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Kn BP, Cs A, Mohammed A, Chitta KK, To XV, Srour H, Nasrallah F. An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury. Med Biol Eng Comput 2023; 61:847-865. [PMID: 36624356 DOI: 10.1007/s11517-022-02752-4] [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: 02/07/2022] [Accepted: 12/24/2022] [Indexed: 01/11/2023]
Abstract
Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra-areas of secondary neural injury which are crucial targets for therapeutic interventions. Segmenting manually areas of ongoing changes like necrosis, edema, hematoma, and inflammation is tedious, error-prone, and biased. Using the multi-parametric MR data from a rodent model study, we demonstrate the effectiveness of an end-end deep learning global-attention-based UNet (GA-UNet) framework for automatic segmentation and quantification of TBI lesions. Longitudinal MR scans (2 h, 1, 3, 7, 14, 30, and 60 days) were performed on eight Sprague-Dawley rats after controlled cortical injury was performed. TBI lesion and sub-regions segmentation was performed using 3D-UNet and GA-UNet. Dice statistics (DSI) and Hausdorff distance were calculated to assess the performance. MR scan variations-based (bias, noise, blur, ghosting) data augmentation was performed to develop a robust model.Training/validation median DSI for U-Net was 0.9368 with T2w and MPRAGE inputs, whereas GA-UNet had 0.9537 for the same. Testing accuracies were higher for GA-UNet than U-Net with a DSI of 0.8232 for the T2w-MPRAGE inputs.Longitudinally, necrosis remained constant while oligemia and penumbra decreased, and edema appearing around day 3 which increased with time. GA-UNet shows promise for multi-contrast MR image-based segmentation/quantification of TBI in large cohort studies.
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Affiliation(s)
- Bhanu Prakash Kn
- Clinical Data Analytics & Radiomics, Cellular Image Informatics, Bioinformatics Institute, A*STAR, 30 Biopolis St Matrix, Singapore, 138671, Singapore. .,Cellular Image Informatics, Bioinformatics Institute, A*STAR Horizontal Technology Centers, Singapore, Singapore.
| | - Arvind Cs
- Clinical Data Analytics & Radiomics, Cellular Image Informatics, Bioinformatics Institute, A*STAR, 30 Biopolis St Matrix, Singapore, 138671, Singapore
| | - Abdalla Mohammed
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Krishna Kanth Chitta
- Signal and Image Processing Group, Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, A*STAR, 02-02 Helios 11, Biopolis Way, Singapore, 138667, Singapore
| | - Xuan Vinh To
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Hussein Srour
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Fatima Nasrallah
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
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Fourier Transform Infrared Imaging-A Novel Approach to Monitor Bio Molecular Changes in Subacute Mild Traumatic Brain Injury. Brain Sci 2021; 11:brainsci11070918. [PMID: 34356152 PMCID: PMC8307811 DOI: 10.3390/brainsci11070918] [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: 12/25/2020] [Revised: 06/28/2021] [Accepted: 07/02/2021] [Indexed: 11/26/2022] Open
Abstract
Traumatic brain injury (TBI) can be defined as a disorder in the function of the brain after a bump, blow, or jolt to the head, or penetrating head injury. Mild traumatic brain injury (mTBI) can cause devastating effects, such as the initiation of long-term neurodegeneration in brain tissue. In the current study, the effects of mTBI were investigated on rat brain regions; cortex (Co) and corpus callosum (CC) after 24 h (subacute trauma) by Fourier transform infrared (FTIR) imaging and immunohistochemistry (IHC). IHC studies showed the formation of amyloid-β (Aβ) plaques in the cortex brain region of mTBI rats. Moreover, staining of myelin basic protein presented the shearing of axons in CC region in the same group of animals. According to FTIR imaging results, total protein and lipid content significantly decreased in both Co and CC regions in mTBI group compared to the control. Due to this significant decrease in both lipid and protein content, remarkable consistency in lipid/protein band ratio in mTBI and control group, was observed. Significant decrease in methyl content and a significant increase in olefinic content were observed in Co and CC regions of mTBI rat brain tissues. Classification amongst distinguishable groups was performed using principal component analysis (PCA) and hierarchical clustering (HCA). This study established the prospective of FTIR imaging for assessing biochemical changes due to mTBI with high sensitivity, precision and high-resolution.
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Neural silences can be localized rapidly using noninvasive scalp EEG. Commun Biol 2021; 4:429. [PMID: 33785813 PMCID: PMC8010113 DOI: 10.1038/s42003-021-01768-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 01/28/2021] [Indexed: 02/01/2023] Open
Abstract
A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.
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Diamond BR, Donald CLM, Frau-Pascual A, Snider SB, Fischl B, Dams-O'Connor K, Edlow BL. Optimizing the accuracy of cortical volumetric analysis in traumatic brain injury. MethodsX 2020; 7:100994. [PMID: 32760659 PMCID: PMC7393399 DOI: 10.1016/j.mex.2020.100994] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 07/08/2020] [Indexed: 01/21/2023] Open
Abstract
Cortical volumetric analysis is widely used to study the anatomic basis of neurological deficits in patients with traumatic brain injury (TBI). However, patients with TBI-related lesions are often excluded from MRI analyses because cortical lesions may compromise the accuracy of reconstructed surfaces upon which volumetric measurements are based. We developed a FreeSurfer-based lesion correction method and tested its impact on cortical volume measures in 87 patients with chronic moderate-to-severe TBI. We reconstructed cortical surfaces from T1-weighted MRI scans, then manually labeled and removed vertices on the cortical surfaces where lesions caused inaccuracies. Next, we measured the surface area of lesion overlap with seven canonical brain networks and the percent volume of each network affected by lesions.The lesion correction method revealed that cortical lesions in patients with TBI are preferentially located in the limbic and default mode networks (95.7% each), with the limbic network also having the largest average surface area (4.4+/−3.7%) and percent volume affected by lesions (12.7+/−9.7%). The method has the potential to improve the accuracy of cortical volumetric measurements and permit inclusion of patients with lesioned brains in MRI analyses. The method also provides new opportunities to elucidate network-based mechanisms of neurological deficits in patients with TBI.
