Integrity of anterior corpus callosum is well related to language impairment after traumatic brain injury

AbstractBackgroundThe corpus callosum (CC) serves as the bridge that relays information between the two cerebral hemispheres, and is one of the most commonly injured areas after traumatic brain injury (TBI). This study was designed to investigate the association between the CC integrity and language function after TBI.MethodsWe retrospectively enrolled 30 patients with TBI who underwent diffusion tensor imaging and language function evaluation using the Western Aphasia Battery. The CC was divided into five segments (C1-C5) according to its projecting fibers using Hofer’s method, and fractional anisotropy (FA) values were measured using DSI studio software. The FA values of the left arcuate fasciculus and cingulum for language function and executive function, respectively, were also evaluated. Twelve healthy controls were also enrolled to compare the FA values of these tracts.ResultsThe FA values of the cingulum and left arcuate fasciculus were significantly correlated with all language scores. The FA values of the entire CC were significantly correlated with the fluency, repetition, and aphasia quotient scores. The FA values of the anterior CC segment (C1 and C2) significantly correlated with the aphasia quotient score; C1 with the fluency score; and C2 with the fluency, comprehension, and repetition scores. However, the FA values of the posterior CC (C3-C5) were not significantly correlated with any of the language subset scores.ConclusionThe language function in patients with TBI is correlated with the integrity of the white matter tracts important for language and attention processes. Moreover, disruption of the CC is common after TBI, and the anterior CC segment plays an important role in language impairment after TBI. Therefore, analyzing CC integrity using diffusion tensor imaging can help predict language impairment in patients with TBI.

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Diffusion Tensor Imaging Characteristics of the Corpus Callosum in Mild, Moderate, and Severe Traumatic Brain Injury

American Journal of Neuroradiology ◽

10.3174/ajnr.a1213 ◽

2008 ◽

Vol 29 (9) ◽

pp. 1730-1735 ◽

Cited By ~ 139

Author(s):

D.R. Rutgers ◽

P. Fillard ◽

G. Paradot ◽

M. Tadié ◽

P. Lasjaunias ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Diffusion Tensor Imaging ◽

Brain Injury ◽

Corpus Callosum ◽

Severe Traumatic Brain Injury ◽

Diffusion Tensor ◽

Imaging Characteristics

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Diffusion Tensor Imaging in the Corpus Callosum in Children after Moderate to Severe Traumatic Brain Injury

Journal of Neurotrauma ◽

10.1089/neu.2006.23.1412 ◽

2006 ◽

Vol 23 (10) ◽

pp. 1412-1426 ◽

Cited By ~ 174

Author(s):

Elisabeth A. Wilde ◽

Zili Chu ◽

Erin D. Bigler ◽

Jill V. Hunter ◽

Michael A. Fearing ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Diffusion Tensor Imaging ◽

Brain Injury ◽

Corpus Callosum ◽

Severe Traumatic Brain Injury ◽

Diffusion Tensor

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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

10.31224/osf.io/pvyrm ◽

2020 ◽

Author(s):

Andrei Irimia ◽

Di Fan ◽

Nikhil N. Chaudhari ◽

Van Ngo ◽

Fan Zhang ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Differential Geometry ◽

Diffusion Tensor Imaging ◽

Brain Injury ◽

Diffusion Tensor ◽

Brain Connectivity ◽

Arcuate Fasciculus ◽

Post Injury ◽

Elastic Curves ◽

Diffusion Tensor Imaging Dti

Although diffusion tensor imaging (DTI) can identify white matter (WM) alterations due to mild cases of traumatic brain injury (mTBI), the task of within-subject longitudinal matching of DTI streamlines remains challenging in this condition. Here we combine (A) automatic, atlas-informed labeling of WM streamline clusters with (B) streamline prototyping and (C) Riemannian matching of elastic curves to quantitate within-subject WM changes, focusing on the arcuate fasciculus. The approach is demonstrated in a group of geriatric mTBI patients imaged acutely and ~6 months post-injury. Results highlight the utility of differential geometry approaches when quantifying brain connectivity alterations due to mTBI.

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Machine Learning Classification of Traumatic Brain Injury Patients and Healthy Controls Using Multiple Indices of Diffusion Tensor Imaging

10.21203/rs.3.rs-42800/v1 ◽

2020 ◽

Author(s):

Hiba Abuelgasim Fadlelmoula Abdelrahman ◽

Shiho Ubukata ◽

Keita Ueda ◽

Gaku Fujimoto ◽

Naoya Oishi ◽

...

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Diffusion Tensor Imaging ◽

Brain Injury ◽

White Matter ◽

Diffusion Tensor ◽

Area Under The Curve ◽

Support Vector ◽

Healthy Controls

Abstract Background: Diffusion tensor imaging (DTI) indices provide quantitative measures of white matter microstructural changes following traumatic brain injury (TBI). However, there is still insufficient evidence for their use as predictive measures. Recently, there has been growing interest in using machine learning (ML) approaches to aid the diagnosis of many neurological and psychiatric illnesses including TBI. The aim of this study is to examine the potential of using multiple DTI indices in conjunction with ML to automate the classification of healthy subjects and patients with TBI across a spectrum of TBI severity.Methods: Participants were adult patients with chronic TBI (n=26) and age and gender-matched healthy controls (n=26). DTI images were obtained from all the participants. Tract-based spatial statistics (TBSS) analysis was applied to the DTI images. Classification models were built using principle component analysis (PCA) and support vector machines (SVM). Receiver operator characteristic (ROC) curve analysis and area under the curve (AUC) were used to assess the classification performance of the different classifiers.Results: The whole-brain white matter TBSS analyses showed significantly decreased FA, as well as increased MD, AD, and RD in TBI patients compared with healthy controls (all p-value < 0.01). The PCA and SVM-based ML classification using combined DTI indices classified TBI patients and healthy controls with the accuracy of 90.5% with an area under the curve (AUC) of 93 +/- 0.09.Conclusion: This study demonstrates the potential of a joint DTI and ML approach for objective classification of TBI patients and healthy controls.

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