Comparing image processing pipelines for brain MRI data and examining default mode and executive control network white matter correlates of executive function in multiple sclerosis

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2020-01-03
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Pirzada, Salina
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Abstract
Background: Multiple sclerosis is a neurodegenerative disease characterized by demyelinated lesions and axonal loss in white matter regions of the brain. Spatially normalizing brain MRI data is performed to better facilitate comparisons between individuals or groups. MS-related brain pathologies however, can compromise spatial normalization methods. This study therefore compared five normalization methods. This study then used that identified method to investigate relationships between executive function and microstructure throughout the default mode network and executive control network white matter. Methods: Using 20 MS participants and 1 healthy control, we lesion-filled each participant’s T1-weighted brain image to the Montreal Neurological Institute template using 5 normalization approaches (total: 400 normalizations). Inter-subject variability was quantified using mutual information and coefficient of variation and normalization lesion volumes were evaluated using paired sample t-tests. Using SPMCAT12 we used diffusion tensor imaging metrics, FA and MD maps from 103 participants to extract values from DMN and ECN regions via the UManitoba-Functionally-Defined Human White Matter Atlases. Executive function was assessed using the Delis-Kaplan Executive Function System Color-Word Interference Test. One-tailed Spearman correlations assessed relations between DMN and ECN white matter microstructure and individual differences in executive function. Results: SPM CAT12 with lesion filling is the most robust method for spatially normalizing MS brain imaging data. Lowest average Coefficient of Variation maps were SPM: 9.6 and 21.4 was highest for FSL. This study then found executive function scores to be significantly correlated with individual differences in white matter MD measurements from both the DMN (rho = 0.194; 96% CI = 0.0031 to 0.0347; p = 0.027) and the ECN (rho= 0.192; 95% = 0.029 to 0.345; p= 0.029), but not from global white matter (rho = 0.106; 95% CI = -0.059 to 0.0266; p = 0.147). Conclusion: This thesis: 1) compared spatial normalization methods on brain MRI data in the presence of MS lesions and identified an optimal approach for comparing quantitative structural imaging metrics, and 2) used this robust method to investigate relationships between EF and microstructure throughout the DMN and ECN WM. These findings have expanded our understanding of best-practices in MRI data analysis and cognitive functioning in MS.
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multiple sclerosis, spatial normalization, cognition, executive function, structural connectivity, functional connectivity, diffusion tensor imaging, brain networks, default mode network, executive control network
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