Are MoCA subdomains deficits linked to white matter changes in early Huntington’s disease? A diffusion MRI approach

Are MoCA subdomains deficits linked to white matter changes in early Huntington’s disease? 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A diffusion MRI approach Esquiliano Ricardo, Cancino-Pérez Valeria, Fernandez-Ruiz Juan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8971620/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study explored the relationship between the Montreal Cognitive Assessment’s (MoCA) subdomains and white matter changes in Huntington's disease (HD). Fractional anisotropy (FA) and mean diffusivity (MD) were measured using Diffusion Tensor Imaging (DTI) in 22 early manifest HD patients and 20 healthy controls. Tract-Based Spatial Statistics was used to compare the two groups and correlate patients’ FA and MD values with their performance in MoCA subdomains. Among all MoCA subdomains, only visuospatial performance was associated with white matter integrity in the corpus callosum, corona radiata, and superior longitudinal fasciculus, supporting the feasibility of combining DTI analysis with screening tools to monitor non-motor symptoms. Huntington’s Disease MoCA Visuospatial White Matter fMRI Fractional Anisotropy Mean diffusivity TBSS Figures Figure 1 Figure 2 Figure 3 Introduction Huntington's disease (HD) is a genetic neurodegenerative disorder characterized by neuropsychiatric, cognitive, and motor symptoms, typically manifesting in early adulthood (Philips et al., 2013). Cognitive impairment becomes prominent as the disease progresses, especially affecting executive functions and visuospatial performance (Migliore et al., 2021 ; Hart et al., 2013 ). Although cognitive deterioration is a characteristic non-motor symptom of HD, premanifest and manifest patients exhibit different features of decline (Del Pino et al., 2025 ). While basal ganglia degeneration is the hallmark of HD neuro-pathophysiology (Liu et al., 2023 ), early changes in white matter (WM) also play a critical role. These changes result from the breakdown of myelinated fibers and axonal degeneration, particularly in the prefrontal tracts, corpus callosum (CC), and corticospinal tracts (Bourbon-Teles et al., 2019 ; Estevez-Fraga et al., 2020). Magnetic resonance imaging (MRI) studies have correlated executive functions with WM changes in patients, quantifying diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA) and mean diffusivity (MD) (Bohanna et al., 2011 ). However, cognitive deficits are often detected using extensive neuropsychological batteries, which are time-consuming and require highly trained personnel (Gluhm et al., 2013 ; Rosca & Simu, 2020). As an alternative, the Montreal Cognitive Assessment (MoCA) has emerged as a brief, easy-to-administer tool for detecting mild cognitive impairment (MCI; MoCA score < 26/30) in HD patients (Gluhm et al., 2013 ; Nasreddine et al., 2005 ). Furthermore, MoCA assesses a broad range of cognitive subdomains, facilitating research into functional and structural brain deterioration (Videnovic et al., 2010 ). For example, fMRI studies in HD patients have shown a correlation between language subdomain scores and cortical thinning in regions such as the lingual and fusiform gyri, supramarginal gyrus, and lateral orbitofrontal lobe (Sweidan et al., 2020 ). Despite these findings, the relationship between MoCA-assessed cognitive subdomains and WM changes in HD remains unclear. This study aims to explore the link between cognitive deficits and WM differences in early manifest HD by correlating MoCA subdomain scores with DTI parameters. The hypothesis is that low performance on MoCA subdomains is associated with interhemispheric and prefrontal WM changes in patients. Methods Participants Twenty-two patients with a molecular diagnosis of HD and 20 age-matched healthy controls participated in the study (Table 1 ). Patients were recruited at the National Institute of Neurology and Neurosurgery (INNN) in Mexico City. MoCA scores were grouped into five cognitive subdomains: attention/executive (digit span, target detection, serial seven subtraction, Trail Making Test B, fluency, and abstraction), visuospatial (clock drawing and three-dimensional cube copying), language (object naming and sentence repetition), memory (recall of five previously presented words), and orientation (temporal and spatial) (Gluhm et al., 2013 ). Image Acquisition All images were acquired using a 3T MRI scanner from Philips Medical Systems in Eindhoven, The Netherlands. The high-resolution anatomical images were obtained using a T1-3D Fast Field Echo sequence with the following parameters: TR/TE of 8/3.7 ms, a field of view (FOV) of 256 × 256 mm², a flip angle of 8°, an acquisition and reconstruction matrix of 256 × 256, and an isometric resolution of 1 × 1 × 1 mm³. The DTI sequences were conducted using Single-Shot Echo Planar Imaging. This involved acquiring 33 volumes of 70 axial slices (with a slice thickness of 2 mm and no separation), corresponding to each of the 32 independent diffusion directions with a b-value of 800 s/mm², along with one volume for b = 0 s/mm². The DTI sequence parameters included a TR/TE of 8467/60 ms, a FOV of 256 × 256 mm², and an acquisition and reconstruction matrix of 128 × 128, resulting in an isometric resolution of 2 × 2 × 2 mm³. Diffusion Tensor Analysis The DTI images were processed using the FSL Diffusion toolbox (Smith et al., 2007 ). First, the effects of eddy currents were corrected, and the diffusion tensor model was adjusted to generate FA and MD maps for each participant. Subsequently, statistical analysis was performed using the Tract-Based Spatial Statistics (TBSS) approach, following: 1) identification of a common registration target and alignment of all participants' FA images to this target; 2) creation of a mean FA map using the mean of all aligned FA images and of a thresholded skeletonized mean FA image (threshold = 0.2); 3) and projection of each participant's FA image onto the skeleton and voxel-wise statistical analysis across subjects on the skeleton-space FA data; 4) Using the same nonlinear registration, derived from the FA analysis, MD data were projected onto the skeleton vectors before voxel-wise statistical analysis across subjects (Smith et al., 2007 , Smith et al., 2004 ). 