Results: A group comparison showed significantly higher FC in PD-MCI than HC (p=0.031, g=1.30), suggesting altered bundle geometry rather than axonal proliferation. Within the PD cohort, FD showed stronger correlation-cognition coupling in PD-NC that PD-MCI across attention and working memory (p=0.025, g=1.27), learning and memory (p=0.023, g=1.38) and visuospatial memory (p=0.033, g=1.01). This pattern held for executive function on category switching (p=0.028, g=1.09), through category fluency total raw score instead showed a positive FD correlation within PD-MCI alone (p=0.032, g=1.30), suggesting domain specific preservation of coupling. Within learning and memory, PD-MCI additionally showed higher FC than PD-NC for BVMT Delayed Recall score (p=0.043, g=1.46). Overlapping FC and FDC fixels showed convergent significance across the four domains. this is the reslut jus the brief which i had witten in my abstrat now i have to write this in my manuscript so ill give uh the format how it should be written Fixel-Based Group Differences At the FWE-corrected threshold, the only group difference in fixel metrics was reduced FC in PD-FOG relative to HC (g = 1.29, pFWE = 0.034; Figure 1), localized to the corpus callosum and left thalamo-parietal tracts. PD-FOG and PD-nFOG did not differ on FD, FC, or FDC. At the exploratory uncorrected threshold (p < 0.001, D > 1.57), sub-threshold differences were observed between PD-FOG and PD-nFOG and between each PD group and HC. thats the format ill give mine what should be written PD-MCI>HC Mean difference_3group FC 3.97 ± 0.33 0.031 ± 0.011 the picture and the this numbers

its not uncorrected its fwep0.05 FC

see how my frnd has written

no not nice write like he has writen

Fixel-Based Analysis Reveals Phenotype-Specific White Matter–Behavior Coupling in Parkinson’s Disease With and Without Freezing of Gait i want something intresting topic like this for my paper

a. Attention/Working Memory In the attention/working memory domain, Digit Span Forward showed a significantly stronger positive correlation with FD in nFOG than in FOG (g = 1.35, p = 0.032; Figure 6a). Conversely, Digit Span Backward showed a significantly stronger positive correlation with FD in FOG than in nFOG (g = 1.36, p = 0.036; Figure 6b). The PD-FOG > PD-nFOG Digit Span Backward contrast uniquely involved the anterior commissure (CA), rostrum of the corpus callosum (CC1), right inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculi (UF). No tracts were unique to nFOG > FOG Digit Span Forwards, but notably absent tracts present in all other analyses included the left middle longitudinal fascicle (L_MLF) and left striato-occipital (OCC_ST_L) fascicle. Digit Span Forward in nFOG was also correlated with FC (g = 1.55, p = 0.047; Figure 6c), FD (g = 1.22, p = 0.031; Figure 6d), and FDC (g = 1.23, p = 0.046; Figure 6e). b. Executive Functioning Executive measures showed the broadest differences between PD subgroups. DKEFS Category Switching showed a stronger positive correlation with FDC in FOG than in nFOG (g = 1.54, p = 0.038; Figure 7a). No tracts were unique to this contrast, but significant fixels included the corpus callosum and several left-hemisphere pathways, including the left middle longitudinal fasciculus (MLF), left parieto-occipital pontine tract (POPT), left striato-parietal tract (ST_PAR), and left thalamo-parietal tract (T_PAR). In nFOG, DKEFS Switching was found to be correlated with FC (g = 1.76, p = 0.043; Figure 7b), FD (g = 1.25, p = 0.034; Figure 7c), and FDC (g = 1.59, p = 0.024; Figure 7d). DKEFS Category Fluency was found to be correlated with FC (g = 1.52, p = 0.032; Figure 7e), FD (g = 1.28, p = 0.041; Figure 7f), and FDC (g = 1.37, p = 0.038; Figure 7g) in FOG. DKEFS Category Fluency was found to be correlated with FC (g = 1.42, p = 0.032; Figure 7h), FD (g = 1.32, p = 0.033; Figure 7i), and FDC (g = 1.38, p = 0.023; Figure 7j). Finally, DKEFS FAS Fluency was found to be correlated with FDC in FOG (g = 1.42, p = 0.048; Figure 7k). The visuospatial domain was unable to be analyzed for correlational differences, as there was no variation in DRS-II Construction performance in nFOG. c. Memory Within memory, BVMT Learning was negatively correlated with FC (g = 1.56, p = 0.037; Figure 8a), FD (g = 1.50, p = 0.027; Figure 8b), and FDC (g = 1.39, p = 0.032; Figure 8c) in the nFOG group. BVMT Delay in the nFOG group was positively correlated with FC (g = 1.42, p = 0.050; Figure 8d), and FDC (g = 1.49, p = 0.037; Figure 8e). In FOG, FC was found to be negatively correlated with AVLT Delay (g = 1.70, p = 0.041; Figure 8f). AVLT Delay was also negatively correlated with FDC in nFOG (g = 1.25, p = 0.039; Figure 8g). No FWE corrected correlational differences were observed between FOG and nFOG groups for memory. d. Language Language did not yield corrected fixel-based correlation findings. see like this i need to write for my data ill give uh my data first for attention/working memory that i have pasted first i need to write about fd drs attention and then later fc and fdc goes to supplementary but need to mention that also saying overlapping fixels