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Affiliation(s)
- Bram R Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
| | | | - Aina Frau-Pascual
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
| | - Samuel B Snider
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA.,Harvard-MIT Health Sciences and Technology, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA
| | - Kristen Dams-O'Connor
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
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Moore BA, Brock MS, Brager A, Collen J, LoPresti M, Mysliwiec V. Posttraumatic Stress Disorder, Traumatic Brain Injury, Sleep, and Performance in Military Personnel. Sleep Med Clin 2020; 15:87-100. [DOI: 10.1016/j.jsmc.2019.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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van der Kleij LA, De Vis JB, Restivo MC, Turtzo LC, Hendrikse J, Latour LL. Subarachnoid Hemorrhage and Cerebral Perfusion Are Associated with Brain Volume Decrease in a Cohort of Predominantly Mild Traumatic Brain Injury Patients. J Neurotrauma 2020; 37:600-607. [PMID: 31642407 PMCID: PMC7045349 DOI: 10.1089/neu.2019.6514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Biomarkers are needed to identify traumatic brain injury (TBI) patients at risk for accelerated brain volume loss and its associated functional impairment. Subarachnoid hemorrhage (SAH) has been shown to affect cerebral volume and perfusion, possibly by induction of inflammation and vasospasm. The purpose of this study was to assess the impact of SAH due to trauma on cerebral perfusion and brain volume. For this, magnetic resonance imaging (MRI) was performed <48 h and at 90 days after TBI. The <48-h scan was used to assess SAH presence and perfusion. Brain volume changes were assessed quantitatively over time. Differences in brain volume change and perfusion were compared between SAH and non-SAH patients. A linear regression analysis with clinical and imaging variables was used to identify predictors of brain volume change. All patients had a relatively good status on admission, and 83% presented with the maximum Glasgow Coma Scale (GCS) score. Brain volume decrease was greater in the 11 SAH patients (-3.2%, interquartile range [IQR] -4.8 to -1.3%) compared with the 46 non-SAH patients (-0.4%, IQR -1.8 to 0.9%; p < 0.001). Brain perfusion was not affected by SAH, but it was correlated with brain volume change (ρ = 0.39; p < 0.01). Forty-three percent of brain volume change was explained by SAH (β -0.40, p = 0.001), loss of consciousness (β -0.24, p = 0.035), and peak perfusion curve signal intensity height (0.27, p = 0.012). SAH and lower perfusion in the acute phase may identity TBI patients at increased risk for accelerated brain volume loss, in addition to loss of consciousness occurrence. Future studies should determine whether the findings apply to TBI patients with worse clinical status on admission. SAH predicts brain volume decrease independent of brain perfusion. This indicates the adverse effects of SAH extend beyond vasoconstriction, and that hypoperfusion also occurs separately from SAH.
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Affiliation(s)
- Lisa A. van der Kleij
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
- Acute Cerebrovascular Diagnostics Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
| | - Jill B. De Vis
- Acute Cerebrovascular Diagnostics Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
| | - Matthew C. Restivo
- Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - L. Christine Turtzo
- Acute Cerebrovascular Diagnostics Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
- Acute Studies Core, Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lawrence L. Latour
- Acute Cerebrovascular Diagnostics Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
- Acute Studies Core, Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland
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Bhattrai A, Irimia A, Van Horn JD. Neuroimaging of traumatic brain injury in military personnel: An overview. J Clin Neurosci 2019; 70:1-10. [PMID: 31331746 PMCID: PMC6861663 DOI: 10.1016/j.jocn.2019.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 07/04/2019] [Indexed: 02/02/2023]
Abstract
BACKGROUND The incidence of blunt-force traumatic brain injury (TBI) is especially prevalent in the military, where the emergency care admission rate has been reported to be 24.6-41.8 per 10,000 soldier-years. Given substantial advancements in modern neuroimaging techniques over the past decade in terms of structural, functional, and connectomic approaches, this mode of exploration can be viewed as best suited for understanding the underlying pathology and for providing proper intervention at effective time-points. APPROACH Here we survey neuroimaging studies of mild-to-severe TBI in military veterans with the intent to aid the field in the creation of a roadmap for clinicians and researchers whose aim is to understand TBI progression. DISCUSSION Recent advancements on the quantification of neurocognitive dysfunction, cellular dysfunction, intracranial pressure, cerebral blood flow, inflammation, post-traumatic neuropathophysiology, on blood serum biomarkers and on their correlation to neuroimaging findings are reviewed to hypothesize how they can be used in conjunction with one another. This may allow clinicians and scientists to comprehensively study TBI in military service members, leading to new treatment strategies for both currently-serving as well as veteran personnel, and to improve the study of TBI more broadly.
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Affiliation(s)
- Avnish Bhattrai
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, SHN, Los Angeles, CA 90033, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, USC Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Ave., Room 228C, Los Angeles, CA 90089-0191, USA.
| | - John Darrell Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, SHN, Los Angeles, CA 90033, USA.
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King DJ, Ellis KR, Seri S, Wood AG. A systematic review of cross-sectional differences and longitudinal changes to the morphometry of the brain following paediatric traumatic brain injury. Neuroimage Clin 2019; 23:101844. [PMID: 31075554 PMCID: PMC6510969 DOI: 10.1016/j.nicl.2019.101844] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 04/26/2019] [Accepted: 04/29/2019] [Indexed: 01/27/2023]
Abstract
Paediatric traumatic brain injury (pTBI) is a leading cause of disability for children and young adults. Children are a uniquely vulnerable group with the disease process that occurs following a pTBI interacting with the trajectory of normal brain development. Quantitative MRI post-injury has suggested a long-term, neurodegenerative effect of TBI on the morphometry of the brain, in both adult and childhood TBI. Changes to the brain beyond that of anticipated, age-dependant differences may allow us to estimate the state of the brain post-injury and produce clinically relevant predictions for long-term outcome. The current review synthesises the existing literature to assess whether, following pTBI, the morphology of the brain exhibits either i) longitudinal change and/or ii) differences compared to healthy controls and outcomes. The current literature suggests that morphometric differences from controls are apparent cross-sectionally at both acute and late-chronic timepoints post-injury, thus suggesting a non-transient effect of injury. Developmental trajectories of morphometry are altered in TBI groups compared to patients, and it is unlikely that typical maturation overcomes damage post-injury, or even 'catches up' with that of typically-developing peers. However, there is limited evidence for diverted developmental trajectories being associated with cognitive impairment post-injury. The current review also highlights the apparent challenges to the existing literature and potential methods by which these can be addressed.
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Affiliation(s)
- D J King
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - K R Ellis
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - S Seri
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - A G Wood
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK; Child Neuropsychology, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.
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Duncan D, Barisano G, Cabeen R, Sepehrband F, Garner R, Braimah A, Vespa P, Pitkänen A, Law M, Toga AW. Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients. Front Neuroinform 2018; 12:86. [PMID: 30618695 PMCID: PMC6307529 DOI: 10.3389/fninf.2018.00086] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 11/02/2018] [Indexed: 12/16/2022] Open
Abstract
Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms of the disorder, and the development of antiepileptogenic interventions could potentially prevent or cure epilepsy in many of them. However, the discovery of potential antiepileptogenic treatments and clinical validation would require a means to identify populations of patients at very high risk for epilepsy after a potential epileptogenic insult, to know when to treat and to document prevention or cure. A fundamental challenge in discovering biomarkers of epileptogenesis is that this process is likely multifactorial and crosses multiple modalities. Investigators must have access to a large number of high quality, well-curated data points and study subjects for biomarker signals to be detectable above the noise inherent in complex phenomena, such as epileptogenesis, traumatic brain injury (TBI), and conditions of data collection. Additionally, data generating and collecting sites are spread worldwide among different laboratories, clinical sites, heterogeneous data types, formats, and across multi-center preclinical trials. Before the data can even be analyzed, these data must be standardized. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a multi-center project with the overarching goal that epileptogenesis after TBI can be prevented with specific treatments. The identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies. We have been analyzing human data collected from UCLA and rat data collected from the University of Eastern Finland, both centers collecting data for EpiBioS4Rx, to identify biomarkers of epileptogenesis. Big data techniques and rigorous analysis are brought to longitudinal data collected from humans and an animal model of TBI, epilepsy, and their interaction. The prolonged continuous data streams of intracranial, cortical surface, and scalp EEG from humans and an animal model of epilepsy span months. By applying our innovative mathematical tools via supervised and unsupervised learning methods, we are able to subject a robust dataset to recently pioneered data analysis tools and visualize multivariable interactions with novel graphical methods.