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, we applied the Threshold-Free Cluster Enhancement (TFCE) method (Winkler et al., 2014 ). Only voxels that survived family-wise error correction (FWE) with p < 0.05 were considered significant between the two groups. The final parametric maps were parcellated, binarized, and labeled using the WM atlas developed at Johns Hopkins University. Cognitive correlations of WM with MoCA and its subdomains The associations between WM density and the cognitive deficits in patients with HD were examined using analysis of covariance (ANCOVA) by TBSS. Six different analyses were run. In the first analysis, FSL randomise was fed with the total MoCA scores. The subsequent analyses used the scores of each cognitive subdomain. To correct for multiple comparisons, randomized permutations were employed using the TFCE method (Winkler et al., 2014 ). Only voxels that survived FWE correction with p < 0.05 were considered statistically significant. In all analyses, the disease burden score (calculated as age (years) × (CAG repeat length − 35.5)) was included as a nuisance variable (Poudel et al., 2014 ). Subsequently, the FA and MD peak values in the significant voxels were selected for each patient. As the cognitive measurements did not follow a normal distribution, as determined by the Shapiro-Wilk test (p < 0.05), Spearman correlations were conducted between the diffusion measurements with the MoCA score and its five cognitive subdomains. Significant correlations were defined as those with p-values less than 0.05. Multiple linear regression analyses between diffusion and gray matter To dissociate the role of volumetric changes in gray matter from those in WM, a multiple linear regression was conducted to assess the combined subcortical effects on cognitive performance. Volumetric data were obtained from T1-3D images of patients using the volBrain pipeline ( https://www.volbrain.net/home ) (Coupé et al., 2020 ). Statistical analyses were conducted in RStudio version 3.6.0. Ethical and Data Availability Statement The study was approved by the INNN’s Research and Ethics Committee (N’ DIC/419/14 and N’ 41/14). All participants provided written informed consent, as outlined in the Declaration of Helsinki (World Medical Association, 2024 ). Results Demographic and clinical data Controls and patients were matched for age (U = 198.5), area of residence, and education level (U = 275). The volunteers included in the control group had no history of neurological injury or psychiatric diseases reported by clinical interview (Table 1 ). The TFC scale mean score for HD subjects was 11.8 ± 1.8, detecting 17 patients (77%) in Stage I (scores 11–13) and 5 patients (22.7%) in Stage II (scores 7–10). The mean UHDRS score was 14.6 ± 10.6 out of a maximum of 124. One patient (4.5%) showed abnormal movements, with the most affected patient scoring 40 points. Seventeen patients (77.3%) scored less than 17 points, and two patients (9.5%) scored 0 points. The above data confirmed their early clinical status, as reflected in functional and motor decline (Schobel et al., 2017 ). Table 1 Clinical data Male-to-female ratio Control healthy group mean ± standard deviation (range) HD group mean ± standard deviation (range) 9:11 9:13 Age in years 45.8 ± 11.9 (26.5–67.5) 46.1 ± 12.1 (27.6–67.7) Education in years 16.6 ± 2.8 (9–22) 14.1 ± 3.2 (9–19) Age onset in years 48.8 ± 11.5 (28–70) Symptoms Duration in months 59.5 ± 53.4 (0-137.36) CAG-Repeat Length 44.5 ± 3.8 (40–54) Disease Burden 385.6 ± 100.3 (153-536.5) UHDRS motor scale 14.6 ± 10.6 (0–40) TFC 11.8 ± 1.8 (8–13) Diseased Burden: Age (years) × (CAG-repeat length − 35.5). To classify patients with early-onset Huntington’s disease, symptomatic HD individuals were identified based on their Total Functional Capacity (TFC) scores, with lower values indicating reduced functional capacity. TFC assessment, ranging from 0 to 13 (early symptomatic HD > 7), and the UHDRS (Unified Huntington's Disease Rating Scale) were used to assess disease severity. A single outlier pushed the UHDRS range to 40. MoCA performance MoCA performance in HD patients (median = 24; U = 344.5, p < .05) was statistically lower than that of the control group (median = 28). In addition, the HD group obtained low scores in the following MoCA subdomains: language (median = 4; U = 294, p < .03), attention/executive (median = 8; U = 342, p < .05), and visuospatial (median = 4; U = 296, p < .01) (Fig. 1 ). WM changes in HD patients TBSS group comparison revealed significantly lower FA values (Fig. 2 a) in patients’ right/left external capsules, the body of the corpus callosum (BCC), and the posterior/anterior right corona radiata. Conversely, there were significantly high MD values (Fig. 2 b), mainly in the right superior longitudinal fasciculus (SLF), the right superior corona radiata (SCR), and the splenium of the corpus callosum (Supplementary Table 1). MoCA subdomain performance and its correlation with WM changes in HD patients Although patients exhibited lower language and attention/executive performance than the control group, no significant relationship was found between their scores and WM integrity (p = .48 and p = .09, respectively). Nonetheless, the visuospatial subdomain was the only one correlated with DTI parameters in the HD group. As performance increased, the higher voxel peak FA were identified in the BCC, left SLF, and left SCR (Fig. 3 a). Additionally, a negative correlation was observed between the higher voxel-peak MD and visuospatial scores in the BCC, right SLF, and left SCR (Fig. 3 b). Although other WM regions were also identified (Table 2 ), the subsequent analysis used the higher FA and MD voxel peak values along with visuospatial scores. Table 2 Visuospatial correlation with FA and MD FA Correlations Anatomical Regions MNI Coordinates r p < x y z Body of the corpus callosum -10 -22 27 .80 .001 Left superior longitudinal fasciculus -42 -47 33 .83 .001 Left superior corona radiata -19 -23 38 .71 .001 Inferior fronto-occipital fasciculus 28 -54 19 .81 .001 Right arcuate fasciculus 38 -37 18 .76 .001 Left uncinate fasciculus -23 9 -13 .78 .001 Forceps minor 11 28 -7 .78 .001 Right anterior thalamic radiation 22 32 15 .76 .001 Left external capsule -32 -11 8 .74 .001 MD Correlations Anatomical Regions MNI Coordinates r p < x y z Body of the corpus callosum -13 -30 27 − .88 .001 Right superior longitudinal fasciculus 49 -33 37 − .86 .001 Left superior corona radiata -22 -16 37 − .79 .005 Forceps minor 10 31 8 − .79 .001 Left dorsal cingulum subsection -11 -35 31 − .94 .001 Right dorsal cingulum subsection 8 -23 36 − .90 .001 Left posterior limb of the internal capsule -21 -4 15 − .73 .001 Right inferior fronto-occipital fasciculus 40 35 1 − .69 .008 Right posterior corona radiata 25 -24 23 − .75 .001 Left anterior limb of the internal capsule -19 11 12 − .76 .001 All correlations were performed using Spearman's rank correlation, with p < .05 considered significant. FA, Fractional Anisotropy; MD, Mean Diffusivity; MNI, Montreal Neurological Institute 152 Atlas. Correlation between MoCA scores and subcortical brain structures A moderate correlation was found between MoCA scores and the volumes of the left (r = 