a. Attention/Working Memory In the attention/working memory domain, Digit Span Forward showed a significantly stronger positive correlation with FD in nFOG than in FOG (g = 1.35, p = 0.032; Figure 6a). Conversely, Digit Span Backward showed a significantly stronger positive correlation with FD in FOG than in nFOG (g = 1.36, p = 0.036; Figure 6b). The PD-FOG > PD-nFOG Digit Span Backward contrast uniquely involved the anterior commissure (CA), rostrum of the corpus callosum (CC1), right inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculi (UF). No tracts were unique to nFOG > FOG Digit Span Forwards, but notably absent tracts present in all other analyses included the left middle longitudinal fascicle (L_MLF) and left striato-occipital (OCC_ST_L) fascicle. Digit Span Forward in nFOG was also correlated with FC (g = 1.55, p = 0.047; Figure 6c), FD (g = 1.22, p = 0.031; Figure 6d), and FDC (g = 1.23, p = 0.046; Figure 6e). b. Executive Functioning Executive measures showed the broadest differences between PD subgroups. DKEFS Category Switching showed a stronger positive correlation with FDC in FOG than in nFOG (g = 1.54, p = 0.038; Figure 7a). No tracts were unique to this contrast, but significant fixels included the corpus callosum and several left-hemisphere pathways, including the left middle longitudinal fasciculus (MLF), left parieto-occipital pontine tract (POPT), left striato-parietal tract (ST_PAR), and left thalamo-parietal tract (T_PAR). In nFOG, DKEFS Switching was found to be correlated with FC (g = 1.76, p = 0.043; Figure 7b), FD (g = 1.25, p = 0.034; Figure 7c), and FDC (g = 1.59, p = 0.024; Figure 7d). DKEFS Category Fluency was found to be correlated with FC (g = 1.52, p = 0.032; Figure 7e), FD (g = 1.28, p = 0.041; Figure 7f), and FDC (g = 1.37, p = 0.038; Figure 7g) in FOG. DKEFS Category Fluency was found to be correlated with FC (g = 1.42, p = 0.032; Figure 7h), FD (g = 1.32, p = 0.033; Figure 7i), and FDC (g = 1.38, p = 0.023; Figure 7j). Finally, DKEFS FAS Fluency was found to be correlated with FDC in FOG (g = 1.42, p = 0.048; Figure 7k). The visuospatial domain was unable to be analyzed for correlational differences, as there was no variation in DRS-II Construction performance in nFOG. c. Memory Within memory, BVMT Learning was negatively correlated with FC (g = 1.56, p = 0.037; Figure 8a), FD (g = 1.50, p = 0.027; Figure 8b), and FDC (g = 1.39, p = 0.032; Figure 8c) in the nFOG group. BVMT Delay in the nFOG group was positively correlated with FC (g = 1.42, p = 0.050; Figure 8d), and FDC (g = 1.49, p = 0.037; Figure 8e). In FOG, FC was found to be negatively correlated with AVLT Delay (g = 1.70, p = 0.041; Figure 8f). AVLT Delay was also negatively correlated with FDC in nFOG (g = 1.25, p = 0.039; Figure 8g). No FWE corrected correlational differences were observed between FOG and nFOG groups for memory. d. Language Language did not yield corrected fixel-based correlation findings. see like this i need to write for my data ill give uh my data first for attention/working memory that i have pasted first i need to write about fd drs attention and then later fc and fdc goes to supplementary but need to mention that also saying overlapping fixels and the p and t value of fd is Contrast Measures WM Metric Mean t (±SD) Mean p_FWE (±SD) Group Level Differences PD-MCI>HC Mean difference_3group FC 3.97 ± 0.33 0.031 ± 0.011 Correlation PD-NC>PD-MCI DRS Attention FC 3.47 ± 0.52 0.038 ± 0.009 PD-NC>PD-MCI DRS Attention FD 3.58 ± 0.65 0.025 ± 0.013 PD-NC>PD-MCI DRS Attention FDC 3.25 ± 0.50 0.026 ± 0.012 MCI is +vely significant Category Fluency Total Raw Score FD 3.65 ± 0.27 0.042 ± 0.005 PD-NC>PD-MCI Category Switching FD 3.06 ± 0.39 0.028 ± 0.011 PD-NC>PD-MCI Category Switching FDC 3.26 ± 0.39 0.042 ± 0.006 PD-MCI>PD-NC BVMT Delayed Recall FC 3.70 ± 0.14 0.043 ± 0.004 PD-NC>PD-MCI DRS Memory FC 3.95 ± 0.77 0.018 ± 0.013 PD-NC>PD-MCI DRS Memory FD 3.51 ± 0.62 0.023 ± 0.015 PD-NC>PD-MCI DRS Memory FDC 3.78 ± 0.78 0.024 ± 0.015 PD-NC>PD-MCI DRS Construction FC 3.52 ± 0.55 0.027 ± 0.013 PD-NC>PD-MCI DRS Construction FD 3.10 ± 0.51 0.033 ± 0.011 PD-NC>PD-MCI DRS Construction FDC 3.51 ± 0.55 0.037 ± 0.008