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Affiliation(s)
- Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Giuseppe Barisano
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Ryan Cabeen
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Farshid Sepehrband
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Adebayo Braimah
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Paul Vespa
- Division of Neurosurgery, Department of Neurology, University of California at Los Angeles School of Medicine Los Angeles, CA, United States
| | - Asla Pitkänen
- A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland, Kuopio, Finland
| | - Meng Law
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
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Stewan Feltrin F, Zaninotto AL, Guirado VMP, Macruz F, Sakuno D, Dalaqua M, Magalhães LGA, Paiva WS, Andrade AFD, Otaduy MCG, Leite CC. Longitudinal changes in brain volumetry and cognitive functions after moderate and severe diffuse axonal injury. Brain Inj 2018; 32:1208-1217. [PMID: 30024781 DOI: 10.1080/02699052.2018.1494852] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
BACKGROUND AND OBJECTIVE Diffuse axonal injury (DAI) induces a long-term process of brain atrophy and cognitive deficits. The goal of this study was to determine whether there are correlations between brain volume loss, microhaemorrhage load (MHL) and neuropsychological performance during the first year after DAI. METHODS Twenty-four patients with moderate or severe DAI were evaluated at 2, 6 and 12 months post-injury. MHL was evaluated at 3 months, and brain volumetry was evaluated at 3, 6 and 12 months. The trail making test (TMT) was used to evaluate executive function (EF), and the Hopkins verbal learning test (HVLT) was used to evaluate episodic verbal memory (EVM) at 6 and 12 months. RESULTS There were significant white matter volume (WMV), subcortical grey matter volume and total brain volume (TBV) reductions during the study period (p < 0.05). MHL was correlated only with WMV reduction. EF and EVM were not correlated with MHL but were, in part, correlated with WMV and TBV reductions. CONCLUSIONS Our findings suggest that MHL may be a predictor of WMV reduction but cannot predict EF or EVM in DAI. Brain atrophy progresses over time, but patients showed better EF and EVM in some of the tests, which could be due to neuroplasticity.
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Affiliation(s)
- Fabrício Stewan Feltrin
- a Laboratory of Magnetic Resonance, LIM44, Department of Radiology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Ana Luiza Zaninotto
- b Division of Psychology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Vinícius M P Guirado
- c Division of Neurosurgery , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Fabiola Macruz
- a Laboratory of Magnetic Resonance, LIM44, Department of Radiology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Daniel Sakuno
- d Department of Radiology , Hospital Universitário HU-UEPG, Universidade Estadual de Ponta Grossa , Ponta Grossa , Brazil
| | - Mariana Dalaqua
- e Department of Radiology , Hospital Israelita Albert Einstein , São Paulo , Brazil
| | | | - Wellingson Silva Paiva
- c Division of Neurosurgery , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Almir Ferreira de Andrade
- c Division of Neurosurgery , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Maria C G Otaduy
- a Laboratory of Magnetic Resonance, LIM44, Department of Radiology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
| | - Claudia C Leite
- a Laboratory of Magnetic Resonance, LIM44, Department of Radiology , Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo , Sao Paulo , SP , Brazil
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Irimia A, Van Horn JD, Vespa PM. Cerebral microhemorrhages due to traumatic brain injury and their effects on the aging human brain. Neurobiol Aging 2018; 66:158-164. [PMID: 29579686 PMCID: PMC5924627 DOI: 10.1016/j.neurobiolaging.2018.02.026] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 02/24/2018] [Accepted: 02/27/2018] [Indexed: 01/08/2023]
Abstract
Although cerebral microbleeds (CMBs) are frequently associated with traumatic brain injury (TBI), their effects on clinical outcome after TBI remain controversial and poorly understood, particularly in older adults. Here we (1) highlight major challenges and opportunities associated with studying the effects of TBI-mediated CMBs; (2) review the evidence on their potential effects on cognitive and neural outcome as a function of age at injury; and (3) suggest priorities for future research on understanding the clinical implications of CMBs. Although TBI-mediated CMBs are likely distinct from those due to cerebral amyloid angiopathy or other neurodegenerative diseases, the effects of these 2 CMB types on brain function may share common features. Furthermore, in older TBI victims, the incidence of TBI-mediated CMBs may approximate that of cerebral amyloid angiopathy-related CMBs, and thus warrants detailed study. Because the alterations effected by CMBs on brain structure and function are both unique and age-dependent, it seems likely that novel, age-tailored therapeutic approaches are necessary for the adequate clinical interpretation and treatment of these ubiquitous and underappreciated TBI sequelae.
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Affiliation(s)
- Andrei Irimia
- Ethel Percy Andrus Gerontology Center, USC Leonard Davis School of Gerontology, University of Southern California, Los Angeles CA, USA.
| | - John D Van Horn
- USC Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Paul M Vespa
- Departments of Neurosurgery and Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
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12
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Ledig C, Kamnitsas K, Koikkalainen J, Posti JP, Takala RSK, Katila A, Frantzén J, Ala-Seppälä H, Kyllönen A, Maanpää HR, Tallus J, Lötjönen J, Glocker B, Tenovuo O, Rueckert D. Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging. PLoS One 2017; 12:e0188152. [PMID: 29182625 PMCID: PMC5705131 DOI: 10.1371/journal.pone.0188152] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Accepted: 11/01/2017] [Indexed: 02/02/2023] Open
Abstract
Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute- and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e.g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%).
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Affiliation(s)
- Christian Ledig
- Imperial College London, Department of Computing, London, United Kingdom
- * E-mail:
| | | | - Juha Koikkalainen
- Combinostics, Tampere, Finland
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Jussi P. Posti
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
- Division of Clinical Neurosciences, Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Riikka S. K. Takala
- Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku, Turku, Finland
| | - Ari Katila
- Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku, Turku, Finland
| | - Janek Frantzén
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
- Division of Clinical Neurosciences, Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Henna Ala-Seppälä
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Anna Kyllönen
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | | | - Jussi Tallus
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Jyrki Lötjönen
- Combinostics, Tampere, Finland
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Ben Glocker
- Imperial College London, Department of Computing, London, United Kingdom
| | - Olli Tenovuo
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
| | - Daniel Rueckert
- Imperial College London, Department of Computing, London, United Kingdom
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13
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Van Horn JD, Bhattrai A, Irimia A. Multimodal Imaging of Neurometabolic Pathology due to Traumatic Brain Injury. Trends Neurosci 2016; 40:39-59. [PMID: 27939821 DOI: 10.1016/j.tins.2016.10.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 10/21/2016] [Accepted: 10/25/2016] [Indexed: 12/28/2022]
Abstract
The impact of traumatic brain injury (TBI) involves a combination of complex biochemical processes beginning with the initial insult and lasting for days, months and even years post-trauma. These changes range from neuronal integrity losses to neurotransmitter imbalance and metabolite dysregulation, leading to the release of pro- or anti-apoptotic factors which mediate cell survival or death. Such dynamic processes affecting the brain's neurochemistry can be monitored using a variety of neuroimaging techniques, whose combined use can be particularly useful for understanding patient-specific clinical trajectories. Here, we describe how TBI changes the metabolism of essential neurochemical compounds, summarize how neuroimaging approaches facilitate the study of such alterations, and highlight promising ways in which neuroimaging can be used to investigate post-TBI changes in neurometabolism.
