articles were excluded if they were not full-length, did not contain original data, if they were case studies, pertained to military populations, had inadequate injury severity classification, or did not report post-injury interval. Findings were reported independently for four subgroups: acute/subacute pediatric mTBI, acute/subacute adult mTBI, chronic adult mTBI, and sport-related concussion, and all DWI acquisition and analysis methods used were included. Patterns of findings between studies were reported, along with strengths and weaknesses of the current state of the literature. Although heterogeneity of sample characteristics and study methods limited the consistency of findings, alterations in DWI metrics were most commonly reported in the corpus callosum, corona radiata, internal capsule, and long association pathways. Many acute/subacute pediatric studies reported higher FA and lower ADC or MD in various regions. In contrast, acute/subacute adult studies most commonly indicate lower FA within the context of higher MD and RD. In the chronic phase of recovery, FA may remain low, possibly indicating overall demyelination or Wallerian degeneration over time. Longitudinal studies, though limited, generally indicate at least a partial normalization of DWI metrics over time, which is often associated with functional improvement. We conclude that DWI is able to detect structural mTBI-related abnormalities that may persist over time, although future DWI research will benefit from larger samples, improved data analysis methods, standardized reporting, and increasing transparency.
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18
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Liu Y, Li F, Shang S, Wang P, Yin X, Krishnan Muthaiah VP, Lu L, Chen YC. Functional-structural large-scale brain networks are correlated with neurocognitive impairment in acute mild traumatic brain injury. Quant Imaging Med Surg 2023; 13:631-644. [PMID: 36819289 PMCID: PMC9929413 DOI: 10.21037/qims-22-450] [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: 05/04/2022] [Accepted: 11/07/2022] [Indexed: 12/02/2022]
Abstract
Background This study was conducted to investigate topological changes in large-scale functional connectivity (FC) and structural connectivity (SC) networks in acute mild traumatic brain injury (mTBI) and determine their potential relevance to cognitive impairment. Methods Seventy-one patients with acute mTBI (29 males, 42 females, mean age 43.54 years) from Nanjing First Hospital and 57 matched healthy controls (HC) (33 males, 24 females, mean age 46.16 years) from the local community were recruited in this prospective study. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were acquired within 14 days (mean 3.29 days) after the onset of mTBI. Then, large-scale FC and SC networks with 116 regions from the automated anatomical labeling (AAL) brain atlas were constructed. Graph theory analysis was used to analyze global and nodal metrics. Finally, correlations were assessed between topological properties and neurocognitive performances evaluated by the Montreal Cognitive Assessment (MoCA). Bonferroni correction was performed out for multiple comparisons in all involved analyses. Results Compared with HC, acute mTBI patients had a higher normalized clustering coefficient (γ) for FC (Cohen's d=4.076), and higher γ and small worldness (σ) for SC (Cohen's d=0.390 and Cohen's d=0.395). The mTBI group showed aberrant nodal degree (Dc), nodal efficiency (Ne), and nodal local efficiency (Nloc) for FC and aberrant Dc, nodal betweenness (Bc), nodal clustering coefficient (NCp) and Ne for SC mainly in the frontal and temporal, cerebellum, and subcortical areas. Acute mTBI patients also had higher functional-structural coupling strength at both the group and individual levels (Cohen's d=0.415). These aberrant global and nodal topological properties at functional and structural levels were associated with attention, orientation, memory, and naming performances (all P<0.05). Conclusions Our findings suggested that large-scale FC and SC network changes, higher correlation between FC and SC and cognitive impairment can be detected in the acute stage of mTBI. These network aberrances may be a compensatory mechanism for cognitive impairment in acute mTBI patients.
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Affiliation(s)
- Yin Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Fengfang Li
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Song’an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Vijaya Prakash Krishnan Muthaiah
- Department of Rehabilitation Sciences, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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19
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Newlin NR, Cai LY, Yao T, Archer D, Pechman KR, Schilling KG, Jefferson A, Resnick SM, Hohman TJ, Shafer AT, Landman BA. Comparing voxel- and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12464:124642B. [PMID: 37123017 PMCID: PMC10139749 DOI: 10.1117/12.2653947] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Complex graph theory measures of brain structural connectomes derived from diffusion weighted images (DWI) provide insight into the network structure of the brain. Further, as the number of available DWI datasets grows, so does the ability to investigate associations in these measures with major biological factors, like age. However, one key hurdle that remains is the presence of scanner effects that can arise from different DWI datasets and confound multisite analyses. Two common approaches to correct these effects are voxel-wise and feature-wise harmonization. However, it is still unclear how to best leverage them for graph-theory analysis of an aging population. Thus, there is a need to better characterize the impact of each harmonization method and their ability to preserve age related features. We investigate this by characterizing four complex graph theory measures (modularity, characteristic path length, global efficiency, and betweenness centrality) in 48 participants aged 55 to 86 from Baltimore Longitudinal Study of Aging (BLSA) and Vanderbilt Memory and Aging Project (VMAP) before and after voxel- and feature-wise harmonization with the Null Space Deep Network (NSDN) and ComBat, respectively. First, we characterize across dataset coefficients of variation (CoV) and find the combination of NSDN and ComBat causes the greatest reduction in CoV followed by ComBat alone then NSDN alone. Second, we reproduce published associations of modularity with age after correcting for other covariates with linear models. We find that harmonization with ComBat or ComBat and NSDN together improves the significance of existing age effects, reduces model residuals, and qualitatively reduces separation between datasets. These results reinforce the efficiency of statistical harmonization on the feature-level with ComBat and suggest that harmonization on the voxel-level is synergistic but may have reduced effect after running through the multiple layers of the connectomics pipeline. Thus, we conclude that feature-wise harmonization improves statistical results, but the addition of biologically informed voxel-based harmonization offers further improvement.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
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20
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Adegoke MA, Teter O, Meaney DF. Flexibility of in vitro cortical circuits influences resilience from microtrauma. Front Cell Neurosci 2022; 16:991740. [PMID: 36589287 PMCID: PMC9803265 DOI: 10.3389/fncel.2022.991740] [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: 07/11/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Background Small clusters comprising hundreds to thousands of neurons are an important level of brain architecture that correlates single neuronal properties to fulfill brain function, but the specific mechanisms through which this scaling occurs are not well understood. In this study, we developed an in vitro experimental platform of small neuronal circuits (islands) to probe the importance of structural properties for their development, physiology, and response to microtrauma. Methods Primary cortical neurons were plated on a substrate patterned to promote attachment in clusters of hundreds of cells (islands), transduced with GCaMP6f, allowed to mature until 10-13 days in vitro (DIV), and monitored with Ca2+ as a non-invasive proxy for electrical activity. We adjusted two structural factors-island size and cellular density-to evaluate their role in guiding spontaneous activity and network formation in neuronal islands. Results We found cellular density, but not island size, regulates of circuit activity and network function in this system. Low cellular density islands can achieve many states of activity, while high cellular density biases islands towards a limited regime characterized by low rates of activity and high synchronization, a property we summarized as "flexibility." The injury severity required for an island to lose activity in 50% of its population was significantly higher in low-density, high flexibility islands. Conclusion Together, these studies demonstrate flexible living cortical circuits are more resilient to microtrauma, providing the first evidence that initial circuit state may be a key factor to consider when evaluating the consequences of trauma to the cortex.