a. Attention/Working Memory In the attention/working memory domain, Digit Span Forward showed a significantly stronger positive correlation with FD in nFOG than in FOG (g = 1.35, p = 0.032; Figure 6a). Conversely, Digit Span Backward showed a significantly stronger positive correlation with FD in FOG than in nFOG (g = 1.36, p = 0.036; Figure 6b). The PD-FOG > PD-nFOG Digit Span Backward contrast uniquely involved the anterior commissure (CA), rostrum of the corpus callosum (CC1), right inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculi (UF). No tracts were unique to nFOG > FOG Digit Span Forwards, but notably absent tracts present in all other analyses included the left middle longitudinal fascicle (L_MLF) and left striato-occipital (OCC_ST_L) fascicle. Digit Span Forward in nFOG was also correlated with FC (g = 1.55, p = 0.047; Figure 6c), FD (g = 1.22, p = 0.031; Figure 6d), and FDC (g = 1.23, p = 0.046; Figure 6e). b. Executive Functioning Executive measures showed the broadest differences between PD subgroups. DKEFS Category Switching showed a stronger positive correlation with FDC in FOG than in nFOG (g = 1.54, p = 0.038; Figure 7a). No tracts were unique to this contrast, but significant fixels included the corpus callosum and several left-hemisphere pathways, including the left middle longitudinal fasciculus (MLF), left parieto-occipital pontine tract (POPT), left striato-parietal tract (ST_PAR), and left thalamo-parietal tract (T_PAR). In nFOG, DKEFS Switching was found to be correlated with FC (g = 1.76, p = 0.043; Figure 7b), FD (g = 1.25, p = 0.034; Figure 7c), and FDC (g = 1.59, p = 0.024; Figure 7d). DKEFS Category Fluency was found to be correlated with FC (g = 1.52, p = 0.032; Figure 7e), FD (g = 1.28, p = 0.041; Figure 7f), and FDC (g = 1.37, p = 0.038; Figure 7g) in FOG. DKEFS Category Fluency was found to be correlated with FC (g = 1.42, p = 0.032; Figure 7h), FD (g = 1.32, p = 0.033; Figure 7i), and FDC (g = 1.38, p = 0.023; Figure 7j). Finally, DKEFS FAS Fluency was found to be correlated with FDC in FOG (g = 1.42, p = 0.048; Figure 7k). The visuospatial domain was unable to be analyzed for correlational differences, as there was no variation in DRS-II Construction performance in nFOG. c. Memory Within memory, BVMT Learning was negatively correlated with FC (g = 1.56, p = 0.037; Figure 8a), FD (g = 1.50, p = 0.027; Figure 8b), and FDC (g = 1.39, p = 0.032; Figure 8c) in the nFOG group. BVMT Delay in the nFOG group was positively correlated with FC (g = 1.42, p = 0.050; Figure 8d), and FDC (g = 1.49, p = 0.037; Figure 8e). In FOG, FC was found to be negatively correlated with AVLT Delay (g = 1.70, p = 0.041; Figure 8f). AVLT Delay was also negatively correlated with FDC in nFOG (g = 1.25, p = 0.039; Figure 8g). No FWE corrected correlational differences were observed between FOG and nFOG groups for memory. d. Language Language did not yield corrected fixel-based correlation findings. see like this i need to write for my data ill give uh my data first for attention/working memory that i have pasted first i need to write about fd drs attention and then later fc and fdc goes to supplementary but need to mention that also saying overlapping fixels and the p and t value of fd is Contrast Measures WM Metric Mean t (±SD) Mean p_FWE (±SD) Group Level Differences PD-MCI>HC Mean difference_3group FC 3.97 ± 0.33 0.031 ± 0.011 Correlation PD-NC>PD-MCI DRS Attention FC 3.47 ± 0.52 0.038 ± 0.009 PD-NC>PD-MCI DRS Attention FD 3.58 ± 0.65 0.025 ± 0.013 PD-NC>PD-MCI DRS Attention FDC 3.25 ± 0.50 0.026 ± 0.012 MCI is +vely significant Category Fluency Total Raw Score FD 3.65 ± 0.27 0.042 ± 0.005 PD-NC>PD-MCI Category Switching FD 3.06 ± 0.39 0.028 ± 0.011 PD-NC>PD-MCI Category Switching FDC 3.26 ± 0.39 0.042 ± 0.006 PD-MCI>PD-NC BVMT Delayed Recall FC 3.70 ± 0.14 0.043 ± 0.004 PD-NC>PD-MCI DRS Memory FC 3.95 ± 0.77 0.018 ± 0.013 PD-NC>PD-MCI DRS Memory FD 3.51 ± 0.62 0.023 ± 0.015 PD-NC>PD-MCI DRS Memory FDC 3.78 ± 0.78 0.024 ± 0.015 PD-NC>PD-MCI DRS Construction FC 3.52 ± 0.55 0.027 ± 0.013 PD-NC>PD-MCI DRS Construction FD 3.10 ± 0.51 0.033 ± 0.011 PD-NC>PD-MCI DRS Construction FDC 3.51 ± 0.55 0.037 ± 0.008

