Mapping Cerebral Connectivity Changes after Mild Traumatic Brain Injury in Older Adults Using Diffusion Tensor Imaging and Riemannian Matching of Elastic Curves

Irimia, Andrei; Fan, Di; Chaudhari, Nikhil; Ngo, Van; Zhang, Fan; Joshi, Shantanu H.; O’Donnell, Lauren J.

doi:10.1109/isbi45749.2020.9098476

Cited by 7 publications

(10 citation statements)

References 8 publications

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“…For each subject, we identified white matter association tracts using a robust machine learning approach that has been shown to consistently identify white matter tracts across the full human lifespan, across health conditions including brain tumors, and across different image acquisitions ( Zhang et al, 2018b ). Furthermore, the method has high test-retest reproducibility ( Zhang et al, 2019 ) and has been employed in multiple recent studies ( Gong et al, 2018 ; Irimia et al, 2020 ; Kochsiek et al, 2021 ; Levitt et al, 2021 ). This method is implemented in the WMA package, which uses a well-established fiber clustering pipeline ( O’Donnell et al, 2012 ; O’Donnell and Westin, 2007 ) in conjunction with an anatomical white matter tract atlas ( Zhang et al, 2018b ).…”

Section: Methodsmentioning

confidence: 99%

White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project

et al. 2022

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Section: Methodsmentioning

confidence: 99%

White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project

et al. 2022

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“…This can enable the study of the tissue microstructure in local regions along the fiber pathway. This approach requires definition of a coordinate system, sampling framework, or surface-based representation to define how to sample the microstructural data of interest [510,316,40,230,96,89,78,212]. Most often, data are averaged across corresponding points along each streamline in the pathway, such that data can be analyzed (e.g., an along-tract or along-pathway plot) versus streamline arc length or other parameterization [96,89,510].…”

Section: Domain Of Analysis: Extraction Of Quantitative Measuresmentioning

confidence: 99%

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review

Zhang¹,

Daducci²,

He³

et al. 2021

Preprint

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Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections at macro scale. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

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“…Furthermore, the potential relationship between TBI and AD remains underexplored despite the epidemiological significance of both conditions. Independent investigations that used dMRI to compare the FAs of healthy controls (HCs) to those of AD patients and acute mTBI victims found significantly lower FA [4,12], which is indicative of damage, along WM tracts projecting to the hippocampi and to temporal regions in the brains of AD and mTBI patients [13,14]. Researchers have also utilized cortical thickness as a measure of gray matter (GM) atrophy and have independently observed similar spatial patterns of cortical thinning in AD and mTBI patients compared to HCs [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer’s disease

Rostowsky

Irimia

2021

GeroScience

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Traumatic brain injuries (TBIs) are often followed by persistent structural brain alterations and by cognitive sequalae, including memory deficits, reduced neural processing speed, impaired social function, and decision-making difficulties. Although mild TBI (mTBI) is a risk factor for Alzheimer's disease (AD), the extent to which these conditions share patterns of macroscale neurodegeneration has not been quantified. Comparing such patterns can not only reveal how the neurodegenerative trajectories of TBI and AD are similar, but may also identify brain atrophy features which can be leveraged to prognosticate AD risk after TBI. The primary aim of this study is to systematically map how TBI affects white matter (WM) and gray matter (GM) properties in AD-analogous patterns. Our findings identify substantial similarities in the regional macroscale neurodegeneration patterns associated with mTBI and AD. In cerebral GM, such similarities are most extensive in brain areas involved in memory and executive function, such as the temporal poles and orbitofrontal cortices, respectively. Our results indicate that the spatial pattern of cerebral WM degradation observed in AD is broadly similar to the pattern of diffuse axonal injury observed in TBI, which frequently affects WM structures like the fornix, corpus callosum, and corona radiata. Using machine learning, we find that the severity of AD-like brain changes observed during the chronic stage of mTBI can be accurately prognosticated based on acute assessments of post-traumatic mild cognitive impairment. These findings suggest that acute post-traumatic cognitive impairment predicts the magnitude of AD-like brain atrophy, which is itself associated with AD risk.

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Mapping Cerebral Connectivity Changes after Mild Traumatic Brain Injury in Older Adults Using Diffusion Tensor Imaging and Riemannian Matching of Elastic Curves

Cited by 7 publications

References 8 publications

White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project

White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review

Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer’s disease

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