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Affiliation(s)
- Modupe A. Adegoke
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - Olivia Teter
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - David F. Meaney
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States,Department of Neurosurgery, Penn Center for Brain Injury and Repair, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States,*Correspondence: David F. Meaney,
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21
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Nakuci J, McGuire M, Schweser F, Poulsen D, Muldoon SF. Differential Patterns of Change in Brain Connectivity Resulting from Severe Traumatic Brain Injury. Brain Connect 2022; 12:799-811. [PMID: 35302399 PMCID: PMC9805864 DOI: 10.1089/brain.2021.0168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background: Traumatic brain injury (TBI) damages white matter tracts, disrupting brain network structure and communication. There exists a wide heterogeneity in the pattern of structural damage associated with injury, as well as a large heterogeneity in behavioral outcomes. However, little is known about the relationship between changes in network connectivity and clinical outcomes. Materials and Methods: We utilize the rat lateral fluid-percussion injury model of severe TBI to study differences in brain connectivity in 8 animals that received the insult and 11 animals that received only a craniectomy. Diffusion tensor imaging is performed 5 weeks after the injury and network theory is used to investigate changes in white matter connectivity. Results: We find that (1) global network measures are not able to distinguish between healthy and injured animals; (2) injury induced alterations predominantly exist in a subset of connections (subnetworks) distributed throughout the brain; and (3) injured animals can be divided into subgroups based on changes in network motifs-measures of local structural connectivity. In addition, alterations in predicted functional connectivity indicate that the subgroups have different propensities to synchronize brain activity, which could relate to the heterogeneity of clinical outcomes. Discussion: These results suggest that network measures can be used to quantify progressive changes in brain connectivity due to injury and differentiate among subpopulations with similar injuries, but different pathological trajectories.
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Affiliation(s)
- Johan Nakuci
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Matthew McGuire
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
- Department of Neurosurgery, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, SUNY, Buffalo, New York, USA
| | - David Poulsen
- Department of Neurosurgery, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Sarah F. Muldoon
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, New York, USA
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22
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Osmanlıoğlu Y, Parker D, Alappatt JA, Gugger JJ, Diaz-Arrastia RR, Whyte J, Kim JJ, Verma R. Connectomic assessment of injury burden and longitudinal structural network alterations in moderate-to-severe traumatic brain injury. Hum Brain Mapp 2022; 43:3944-3957. [PMID: 35486024 PMCID: PMC9374876 DOI: 10.1002/hbm.25894] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/08/2022] [Accepted: 04/14/2022] [Indexed: 11/14/2022] Open
Abstract
Traumatic brain injury (TBI) is a major public health problem. Caused by external mechanical forces, a major characteristic of TBI is the shearing of axons across the white matter, which causes structural connectivity disruptions between brain regions. This diffuse injury leads to cognitive deficits, frequently requiring rehabilitation. Heterogeneity is another characteristic of TBI as severity and cognitive sequelae of the disease have a wide variation across patients, posing a big challenge for treatment. Thus, measures assessing network-wide structural connectivity disruptions in TBI are necessary to quantify injury burden of individuals, which would help in achieving personalized treatment, patient monitoring, and rehabilitation planning. Despite TBI being a disconnectivity syndrome, connectomic assessment of structural disconnectivity has been relatively limited. In this study, we propose a novel connectomic measure that we call network normality score (NNS) to capture the integrity of structural connectivity in TBI patients by leveraging two major characteristics of the disease: diffuseness of axonal injury and heterogeneity of the disease. Over a longitudinal cohort of moderate-to-severe TBI patients, we demonstrate that structural network topology of patients is more heterogeneous and significantly different than that of healthy controls at 3 months postinjury, where dissimilarity further increases up to 12 months. We also show that NNS captures injury burden as quantified by posttraumatic amnesia and that alterations in the structural brain network is not related to cognitive recovery. Finally, we compare NNS to major graph theory measures used in TBI literature and demonstrate the superiority of NNS in characterizing the disease.
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Affiliation(s)
- Yusuf Osmanlıoğlu
- Department of Computer Science, College of Computing and Informatics, Drexel University, Philadelphia, Pennsylvania, USA
| | - Drew Parker
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jacob A Alappatt
- Speech and hearing, bioscience and technology program, Harvard Medical School, Harvard University, Boston, MA, USA
| | - James J Gugger
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ramon R Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Brain Injury and Repair, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John Whyte
- Moss Rehabilitation Research Institute, TBI Rehabilitation Research LaboratoryEinstein Medical Center, Elkins Park, Pennsylvania, USA
| | - Junghoon J Kim
- Department of Molecular, Cellular, and Biomedical Sciences, CUNY School of Medicine, The City College of New York, New York, New York, USA
| | - Ragini Verma
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Brain Injury and Repair, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Neurosurgery, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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23
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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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24
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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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25
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Oyefiade A, Moxon-Emre I, Beera K, Bouffet E, Taylor M, Ramaswamy V, Laughlin S, Skocic J, Mabbott D. Structural connectivity and intelligence in brain-injured children. Neuropsychologia 2022; 173:108285. [PMID: 35690116 DOI: 10.1016/j.neuropsychologia.2022.108285] [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: 09/23/2021] [Revised: 05/28/2022] [Accepted: 05/31/2022] [Indexed: 11/29/2022]
Abstract
In children, higher general intelligence corresponds with better processing speed ability. However, the relationship between structural brain connectivity and processing speed in the context of intelligence is unclear. Furthermore, the impact of brain injury on this relationship is also unknown. Structural networks were constructed for 36 brain tumor patients (mean age: 13.45 ± 2.73, 58% males) and 35 typically developing children (13.30 ± 2.86, 51% males). Processing speed and general intelligence scores were acquired using standard batteries. The relationship between network properties, processing speed, and intelligence was assessed using a partial least squares analysis. Results indicated that structural networks in brain-injured children were less integrated (β = -.38, p = 0.001) and more segregated (β = 0.4, p = 0.0005) compared to typically developing children. There was an indirect effect of network segregation on general intelligence via processing speed, where greater network segregation predicted slower processing speed which in turn predicted worse general intelligence (GoF = 0.37). These findings provide the first evidence of relations between structural connectivity, processing speed, and intelligence in children. Injury-related disruption to the structural network may result in worse intelligence through impacts on information processing. Our findings are discussed in the context of a network approach to understanding brain-behavior relationships.
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Affiliation(s)
- Adeoye Oyefiade
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA; Department of Psychology, University of Toronto, Toronto, Ontario, CANADA
| | - Iska Moxon-Emre
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Kiran Beera
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Eric Bouffet
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Michael Taylor
- Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Vijay Ramaswamy
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Suzanne Laughlin
- Division of Radiology, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Jovanka Skocic
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA
| | - Donald Mabbott
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, CANADA; Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, CANADA; Department of Psychology, University of Toronto, Toronto, Ontario, CANADA.
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26
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Oyefiade A, Moxon-Emre I, Beera K, Bouffet E, Taylor M, Ramaswamy V, Laughlin S, Skocic J, Mabbott D. Abnormalities of Structural Brain Connectivity in Pediatric Brain Tumor Survivors. Neurooncol Adv 2022; 4:vdac064. [PMID: 35875689 PMCID: PMC9297943 DOI: 10.1093/noajnl/vdac064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Background Pediatric brain tumor survivors are at an increased risk for white matter (WM) injury. However, damage to whole-brain structural connectivity is unelucidated. The impact of treatment on WM connectivity was investigated. Methods Whole-brain WM networks were derived from diffusion tensor imaging data acquired for 28 irradiated patients (radiotherapy, RT) (mean age = 13.74 ± 3.32 years), 13 patients not irradiated (No RT) (mean age = 12.57 ± 2.87), and 41 typically developing children (TDC) (mean age = 13.32 ± 2.92 years). Differences in network properties were analyzed using robust regressions. Results Participation coefficient was lower in both patient groups (RT: adj. P = .015; No RT: adj. P = .042). Compared to TDC, RT had greater clustering (adj. P = .015), local efficiency (adj. P = .003), and modularity (adj. P = .000003). WM traced from hubs was damaged in patients: left hemisphere pericallosal sulcus (FA [F = 4.97; q < 0.01]; MD [F = 11.02; q < 0.0001]; AD [F = 10.00; q < 0.0001]; RD [F = 8.53; q < 0.0001]), right hemisphere pericallosal sulcus (FA [F = 8.87; q < 0.0001]; RD [F = 8.27; q < 0.001]), and right hemisphere parietooccipital sulcus (MD [F = 5.78; q < 0.05]; RD [F = 5.12; q < 0.05]). Conclusions Findings indicate greater segregation of WM networks after RT. Intermodular connectivity was lower after treatment with and without RT. No significant network differences were observed between patient groups. Our results are discussed in the context of a network approach that emphasizes interactions between brain regions.