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Affiliation(s)
- John Darrell Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, Keck School of Medicine of USC, University of Southern California, Los Angeles, California 90033, USA.
| | - Avnish Bhattrai
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, Keck School of Medicine of USC, University of Southern California, Los Angeles, California 90033, USA
| | - Andrei Irimia
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, Keck School of Medicine of USC, University of Southern California, Los Angeles, California 90033, USA
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14
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Wang B, Prastawa M, Irimia A, Saha A, Liu W, Goh SM, Vespa PM, Van Horn JD, Gerig G. Modeling 4D Pathological Changes by Leveraging Normative Models. COMPUTER VISION AND IMAGE UNDERSTANDING : CVIU 2016; 151:3-13. [PMID: 27818606 PMCID: PMC5094466 DOI: 10.1016/j.cviu.2016.01.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
With the increasing use of efficient multimodal 3D imaging, clinicians are able to access longitudinal imaging to stage pathological diseases, to monitor the efficacy of therapeutic interventions, or to assess and quantify rehabilitation efforts. Analysis of such four-dimensional (4D) image data presenting pathologies, including disappearing and newly appearing lesions, represents a significant challenge due to the presence of complex spatio-temporal changes. Image analysis methods for such 4D image data have to include not only a concept for joint segmentation of 3D datasets to account for inherent correlations of subject-specific repeated scans but also a mechanism to account for large deformations and the destruction and formation of lesions (e.g., edema, bleeding) due to underlying physiological processes associated with damage, intervention, and recovery. In this paper, we propose a novel framework that provides a joint segmentation-registration framework to tackle the inherent problem of image registration in the presence of objects not present in all images of the time series. Our methodology models 4D changes in pathological anatomy across time and and also provides an explicit mapping of a healthy normative template to a subject's image data with pathologies. Since atlas-moderated segmentation methods cannot explain appearance and locality pathological structures that are not represented in the template atlas, the new framework provides different options for initialization via a supervised learning approach, iterative semisupervised active learning, and also transfer learning, which results in a fully automatic 4D segmentation method. We demonstrate the effectiveness of our novel approach with synthetic experiments and a 4D multimodal MRI dataset of severe traumatic brain injury (TBI), including validation via comparison to expert segmentations. However, the proposed methodology is generic in regard to different clinical applications requiring quantitative analysis of 4D imaging representing spatio-temporal changes of pathologies.
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Affiliation(s)
- Bo Wang
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Drive, Salt Lake City, UT 84112 USA
- School of Computing, University of Utah, 50 S., Central Campus Drive, Salt Lake City, UT 84112 USA
| | - Marcel Prastawa
- Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029 USA
| | - Andrei Irimia
- The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles CA 90089 USA
| | - Avishek Saha
- Yahoo Labs, 701 1st Ave, Sunnyvale, CA 94089 USA
| | - Wei Liu
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Drive, Salt Lake City, UT 84112 USA
- School of Computing, University of Utah, 50 S., Central Campus Drive, Salt Lake City, UT 84112 USA
| | - S.Y. Matthew Goh
- The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles CA 90089 USA
| | - Paul M. Vespa
- Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - John D. Van Horn
- The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles CA 90089 USA
| | - Guido Gerig
- Tandon School of Engineering, Department of Computer Science and Engineering, NYU, USA
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15
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Hernandez A, Donovan V, Grinberg YY, Obenaus A, Carson MJ. Differential detection of impact site versus rotational site injury by magnetic resonance imaging and microglial morphology in an unrestrained mild closed head injury model. J Neurochem 2016; 136 Suppl 1:18-28. [PMID: 26806371 PMCID: PMC5047732 DOI: 10.1111/jnc.13402] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 10/05/2015] [Accepted: 10/06/2015] [Indexed: 01/31/2023]
Abstract
Seventy‐five percent of all traumatic brain injuries are mild and do not cause readily visible abnormalities on routine medical imaging making it difficult to predict which individuals will develop unwanted clinical sequelae. Microglia are brain‐resident macrophages and early responders to brain insults. Their activation is associated with changes in morphology or expression of phenotypic markers including P2Y12 and major histocompatibility complex class II. Using a murine model of unrestrained mild closed head injury (mCHI), we used microglia as reporters of acute brain injury at sites of impact versus sites experiencing rotational stress 24 h post‐mCHI. Consistent with mild injury, a modest 20% reduction in P2Y12 expression was detected by quantitative real‐time PCR (qPCR) analysis but only in the impacted region of the cortex. Furthermore, neither an influx of blood‐derived immune cells nor changes in microglial expression of CD45, TREM1, TREM2, major histocompatibility complex class II or CD40 were detected. Using magnetic resonance imaging (MRI), small reductions in T2 weighted values were observed but only near the area of impact and without overt tissue damage (blood deposition, edema). Microglial morphology was quantified without cryosectioning artifacts using ScaleA2 clarified brains from CX3CR1‐green fluorescence protein (GFP) mice. The cortex rostral to the mCHI impact site receives greater rotational stress but neither MRI nor molecular markers of microglial activation showed significant changes from shams in this region. However, microglia in this rostral region did display signs of morphologic activation equivalent to that observed in severe CHI. Thus, mCHI‐triggered rotational stress is sufficient to cause injuries undetectable by routine MRI that could result in altered microglial surveillance of brain homeostasis.
Acute changes in microglial morphology reveal brain responses to unrestrained mild traumatic brain injury
In areas subjected to rotational stress distant from impact site In the absence of detectable changes in standard molecular indicators of brain damage, inflammation or microglial activation. That might result in decreased surveillance of brain function and increased susceptibility to subsequent brain insults.
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Affiliation(s)
- Alfredo Hernandez
- Center for Glial-Neuronal Interactions, University of California Riverside, School of Medicine, Riverside, California, USA.,MarcU Program, University of California Riverside, Riverside, California, USA.,Division of Biomedical Sciences, University of California Riverside, School of Medicine, Riverside, California, USA
| | - Virgina Donovan
- Center for Glial-Neuronal Interactions, University of California Riverside, School of Medicine, Riverside, California, USA.,Division of Biomedical Sciences, University of California Riverside, School of Medicine, Riverside, California, USA.,Cell Molecular and Developmental Biology Program, University of California Riverside, Riverside, California, USA.,Loma Linda University School of Medicine, Loma Linda California, Loma Linda, CA, USA
| | - Yelena Y Grinberg
- Center for Glial-Neuronal Interactions, University of California Riverside, School of Medicine, Riverside, California, USA.,Division of Biomedical Sciences, University of California Riverside, School of Medicine, Riverside, California, USA
| | - Andre Obenaus
- Center for Glial-Neuronal Interactions, University of California Riverside, School of Medicine, Riverside, California, USA.,Cell Molecular and Developmental Biology Program, University of California Riverside, Riverside, California, USA.,Loma Linda University School of Medicine, Loma Linda California, Loma Linda, CA, USA
| | - Monica J Carson
- Center for Glial-Neuronal Interactions, University of California Riverside, School of Medicine, Riverside, California, USA.,Division of Biomedical Sciences, University of California Riverside, School of Medicine, Riverside, California, USA.,Cell Molecular and Developmental Biology Program, University of California Riverside, Riverside, California, USA
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16
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Menze BH, Van Leemput K, Lashkari D, Riklin-Raviv T, Geremia E, Alberts E, Gruber P, Wegener S, Weber MA, Szekely G, Ayache N, Golland P. A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:933-46. [PMID: 26599702 PMCID: PMC4854961 DOI: 10.1109/tmi.2015.2502596] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.