4. Discussion The principal finding of this study is that white matter (WM) organization is coupled to gait and cognition differently in PD with freezing of gait (PD-FOG) than in PD without freezing (PD-nFOG) or healthy controls (HC). PD-FOG and PD-nFOG did not differ on any fixel-based metric at the family-wise error (FWE)-corrected threshold; the only surviving group-level structural difference was reduced fiber cross-section (FC) in PD-FOG relative to HC (Figure 1). The more informative distinctions between freezers and non-freezers were therefore not in mean WM organization but in how that organization related to behavior, with phenotype-sensitive correlations for off-state gait, attention/working memory, and verbal fluency and set-shifting (Figures 2–8). This contrasts with the findings of Zhou et al., 2023, who reported multiple fixel-based group differences via single-tensor FBA, emphasizing the added specificity gained from multi-shell, multi-tensor FBA. The FWE-corrected reduction in FC in PD-FOG relative to HC was generally localized to the corpus callosum and to left-lateralized pathways including the left thalamo-parietal tracts (Figure 1). FC is a macrostructural index of the cross-sectional area, or morphology, of a fiber bundle in template space and could therefore be interpreted as bundle-level atrophy or reduced caliber of this fiber (Raffelt et al., 2017). The left-lateralized pattern parallels prior reports of left globus pallidus–somatosensory cortex disconnection in PD-FOG (Miranda-Domínguez et al., 2020), whereas most single-tensor DTI studies have emphasized right-predominant, bilateral, or distributed abnormalities (Fling et al., 2013; Canu et al., 2015; Jin et al., 2021; Lin et al., 2024); this divergence may reflect the greater specificity of FBA in regions of crossing fibers and adds to evidence implicating interhemispheric and parietal connectivity in freezing (Li et al., 2018; Jin et al., 2021). The majority of physical performance-based measures examined were not found to be significantly related to FBA metrics, including observed FOG score. The clearest phenotype-specific gait finding involved off-state TUG and cerebellar pathways. In PD-nFOG, FDC correlated negatively with TUG (Figure 2a), and this correlation was significantly more negative than in PD-FOG (Figure 2b). This contrast could suggest a reorganization of the normal cerebellar structure–function relationship in freezers, consistent with work linking reduced gait automaticity in FOG to altered cortico-cerebellar coupling (Lench et al., 2025). MDS-UPDRS-III in the PD-FOG group showed a significant negative relationship with FC (Figure 2c) in fronto-pontine, fronto-striatal/prefrontal, callosal, and thalamic tracts. The presence of fronto-cortical tracts in PD-FOG and not in PD-nFOG aligns with prior reported maladaptive increases in frontal connectivity in freezers (Xie et al., 2025; Gan et al., 2023., Guo et al., 2020). Multiple phenotype-specific brain-behavior correlations emerged across neuropsychological measures. Although PD-FOG and PD-nFOG differed in mean performance on Digit Span Forward and Sequencing, they did not differ on Digit Span Backward (Table 2). Yet the coupling between Digit Span Backward and fiber density was significantly more positive in PD-FOG than in PD-nFOG , whereas the coupling between Digit Span Forward and fiber density showed the opposite pattern, being more positive in PD-nFOG than in PD-FOG (g = 1.35, P = 0.032; Figure 6). The PD-FOG Digit Span Backward relationship was also stronger than in healthy controls, for both FDC and FD (Figure 3a–b). This PD-FOG–specific effect was anatomically circumscribed, involving the anterior commissure, the rostrum of the corpus callosum, the right inferior longitudinal fasciculus, and the bilateral uncinate fasciculi, all commissural and ventral association pathways that support interhemispheric integration and the maintenance and manipulation of verbal information (Hall et al., 2018). We interpret this dissociation through the lens of cognitive reserve (Stern, 2009): the capacity to sustain cognitive performance despite neural compromise by drawing on the residual integrity and flexible recruitment of supporting networks. This could explain why performance on the more cognitively complex digit span backwards tracks the intra-axonal volume of commissural and association tracts in PD-FOG, consistent with freezers operating closer to the limits of that reserve. A