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Affiliation(s)
- Adeoye Oyefiade
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario
- Department of Psychology, University of Toronto, Toronto, Ontario
| | - Iska Moxon-Emre
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Kiran Beera
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario
| | - Eric Bouffet
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario
| | - Michael Taylor
- Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario
| | - Vijay Ramaswamy
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario
| | - Suzanne Laughlin
- Division of Radiology, The Hospital for Sick Children, Toronto, Ontario
| | - Jovanka Skocic
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Donald Mabbott
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario
- Division of Hematology/Oncology, The Hospital for Sick Children, Toronto, Ontario
- Department of Psychology, University of Toronto, Toronto, Ontario
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27
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Thanjavur K, Hristopulos DT, Babul A, Yi KM, Virji-Babul N. Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors. Front Hum Neurosci 2021; 15:734501. [PMID: 34899212 PMCID: PMC8654150 DOI: 10.3389/fnhum.2021.734501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments. We recently developed a deep learning long short-term memory (LSTM) based recurrent neural network to classify concussion using raw, resting state data using 64 EEG channels and achieved high accuracy in classifying concussion. Here, we report on our efforts to develop a clinically practical system using a minimal subset of EEG sensors. EEG data from 23 athletes who had suffered a sport-related concussion and 35 non-concussed, control athletes were used for this study. We tested and ranked each of the original 64 channels based on its contribution toward the concussion classification performed by the original LSTM network. The top scoring channels were used to train and test a network with the same architecture as the previously trained network. We found that with only six of the top scoring channels the classifier identified concussions with an accuracy of 94%. These results show that it is possible to classify concussion using raw, resting state data from a small number of EEG sensors, constituting a first step toward developing portable, easy to use EEG systems that can be used in a clinical setting.
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Affiliation(s)
- Karun Thanjavur
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada
| | | | - Arif Babul
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada
| | - Kwang Moo Yi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Naznin Virji-Babul
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
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28
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King DJ, Seri S, Catroppa C, Anderson VA, Wood AG. Structural-covariance networks identify topology-based cortical-thickness changes in children with persistent executive function impairments after traumatic brain injury. Neuroimage 2021; 244:118612. [PMID: 34563681 PMCID: PMC8591373 DOI: 10.1016/j.neuroimage.2021.118612] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 09/14/2021] [Accepted: 09/20/2021] [Indexed: 11/05/2022] Open
Abstract
Paediatric traumatic brain injury (pTBI) results in inconsistent changes to regional morphometry of the brain across studies. Structural-covariance networks represent the degree to which the morphology (typically cortical-thickness) of cortical-regions co-varies with other regions, driven by both biological and developmental factors. Understanding how heterogeneous regional changes may influence wider cortical network organization may more appropriately capture prognostic information in terms of long term outcome following a pTBI. The current study aimed to investigate the relationships between cortical organisation as measured by structural-covariance, and long-term cognitive impairment following pTBI. T1-weighted magnetic resonance imaging (MRI) from n = 83 pTBI patients and 33 typically developing controls underwent 3D-tissue segmentation using Freesurfer to estimate cortical-thickness across 68 cortical ROIs. Structural-covariance between regions was estimated using Pearson's correlations between cortical-thickness measures across 68 regions-of-interest (ROIs), generating a group-level 68 × 68 adjacency matrix for patients and controls. We grouped a subset of patients who underwent executive function testing at 2-years post-injury using a neuropsychological impairment (NPI) rule, defining impaired- and non-impaired subgroups. Despite finding no significant reductions in regional cortical-thickness between the control and pTBI groups, we found specific reductions in graph-level strength of the structural covariance graph only between controls and the pTBI group with executive function (EF) impairment. Node-level differences in strength for this group were primarily found in frontal regions. We also investigated whether the top n nodes in terms of effect-size of cortical-thickness reductions were nodes that had significantly greater strength in the typically developing brain than n randomly selected regions. We found that acute cortical-thickness reductions post-pTBI are loaded onto regions typically high in structural covariance. This association was found in those patients with persistent EF impairment at 2-years post-injury, but not in those for whom these abilities were spared. This study posits that the topography of post-injury cortical-thickness reductions in regions that are central to the typical structural-covariance topology of the brain, can explain which patients have poor EF at follow-up.
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Affiliation(s)
- Daniel J King
- College of Health and Life Sciences and Aston Institute of Health and Neurodevelopment, Aston University, Birmingham B4 7ET, UK.
| | - Stefano Seri
- College of Health and Life Sciences and Aston Institute of Health and Neurodevelopment, Aston University, Birmingham B4 7ET, UK; Department of Clinical Neurophysiology, Birmingham Women's and Children's Hospital NHS Foundation Trust, UK
| | - Cathy Catroppa
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Psychology, Royal Children's Hospital, Melbourne, Australia
| | - Vicki A Anderson
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Psychology, Royal Children's Hospital, Melbourne, Australia
| | - Amanda G Wood
- College of Health and Life Sciences and Aston Institute of Health and Neurodevelopment, Aston University, Birmingham B4 7ET, UK; Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, Victoria, Australia
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29
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Ware AL, Yeates KO, Geeraert B, Long X, Beauchamp MH, Craig W, Doan Q, Freedman SB, Goodyear BG, Zemek R, Lebel C. Structural connectome differences in pediatric mild traumatic brain and orthopedic injury. Hum Brain Mapp 2021; 43:1032-1046. [PMID: 34748258 PMCID: PMC8764485 DOI: 10.1002/hbm.25705] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/23/2021] [Accepted: 10/18/2021] [Indexed: 01/06/2023] Open
Abstract
Sophisticated network‐based approaches such as structural connectomics may help to detect a biomarker of mild traumatic brain injury (mTBI) in children. This study compared the structural connectome of children with mTBI or mild orthopedic injury (OI) to that of typically developing (TD) children. Children aged 8–16.99 years with mTBI (n = 83) or OI (n = 37) were recruited from the emergency department and completed 3T diffusion MRI 2–20 days postinjury. TD children (n = 39) were recruited from the community and completed diffusion MRI. Graph theory metrics were calculated for the binarized average fractional anisotropy among 90 regions. Multivariable linear regression and linear mixed effects models were used to compare groups, with covariates age, hemisphere, and sex, correcting for multiple comparisons. The two injury groups did not differ on graph theory metrics, but both differed from TD children in global metrics (local network efficiency: TD > OI, mTBI, d = 0.49; clustering coefficient: TD < OI, mTBI, d = 0.49) and regional metrics for the fusiform gyrus (lower degree centrality and nodal efficiency: TD > OI, mTBI, d = 0.80 to 0.96; characteristic path length: TD < OI, mTBI, d = −0.75 to −0.90) and in the superior and middle orbital frontal gyrus, paracentral lobule, insula, and thalamus (clustering coefficient: TD > OI, mTBI, d = 0.66 to 0.68). Both mTBI and OI demonstrated reduced global and regional network efficiency and segregation as compared to TD children. Findings suggest a general effect of childhood injury that could reflect pre‐ and postinjury factors that can alter brain structure. An OI group provides a more conservative comparison group than TD children for structural neuroimaging research in pediatric mTBI.