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17
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Three-dimensional visualization of the distribution of melanin-concentrating hormone producing neurons in the mouse hypothalamus. J Chem Neuroanat 2016; 71:20-5. [DOI: 10.1016/j.jchemneu.2015.11.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 11/27/2015] [Accepted: 11/27/2015] [Indexed: 01/03/2023]
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18
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Ou Y, Gollub RL, Retzepi K, Reynolds N, Pienaar R, Pieper S, Murphy SN, Grant PE, Zöllei L. Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images. Neuroimage 2015; 122:246-61. [PMID: 26260429 PMCID: PMC4966541 DOI: 10.1016/j.neuroimage.2015.08.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Revised: 07/29/2015] [Accepted: 08/03/2015] [Indexed: 01/18/2023] Open
Abstract
Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect abnormalities in the developing brain. However, given the normal variation in regional ADC with myelination, detection of abnormalities is difficult when based on visual assessment. Quantitative and automated analysis of pediatric ADC maps is thus desired but requires accurate brain extraction as the first step. Currently, most existing brain extraction methods are optimized for structural T1-weighted MR images of fully myelinated brains. Due to differences in age and image contrast, these approaches do not translate well to pediatric ADC maps. To address this problem, we present a multi-atlas brain extraction framework that has 1) specificity: designed and optimized specifically for pediatric ADC maps; 2) generality: applicable to multi-platform and multi-institution data, and to subjects at various neuro-developmental stages across the first 6 years of life; 3) accuracy: highly accurate compared to expert annotations; and 4) consistency: consistently accurate regardless of sources of data and ages of subjects. We show how we achieve these goals, via optimizing major components in a multi-atlas brain extraction framework, and via developing and evaluating new criteria for its atlas ranking component. Moreover, we demonstrate that these goals can be achieved with a fixed set of atlases and a fixed set of parameters, which opens doors for our optimized framework to be used in large-scale and multi-institution neuro-developmental and clinical studies. In a pilot study, we use this framework in a dataset containing scanner-generated ADC maps from 308 pediatric patients collected during the course of routine clinical care. Our framework leads to successful quantifications of the changes in whole-brain volumes and mean ADC values across the first 6 years of life.
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Affiliation(s)
- Yangming Ou
- Psychiatric Neuroimaging, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 120 2nd Ave, Charlestown, MA 02129, USA; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA.
| | - Randy L Gollub
- Psychiatric Neuroimaging, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 120 2nd Ave, Charlestown, MA 02129, USA; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
| | - Kallirroi Retzepi
- Psychiatric Neuroimaging, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 120 2nd Ave, Charlestown, MA 02129, USA; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
| | - Nathaniel Reynolds
- Psychiatric Neuroimaging, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 120 2nd Ave, Charlestown, MA 02129, USA; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
| | - Rudolph Pienaar
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Children's Hospital Boston, Harvard Medical School, 1 Autumn St, Boston, MA 02115, USA
| | - Steve Pieper
- Isomics, Inc., 55 Kirkland St, Cambridge, MA 02138, USA
| | - Shawn N Murphy
- Research Computing, Partners HealthCare, 1 Constitution Center, Charlestown, MA 02129, USA; Laboratory of Computer Science, Massachusetts General Hospital, Harvard Medical School, 50 Staniford St, Boston, MA 02114, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Children's Hospital Boston, Harvard Medical School, 1 Autumn St, Boston, MA 02115, USA
| | - Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
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19
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: the workflows, methods, and platforms. Brain Inform 2015; 2:181-195. [PMID: 27747508 PMCID: PMC4737665 DOI: 10.1007/s40708-015-0020-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 08/20/2015] [Indexed: 12/20/2022] Open
Abstract
The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- School of IT, The University of Sydney, Sydney, Australia
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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20
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform 2015; 2:167-180. [PMID: 27747507 PMCID: PMC4737664 DOI: 10.1007/s40708-015-0019-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 08/08/2015] [Indexed: 12/20/2022] Open
Abstract
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, and the Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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21
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Irimia A, Van Horn JD. Functional neuroimaging of traumatic brain injury: advances and clinical utility. Neuropsychiatr Dis Treat 2015; 11:2355-65. [PMID: 26396520 PMCID: PMC4576900 DOI: 10.2147/ndt.s79174] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Functional deficits due to traumatic brain injury (TBI) can have significant and enduring consequences upon patients' life quality and expectancy. Although functional neuroimaging is essential for understanding TBI pathophysiology, an insufficient amount of effort has been dedicated to the task of translating functional neuroimaging findings into information with clinical utility. The purpose of this review is to summarize the use of functional neuroimaging techniques - especially functional magnetic resonance imaging, diffusion tensor imaging, positron emission tomography, magnetic resonance spectroscopy, and electroencephalography - for advancing current knowledge of TBI-related brain dysfunction and for improving the rehabilitation of TBI patients. We focus on seven core areas of functional deficits, namely consciousness, motor function, attention, memory, higher cognition, personality, and affect, and, for each of these, we summarize recent findings from neuroimaging studies which have provided substantial insight into brain function changes due to TBI. Recommendations are also provided to aid in setting the direction of future neuroimaging research and for understanding brain function changes after TBI.
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Affiliation(s)
- Andrei Irimia
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John Darrell Van Horn
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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22
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Bigler ED, Stern Y. Traumatic brain injury and reserve. HANDBOOK OF CLINICAL NEUROLOGY 2015; 128:691-710. [DOI: 10.1016/b978-0-444-63521-1.00043-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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23
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Bianchi A, Bhanu B, Obenaus A. Dynamic Low-Level Context for the Detection of Mild Traumatic Brain Injury. IEEE Trans Biomed Eng 2015; 62:145-53. [DOI: 10.1109/tbme.2014.2342653] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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24
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Robust whole-brain segmentation: application to traumatic brain injury. Med Image Anal 2014; 21:40-58. [PMID: 25596765 DOI: 10.1016/j.media.2014.12.003] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 12/14/2014] [Accepted: 12/15/2014] [Indexed: 11/23/2022]
Abstract
We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.
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25
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Goh SYM, Irimia A, Torgerson CM, Tubi MA, Real CR, Hanley DF, Martin NA, Vespa PM, Van Horn JD. Longitudinal quantification and visualization of intracerebral haemorrhage using multimodal magnetic resonance and diffusion tensor imaging. Brain Inj 2014; 29:438-45. [PMID: 25518865 DOI: 10.3109/02699052.2014.989907] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVE To demonstrate a set of approaches using diffusion tensor imaging (DTI) tractography whereby pathology-affected white matter (WM) fibres in patients with intracerebral haemorrhage (ICH) can be selectively visualized. METHODS Using structural neuroimaging and DTI volumes acquired longitudinally from three representative patients with ICH, the spatial configuration of ICH-related trauma is delineated and the WM fibre bundles intersecting each ICH lesion are identified and visualized. Both the extent of ICH lesions as well as the proportion of WM fibres intersecting the ICH pathology are quantified and compared across subjects. RESULTS This method successfully demonstrates longitudinal volumetric differences in ICH lesion load and differences across time in the percentage of fibres which intersect the primary injury. CONCLUSIONS Because neurological conditions such as intracerebral haemorrhage (ICH) frequently exhibit pathology-related effects which lead to the exertion of mechanical pressure upon surrounding tissues and, thereby, to the deformation and/or displacement of WM fibres, DTI fibre tractography is highly suitable for assessing longitudinal changes in WM fibre integrity and mechanical displacement.