similar dissociative pattern appears to have taken place in executive functioning. DKEFS Category Switching, which requires simultaneous semantic generation and set alternation, was significantly more positively correlated with FDC in PD-FOG than in PD-nFOG, with the effect distributed across frontoparietal, cingulum, inferior fronto-occipital, inferior longitudinal, and thalamic pathways (Figure 7a–c). Category Fluency likewise coupled more strongly with white matter in PD-FOG than in controls across all three metrics (Figure 4d–f). FOG is associated with reduced movement automaticity and a compensatory shift toward effortful, controlled processing (Lench et al., 2025; Vandenbossche et al., 2012). Tasks such as category switching and digit span backwards impose these demands, rapid semantic retrieval, set maintenance, and flexible reconfiguration—which depend on the frontoparietal, cingulum, and ventral occipito-temporal pathways implicated here (Giampiccolo et al., 2025; Goldstone et al., 2018; Herbet et al., 2018; Nelson, 2021). When those pathways are compromised, performance in PD-FOG could become correspondingly more dependent on their residual integrity (Hall et al., 2018; Shine et al., 2013a, 2013), which is the coupling we observe. Taken together, these findings suggest that simple and complex attention, verbal fluency, and set-shifting may not only separate PD-FOG from PD-nFOG on mean performance but may also help identify the patients whose cognition and gait are most constrained by network-level white matter vulnerability. This study has some key limitations. Firstly, the sample size, especially for the PD-FOG group, was relatively small (n = 16), which may limit generalizability, or impact the stability of statistical models. Our FOG classification was performed via rigorous independent consensus review, following established best practices for laboratory FOG detection (Snijders et al., 2008, 2012) and the updated consensus definition of FOG (Gilat et al., 2026). However, the best methodology for the accurate diagnosis of FOG is an ongoing topic of discussion within the literature (Bansal et al., 2023). It is possible that another classification method would have yielded better results, especially given that 3 (14%) of nFOG individuals in our study responded on the FOGQ within two standard deviations of the PD-FOG group mean (30.89 ± 2 * 10.58). This study was also cross-sectional, meaning that changes such as conversion from PD-nFOG to PD-FOG could not be observed. This could be potentially problematic, as structural changes or neuroanatomical vulnerability to PD-FOG development could be present in some subjects within our PD-nFOG group, obfuscating the true extent of our findings. A longitudinal component could also determine whether these correlational WM changes precede the development of PD-FOG, if they are a key component of PD-FOG development, or if they are compensatory changes to mitigate the functional impact of PD-FOG. While the focus of this work was on WM, it is possible that the inclusion of other neuroanatomical features (ROI volume, connectomic features, fMRI activation, and PET) in future analyses alongside these fixel-based metrics would provide a more complete picture of the exact involvement of white matter microstructure in PD-FOG. FBA-derived metrics are also not a direct measurement of WM microstructural organization, and it is possible that other methodologies (post-mortem dissection, NODDI, DKI, etc.) could differ. Finally, performance on many of the neuropsychological measures included here, such as the verbal fluency tasks, could be influenced by processing speed. As processing speed was not evaluated independently or included as a covariate, it is possible that it could drive some of the present structure-function results. In summary, multi-shell FBA revealed anatomically specific differences in WM–behavior coupling between PD patients with and without freezing of gait, even in the absence of a corrected group-level structural difference between them. The pattern—reversed cerebellar coupling with off-state gait and heightened reliance on association and commissural tracts for attention and executive performance—aligns with prior work implicating cerebellar automaticity and hemispheric lateralization in freezing (Lench et al., 2025). Rather than offering a diagnostic structural biomarker, these results identify candidate tract–behavior relationships and suggest that brief measures of simple