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Affiliation(s)
- Ashley L Ware
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Bryce Geeraert
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Xiangyu Long
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Miriam H Beauchamp
- Department of Psychology, University of Montreal & CHU Sainte-Justine Hospital Research Center, Montreal, Quebec, Canada
| | - William Craig
- University of Alberta and Stollery Children's Hospital, Edmonton, Alberta, Canada
| | - Quynh Doan
- Pediatric Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stephen B Freedman
- Department of Emergency Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Bradley G Goodyear
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Roger Zemek
- Department of Pediatrics and Emergency Medicine, University of Ottawa, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Catherine Lebel
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
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30
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Yuan W, Diekfuss JA, Barber Foss KD, Dudley JA, Leach JL, Narad ME, DiCesare CA, Bonnette S, Epstein JN, Logan K, Altaye M, Myer GD. High School Sports-Related Concussion and the Effect of a Jugular Vein Compression Collar: A Prospective Longitudinal Investigation of Neuroimaging and Neurofunctional Outcomes. J Neurotrauma 2021; 38:2811-2821. [PMID: 34375130 DOI: 10.1089/neu.2021.0141] [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] [Indexed: 11/13/2022] Open
Abstract
Sports-related concussion (SRC) can exert serious acute and long-term consequences on brain microstructure, function, and behavioral outcomes. We aimed to quantify the alterations in white matter (WM) microstructure and global network organization, and the decrements in behavioral and cognitive outcomes from pre-season to post-concussion in youth athletes who experienced SRC. We also aimed to evaluate whether wearing a jugular compression neck collar, a device designed to mitigate brain "slosh" injury, would mitigate the pre-season to post-concussion alterations in neuroimaging, behavioral, and cognitive outcomes. A total of 488 high school football and soccer athletes (14-18 years old) were prospectively enrolled and assigned to the non-collar group (n = 237) or the collar group (n = 251). The outcomes of the study were the pre-season to post-concussion neuroimaging, behavioral, and cognitive alterations. Forty-six participants (non-collar: n = 24; collar: n = 22) were diagnosed with a SRC during the season. Forty of these 46 athletes (non-collar: n = 20; collar: n = 20) completed neuroimaging assessment. Significant pre-season to post-concussion alterations in WM microstructural integrity and brain network organization were found in these athletes (corrected p < 0.05). The alterations were significantly reduced in collar-wearing athletes compared to non-collar-wearing athletes (corrected p < 0.05). Concussion and collar main effects were identified for some of the behavioral and cognitive outcomes, but no collar by SRC interaction effects were observed in any outcomes. In summary, young athletes exhibited significant WM microstructural and network organizational, and cognitive alterations following SRC. The use of the jugular vein compression collar showed promising evidence to reduce these alterations in high school contact sport athletes.
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Affiliation(s)
- Weihong Yuan
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Jed A Diekfuss
- Emory Sports Performance and Research Center, Flowery Branch, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Kim D Barber Foss
- Emory Sports Performance and Research Center, Flowery Branch, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Jonathan A Dudley
- Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - James L Leach
- Division of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Megan E Narad
- Division of Behavioral Medicine & Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Christopher A DiCesare
- Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Scott Bonnette
- Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Jeffery N Epstein
- Division of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Division of Behavioral Medicine & Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Kelsey Logan
- Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Department of Internal Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Mekibib Altaye
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Gregory D Myer
- Emory Sports Performance and Research Center, Flowery Branch, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
- Emory Sports Medicine Center, Atlanta, Georgia, USA
- The Micheli Center for Sports Injury Prevention, Waltham, Massachusetts, USA
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31
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Moody JF, Adluru N, Alexander AL, Field AS. The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI. Semin Ultrasound CT MR 2021; 42:507-522. [PMID: 34537118 DOI: 10.1053/j.sult.2021.07.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.
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Affiliation(s)
- Jason F Moody
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI; Department of Radiology, University of Wisconsin-Madison, Madison, WI
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Aaron S Field
- Department of Radiology, University of Wisconsin-Madison, Madison, WI.
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32
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Thanjavur K, Babul A, Foran B, Bielecki M, Gilchrist A, Hristopulos DT, Brucar LR, Virji-Babul N. Recurrent neural network-based acute concussion classifier using raw resting state EEG data. Sci Rep 2021; 11:12353. [PMID: 34117309 PMCID: PMC8196170 DOI: 10.1038/s41598-021-91614-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 05/24/2021] [Indexed: 02/05/2023] Open
Abstract
Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.
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Affiliation(s)
- Karun Thanjavur
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, V8P 5C2, Canada.
| | - Arif Babul
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, V8P 5C2, Canada
| | - Brandon Foran
- Department of Computer Science, Middlesex College, Western University, London, ON, N6A 5B7, Canada
| | - Maya Bielecki
- Department of Computer Science, Middlesex College, Western University, London, ON, N6A 5B7, Canada
| | - Adam Gilchrist
- Department of Computer Science, Middlesex College, Western University, London, ON, N6A 5B7, Canada
| | - Dionissios T Hristopulos
- School of Electrical and Computer Engineering, Technical University of Crete, 73100, Chania, Greece
| | - Leyla R Brucar
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Naznin Virji-Babul
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
- Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
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33
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Messaritaki E, Foley S, Schiavi S, Magazzini L, Routley B, Jones DK, Singh KD. Predicting MEG resting-state functional connectivity from microstructural information. Netw Neurosci 2021; 5:477-504. [PMID: 34189374 PMCID: PMC8233113 DOI: 10.1162/netn_a_00187] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/01/2021] [Indexed: 12/18/2022] Open
Abstract
Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.
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Affiliation(s)
- Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Lorenzo Magazzini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Bethany Routley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, UK
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34
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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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Wang S, Gan S, Yang X, Li T, Xiong F, Jia X, Sun Y, Liu J, Zhang M, Bai L. Decoupling of structural and functional connectivity in hubs and cognitive impairment after mild traumatic brain injury. Brain Connect 2021; 11:745-758. [PMID: 33605188 DOI: 10.1089/brain.2020.0852] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION Mild traumatic brain injury (mild TBI) exhibited abnormal brain network topologies associated with cognitive dysfunction. However, it was still unclear which aspects of network organization were critical underlying the key pathology of mild TBI. Here, a multi-imaging strategy was applied to capture dynamic topological features of both structural and functional connectivity networks (SCN and FCN), to provide more sensitive detection of altered FCN from its anatomical backbone and identify novel biomarkers of mild TBI outcomes. METHODS 62 mild TBI patients (30 subjects as an original sample with 3-12 months follow-up, 32 subjects as independent replicated sample), and 37 healthy controls were recruited. Both diffusion tensor imaging (DTI) and resting-state fMRI were used to create global connectivity matrices in the same individuals. Global and regional network analyses were applied to identify group differences and correlations with clinical assessments. RESULTS Most global network properties were conserved in both SCNs and FCNs in subacute mild TBI, whereas SCNs presented decreased global efficiency and characteristic path length at follow-up. Specifically, some hubs in healthy brain networks typically became non-hubs in patients and vice versa, such as the medial prefrontal cortex, superior temporal gyrus, middle frontal gyrus. The relationship between structural and functional connectivity (SC and FC) in patients also showed salient decoupling as a function of time, primarily located in the hubs. CONCLUSIONS These results suggested mild TBI influences the relationship between SCN and FCN, and the SC-FC coupling strength may be used as a potential biomarker to predict long-term outcomes after injury.