0.46; p < .03) and right (r = 0.44; p < .04) caudate. All other subcortical structures did not meet the significant correlations of p < .05. Multiple linear regression analyses between diffusion and gray matter The linear regression models were conducted using FA and MD values from visuospatially relevant tracts: BCC, SLF, and SCR, as dependent variables. Ventricular and caudate nucleus volumes were entered simultaneously as predictors to assess whether their changes served as strong predictors of the previously observed diffusion associations. A significant association was found between right ventricular volume and BCC integrity (β = −0.01, p < .04). No significant effects were observed for ventricular volumes in the SLF or SCR, nor for caudate nucleus volumes in any tract. A significant association was found between right ventricular volume and BCC integrity (β = −0.01, p < .04). No significant effects were observed for ventricular volumes in the SLF or SCR, nor for caudate nucleus volumes over any tract (Supplementary Table 2). The volumetric list, including all regions of interest (ROIs), is available at: https://doi.org/10.6084/m9.figshare.27893361.v2 . Discussion This study aimed to explore the link between cognitive deficits and WM differences in early HD by correlating MoCA subdomain scores with diffusion parameters. The first TBSS analysis identified the main WM changes in HD patients. The second analysis explored the correlation between scores on all MoCA subdomains and WM changes in the patients. The main finding was a significant relationship between visuospatial scores and the integrity of the BCC, SLF, and SCR. Cognitive deficits in HD are primarily driven by early WM pathophysiology. Patients exhibited lower overall performance on the global MoCA and reduced WM integrity compared to the control group. The structural changes observed in the BCC and CR tracts, which are heavily connected to cortical and subcortical areas, align with findings from other studies showing a pattern of degeneration that starts in deep brain regions of patients with HD (Della Nave et al., 2010 ). Therefore, these WM alterations reduce the efficiency of signal transmission between brain regions, affecting both motor and cognitive functions from the early stages of HD (Rosas et al., 2018 ), as shown in studies using the MoCA to assess patients' status (Rosca & Simu, 2020). Visuospatial deficits in HD patients are linked to WM disruption in communication pathways between motor and perceptual brain areas. The patients' SLF, CR, and BCC integrity were the primary effects associated with low MoCA visuospatial scores. These results are followed by a previous report that describes SLF and CR changes in HD patients (Della Nave et al., 2010 ). Particularly, the CR is involved in motor planning, cognitive flexibility, and processing speed (Yu et al., 2022 ). Similarly, SLF is a crucial component of the visuospatial attentional network, since it connects the frontal lobe with the parietal cortex (Bernard et al., 2020 ). This network processes and integrates visual and spatial information, facilitating orientation, attention allocation, visual search, hand-eye coordination, object recognition, and spatial memory (Kargar & Jalilan, 2024). Also, the BCC contributes significantly to precise motor control, including real-time adjustments based on perceptual feedback (Phillips et al., 2013 ; Bohana et al., 2011). Therefore, changes in BCC, SLF, and CR would impair performance in copying and drawing tasks, typically observed in the early HD stages (Dridan et al., 2012; Johnson et al., 2015 ). Visuospatial deficits in HD patients are also driven by reduced interhemispheric connectivity. The MoCA visuospatial subdomain correlated with the BCC integrity. Despite the regression analysis suggesting that ventricular enlargement, a common neuroanatomical hallmark of HD, may partially contribute to alterations in BCC diffusion, the absence of significant ventricular effects on the SLF and SCR, together with the lack of associations between caudate nucleus volumes and across all examined tracts, suggests that ventricular expansion alone does not fully account for the visuospatial deficits. It is important to highlight that the BCC alteration is associated with decreased pyramidal cells, as its fibers originate from these neurons, which are affected from the manifest to advanced stages of the illness (Blumenstock & Dudanova, 2020 ). Then, interhemispheric disruption could hinder the coordination of motor and perceptual abilities required for visuospatial performance as observed in manifest HD patients (Coppen et al., 2019 ). MoCA’s visuospatial component can be used to track brain changes in HD. Although brief, the visuospatial tasks were a feasible measure that enabled the detection of correlations between FA and MD values and neuroanatomical changes. Previous studies support the notion that this skill is among the earliest cognitive deficits in HD, making the findings coherent (Labuschagne et al., 2016 ). However, some studies have struggled to find a correlation between MoCA visuospatial tasks and brain changes (Sweidan et al., 2020 ; Poudel et al., 2014 ), possibly because they overlooked the proposed arrangement of MoCA’s subdomains, specifically for HD patients (Gluhm et al., 2013 ). The limitations of the present study to be addressed in future designs were: 1) conducting cognitive follow-up to observe participants' progress, and 2) further addressing fiber bundle overlap by using techniques such as Fixel-Based Analysis, which can differentiate between fiber bundles within a voxel (Raffelt et al., 2015 ). Conclusion The visuospatial deficits identified by the MoCA were associated with the main WM regions affected in early manifest HD, specifically the SLF, BCC, and CR. The above supports the advantages of combining DTI analysis with cognitive screening tools in HD research on non-motor symptoms. Declarations Author Contribution E. R contributed on the main writing of the paper, as well as the statistical analysis of the fMRI images CP. V writing riview and editing, running statistical analysisFR. J revision of the statistical analysys, disigner of the study BC.M review and editing of the manuscript G.V conceptualization of the study , approval and supervition of the study, funding acquisition and review and revition of the manuscript Declaration of Conflicting Interest The authors declare no potential conflicts of interest concerning this article. Funding Declaration This research was funded by "Fondo de apoyo a la Investigación de la Facultad de Ciencias de la Salud - Universidad Panamericana" CIP-PI-115-3, granted to Victor Galvez, for the period 2025–2027. Human Ethics and Consent to Participate declarations We confirm that all participants have provided written informed consent. The Ethical Committee from the National Neurology Institute approved the informed consent letter (N’ 41/14). Clinical trial number: not applicable References Bernard, F., Lemée, J., Mazerand, E., Leiber, L., Menei, P., & Minassian, T., A (2020). 