and complex attention (digit span forward and backward), verbal fluency and switching, and off-state gait (TUG) may be useful probes for future studies of WM structure–function coupling in PD-FOG. Whether these relationships can inform prognosis or guide cueing-based and other interventions will require validation in larger, longitudinal, and multimodal cohorts. see this is how my frnd has written the discussion part now how can i write mine ill paste my results and everything Methods Participants A total of 45 participants were enrolled through the center for Neurodegeneration and Translational Neuroscience (CNTN)] ([www.nevadacntn.org](http://www.nevadacntn.org)).database comprising three group:15 healthy controls(HC),12 individuals with Parkinson’s disease and normal cognition(PD-NC),and 18 individuals with Parkinson’s disease and mild cognitive impairment (PD-MCI).Written informed consent was obtained from all participants prior to enrollment, in accordance with the Declaration of Helsinki, and all procedure were approved by Cleveland Clinic Institutional Review Board. Eligibility criteria required the absence of MRI contraindications including certain implants or metallic foreign bodies in the eye, as well as no prior history of stroke, brain tumor, psychiatric conditions, or neurological conditions other than PD. Clinical and Neuropsychological Assessment All participants underwent clinical evaluation according to Level II Movement Disorder Society (MDS) criteria(Dubois, et al. 2007). Collected demographics included age at recruitment, sex, years of education (YOE), and dominant hand. For individuals with PD, additional clinical variables were recorded including disease duration(DDX), Levodopa Equivalent Daily Dose(LEDD), Unified Parkinson’s Disease Rating Scale score in the OFF state(UPDRS-OFF), and affected side.A summary of demographic and clinical characteristics is presented in Table 1 and Table 2. A comprehensive neuropsychological battery was administered to evaluate cognitive function across five domains. Attention and working memory were assessed via the Wechsler Adult Intelligence Scale Fourth Edition(Ryan, et al. 2012) (WAIS-IV) Digit span Forward and Backward subtests, as well as the Dementia Rating Scale-2 (Matteau, et al. 2011)(DRS-2) Attention subscale. Executive function was evaluated through the Delis-Kaplan Executive Function System(Heled, et al. 2012) (D-KEFS) Letter Fluency, Category Fluency Total Raw Score, and Category Switching tests. Language function was assessed using the Boston Naming Test (BNT). Verbal memory was assessed with Rey Auditory Verbal Test (RAVLT)(Loring, et al. 2023) while visual memory was assessed via Brief Visuospatial Memory test (BVMT) Learning and Delayed Recall trials, Visuospatial memory was assessed via DRS-2 Construction subtest. A summary of neuropsychological performance across groups is provided in Table 3. Determination of Cognitive Status A panel of clinical experts reviewed all clinical and neuropsychological assessments to confirm the presence or absence of mild cognitive impairment. PD-MCI was classified in accordance with the Litvan Level II Movement Disorder Society (MDS) (Litvan, et al. 2012)diagnostic criteria, using an impairment threshold of ≥ 1.5 standard deviations below normative expectations on at least two tests within a cognitive domain. Based on these criteria, the study cohort was divided into three groups:18 participants with PD-MCI, 12 with PD-NC, and 15 with healthy controls (HC) MRI data acquisition All scans were acquired on a 32-channel transmit receive head coil using a 3T Siemens Skyra scanner. Diffusion MRI (dMRI) data were acquired using the following parameters: TR=5218 ms, TE=100 ms, flip angle=780, with 1.5×1.5×1.5 mm3 voxel resolution, in-plane acceleration=2, and multiband factor=3. Diffusion weighting comprised 25 b0 volumes interleaved among 213 diffusion weighted volumes across three shells (b=500,1000,2500 s/mm2). The phase-encoding direction was P>>A, with an effective echo spacing of 0.47 ms and a total readout time of 63.45ms. Opposite phase-encoding A>>P b0 images were additionally acquired to correct for susceptibility and eddy current distortions(Andersson and Sotiropoulos 2016). High-resolution T1 - weighted structural images were obtained using a magnetization-prepared rapid gradient echo (MPRAGE) sequence with 1×1×1 mm isotropic resolution and 176 sagittal slices (TR=2300 ms, TE=2.96 ms, inversion time =900 ms, flip angle=90). Total