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Affiliation(s)
- Shan Wang
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xianning Road, Xi'an, China, 710049;
| | - Shuoqiu Gan
- Xi'an Jiaotong University Medical College First Affiliated Hospital, 162798, Department of Medical Imaging, Xi'an, Shaanxi, China;
| | - Xuefei Yang
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
| | - Tianhui Li
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
| | - Feng Xiong
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
| | - Xiaoyan Jia
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
| | - Yingxiang Sun
- Xi'an Jiaotong University Medical College First Affiliated Hospital, 162798, Department of Medical Imaging, Xi'an, Shaanxi, China;
| | - Jun Liu
- Xiangya Hospital Central South University, 159374, Department of Radiology, Changsha, Hunan, China;
| | - Ming Zhang
- Xi'an Jiaotong University Medical College First Affiliated Hospital, 162798, Department of Medical Imaging, Xi'an, Shaanxi, China;
| | - Lijun Bai
- Xi'an Jiaotong University, 12480, Department of Biomedical Engineering, Xi'an, Shaanxi, China;
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36
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Fleck DE, Ernest N, Asch R, Adler CM, Cohen K, Yuan W, Kunkel B, Krikorian R, Wade SL, Babcock L. Predicting Post-Concussion Symptom Recovery in Adolescents Using a Novel Artificial Intelligence. J Neurotrauma 2020; 38:830-836. [PMID: 33115345 DOI: 10.1089/neu.2020.7018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
This pilot study explores the possibility of predicting post-concussion symptom recovery at one week post-injury using only objective diffusion tensor imaging (DTI) data inputs to a novel artificial intelligence (AI) system composed of Genetic Fuzzy Trees (GFT). Forty-three adolescents age 11 to 16 years with either mild traumatic brain injury or traumatic orthopedic injury were enrolled on presentation to the emergency department. Participants received a DTI scan three days post-injury, and their symptoms were assessed by the Post-Concussion Symptom Scale (PCSS) at 6 h and one week post-injury. The GFT system was trained using one-week total PCSS scores, 48 volumetric magnetic resonance imaging inputs, and 192 DTI inputs per participant over 225 training runs. Each training run contained a randomly selected 80% of the total sample followed by a 20% validation run. Over a different randomly selected sample distribution, GFT was also compared with six common classification methods. The cascading GFT structure controlled an effectively infinite solution space that classified participants as recovered or not recovered significantly better than chance. It demonstrated 100% and 62% classification accuracy in training and validation, respectively, better than any of the six comparison methods. Recovery sensitivity and specificity were 59% and 65% in the GFT validation set, respectively. These results provide initial evidence for the effectiveness of a GFT system to make clinical predictions of trauma symptom recovery using objective brain measures. Although clinical and research applications will necessitate additional optimization of the system, these results highlight the future promise of AI in acute care.
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Affiliation(s)
- David E Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | | | - Ruth Asch
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Caleb M Adler
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Kelly Cohen
- Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati College of Engineering and Applied Science, Cincinnati, Ohio, USA
| | - Weihong Yuan
- Imaging Research Center, Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | | | - Robert Krikorian
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Shari L Wade
- Divisions of Emergency Medicine, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Lynn Babcock
- Divisions of Physical Medicine and Rehabilitation, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
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Chen M, Li H, Wang J, Yuan W, Altaye M, Parikh NA, He L. Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks. Front Neurosci 2020; 14:858. [PMID: 33041749 PMCID: PMC7530168 DOI: 10.3389/fnins.2020.00858] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 07/23/2020] [Indexed: 12/22/2022] Open
Abstract
Up to 40% of very preterm infants (≤32 weeks’ gestational age) were identified with a cognitive deficit at 2 years of age. Yet, accurate clinical diagnosis of cognitive deficit cannot be made until early childhood around 3–5 years of age. Recently, brain structural connectome that was constructed by advanced diffusion tensor imaging (DTI) technique has been playing an important role in understanding human cognitive functions. However, available annotated neuroimaging datasets with clinical and outcome information are usually limited and expensive to enlarge in the very preterm infants’ studies. These challenges hinder the development of neonatal prognostic tools for early prediction of cognitive deficit in very preterm infants. In this study, we considered the brain structural connectome as a 2D image and applied established deep convolutional neural networks to learn the spatial and topological information of the brain connectome. Furthermore, the transfer learning technique was utilized to mitigate the issue of insufficient training data. As such, we developed a transfer learning enhanced convolutional neural network (TL-CNN) model for early prediction of cognitive assessment at 2 years of age in very preterm infants using brain structural connectome. A total of 110 very preterm infants were enrolled in this work. Brain structural connectome was constructed using DTI images scanned at term-equivalent age. Bayley III cognitive assessments were conducted at 2 years of corrected age. We applied the proposed model to both cognitive deficit classification and continuous cognitive score prediction tasks. The results demonstrated that TL-CNN achieved improved performance compared to multiple peer models. Finally, we identified the brain regions most discriminative to the cognitive deficit. The results suggest that deep learning models may facilitate early prediction of later neurodevelopmental outcomes in very preterm infants at term-equivalent age.
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Affiliation(s)
- Ming Chen
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, United States
| | - Hailong Li
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Jinghua Wang
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Weihong Yuan
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Mekbib Altaye
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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38
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Rausa VC, Shapiro J, Seal ML, Davis GA, Anderson V, Babl FE, Veal R, Parkin G, Ryan NP, Takagi M. Neuroimaging in paediatric mild traumatic brain injury: a systematic review. Neurosci Biobehav Rev 2020; 118:643-653. [PMID: 32905817 DOI: 10.1016/j.neubiorev.2020.08.017] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 08/02/2020] [Accepted: 08/29/2020] [Indexed: 01/05/2023]
Abstract
Neuroimaging is being increasingly applied to the study of paediatric mild traumatic brain injury (mTBI) to uncover the neurobiological correlates of delayed recovery post-injury. The aims of this systematic review were to: (i) evaluate the neuroimaging research investigating neuropathology post-mTBI in children and adolescents from 0-18 years, (ii) assess the relationship between advanced neuroimaging abnormalities and PCS in children, (iii) assess the quality of the evidence by evaluating study methodology and reporting against best practice guidelines, and (iv) provide directions for future research. A literature search of MEDLINE, PsycINFO, EMBASE, and PubMed was conducted. Abstracts and titles were screened, followed by full review of remaining articles where specific eligibility criteria were applied. This systematic review identified 58 imaging studies which met criteria. Based on several factors including methodological heterogeneity and relatively small sample sizes, the literature currently provides insufficient evidence to draw meaningful conclusions about the relationship between MRI findings and clinical outcomes. Future research is needed which incorporates prospective, longitudinal designs, minimises potential confounds and utilises multimodal imaging techniques.
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Affiliation(s)
- Vanessa C Rausa
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Jesse Shapiro
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia; Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia.
| | - Marc L Seal
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia.
| | - Gavin A Davis
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Vicki Anderson
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia; Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia; Psychology Service, The Royal Children's Hospital, Melbourne, Australia.
| | - Franz E Babl
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia; Emergency Department, Royal Children's Hospital, Melbourne, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia.
| | - Ryan Veal
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Georgia Parkin
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Nicholas P Ryan
- Department of Paediatrics, University of Melbourne, Victoria, Australia; Cognitive Neuroscience Unit, Deakin University, Geelong, Australia.
| | - Michael Takagi
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia; Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia.
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39
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Meningher I, Bernstein-Eliav M, Rubovitch V, Pick CG, Tavor I. Alterations in Network Connectivity after Traumatic Brain Injury in Mice. J Neurotrauma 2020; 37:2169-2179. [PMID: 32434427 DOI: 10.1089/neu.2020.7063] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Victims of mild traumatic brain injury (mTBI) usually do not display clear morphological brain defects, but frequently have long-lasting cognitive deficits, emotional difficulties, and behavioral disturbances. In the present study we used diffusion magnetic resonance imaging (dMRI) combined with graph theory measurements to investigate the effects of mTBI on brain network connectivity. We employed a non-invasive closed-head weight-drop mouse model to produce mTBI. Mice were scanned at two time points, 24 h before the injury and either 7 or 30 days following the injury. Connectivity matrices were computed for each animal at each time point, and these were subsequently used to extract graph theory measures reflecting network integration and segregation, on both the global (i.e., whole brain) and local (i.e., single regions) levels. We found that cluster coefficient, reflecting network segregation, decreased 7 days post-injury and then returned to baseline level 30 days following the injury. Global efficiency, reflecting network integration, demonstrated opposite patterns in the left and right hemispheres, with an increase of right hemisphere efficiency at 7 days and then a decrease in efficiency following 30 days, and vice versa in the left hemisphere. These findings suggest a possible compensation mechanism acting to moderate the influence of mTBI on the global network. Moreover, these results highlight the importance of tracking the dynamic changes in mTBI over time, and the potential of structural connectivity as a promising approach for studying network integrity and pathology progression in mTBI.