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Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Apr, 2026 Reviews received at journal 24 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8971620","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617795596,"identity":"3013c056-49cb-491d-9408-fecea918a294","order_by":0,"name":"Esquiliano Ricardo","email":"","orcid":"","institution":"Universidad Panamericana","correspondingAuthor":false,"prefix":"","firstName":"Esquiliano","middleName":"","lastName":"Ricardo","suffix":""},{"id":617795597,"identity":"0c9e0f54-cdc9-43e2-ab5f-10652412f8ed","order_by":1,"name":"Cancino-Pérez Valeria","email":"","orcid":"","institution":"Universidad Panamericana","correspondingAuthor":false,"prefix":"","firstName":"Cancino-Pérez","middleName":"","lastName":"Valeria","suffix":""},{"id":617795598,"identity":"b16a074c-78d1-4bf1-886e-326deadfad50","order_by":2,"name":"Fernandez-Ruiz Juan","email":"","orcid":"","institution":"Universidad Nacional Autónoma de México","correspondingAuthor":false,"prefix":"","firstName":"Fernandez-Ruiz","middleName":"","lastName":"Juan","suffix":""},{"id":617795599,"identity":"e9371821-65dd-48ef-8ca9-9b5d1dab0a8c","order_by":3,"name":"Blanco-Cortes Milka","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Blanco-Cortes","middleName":"","lastName":"Milka","suffix":""},{"id":617795600,"identity":"54843a17-2392-4831-b325-ed98200815f5","order_by":4,"name":"Galvez Victor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYHACxgNAQg7M5CFWD0iLMelaEhuI1mJwfvGDwzwVdekbbjcwPnjbxhBtcICQlhvPDA7znGHL3XDnALPh3DaG3JkNBLRIzjhgcHBmG0/uhhsJbNK8QC39hBwmOeP4h4Mz/0mkG9xIYP8N0tJGSAs/f4/BgY8NBglALWzMRNnCL8FTcODDsQTDmXcONkvOOSdB2C9s/Mc3PkioqZPnu9188MObMpvcDQcIWSORAGMwgoyXIKQe5DKYocQoHgWjYBSMgpEJABgdRLPB4G7wAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Panamericana","correspondingAuthor":true,"prefix":"","firstName":"Galvez","middleName":"","lastName":"Victor","suffix":""}],"badges":[],"createdAt":"2026-02-25 22:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8971620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8971620/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106534748,"identity":"b6cb1620-84de-4a6e-843d-35c7db3e1386","added_by":"auto","created_at":"2026-04-09 15:06:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69118,"visible":true,"origin":"","legend":"\u003cp\u003eThe MoCA global performance and its five cognitive subdomains. Mean scores for each plot were as follows: a) Total MoCA Score: control = 27.6, HD= 24.09; b) MoCA Attention Executive: control = 9.8; HD = 7.8; c) MoCA Language: control = 4.7, HD = 4.2; d) MoCA Visuospatial: control = 3.8, HD = 3.05; e) MoCA Memory: control = 3.3, HD = 3; f)MoCA Orientation: control = 6, HD = 5.6. Blue dots indicate identified outliers. HD: Huntington's Disease.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8971620/v1/8664866d2e9e69ce4bcf12b0.jpg"},{"id":106724688,"identity":"b020b595-3ea7-4e43-a738-67b1f9fccbd1","added_by":"auto","created_at":"2026-04-12 18:29:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154138,"visible":true,"origin":"","legend":"\u003cp\u003eTBSS differences in diffusion parameters between HD and healthy controls.\u003cstrong\u003e \u003c/strong\u003ea) Fractional anisotropy decrease in the HD group. b) Mean diffusivity increase in the HD group. TFCE: Threshold-Free Cluster Enhancement method; R: right hemisphere; Z: MNI Atlas Coordinates.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8971620/v1/1014417364ff23ca95d681d1.jpg"},{"id":106534750,"identity":"8ef0cda1-bf0d-46ee-be25-585f3d6c5696","added_by":"auto","created_at":"2026-04-09 15:06:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139085,"visible":true,"origin":"","legend":"\u003cp\u003eTBSS correlations between diffusion changes and visuospatial MoCA scores.\u003cstrong\u003e \u003c/strong\u003ea) Fractional anisotropy. b) Mean diffusivity. BCC: body of corpus callosum; l-SLF: left superior longitudinal fasciculus; l-SCR: left superior corona radiata; r-SLF: right superior longitudinal fasciculus. The scatterplots were generated using the FA and MD voxel’s peak values with visuospatial scores.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8971620/v1/5df839ad4693d44858f59189.jpg"},{"id":108976038,"identity":"fc0f760d-6b1e-4a0f-9ba5-f55ff67e1229","added_by":"auto","created_at":"2026-05-11 10:58:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":699553,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8971620/v1/7f0704b4-ed77-4da5-8a24-395da2572fb5.pdf"},{"id":106534747,"identity":"bae4f1b8-c80f-40c9-814d-2ec705747558","added_by":"auto","created_at":"2026-04-09 15:06:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12886,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8971620/v1/276632cfe5802998ace36579.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Are MoCA subdomains deficits linked to white matter changes in early Huntington’s disease? A diffusion MRI approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuntington's disease (HD) is a genetic neurodegenerative disorder characterized by neuropsychiatric, cognitive, and motor symptoms, typically manifesting in early adulthood (Philips et al., 2013). Cognitive impairment becomes prominent as the disease progresses, especially affecting executive functions and visuospatial performance (Migliore et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hart et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although cognitive deterioration is a characteristic non-motor symptom of HD, premanifest and manifest patients exhibit different features of decline (Del Pino et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile basal ganglia degeneration is the hallmark of HD neuro-pathophysiology (Liu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), early changes in white matter (WM) also play a critical role. These changes result from the breakdown of myelinated fibers and axonal degeneration, particularly in the prefrontal tracts, corpus callosum (CC), and corticospinal tracts (Bourbon-Teles et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Estevez-Fraga et al., 2020). Magnetic resonance imaging (MRI) studies have correlated executive functions with WM changes in patients, quantifying diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA) and mean diffusivity (MD) (Bohanna et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, cognitive deficits are often detected using extensive neuropsychological batteries, which are time-consuming and require highly trained personnel (Gluhm et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rosca \u0026amp; Simu, 2020).