acquisition time was approximately 25 minutes. Data Processing All diffusion MRI data were processed using the MRtrix3 freeware. Noise reduction was first applied via the dwidenoise(Tournier, et al. 2019) command using a Marchenko-Pastur principal component analysis filter before further preprocessing(Veraart, et al. 2016a; Veraart, et al. 2016b). Head motion and eddy-current distortions were subsequently corrected using dwifslpreproc(Tournier, et al. 2019) command, applying FSL-based(Smith, et al. 2004) eddy-current correction via sub-voxel shifting and motion realignment, while additionally correcting for b=0 susceptibility distortion(Andersson and Sotiropoulos 2016). A brain mask was then computed using dwi2mask(Tournier, et al. 2019) and applied through dwibiascorrect(Tournier, et al. 2019) command

???

?

Final Discussion for Multi-Shell Fixel-Based Analysis in Parkinson’s Disease Download the Word document: PD_FBA_Discussion_Final.docx Executive summary The central finding of this study is that cognitive impairment in Parkinson’s disease was expressed more clearly through altered white matter–cognition relationships than through large corrected mean structural differences between cognitive subgroups. At the whole-brain level, the only family-wise-error-corrected group difference was higher fiber cross-section in PD-MCI relative to healthy controls. By contrast, domain-specific analyses showed repeated reductions in the strength of structure–cognition coupling in PD-MCI relative to PD-NC, most consistently for fiber density, across attention/working memory, executive function, learning and memory, and visuospatial performance. Taken together, these findings support a distributed network interpretation of PD-MCI, in which cognitive decline is associated with weakening relationships between cognition and fiber-specific white matter integrity across commissural, projection, and association pathways, rather than with a single dominant tract lesion pattern. This interpretation is consistent with the FBA framework, which separates microstructural fiber density from macrostructural bundle morphology, and with contemporary models of Parkinson’s disease cognition that emphasize large-scale disconnection across frontostriatal, thalamocortical, and posterior cortical systems. Key statistical summary Contrast Measure WM metric Mean t ± SD Mean p_FWE ± SD PD-MCI > HC Mean difference_3group FC 3.97 ± 0.33 0.031 ± 0.011 PD-NC > PD-MCI DRS Attention FC 3.47 ± 0.52 0.038 ± 0.009 PD-NC > PD-MCI DRS Attention FD 3.58 ± 0.65 0.025 ± 0.013 PD-NC > PD-MCI DRS Attention FDC 3.25 ± 0.50 0.026 ± 0.012 PD-MCI positive within-group Category Fluency Total Raw Score FD 3.65 ± 0.27 0.042 ± 0.005 PD-NC > PD-MCI Category Switching FD 3.06 ± 0.39 0.028 ± 0.011 PD-NC > PD-MCI Category Switching FDC 3.26 ± 0.39 0.042 ± 0.006 PD-MCI > PD-NC BVMT Delayed Recall FC 3.70 ± 0.14 0.043 ± 0.004 PD-NC > PD-MCI DRS Memory FC 3.95 ± 0.77 0.018 ± 0.013 PD-NC > PD-MCI DRS Memory FD 3.51 ± 0.62 0.023 ± 0.015 PD-NC > PD-MCI DRS Memory FDC 3.78 ± 0.78 0.024 ± 0.015 PD-NC > PD-MCI DRS Construction FC 3.52 ± 0.55 0.027 ± 0.013 PD-NC > PD-MCI DRS Construction FD 3.10 ± 0.51 0.033 ± 0.011 PD-NC > PD-MCI DRS Construction FDC 3.51 ± 0.55 0.037 ± 0.008 Section flow Executive summary Main findings Group-level FC interpretation Attention and working memory Executive function Learning and memory Visuospatial function Biological interpretation of FD FC and FDC Comparison with previous FBA and DTI studies Clinical significance Strengths Limitations Future directions Conclusion Show code Discussion Main findings This study used multi-shell diffusion MRI and fixel-based analysis to characterize fiber-specific white matter changes across healthy controls, Parkinson’s disease with normal cognition, and Parkinson’s disease with mild cognitive impairment. The dominant pattern was not a large corrected structural separation between PD-NC and PD-MCI, but a repeated alteration in how white matter related to cognitive performance across disease stages. Only one whole-brain group effect survived correction, namely higher fiber cross-section in PD-MCI relative to healthy controls. In contrast, attention/working memory, executive function, learning and memory, and visuospatial analyses all showed stronger cognition–white matter relationships in PD-NC than PD-MCI, most consistently for fiber density. These findings suggest that at the predementia stage, cognitive decline may be captured more