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Affiliation(s)
- Inbar Meningher
- Department of Anatomy and Anthropology, Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Michal Bernstein-Eliav
- Department of Anatomy and Anthropology, Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Vardit Rubovitch
- Department of Anatomy and Anthropology, Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Chaim G Pick
- Department of Anatomy and Anthropology, Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel.,Dr. Miriam and Sheldon G. Adelson Chair and Center for the Biology of Addictive Diseases, Tel-Aviv University, Tel-Aviv, Israel
| | - Ido Tavor
- Department of Anatomy and Anthropology, Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
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40
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Raizman R, Tavor I, Biegon A, Harnof S, Hoffmann C, Tsarfaty G, Fruchter E, Tatsa-Laur L, Weiser M, Livny A. Traumatic Brain Injury Severity in a Network Perspective: A Diffusion MRI Based Connectome Study. Sci Rep 2020; 10:9121. [PMID: 32499553 PMCID: PMC7272462 DOI: 10.1038/s41598-020-65948-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 05/11/2020] [Indexed: 11/08/2022] Open
Abstract
Traumatic brain injury (TBI) is often characterized by alterations in brain connectivity. We explored connectivity alterations from a network perspective, using graph theory, and examined whether injury severity affected structural connectivity and modulated the association between brain connectivity and cognitive deficits post-TBI. We performed diffusion imaging network analysis on chronic TBI patients, with different injury severities and healthy subjects. From both global and local perspectives, we found an effect of injury severity on network strength. In addition, regions which were considered as hubs differed between groups. Further exploration of graph measures in the determined hub regions showed that efficiency of six regions differed between groups. An association between reduced efficiency in the precuneus and nonverbal abstract reasoning deficits (calculated using actual pre-injury scores) was found in the controls but was lost in TBI patients. Our results suggest that disconnection of network hubs led to a less efficient network, which in turn may have contributed to the cognitive impairments manifested in TBI patients. We conclude that injury severity modulates the disruption of network organization, reflecting a "dose response" relationship and emphasize the role of efficiency as an important diagnostic tool to detect subtle brain injury specifically in mild TBI patients.
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Affiliation(s)
- Reut Raizman
- Division of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Ido Tavor
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Sagol School of neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Anat Biegon
- Department of Radiology and Neurology, Stony Brook University School of Medicine, Stony Brook, NY, USA
| | - Sagi Harnof
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Neurosurgery, Rabin Medical Center, Belinson, Israel
| | - Chen Hoffmann
- Division of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Galia Tsarfaty
- Division of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Eyal Fruchter
- Department of Mental Health, Israel Defense Forces, Medical Corps, Tel Hashomer, Israel
| | - Lucian Tatsa-Laur
- Department of Mental Health, Israel Defense Forces, Medical Corps, Tel Hashomer, Israel
| | - Mark Weiser
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Psychiatry, Sheba Medical Center, Tel Hashomer, Israel
| | - Abigail Livny
- Division of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Israel.
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel Hashomer, Israel.
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41
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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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42
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Cox E, Bells S, Timmons BW, Laughlin S, Bouffet E, de Medeiros C, Beera K, Harasym D, Mabbott DJ. A controlled clinical crossover trial of exercise training to improve cognition and neural communication in pediatric brain tumor survivors. Clin Neurophysiol 2020; 131:1533-1547. [PMID: 32403066 DOI: 10.1016/j.clinph.2020.03.027] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Revised: 12/10/2019] [Accepted: 03/21/2020] [Indexed: 01/01/2023]
Abstract
OBJECTIVE To assess the efficacy of aerobic exercise training to improve controlled attention, information processing speed and neural communication during increasing task load and rest in pediatric brain tumor survivors (PBTS) treated with cranial radiation. METHODS Participants completed visual-motor Go and Go/No-Go tasks during magnetoencephalography recording prior to and following the completion of 12-weeks of exercise training. Exercise-related changes in response accuracy and visual-motor latency were evaluated with Linear Mixed models. The Phase Lag Index (PLI) was used to estimate functional connectivity during task performance and rest. Changes in PLI values after exercise training were assessed using Partial Least Squares analysis. RESULTS Exercise training predicted sustained (12-weeks) improvement in response accuracy (p<0.05) during No-Go trials. Altered functional connectivity was detected in theta (4-7Hz) alpha (8-12Hz) and high gamma (60-100Hz) frequency bands (p<0.001) during Go and Go/No-Go trials. Significant changes in response latency and resting state connectivity were not detected. CONCLUSION These findings support the efficacy of aerobic exercise to improve controlled attention and enhance functional mechanisms under increasing task load in participants. SIGNIFICANCE It may be possible to harness the beneficial effects of exercise as therapy to promote cognitive recovery and enhance brain function in PBTS.
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Affiliation(s)
- Elizabeth Cox
- Neurosciences & Mental Health, SickKids, 686 Bay Street, Toronto, ON M5G 0A4, Canada; Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada.
| | - Sonya Bells
- Neurosciences & Mental Health, SickKids, 686 Bay Street, Toronto, ON M5G 0A4, Canada.
| | - Brian W Timmons
- Department of Pediatrics, McMaster University, 1200 Main Street W., Hamilton, ON L8N 3Z5, Canada.
| | - Suzanne Laughlin
- Diagnostic Imaging, SickKids, 555 University Avenue, Toronto, ON M5G 1X8, Canada.
| | - Eric Bouffet
- Neurosciences & Mental Health, SickKids, 686 Bay Street, Toronto, ON M5G 0A4, Canada.
| | - Cynthia de Medeiros
- Neurosciences & Mental Health, SickKids, 686 Bay Street, Toronto, ON M5G 0A4, Canada.
| | - Kiran Beera
- Neurosciences & Mental Health, SickKids, 686 Bay Street, Toronto, ON M5G 0A4, Canada.
| | - Diana Harasym
- Neurosciences & Mental Health, SickKids, 686 Bay Street, Toronto, ON M5G 0A4, Canada.
| | - Donald J Mabbott
- Neurosciences & Mental Health, SickKids, 686 Bay Street, Toronto, ON M5G 0A4, Canada; Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada.
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43
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Ware AL, Shukla A, Goodrich-Hunsaker NJ, Lebel C, Wilde EA, Abildskov TJ, Bigler ED, Cohen DM, Mihalov LK, Bacevice A, Bangert BA, Taylor HG, Yeates KO. Post-acute white matter microstructure predicts post-acute and chronic post-concussive symptom severity following mild traumatic brain injury in children. Neuroimage Clin 2019; 25:102106. [PMID: 31896466 PMCID: PMC6940617 DOI: 10.1016/j.nicl.2019.102106] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/15/2019] [Accepted: 11/19/2019] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Mild traumatic brain injury (TBI) is a global public health concern that affects millions of children annually. Mild TBI tends to result in subtle and diffuse alterations in brain tissue, which challenges accurate clinical detection and prognostication. Diffusion tensor imaging (DTI) holds promise as a diagnostic and prognostic tool, but little research has examined DTI in post-acute mild TBI. The current study compared post-acute white matter microstructure in children with mild TBI versus those with mild orthopedic injury (OI), and examined whether post-acute DTI metrics can predict post-acute and chronic post-concussive symptoms (PCS). MATERIALS AND METHODS Children aged 8-16.99 years with mild TBI (n = 132) or OI (n = 69) were recruited at emergency department visits to two children's hospitals, during which parents rated children's pre-injury symptoms retrospectively. Children completed a post-acute (<2 weeks post-injury) assessment, which included a 3T MRI, and 3- and 6-month post-injury assessments. Parents and children rated PCS at each assessment. Mean diffusivity (MD) and fractional anisotropy (FA) were derived from diffusion-weighted MRI using Automatic Fiber Quantification software. Multiple multivariable linear and negative binomial regression models were used to test study aims, with False Discovery Rate (FDR) correction for multiple comparisons. RESULTS No significant group differences were found in any of the 20 white matter tracts after FDR correction. DTI metrics varied by age and sex, and site was a significant covariate. No interactions involving group, age, and sex were significant. DTI metrics in several tracts robustly predicted PCS ratings at 3- and 6-months post-injury, but only corpus callosum genu MD was significantly associated with post-acute PCS after FDR correction. Significant group by DTI metric interactions on chronic PCS ratings indicated that left cingulum hippocampus and thalamic radiation MD was positively associated with 3-month PCS in the OI group, but not in the mild TBI group. CONCLUSIONS Post-acute white matter microstructure did not differ for children with mild TBI versus OI after correcting for multiple comparisons, but was predictive of post-acute and chronic PCS in both injury groups. These findings support the potential prognostic utility of this advanced DTI technique.
