Language Related Tract Integrity and Cognitive Function in Brain Tumor Patients: A DTI Study
Poster No:
802
Submission Type:
Abstract Submission
Authors:
Ting-Hau Huang1, Ko-Ting Chen2, Yi-Ping Chao3
Institutions:
1Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Guishan District, 2Chang Gung Memorial Hospital, Taoyuan, Guishan District, 3Chang Gung University, Taoyuan City, Taiwan
First Author:
Ting-Hau Huang
Department of Computer Science and Information Engineering, Chang Gung University
Taoyuan, Guishan District
Department of Computer Science and Information Engineering, Chang Gung University
Taoyuan, Guishan District
Co-Author(s):
Introduction:
The human brain's language neural system exhibits remarkable complexity. Brain tumors can significantly alter neural pathway trajectories through their size, shape, and location, potentially compromising language cognitive function through compression or invasion of these pathways. Our study investigates the relationship between language-related white matter tracts and language cognitive function, focusing specifically on major language pathways: the Arcuate Fasciculus (AF), Superior Longitudinal Fasciculus (SLF), and Inferior Fronto-Occipital Fasciculus (IFOF). We employ Diffusion Tensor Imaging (DTI) technology and the DSI-Studio fiber tracking software (http://dsi-studio.labsolver.org) to delineate these major language pathways and assess their structural integrity through DTI metrics. The Boston Diagnostic Aphasia Examination (BDAE) provides assessment of language cognitive function in tumor patients. By analyzing correlations between diffusion metrics and aphasia examination scores, we aim to elucidate the relationship between language performance and structural characteristics of language-related neural pathways, ultimately providing deeper insights into the functional roles of specific cerebral language pathways.
Methods:
Our study included 35 brain tumor patients who completed both BDAE and DTI examinations. All patients presented with left hemisphere tumors, spanning ages 25-76 years. Data collection utilized a 3T SIEMENS PET-MR scanner at the Linkou Chang Gung Memorial Hospital Radiology Department. We acquired 3D T1-weighted images using an inversion-recovery fast spoiled gradient-echo sequence, and DTI images comprising one non-diffusion-weighted image (b=0 sec/mm²) and 64 diffusion-encoded gradient images (b=1000 sec/mm²) along non-collinear directions.
We performed language-related white matter fiber tracking using DSI Studio software, selecting ROIs based on established studies. Figure 1 illustrates the ROI placement and resulting tractography for the AF, SLF(I, II, and III), and IFOF. The final analysis included varying numbers of intact tracts across hemispheres for each pathway studied.
Quantitative DTI metrics were extracted for each tract, including Fractional Anisotropy (FA), Axial Diffusivity (AD), Radial Diffusivity (RD), and Mean Diffusivity (MD). These metrics were subsequently analyzed using Pearson Correlation analysis against the patients' BDAE scores.
We performed language-related white matter fiber tracking using DSI Studio software, selecting ROIs based on established studies. Figure 1 illustrates the ROI placement and resulting tractography for the AF, SLF(I, II, and III), and IFOF. The final analysis included varying numbers of intact tracts across hemispheres for each pathway studied.
Quantitative DTI metrics were extracted for each tract, including Fractional Anisotropy (FA), Axial Diffusivity (AD), Radial Diffusivity (RD), and Mean Diffusivity (MD). These metrics were subsequently analyzed using Pearson Correlation analysis against the patients' BDAE scores.
Results:
The analysis showed distinct patterns of correlation between language cognitive function and specific neural tracts. The results are presented in Figure 2. Notably, picture naming response time exhibited a significant negative correlation with the FA value of the left SLF II (r = −0.705, p < 0.05), while showing significant positive correlations with its RD and MD values (r = 0.677, p < 0.05; r = 0.529, p < 0.05). Additionally, the number of correct responses for common sentences demonstrated significant positive correlations exclusively with the AD, RD, and MD values of the left SLF III (r = 0.545, p < 0.05; r = 0.509, p < 0.05; r = 0.471, p < 0.05). Finally, the use of rare sentences showed a significant positive correlation solely with the left IFOF (r = 0.606, p < 0.05). Furthermore, the left AF, Right SLF I, and Right IFOF showed no significant correlations with language cognitive function.
Conclusions:
Our findings integrate with existing studies to advance understanding of language cognitive function and white matter pathway relationships. The correlation analysis of structurally intact pathways largely aligns with theoretical predictions, though we acknowledge the limitation of excluding severely affected tracts. In future research, we plan to conduct individual diffusion analyses for each neural tract and compare diffusion distributions among corresponding tracts across subjects. This approach will facilitate a more comprehensive understanding of whether tumor-induced alterations in neural pathways lead to genuine changes in language cognitive function.
Language:
Language Comprehension and Semantics 1
Speech Production
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
Diffusion MRI 2
Keywords:
Aphasia
Data analysis
Language
Neurological
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
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