\u003c/p\u003e \u003cp\u003eAs an alternative, the Montreal Cognitive Assessment (MoCA) has emerged as a brief, easy-to-administer tool for detecting mild cognitive impairment (MCI; MoCA score\u0026thinsp;\u0026lt;\u0026thinsp;26/30) in HD patients (Gluhm et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nasreddine et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Furthermore, MoCA assesses a broad range of cognitive subdomains, facilitating research into functional and structural brain deterioration (Videnovic et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For example, fMRI studies in HD patients have shown a correlation between language subdomain scores and cortical thinning in regions such as the lingual and fusiform gyri, supramarginal gyrus, and lateral orbitofrontal lobe (Sweidan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite these findings, the relationship between MoCA-assessed cognitive subdomains and WM changes in HD remains unclear.\u003c/p\u003e \u003cp\u003eThis study aims to explore the link between cognitive deficits and WM differences in early manifest HD by correlating MoCA subdomain scores with DTI parameters. The hypothesis is that low performance on MoCA subdomains is associated with interhemispheric and prefrontal WM changes in patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eTwenty-two patients with a molecular diagnosis of HD and 20 age-matched healthy controls participated in the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients were recruited at the National Institute of Neurology and Neurosurgery (INNN) in Mexico City. MoCA scores were grouped into five cognitive subdomains: attention/executive (digit span, target detection, serial seven subtraction, Trail Making Test B, fluency, and abstraction), visuospatial (clock drawing and three-dimensional cube copying), language (object naming and sentence repetition), memory (recall of five previously presented words), and orientation (temporal and spatial) (Gluhm et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImage Acquisition\u003c/h3\u003e\n\u003cp\u003eAll images were acquired using a 3T MRI scanner from Philips Medical Systems in Eindhoven, The Netherlands. The high-resolution anatomical images were obtained using a T1-3D Fast Field Echo sequence with the following parameters: TR/TE of 8/3.7 ms, a field of view (FOV) of 256 \u0026times; 256 mm\u0026sup2;, a flip angle of 8\u0026deg;, an acquisition and reconstruction matrix of 256 \u0026times; 256, and an isometric resolution of 1 \u0026times; 1 \u0026times; 1 mm\u0026sup3;. The DTI sequences were conducted using Single-Shot Echo Planar Imaging. This involved acquiring 33 volumes of 70 axial slices (with a slice thickness of 2 mm and no separation), corresponding to each of the 32 independent diffusion directions with a b-value of 800 s/mm\u0026sup2;, along with one volume for b\u0026thinsp;=\u0026thinsp;0 s/mm\u0026sup2;. The DTI sequence parameters included a TR/TE of 8467/60 ms, a FOV of 256 \u0026times; 256 mm\u0026sup2;, and an acquisition and reconstruction matrix of 128 \u0026times; 128, resulting in an isometric resolution of 2 \u0026times; 2 \u0026times; 2 mm\u0026sup3;.\u003c/p\u003e\n\u003ch3\u003eDiffusion Tensor Analysis\u003c/h3\u003e\n\u003cp\u003eThe DTI images were processed using the FSL Diffusion toolbox (Smith et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). First, the effects of eddy currents were corrected, and the diffusion tensor model was adjusted to generate FA and MD maps for each participant. Subsequently, statistical analysis was performed using the Tract-Based Spatial Statistics (TBSS) approach, following: 1) identification of a common registration target and alignment of all participants' FA images to this target; 2) creation of a mean FA map using the mean of all aligned FA images and of a thresholded skeletonized mean FA image (threshold\u0026thinsp;=\u0026thinsp;0.2); 3) and projection of each participant's FA image onto the skeleton and voxel-wise statistical analysis across subjects on the skeleton-space FA data; 4) Using the same nonlinear registration, derived from the FA analysis, MD data were projected onto the skeleton vectors before voxel-wise statistical analysis across subjects (Smith et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Smith et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStatistical comparison of the groups\u003c/h3\u003e\n\u003cp\u003eA two-sample t-test was conducted using FSL's \u003cem\u003erandomise\u003c/em\u003e function to compare the patient and healthy groups, controlling for age. To address multiple comparisons, we applied the Threshold-Free Cluster Enhancement (TFCE) method (Winkler et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Only voxels that survived family-wise error correction (FWE) with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant between the two groups. The final parametric maps were parcellated, binarized, and labeled using the WM atlas developed at Johns Hopkins University.\u003c/p\u003e\n\u003ch3\u003eCognitive correlations of WM with MoCA and its subdomains\u003c/h3\u003e\n\u003cp\u003eThe associations between WM density and the cognitive deficits in patients with HD were examined using analysis of covariance (ANCOVA) by TBSS. Six different analyses were run. In the first analysis, FSL \u003cem\u003erandomise\u003c/em\u003e was fed with the total MoCA scores. The subsequent analyses used the scores of each cognitive subdomain. To correct for multiple comparisons, randomized permutations were employed using the TFCE method (Winkler et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Only voxels that survived FWE correction with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. In all analyses, the disease burden score (calculated as age (years) \u0026times; (CAG repeat length\u0026thinsp;\u0026minus;\u0026thinsp;35.5)) was included as a nuisance variable (Poudel et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, the FA and MD peak values in the significant voxels were selected for each patient. As the cognitive measurements did not follow a normal distribution, as determined by the Shapiro-Wilk test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Spearman correlations were conducted between the diffusion measurements with the MoCA score and its five cognitive subdomains. Significant correlations were defined as those with p-values less than 0.05.