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Affiliation(s)
- Ashley L Ware
- Department of Psychology, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada.
| | - Ayushi Shukla
- Hotchkiss Brain Institute, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada
| | - Naomi J Goodrich-Hunsaker
- Department of Neurology, University of Utah, USA; Department of Psychology, Brigham Young University, USA
| | - Catherine Lebel
- Hotchkiss Brain Institute, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada
| | | | | | - Erin D Bigler
- Department of Neurology, University of Utah, USA; Department of Psychology, Brigham Young University, USA
| | - Daniel M Cohen
- Abigail Wexner Research Institute at Nationwide Children's Hospital, USA; Department of Pediatrics, The Ohio State University, USA
| | - Leslie K Mihalov
- Abigail Wexner Research Institute at Nationwide Children's Hospital, USA; Department of Pediatrics, The Ohio State University, USA
| | - Ann Bacevice
- Department of Pediatrics, Case Western Reserve University, USA
| | | | - H Gerry Taylor
- Abigail Wexner Research Institute at Nationwide Children's Hospital, USA
| | - Keith O Yeates
- Department of Psychology, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada
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44
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Hristopulos DT, Babul A, Babul S, Brucar LR, Virji-Babul N. Disrupted Information Flow in Resting-State in Adolescents With Sports Related Concussion. Front Hum Neurosci 2019; 13:419. [PMID: 31920584 PMCID: PMC6920175 DOI: 10.3389/fnhum.2019.00419] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 11/12/2019] [Indexed: 11/30/2022] Open
Abstract
Children and youths are at a greater risk of concussions than adults, and once injured, take longer to recover. A key feature of concussion is an increase in functional connectivity, yet it remains unclear how changes in functional connectivity relate to the patterns of information flow within resting state networks following concussion and how these relate to brain function. We applied a data-driven measure of directed effective brain connectivity to compare the patterns of information flow in healthy adolescents and adolescents with subacute concussion during the resting state condition. Data from 32 healthy adolescents (mean age =16 years) and 21 concussed adolescents (mean age = 15 years) within 1 week of injury were included in the study. Five minutes of resting state data EEG were collected while participants sat quietly with their eyes closed. We applied the information flow rate to measure the transfer of information between the EEG time series of each individual at different source locations, and therefore between different brain regions. Based on the ensemble means of the magnitude of normalized information flow rate, our analysis shows that the dominant nexus of information flow in healthy adolescents is primarily left lateralized and anterior-centric, characterized by strong bidirectional information exchange between the frontal regions, and between the frontal and the central/temporal regions. In contrast, adolescents with concussion show distinct differences in information flow marked by a more left-right symmetrical, albeit still primarily anterior-centric, pattern of connections, diminished activity along the central-parietal midline axis, and the emergence of inter-hemispheric connections between the left and right frontal and the left and right temporal regions of the brain. We also find that the statistical distribution of the normalized information flow rates in each group (control and concussed) is significantly different. This paper is the first to describe the characteristics of the source space information flow and the effective connectivity patterns between brain regions in healthy adolescents in juxtaposition with the altered spatial pattern of information flow in adolescents with concussion, statistically quantifying the differences in the distribution of the information flow rate between the two populations. We hypothesize that the observed changes in information flow in the concussed group indicate functional reorganization of resting state networks in response to brain injury.
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Affiliation(s)
- Dionissios T Hristopulos
- Telecommunication Systems Research Institute, Technical University of Crete, Chania, Greece.,School of Mineral Resources Engineering, Technical University of Crete, Chania, Greece
| | - Arif Babul
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada
| | - Shazia'Ayn Babul
- Rockefeller College, Princeton University, Princeton, NJ, United States
| | - Leyla R Brucar
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Naznin Virji-Babul
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.,Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
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45
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Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury. Sci Rep 2019; 9:18833. [PMID: 31827105 PMCID: PMC6906376 DOI: 10.1038/s41598-019-54950-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 11/20/2019] [Indexed: 12/28/2022] Open
Abstract
The diffuse nature of mild traumatic brain injury (mTBI) impacts brain white-matter pathways with potentially long-term consequences, even after initial symptoms have resolved. To understand post-mTBI recovery in adolescents, longitudinal studies are needed to determine the interplay between highly individualised recovery trajectories and ongoing development. To capture the distributed nature of mTBI and recovery, we employ connectomes to probe the brain’s structural organisation. We present a diffusion MRI study on adolescent mTBI subjects scanned one day, two weeks and one year after injury with controls. Longitudinal global network changes over time suggests an altered and more ‘diffuse’ network topology post-injury (specifically lower transitivity and global efficiency). Stratifying the connectome by its back-bone, known as the ‘rich-club’, these network changes were driven by the ‘peripheral’ local subnetwork by way of increased network density, fractional anisotropy and decreased diffusivities. This increased structural integrity of the local subnetwork may be to compensate for an injured network, or it may be robust to mTBI and is exhibiting a normal developmental trend. The rich-club also revealed lower diffusivities over time with controls, potentially indicative of longer-term structural ramifications. Our results show evolving, diffuse alterations in adolescent mTBI connectomes beginning acutely and continuing to one year.
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46
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Optimization of graph construction can significantly increase the power of structural brain network studies. Neuroimage 2019; 199:495-511. [PMID: 31176831 PMCID: PMC6693529 DOI: 10.1016/j.neuroimage.2019.05.052] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/08/2019] [Accepted: 05/19/2019] [Indexed: 12/31/2022] Open
Abstract
Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.
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Imms P, Clemente A, Cook M, D'Souza W, Wilson PH, Jones DK, Caeyenberghs K. The structural connectome in traumatic brain injury: A meta-analysis of graph metrics. Neurosci Biobehav Rev 2019; 99:128-137. [PMID: 30615935 PMCID: PMC7615245 DOI: 10.1016/j.neubiorev.2019.01.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 11/22/2018] [Accepted: 01/03/2019] [Indexed: 12/15/2022]
Abstract
Although recent structural connectivity studies of traumatic brain injury (TBI) have used graph theory to evaluate alterations in global integration and functional segregation, pooled analysis is needed to examine the robust patterns of change in graph metrics across studies. Following a systematic search, 15 studies met the inclusion criteria for review. Of these, ten studies were included in a random-effects meta-analysis of global graph metrics, and subgroup analyses examined the confounding effects of severity and time since injury. The meta-analysis revealed significantly higher values of normalised clustering coefficient (gö=ö1.445, CI=[0.512, 2.378], pö=ö0.002) and longer characteristic path length (gö=ö0.514, CI=[0.190, 0.838], pö=ö0.002) in TBI patients compared with healthy controls. Our findings suggest that the TBI structural network has shifted away from the balanced small-world network towards a regular lattice. Therefore, these graph metrics may be useful markers of neurocognitive dysfunction in TBI. We conclude that the pattern of change revealed by our analysis should be used to guide hypothesis-driven research into the role of graph metrics as diagnostic and prognostic biomarkers.