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Affiliation(s)
- S Y Matthew Goh
- Institute for Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine, University of Southern California , Los Angeles, CA , USA
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26
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Irimia A, Goh SY, Torgerson CM, Vespa P, Van Horn JD. Structural and connectomic neuroimaging for the personalized study of longitudinal alterations in cortical shape, thickness and connectivity after traumatic brain injury. J Neurosurg Sci 2014; 58:129-44. [PMID: 24844173 PMCID: PMC4158854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The integration of longitudinal brain structure analysis with neurointensive care strategies continues to be a substantial difficulty facing the traumatic brain injury (TBI) research community. For patient-tailored case analysis, it remains challenging to establish how lesion profile modulates longitudinal changes in cortical structure and connectivity, as well as how these changes lead to behavioral, cognitive and neural dysfunction. Additionally, despite the clinical potential of morphometric and connectomic studies, few analytic tools are available for their study in TBI. Here we review the state of the art in structural and connectomic neuroimaging for the study of TBI and illustrate a set of recently-developed, patient-tailored approaches for the study of TBI-related brain atrophy and alterations in morphometry as well as inter-regional connectivity. The ability of such techniques to quantify how injury modulates longitudinal changes in cortical shape, structure and circuitry is highlighted. Quantitative approaches such as these can be used to assess and monitor the clinical condition and evolution of TBI victims, and can have substantial translational impact, especially when used in conjunction with measures of neuropsychological function.
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Affiliation(s)
- A Irimia
- Institute for Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine University of Southern California, Los Angeles, CA, USA -
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Glushakova OY, Johnson D, Hayes RL. Delayed Increases in Microvascular Pathology after Experimental Traumatic Brain Injury Are Associated with Prolonged Inflammation, Blood–Brain Barrier Disruption, and Progressive White Matter Damage. J Neurotrauma 2014; 31:1180-93. [DOI: 10.1089/neu.2013.3080] [Citation(s) in RCA: 151] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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Goh SYM, Irimia A, Torgerson CM, Horn JDV. Neuroinformatics challenges to the structural, connectomic, functional and electrophysiological multimodal imaging of human traumatic brain injury. Front Neuroinform 2014; 8:19. [PMID: 24616696 PMCID: PMC3935464 DOI: 10.3389/fninf.2014.00019] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Accepted: 02/11/2014] [Indexed: 01/14/2023] Open
Abstract
Throughout the past few decades, the ability to treat and rehabilitate traumatic brain injury (TBI) patients has become critically reliant upon the use of neuroimaging to acquire adequate knowledge of injury-related effects upon brain function and recovery. As a result, the need for TBI neuroimaging analysis methods has increased in recent years due to the recognition that spatiotemporal computational analyses of TBI evolution are useful for capturing the effects of TBI dynamics. At the same time, however, the advent of such methods has brought about the need to analyze, manage, and integrate TBI neuroimaging data using informatically inspired approaches which can take full advantage of their large dimensionality and informational complexity. Given this perspective, we here discuss the neuroinformatics challenges for TBI neuroimaging analysis in the context of structural, connectivity, and functional paradigms. Within each of these, the availability of a wide range of neuroimaging modalities can be leveraged to fully understand the heterogeneity of TBI pathology; consequently, large-scale computer hardware resources and next-generation processing software are often required for efficient data storage, management, and analysis of TBI neuroimaging data. However, each of these paradigms poses challenges in the context of informatics such that the ability to address them is critical for augmenting current capabilities to perform neuroimaging analysis of TBI and to improve therapeutic efficacy.
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Affiliation(s)
- S Y Matthew Goh
- Department of Neurology, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California Los Angeles, CA, USA
| | - Andrei Irimia
- Department of Neurology, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California Los Angeles, CA, USA
| | - Carinna M Torgerson
- Department of Neurology, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California Los Angeles, CA, USA
| | - John D Van Horn
- Department of Neurology, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California Los Angeles, CA, USA
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Bianchi A, Bhanu B, Donovan V, Obenaus A. Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:11-22. [PMID: 23797243 DOI: 10.1109/tmi.2013.2269317] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care.
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Lou Y, Irimia A, Vela PA, Chambers MC, Van Horn JD, Vespa PM, Tannenbaum AR. Multimodal deformable registration of traumatic brain injury MR volumes via the Bhattacharyya distance. IEEE Trans Biomed Eng 2013; 60:2511-20. [PMID: 23962986 PMCID: PMC4000558 DOI: 10.1109/tbme.2013.2259625] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
An important problem of neuroimaging data analysis for traumatic brain injury (TBI) is the task of coregistering MR volumes acquired using distinct sequences in the presence of widely variable pixel movements which are due to the presence and evolution of pathology. We are motivated by this problem to design a numerically stable registration algorithm which handles large deformations. To this end, we propose a new measure of probability distributions based on the Bhattacharyya distance, which is more stable than the widely used mutual information due to better behavior of the square root function than the logarithm at zero. Robustness is illustrated on two TBI patient datasets, each containing 12 MR modalities. We implement our method on graphics processing units (GPU) so as to meet the clinical requirement of time-efficient processing of TBI data. We find that 6 sare required to register a pair of volumes with matrix sizes of 256 × 256 × 60 on the GPU. In addition to exceptional time efficiency via its GPU implementation, this methodology provides a clinically informative method for the mapping and evaluation of anatomical changes in TBI.
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Affiliation(s)
- Yifei Lou
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Andrei Irimia
- Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA 90095 USA ()
| | - Patricio A. Vela
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA30332 USA ()
| | - Micah C. Chambers
- Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA 90095 USA ()
| | - John D. Van Horn
- Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA 90095 USA ()
| | - Paul M. Vespa
- Brain Injury Research Center, Department of Neurology and Neurosurgery, University of California, Los Angeles, CA 90095 USA ()
| | - Allen R. Tannenbaum
- Departments of Electrical and Computer and Biomedical Engineering, Boston University, Boston, MA 02215 USA ()
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Irimia A, Goh SYM, Torgerson CM, Stein NR, Chambers MC, Vespa PM, Van Horn JD. Electroencephalographic inverse localization of brain activity in acute traumatic brain injury as a guide to surgery, monitoring and treatment. Clin Neurol Neurosurg 2013; 115:2159-65. [PMID: 24011495 DOI: 10.1016/j.clineuro.2013.08.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2013] [Revised: 07/24/2013] [Accepted: 08/04/2013] [Indexed: 10/26/2022]
Abstract
OBJECTIVE To inverse-localize epileptiform cortical electrical activity recorded from severe traumatic brain injury (TBI) patients using electroencephalography (EEG). METHODS Three acute TBI cases were imaged using computed tomography (CT) and multimodal magnetic resonance imaging (MRI). Semi-automatic segmentation was performed to partition the complete TBI head into 25 distinct tissue types, including 6 tissue types accounting for pathology. Segmentations were employed to generate a finite element method model of the head, and EEG activity generators were modeled as dipolar currents distributed over the cortical surface. RESULTS We demonstrate anatomically faithful localization of EEG generators responsible for epileptiform discharges in severe TBI. By accounting for injury-related tissue conductivity changes, our work offers the most realistic implementation currently available for the inverse estimation of cortical activity in TBI. CONCLUSION Whereas standard localization techniques are available for electrical activity mapping in uninjured brains, they are rarely applied to acute TBI. Modern models of TBI-induced pathology can inform the localization of epileptogenic foci, improve surgical efficacy, contribute to the improvement of critical care monitoring and provide guidance for patient-tailored treatment. With approaches such as this, neurosurgeons and neurologists can study brain activity in acute TBI and obtain insights regarding injury effects upon brain metabolism and clinical outcome.