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMultiple linear regression analyses between diffusion and gray matter\u003c/h2\u003e \u003cp\u003eTo dissociate the role of volumetric changes in gray matter from those in WM, a multiple linear regression was conducted to assess the combined subcortical effects on cognitive performance. Volumetric data were obtained from T1-3D images of patients using the volBrain pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.volbrain.net/home\u003c/span\u003e\u003cspan address=\"https://www.volbrain.net/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Coup\u0026eacute; et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Statistical analyses were conducted in RStudio version 3.6.0.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical and Data Availability Statement\u003c/h3\u003e\n\u003cp\u003eThe study was approved by the INNN\u0026rsquo;s Research and Ethics Committee (N\u0026rsquo; DIC/419/14 and N\u0026rsquo; 41/14). All participants provided written informed consent, as outlined in the Declaration of Helsinki (World Medical Association, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical data\u003c/h2\u003e \u003cp\u003eControls and patients were matched for age (U\u0026thinsp;=\u0026thinsp;198.5), area of residence, and education level (U\u0026thinsp;=\u0026thinsp;275). The volunteers included in the control group had no history of neurological injury or psychiatric diseases reported by clinical interview (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The TFC scale mean score for HD subjects was 11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8, detecting 17 patients (77%) in Stage I (scores 11\u0026ndash;13) and 5 patients (22.7%) in Stage II (scores 7\u0026ndash;10). The mean UHDRS score was 14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 out of a maximum of 124. One patient (4.5%) showed abnormal movements, with the most affected patient scoring 40 points. Seventeen patients (77.3%) scored less than 17 points, and two patients (9.5%) scored 0 points. The above data confirmed their early clinical status, as reflected in functional and motor decline (Schobel et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMale-to-female ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl healthy group\u003c/p\u003e \u003cp\u003e\u003cem\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (range)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHD group\u003c/p\u003e \u003cp\u003e\u003cem\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (range)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9:11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9:13\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e45.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9 (26.5\u0026ndash;67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e46.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 (27.6\u0026ndash;67.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 (9\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 (9\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge onset in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5 (28\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms Duration in months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e59.5\u0026thinsp;\u0026plusmn;\u0026thinsp;53.4 (0-137.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAG-Repeat Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 (40\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease Burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e385.6\u0026thinsp;\u0026plusmn;\u0026thinsp;100.3 (153-536.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUHDRS motor scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 (0\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 (8\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiseased Burden: Age (years) \u0026times; (CAG-repeat length\u0026thinsp;\u0026minus;\u0026thinsp;35.5). To classify patients with early-onset Huntington\u0026rsquo;s disease, symptomatic HD individuals were identified based on their Total Functional Capacity (TFC) scores, with lower values indicating reduced functional capacity. TFC assessment, ranging from 0 to 13 (early symptomatic HD\u0026thinsp;\u0026gt;\u0026thinsp;7), and the UHDRS (Unified Huntington's Disease Rating Scale) were used to assess disease severity. A single outlier pushed the UHDRS range to 40.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMoCA performance\u003c/h2\u003e \u003cp\u003eMoCA performance in HD patients (median\u0026thinsp;=\u0026thinsp;24; U\u0026thinsp;=\u0026thinsp;344.5, p \u0026lt; .05) was statistically lower than that of the control group (median\u0026thinsp;=\u0026thinsp;28). In addition, the HD group obtained low scores in the following MoCA subdomains: language (median\u0026thinsp;=\u0026thinsp;4; U\u0026thinsp;=\u0026thinsp;294, p \u0026lt; .03), attention/executive (median\u0026thinsp;=\u0026thinsp;8; U\u0026thinsp;=\u0026thinsp;342, p \u0026lt; .05), and visuospatial (median\u0026thinsp;=\u0026thinsp;4; U\u0026thinsp;=\u0026thinsp;296, p \u0026lt; .01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWM changes in HD patients\u003c/h2\u003e \u003cp\u003eTBSS group comparison revealed significantly lower FA values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) in patients\u0026rsquo; right/left external capsules, the body of the corpus callosum (BCC), and the posterior/anterior right corona radiata. Conversely, there were significantly high MD values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), mainly in the right superior longitudinal fasciculus (SLF), the right superior corona radiata (SCR), and the splenium of the corpus callosum (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMoCA subdomain performance and its correlation with WM changes in HD patients\u003c/h2\u003e \u003cp\u003eAlthough patients exhibited lower language and attention/executive performance than the control group, no significant relationship was found between their scores and WM integrity (p = .48 and p = .09, respectively). Nonetheless, the visuospatial subdomain was the only one correlated with DTI parameters in the HD group. As performance increased, the higher voxel peak FA were identified in the BCC, left SLF, and left SCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Additionally, a negative correlation was observed between the higher voxel-peak MD and visuospatial scores in the BCC, right SLF, and left SCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Although other WM regions were also identified (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the subsequent analysis used the higher FA and MD voxel peak values along with visuospatial scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVisuospatial correlation with FA and MD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eFA Correlations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnatomical Regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMNI Coordinates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep \u0026lt;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody of the corpus callosum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft superior longitudinal fasciculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft superior corona radiata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior fronto-occipital fasciculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight arcuate fasciculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft uncinate fasciculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForceps minor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight anterior thalamic radiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft external capsule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMD Correlations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnatomical Regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMNI Coordinates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep \u0026lt;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody of the corpus callosum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight superior longitudinal fasciculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft superior corona radiata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForceps minor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft dorsal cingulum subsection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight dorsal cingulum subsection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft posterior limb of the internal capsule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight inferior fronto-occipital fasciculus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight posterior corona radiata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft anterior limb of the internal capsule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll correlations were performed using Spearman's rank correlation, with p \u0026lt; .05 considered significant. FA, Fractional Anisotropy; MD, Mean Diffusivity; MNI, Montreal Neurological Institute 152 Atlas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between MoCA scores and subcortical brain structures\u003c/h2\u003e \u003cp\u003eA moderate correlation was found between MoCA scores and the volumes of the left (r\u0026thinsp;=\u0026thinsp;0.46; p \u0026lt; .03) and right (r\u0026thinsp;=\u0026thinsp;0.44; p \u0026lt; .04) caudate. All other subcortical structures did not meet the significant correlations of p \u0026lt; .05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMultiple linear regression analyses between diffusion and gray matter\u003c/h2\u003e \u003cp\u003eThe linear regression models were conducted using FA and MD values from visuospatially relevant tracts: BCC, SLF, and SCR, as dependent variables. Ventricular and caudate nucleus volumes were entered simultaneously as predictors to assess whether their changes served as strong predictors of the previously observed diffusion associations.\u003c/p\u003e \u003cp\u003eA significant association was found between right ventricular volume and BCC integrity (β = \u0026minus;0.01, p \u0026lt; .04). No significant effects were observed for ventricular volumes in the SLF or SCR, nor for caudate nucleus volumes in any tract. A significant association was found between right ventricular volume and BCC integrity (β = \u0026minus;0.01, p \u0026lt; .04). No significant effects were observed for ventricular volumes in the SLF or SCR, nor for caudate nucleus volumes over any tract (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eThe volumetric list, including all regions of interest (ROIs), is available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6084/m9.figshare.27893361.v2\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.27893361.v2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to explore the link between cognitive deficits and WM differences in early HD by correlating MoCA subdomain scores with diffusion parameters. The first TBSS analysis identified the main WM changes in HD patients. The second analysis explored the correlation between scores on all MoCA subdomains and WM changes in the patients. The main finding was a significant relationship between visuospatial scores and the integrity of the BCC, SLF, and SCR.\u003c/p\u003e \u003cp\u003eCognitive deficits in HD are primarily driven by early WM pathophysiology. Patients exhibited lower overall performance on the global MoCA and reduced WM integrity compared to the control group. The structural changes observed in the BCC and CR tracts, which are heavily connected to cortical and subcortical areas, align with findings from other studies showing a pattern of degeneration that starts in deep brain regions of patients with HD (Della Nave et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, these WM alterations reduce the efficiency of signal transmission between brain regions, affecting both motor and cognitive functions from the early stages of HD (Rosas et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), as shown in studies using the MoCA to assess patients' status (Rosca \u0026amp; Simu, 2020).\u003c/p\u003e \u003cp\u003eVisuospatial deficits in HD patients are linked to WM disruption in communication pathways between motor and perceptual brain areas. The patients' SLF, CR, and BCC integrity were the primary effects associated with low MoCA visuospatial scores. These results are followed by a previous report that describes SLF and CR changes in HD patients (Della Nave et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Particularly, the CR is involved in motor planning, cognitive flexibility, and processing speed (Yu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, SLF is a crucial component of the visuospatial attentional network, since it connects the frontal lobe with the parietal cortex (Bernard et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This network processes and integrates visual and spatial information, facilitating orientation, attention allocation, visual search, hand-eye coordination, object recognition, and spatial memory (Kargar \u0026amp; Jalilan, 2024). Also, the BCC contributes significantly to precise motor control, including real-time adjustments based on perceptual feedback (Phillips et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bohana et al., 2011). Therefore, changes in BCC, SLF, and CR would impair performance in copying and drawing tasks, typically observed in the early HD stages (Dridan et al., 2012; Johnson et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVisuospatial deficits in HD patients are also driven by reduced interhemispheric connectivity. The MoCA