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Affiliation(s)
- Phoebe Imms
- Mary MacKillop Institute for Heatlh Research, Faculty of Health Sciences, Australian Catholic University. 115 Victoria Parade, Melbourne, VIC, 3065, Australia.
| | - Adam Clemente
- Mary MacKillop Institute for Heatlh Research, Faculty of Health Sciences, Australian Catholic University. 115 Victoria Parade, Melbourne, VIC, 3065, Australia.
| | - Mark Cook
- Department of Medicine, St. Vincent's Hospital, University of Melbourne. 41 Victoria Parade, Melbourne, VIC, 3065, Australia.
| | - Wendyl D'Souza
- Department of Medicine, St. Vincent's Hospital, University of Melbourne. 41 Victoria Parade, Melbourne, VIC, 3065, Australia.
| | - Peter H Wilson
- Mary MacKillop Institute for Heatlh Research, Faculty of Health Sciences, Australian Catholic University. 115 Victoria Parade, Melbourne, VIC, 3065, Australia.
| | - Derek K Jones
- Mary MacKillop Institute for Heatlh Research, Faculty of Health Sciences, Australian Catholic University. 115 Victoria Parade, Melbourne, VIC, 3065, Australia; Cardiff University Brain Research Imaging Centre, School of Psychology, and Neuroscience and Mental Health Research Institute, Cardiff University, Maindy Rd, Cardiff, CF24 4HQ, United Kingdom.
| | - Karen Caeyenberghs
- Mary MacKillop Institute for Heatlh Research, Faculty of Health Sciences, Australian Catholic University. 115 Victoria Parade, Melbourne, VIC, 3065, Australia.
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Iyer KK, Barlow KM, Brooks B, Ofoghi Z, Zalesky A, Cocchi L. Relating brain connectivity with persistent symptoms in pediatric concussion. Ann Clin Transl Neurol 2019; 6:954-961. [PMID: 31139693 PMCID: PMC6529928 DOI: 10.1002/acn3.764] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 02/28/2019] [Accepted: 03/01/2019] [Indexed: 12/26/2022] Open
Abstract
Persistent post‐concussion symptoms (PCS) in children following a mild traumatic brain injury (mTBI) are a growing public health concern. There is a pressing need to understand the neural underpinning of PCS. Here, we examined whole‐brain functional connectivity from resting‐state fMRI with behavioral assessments in a cohort of 110 children with mTBI. Children with mTBI and controls had similar levels of connectivity. PCS symptoms and behaviors including poor cognition and sleep were associated with connectivity within functional brain networks. The identification of a single “positive‐negative” dimension linking connectivity with behaviors enables better prognosis and stratification toward personalized therapeutic interventions.
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Affiliation(s)
- Kartik K Iyer
- Child Health Research Centre Faculty of Medicine The University of Queensland Brisbane Australia
| | - Karen M Barlow
- Child Health Research Centre Faculty of Medicine The University of Queensland Brisbane Australia.,Department of Neurology Queensland Children's Hospital Brisbane Australia.,Alberta Children's Hospital Research Institute Calgary Canada.,University of Calgary Calgary Canada
| | | | - Zahra Ofoghi
- Alberta Children's Hospital Research Institute Calgary Canada.,University of Calgary Calgary Canada
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre & Department of Biomedical Engineering The University of Melbourne Melbourne Australia
| | - Luca Cocchi
- Clinical Brain Networks group QIMR Berghofer Medical Research Institute Herston Brisbane Australia
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Watson CG, DeMaster D, Ewing-Cobbs L. Graph theory analysis of DTI tractography in children with traumatic injury. Neuroimage Clin 2019; 21:101673. [PMID: 30660661 PMCID: PMC6412099 DOI: 10.1016/j.nicl.2019.101673] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 12/13/2018] [Accepted: 01/07/2019] [Indexed: 01/02/2023]
Abstract
OBJECTIVE To evaluate brai structural connectivity in children with traumatic injury (TI) following a motor vehicle accident using graph theory analysis of DTI tractography data. METHODS DTI scans were acquired on a 3 T Philips scanner from children aged 8-15 years approximately 2 months post-injury. The TI group consisted of children with traumatic brain injury (TBI; n = 44) or extracranial injury (EI; n = 23). Healthy control children (n = 36) were included as an age-matched comparison group. A graph theory approach was applied to DTI tractography data to investigate injury-related differences in connectivity network characteristics. Group differences in structural connectivity evidenced by graph metrics including efficiency, strength, and modularity were assessed using the multi-threshold permutation correction (MTPC) and network-based statistic (NBS) methods. RESULTS At the global network level, global efficiency and mean network strength were lower, and modularity was higher, in the TBI than in the control group. Similarly, strength was lower and modularity higher when comparing the EI to the control group. At the vertex level, nodal efficiency, vertex strength, and average shortest path length were different between all pairwise comparisons of the three groups. Both nodal efficiency and vertex strength were higher in the control than in the EI group, which in turn were higher than in the TBI group. The opposite between-group relationships were seen with path length. These between-group differences were distributed throughout the brain, in both hemispheres. NBS analysis resulted in a cluster of 22 regions and 21 edges with significantly lower connectivity in the TBI group compared to controls. This cluster predominantly involves the frontal lobe and subcortical gray matter structures in both hemispheres. CONCLUSIONS Graph theory analysis of DTI tractography showed diffuse differences in structural brain network connectivity in children 2 months post-TI. Network differences were consistent with lower network integration and higher segregation in the injured groups compared to healthy controls. Findings suggest that inclusion of trauma-exposed comparison groups in studies of TBI outcome is warranted to better characterize the indirect effect of stress on brain networks.
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Affiliation(s)
- Christopher G Watson
- Dept. of Pediatrics, Children's Learning Institute, University of Texas Health Science Center at Houston, United States.
| | - Dana DeMaster
- Dept. of Pediatrics, Children's Learning Institute, University of Texas Health Science Center at Houston, United States
| | - Linda Ewing-Cobbs
- Dept. of Pediatrics, Children's Learning Institute, University of Texas Health Science Center at Houston, United States
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50
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Wang S, Hu L, Cao J, Huang W, Sun C, Zheng D, Wang Z, Gan S, Niu X, Gu C, Bai G, Ye L, Zhang D, Zhang N, Yin B, Zhang M, Bai L. Sex Differences in Abnormal Intrinsic Functional Connectivity After Acute Mild Traumatic Brain Injury. Front Neural Circuits 2018; 12:107. [PMID: 30555304 PMCID: PMC6282647 DOI: 10.3389/fncir.2018.00107] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 11/13/2018] [Indexed: 01/12/2023] Open
Abstract
Mild traumatic brain injury (TBI) is considered to induce abnormal intrinsic functional connectivity within resting-state networks (RSNs). The objective of this study was to estimate the role of sex in intrinsic functional connectivity after acute mild TBI. We recruited a cohort of 54 patients (27 males and 27 females with mild TBI within 7 days post-injury) from the emergency department (ED) and 34 age-, education-matched healthy controls (HCs; 17 males and 17 females). On the clinical scales, there were no statistically significant differences between males and females in either control group or mild TBI group. To detect whether there was abnormal sex difference on functional connectivity in RSNs, we performed independent component analysis (ICA) and a dual regression approach to investigate the between-subject voxel-wise comparisons of functional connectivity within seven selected RSNs. Compared to female patients, male patients showed increased intrinsic functional connectivity in motor network, ventral stream network, executive function network, cerebellum network and decreased connectivity in visual network. Further analysis demonstrated a positive correlation between the functional connectivity in executive function network and insomnia severity index (ISI) scores in male patients (r = 0.515, P = 0.006). The abnormality of the functional connectivity of RSNs in acute mild TBI showed the possibility of brain recombination after trauma, mainly concerning male-specific.
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Affiliation(s)
- Shan Wang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Liuxun Hu
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jieli Cao
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Wenmin Huang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Chuanzhu Sun
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Dongdong Zheng
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuonan Wang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shuoqiu Gan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.,Department of Medical Imaging, The First Affiliated Hospital of 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
| | - Chenghui Gu
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guanghui Bai
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Limei Ye
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Danbin Zhang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Nu Zhang
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Yin
- Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Lijun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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