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Affiliation(s)
- Andrei Irimia
- The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, USA
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Forward and inverse electroencephalographic modeling in health and in acute traumatic brain injury. Clin Neurophysiol 2013; 124:2129-45. [PMID: 23746499 DOI: 10.1016/j.clinph.2013.04.336] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2013] [Revised: 04/04/2013] [Accepted: 04/17/2013] [Indexed: 11/20/2022]
Abstract
OBJECTIVE EEG source localization is demonstrated in three cases of acute traumatic brain injury (TBI) with progressive lesion loads using anatomically faithful models of the head which account for pathology. METHODS Multimodal magnetic resonance imaging (MRI) volumes were used to generate head models via the finite element method (FEM). A total of 25 tissue types-including 6 types accounting for pathology-were included. To determine the effects of TBI upon source localization accuracy, a minimum-norm operator was used to perform inverse localization and to determine the accuracy of the latter. RESULTS The importance of using a more comprehensive number of tissue types is confirmed in both health and in TBI. Pathology omission is found to cause substantial inaccuracies in EEG forward matrix calculations, with lead field sensitivity being underestimated by as much as ≈ 200% in (peri-) contusional regions when TBI-related changes are ignored. Failing to account for such conductivity changes is found to misestimate substantial localization error by up to 35 mm. CONCLUSIONS Changes in head conductivity profiles should be accounted for when performing EEG modeling in acute TBI. SIGNIFICANCE Given the challenges of inverse localization in TBI, this framework can benefit neurotrauma patients by providing useful insights on pathophysiology.
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Bigler ED, Maxwell WL. Neuropathology of mild traumatic brain injury: relationship to neuroimaging findings. Brain Imaging Behav 2012; 6:108-36. [PMID: 22434552 DOI: 10.1007/s11682-011-9145-0] [Citation(s) in RCA: 207] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Neuroimaging identified abnormalities associated with traumatic brain injury (TBI) are but gross indicators that reflect underlying trauma-induced neuropathology at the cellular level. This review examines how cellular pathology relates to neuroimaging findings with the objective of more closely relating how neuroimaging findings reveal underlying neuropathology. Throughout this review an attempt will be made to relate what is directly known from post-mortem microscopic and gross anatomical studies of TBI of all severity levels to the types of lesions and abnormalities observed in contemporary neuroimaging of TBI, with an emphasis on mild traumatic brain injury (mTBI). However, it is impossible to discuss the neuropathology of mTBI without discussing what occurs with more severe injury and viewing pathological changes on some continuum from the mildest to the most severe. Historical milestones in understanding the neuropathology of mTBI are reviewed along with implications for future directions in the examination of neuroimaging and neuropathological correlates of TBI.
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Affiliation(s)
- Erin D Bigler
- Department of Psychology, Brigham Young University, Provo, UT, USA.
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Irimia A, Wang B, Aylward SR, Prastawa MW, Pace DF, Gerig G, Hovda DA, Kikinis R, Vespa PM, Van Horn JD. Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction. NEUROIMAGE-CLINICAL 2012; 1:1-17. [PMID: 24179732 PMCID: PMC3757727 DOI: 10.1016/j.nicl.2012.08.002] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2012] [Revised: 08/14/2012] [Accepted: 08/15/2012] [Indexed: 11/01/2022]
Abstract
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.
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Key Words
- 3D, three-dimensional
- AAL, Automatic Anatomical Labeling
- ADC, apparent diffusion coefficient
- ANTS, Advanced Normalization ToolS
- BOLD, blood oxygen level dependent
- CC, corpus callosum
- CT, computed tomography
- DAI, diffuse axonal injury
- DSI, diffusion spectrum imaging
- DTI, diffusion tensor imaging
- DWI, diffusion weighted imaging
- Diffusion tensor
- FA, fractional anisotropy
- FLAIR, Fluid Attenuated Inversion Recovery
- FSE, Functional Status Examination
- GCS, Glasgow Coma Score
- GM, gray matter
- GOS, Glasgow Outcome Score
- GRE, Gradient Recalled Echo
- HARDI, high-angular-resolution diffusion imaging
- IBA, Individual Brain Atlas
- LDA, linear discriminant analysis
- MRI, magnetic resonance imaging
- MRI/fMRI
- NINDS, National Institute of Neurological Disorders and Stroke
- Neuroimaging
- Outcome measures
- PCA, principal component analysis
- PROMO, PROspective MOtion Correction
- SPM, Statistical Parametric Mapping
- SWI, Susceptibility Weighted Imaging
- TBI, traumatic brain injury
- TBSS, tract-based spatial statistics
- Trauma
- WM, white matter
- fMRI, functional magnetic resonance imaging
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Affiliation(s)
- Andrei Irimia
- Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA 90095, USA
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Computational analysis reveals increased blood deposition following repeated mild traumatic brain injury. NEUROIMAGE-CLINICAL 2012; 1:18-28. [PMID: 24179733 PMCID: PMC3757717 DOI: 10.1016/j.nicl.2012.08.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2012] [Revised: 07/12/2012] [Accepted: 08/04/2012] [Indexed: 11/22/2022]
Abstract
Mild traumatic brain injury (mTBI) has become an increasing public health concern as subsequent injuries can exacerbate existing neuropathology and result in neurological deficits. This study investigated the temporal development of cortical lesions using magnetic resonance imaging (MRI) to assess two mTBIs delivered to opposite cortical hemispheres. The controlled cortical impact model was used to produce an initial mTBI on the right cortex followed by a second injury induced on the left cortex at 3 (rmTBI 3d) or 7 (rmTBI 7d) days later. Histogram analysis was combined with a novel semi-automated computational approach to perform a voxel-wise examination of extravascular blood and edema volumes within the lesion. Examination of lesion volume 1d post last injury revealed increased tissue abnormalities within rmTBI 7d animals compared to other groups, particularly at the site of the second impact. Histogram analysis of lesion T2 values suggested increased edematous tissue within the rmTBI 3d group and elevated blood deposition in the rm TBI 7d animals. Further quantification of lesion composition for blood and edema containing voxels supported our histogram findings, with increased edema at the site of second impact in rmTBI 3d animals and elevated blood deposition in the rmTBI 7d group at the site of the first injury. Histological measurements revealed spatial overlap of regions containing blood deposition and microglial activation within the cortices of all animals. In conclusion, our findings suggest that there is a window of tissue vulnerability where a second distant mTBI, induced 7d after an initial injury, exacerbates tissue abnormalities consistent with hemorrhagic progression.