visuospatial subdomain correlated with the BCC integrity. Despite the regression analysis suggesting that ventricular enlargement, a common neuroanatomical hallmark of HD, may partially contribute to alterations in BCC diffusion, the absence of significant ventricular effects on the SLF and SCR, together with the lack of associations between caudate nucleus volumes and across all examined tracts, suggests that ventricular expansion alone does not fully account for the visuospatial deficits. It is important to highlight that the BCC alteration is associated with decreased pyramidal cells, as its fibers originate from these neurons, which are affected from the manifest to advanced stages of the illness (Blumenstock \u0026amp; Dudanova, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Then, interhemispheric disruption could hinder the coordination of motor and perceptual abilities required for visuospatial performance as observed in manifest HD patients (Coppen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoCA\u0026rsquo;s visuospatial component can be used to track brain changes in HD. Although brief, the visuospatial tasks were a feasible measure that enabled the detection of correlations between FA and MD values and neuroanatomical changes. Previous studies support the notion that this skill is among the earliest cognitive deficits in HD, making the findings coherent (Labuschagne et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, some studies have struggled to find a correlation between MoCA visuospatial tasks and brain changes (Sweidan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Poudel et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), possibly because they overlooked the proposed arrangement of MoCA\u0026rsquo;s subdomains, specifically for HD patients (Gluhm et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe limitations of the present study to be addressed in future designs were: 1) conducting cognitive follow-up to observe participants' progress, and 2) further addressing fiber bundle overlap by using techniques such as Fixel-Based Analysis, which can differentiate between fiber bundles within a voxel (Raffelt et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe visuospatial deficits identified by the MoCA were associated with the main WM regions affected in early manifest HD, specifically the SLF, BCC, and CR. The above supports the advantages of combining DTI analysis with cognitive screening tools in HD research on non-motor symptoms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eE. R contributed on the main writing of the paper, as well as the statistical analysis of the fMRI images CP. V writing riview and editing, running statistical analysisFR. J revision of the statistical analysys, disigner of the study BC.M review and editing of the manuscript G.V conceptualization of the study , approval and supervition of the study, funding acquisition and review and revition of the manuscript\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDeclaration of Conflicting Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no potential conflicts of interest concerning this article.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunding Declaration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis research was funded by \"Fondo de apoyo a la Investigaci\u0026oacute;n de la Facultad de Ciencias de la Salud - Universidad Panamericana\" CIP-PI-115-3, granted to Victor Galvez, for the period 2025\u0026ndash;2027.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eHuman Ethics and Consent to Participate declarations\u003c/h2\u003e \u003cp\u003e We confirm that all participants have provided written informed consent. The Ethical Committee from the National Neurology Institute approved the informed consent letter (N\u0026rsquo; 41/14).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClinical trial number: not applicable\u003c/h2\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBernard, F., Lem\u0026eacute;e, J., Mazerand, E., Leiber, L., Menei, P., \u0026amp; Minassian, T., A (2020). 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(2022). Pathway-specific mediation effect between structure, function, and motor impairment after subcortical stroke. \u003cem\u003eNeurology\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000201495\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000201495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Huntington’s Disease, MoCA, Visuospatial, White Matter, fMRI, Fractional Anisotropy, Mean diffusivity, TBSS","lastPublishedDoi":"10.21203/rs.3.rs-8971620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8971620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explored the relationship between the Montreal Cognitive Assessment\u0026rsquo;s (MoCA) subdomains and white matter changes in Huntington's disease (HD). Fractional anisotropy (FA) and mean diffusivity (MD) were measured using Diffusion Tensor Imaging (DTI) in 22 early manifest HD patients and 20 healthy controls. Tract-Based Spatial Statistics was used to compare the two groups and correlate patients\u0026rsquo; FA and MD values with their performance in MoCA subdomains. Among all MoCA subdomains, only visuospatial performance was associated with white matter integrity in the corpus callosum, corona radiata, and superior longitudinal fasciculus, supporting the feasibility of combining DTI analysis with screening tools to monitor non-motor symptoms.\u003c/p\u003e","manuscriptTitle":"Are MoCA subdomains deficits linked to white matter changes in early Huntington’s disease? A diffusion MRI approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 15:06:12","doi":"10.21203/rs.3.rs-8971620/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-27T15:55:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T13:19:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T00:50:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137497169034646181931558679769560673739","date":"2026-04-05T13:14:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302967941447464644424993309247252459616","date":"2026-04-02T18:41:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T15:21:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T15:06:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T10:25:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2026-02-25T22:50:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"35f29825-3eb4-4e33-b7cd-e5fe9cf05635","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T20:53:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 15:06:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8971620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8971620","identity":"rs-8971620","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}