<b>MoCA Cognitive Deficits Associated with White Matter Changes in Huntington’s Disease</b> .xlsx

Image Acquisition All images were acquired using a 3T MRI scanner from Philips Medical Systems in Eindhoven, The Netherlands. High-resolution anatomical images were obtained using a T1-3D Fast Field Echo sequence (TR/TE = 8/3.7 ms; FOV = 256 × 256 mm²; flip angle = 8°; acquisition and reconstruction matrix = 256 × 256; isometric resolution = 1 × 1 × 1 mm³). The DTI sequences were conducted using Single-Shot Echo Planar Imaging. Thirty-three volumes of 70 axial slices were acquired (slice thickness = 2 mm, no separation), corresponding to 32 independent diffusion directions (b = 800 s/mm²) and one volume with b = 0 s/mm². The DTI sequence parameters included TR/TE = 8467/60 ms, FOV = 256 × 256 mm², an acquisition and reconstruction matrix of 128 × 128, and an isometric resolution of 2 × 2 × 2 mm³.Diffusion Tensor Analysis The DTI images were processed using the FSL Diffusion toolbox. The effects of eddy currents were corrected, and the diffusion tensor model was adjusted to generate FA and MD maps for each participant. Statistical analysis was performed using the Tract-Based Spatial Statistics (TBSS) approach.All participants’ FA images were aligned to a common registration target. A mean FA map and a thresholded skeletonized mean FA image were created (threshold = 0.2). Using the same nonlinear registration derived from the FA analysis, MD data were projected onto the skeleton before voxel-wise statistical analysis across subjects. Statistical Comparison of the Groups A two-sample t-test was conducted using FSL’s randomise function to compare the patient and healthy groups, controlling for age. To address multiple comparisons, Threshold-Free Cluster Enhancement (TFCE) was applied. Only voxels surviving family-wise error correction with p < 0.05 were considered significant. The final parametric maps were parcellated, binarized, and labeled using the white matter atlas developed at Johns Hopkins University. Cognitive Correlations of WM with MoCA and Its Subdomains Associations between white matter integrity and cognition (MoCA total score and subdomains) were assessed using ANCOVA within TBSS, including disease burden score as a nuisance variable. Post-hoc Spearman correlations were performed between peak FA/MD values and cognitive scores (p < 0.05).References Estevez-Fraga C, Scahill R, Rees G, Tabrizi SJ, Gregory S. Diffusion imaging in Huntington’s disease: comprehensive review. J Neurol Neurosurg Psychiatry 2020;92:62–9. https://doi.org/10.1136/jnnp-2020-324377.De Azevedo PC, Guimarães RP, Piccinin CC, Piovesana LG, Campos LS, Zuiani JR, et al. Cerebellar Gray Matter Alterations in Huntington Disease: A Voxel-Based Morphometry Study. The Cerebellum [Internet]. 20 de mayo de 2017;16(5-6):923-8. https://doi.org/10.1007/s12311-017-0865-6.Cauda F, Nani A, Manuello J, Premi E, Palermo S, Tatu K, et al. Brain structural alterations are distributed following functional, anatomic and genetic connectivity. Brain. 12 de septiembre de 2018;141(11):3211-32. https://doi.org/10.1093/brain/awy252.Penney JB Jr, Vonsattel J-P, Macdonald ME, Gusella JF, Myers RH. CAG repeat number governs the development rate of pathology in Huntington’s disease. Ann Neurol 1997;41:689–92. https://doi.org/10.1002/ana.410410521.Hernandez-Castillo CR, Galvez V, Mercadillo R, Diaz R, Campos-Romo A, Fernandez-Ruiz J. Extensive White Matter Alterations and Its Correlations with Ataxia Severity in SCA 2 Patients. PLoS ONE. 2015;10(8):e0135449. https://doi.org/10.1371/journal.pone.0135449.