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Shenton ME, Hamoda HM, Schneiderman JS, Bouix S, Pasternak O, Rathi Y, Vu MA, Purohit MP, Helmer K, Koerte I, Lin AP, Westin CF, Kikinis R, Kubicki M, Stern RA, Zafonte R. A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav 2012; 6:137-92. [PMID: 22438191 PMCID: PMC3803157 DOI: 10.1007/s11682-012-9156-5] [Citation(s) in RCA: 605] [Impact Index Per Article: 50.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Mild traumatic brain injury (mTBI), also referred to as concussion, remains a controversial diagnosis because the brain often appears quite normal on conventional computed tomography (CT) and magnetic resonance imaging (MRI) scans. Such conventional tools, however, do not adequately depict brain injury in mTBI because they are not sensitive to detecting diffuse axonal injuries (DAI), also described as traumatic axonal injuries (TAI), the major brain injuries in mTBI. Furthermore, for the 15 to 30 % of those diagnosed with mTBI on the basis of cognitive and clinical symptoms, i.e., the "miserable minority," the cognitive and physical symptoms do not resolve following the first 3 months post-injury. Instead, they persist, and in some cases lead to long-term disability. The explanation given for these chronic symptoms, i.e., postconcussive syndrome, particularly in cases where there is no discernible radiological evidence for brain injury, has led some to posit a psychogenic origin. Such attributions are made all the easier since both posttraumatic stress disorder (PTSD) and depression are frequently co-morbid with mTBI. The challenge is thus to use neuroimaging tools that are sensitive to DAI/TAI, such as diffusion tensor imaging (DTI), in order to detect brain injuries in mTBI. Of note here, recent advances in neuroimaging techniques, such as DTI, make it possible to characterize better extant brain abnormalities in mTBI. These advances may lead to the development of biomarkers of injury, as well as to staging of reorganization and reversal of white matter changes following injury, and to the ability to track and to characterize changes in brain injury over time. Such tools will likely be used in future research to evaluate treatment efficacy, given their enhanced sensitivity to alterations in the brain. In this article we review the incidence of mTBI and the importance of characterizing this patient population using objective radiological measures. Evidence is presented for detecting brain abnormalities in mTBI based on studies that use advanced neuroimaging techniques. Taken together, these findings suggest that more sensitive neuroimaging tools improve the detection of brain abnormalities (i.e., diagnosis) in mTBI. These tools will likely also provide important information relevant to outcome (prognosis), as well as play an important role in longitudinal studies that are needed to understand the dynamic nature of brain injury in mTBI. Additionally, summary tables of MRI and DTI findings are included. We believe that the enhanced sensitivity of newer and more advanced neuroimaging techniques for identifying areas of brain damage in mTBI will be important for documenting the biological basis of postconcussive symptoms, which are likely associated with subtle brain alterations, alterations that have heretofore gone undetected due to the lack of sensitivity of earlier neuroimaging techniques. Nonetheless, it is noteworthy to point out that detecting brain abnormalities in mTBI does not mean that other disorders of a more psychogenic origin are not co-morbid with mTBI and equally important to treat. They arguably are. The controversy of psychogenic versus physiogenic, however, is not productive because the psychogenic view does not carefully consider the limitations of conventional neuroimaging techniques in detecting subtle brain injuries in mTBI, and the physiogenic view does not carefully consider the fact that PTSD and depression, and other co-morbid conditions, may be present in those suffering from mTBI. Finally, we end with a discussion of future directions in research that will lead to the improved care of patients diagnosed with mTBI.
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Affiliation(s)
- M E Shenton
- Clinical Neuroscience Laboratory, Department of Psychiatry, VA Boston Healthcare System, Brockton, MA, USA.
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Wang B, Prastawa M, Irimia A, Chambers MC, Vespa PM, Van Horn JD, Gerig G. A Patient-Specific Segmentation Framework for Longitudinal MR Images of Traumatic Brain Injury. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2012; 8314:831402. [PMID: 24465115 DOI: 10.1117/12.911043] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Robust, reproducible segmentations of MR images with TBI are crucial for quantitative analysis of recovery and treatment efficacy. However, this is a significant challenge due to severe anatomy changes caused by edema (swelling), bleeding, tissue deformation, skull fracture, and other effects related to head injury. In this paper, we introduce a multi-modal image segmentation framework for longitudinal TBI images. The framework is initialized through manual input of primary lesion sites at each time point, which are then refined by a joint approach composed of Bayesian segmentation and construction of a personalized atlas. The personalized atlas construction estimates the average of the posteriors of the Bayesian segmentation at each time point and warps the average back to each time point to provide the updated priors for Bayesian segmentation. The difference between our approach and segmenting longitudinal images independently is that we use the information from all time points to improve the segmentations. Given a manual initialization, our framework automatically segments healthy structures (white matter, grey matter, cerebrospinal fluid) as well as different lesions such as hemorrhagic lesions and edema. Our framework can handle different sets of modalities at each time point, which provides flexibility in analyzing clinical scans. We show results on three subjects with acute baseline scans and chronic follow-up scans. The results demonstrate that joint analysis of all the points yields improved segmentation compared to independent analysis of the two time points.
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Affiliation(s)
- Bo Wang
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah ; School of Computing, University of Utah, Salt Lake City, Utah
| | - Marcel Prastawa
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah ; School of Computing, University of Utah, Salt Lake City, Utah
| | - Andrei Irimia
- Laboratory of Neuro Imaging, University of California, Los Angeles, California
| | - Micah C Chambers
- Laboratory of Neuro Imaging, University of California, Los Angeles, California ; Henri Samueli School of Engineering and Applied Science, University of California, Los Angeles, California
| | - Paul M Vespa
- Brain Injury Research Center, Departments of Neurosurgery and Neurology, University of California, Los Angeles, California
| | - John D Van Horn
- Laboratory of Neuro Imaging, University of California, Los Angeles, California
| | - Guido Gerig
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah ; School of Computing, University of Utah, Salt Lake City, Utah
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Irimia A, Chambers MC, Torgerson CM, Filippou M, Hovda DA, Alger JR, Gerig G, Toga AW, Vespa PM, Kikinis R, Van Horn JD. Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury. Front Neurol 2012; 3:10. [PMID: 22363313 PMCID: PMC3275792 DOI: 10.3389/fneur.2012.00010] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2011] [Accepted: 01/16/2012] [Indexed: 01/21/2023] Open
Abstract
Available approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TBI-related pathology. In this paper, we describe a patient-tailored framework which allows mapping and characterization of TBI-related structural damage to the brain via multimodal neuroimaging and personalized connectomics. Specifically, we introduce a graphically driven approach for the assessment of trauma-related atrophy of white matter connections between cortical structures, with relevance to the quantification of TBI chronic case evolution. This approach allows one to inform the formulation of graphical neurophysiological and neuropsychological TBI profiles based on the particular structural deficits of the affected patient. In addition, it allows one to relate the findings supplied by our workflow to the existing body of research that focuses on the functional roles of the cortical structures being targeted. A graphical means for representing patient TBI status is relevant to the emerging field of personalized medicine and to the investigation of neural atrophy.
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Affiliation(s)
- Andrei Irimia
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles Los Angeles, CA, USA
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