sensitively by disturbed structure–cognition coupling across distributed networks than by large uniform between-group reductions in fixel metrics. That interpretation is aligned with MDS concepts of PD-MCI as a clinically meaningful transitional state and with the technical premise of FBA, which is designed to detect changes in specific fiber populations even in regions of complex crossing architecture. [1,8,17,26] A second overarching finding is that the implicated pathways were not confined to one anatomical system. The significant effects repeatedly involved the corpus callosum, posterior thalamic and striatal projections, optic radiations, fronto-orbital pathways, and long temporo-occipital association tracts. This recurrence across domains argues against an isolated tract explanation for PD-MCI and instead supports a network-level structural disconnection model. Such a reading is consistent with classical theories of distributed cognition and with modern accounts of Parkinson’s disease cognition, which emphasize interacting frontostriatal, thalamocortical, temporolimbic, and posterior cortical systems rather than a purely frontal dysexecutive syndrome. [17,19–21] Group-level FC interpretation The isolated whole-brain fiber cross-section increase in PD-MCI relative to healthy controls requires careful interpretation. Within the FBA framework, FC reflects bundle cross-sectional morphology in template space rather than within-fixel axonal packing. Accordingly, increased FC should not be interpreted as simple evidence of healthier tissue. A more cautious interpretation is that this effect may reflect selective preservation of bundle caliber, stage-dependent tract remodeling, or nonlinear reorganization in vulnerable pathways. Importantly, FC increases are not unprecedented in the PD FBA literature. Li et al. reported increased FC in the superior cerebellar peduncle in PD, and Andica et al. described increased FC or FDC in corticospinal and striatal-thalamo-cortical pathways in idiopathic or tremor-dominant PD, raising the possibility that macrostructural expansion may coexist with clinical burden in some phenotypes or stages. [8,10,14,15,26] The anatomical emphasis of the present FC finding on callosal and posterior pathways is biologically meaningful. The corpus callosum is the major commissural conduit for interhemispheric integration, and tractography-based work has shown that its segments contain organized prefrontal, sensorimotor, parietal, temporal, and occipital projections. Normal aging also affects callosal microstructure and macrostructure heterogeneously, particularly along anterior-posterior gradients, which is relevant because aging and neurodegeneration can influence FC in different ways. [23,24,35] In Parkinson’s disease, callosal abnormalities have repeatedly been linked to cognitive impairment using both DTI and FBA approaches, including the studies of Bledsoe et al. and Liao et al. [9,12] Relative to those reports, the present study identified a more subtle group-level phenotype, which may reflect tighter clinical matching, Level II cognitive classification, and the possibility that cognition-linked network abnormalities are more prominent than mean tract loss in this cohort. [1,9,12] Attention and working memory Attention and working memory showed one of the clearest domain-level patterns. DRS Attention demonstrated stronger positive relationships in PD-NC than PD-MCI across FD, with convergent FC and FDC effects in overlapping fixels. The implicated pathways included the corpus callosum, optic radiations, thalamo-occipital and striato-occipital projections, the striato-frontal/orbital tract, and the uncinate fasciculus. This anatomy is coherent with contemporary models of attention because attentional control depends on distributed networks that integrate posterior sensory processing, thalamic gating, frontal control, and interhemispheric coordination. Classical and modern network accounts emphasize that attention is implemented by large-scale interacting systems rather than by a single cortical node, and the thalamus is increasingly recognized as an active regulator of information selection and relay rather than a passive conduit. [19,20,36] The PD-NC > PD-MCI pattern therefore suggests that attentional performance remains tightly coupled to the integrity of interhemispheric and posterior projection systems while cognition is still clinically preserved, but that this structure–function relationship weakens once MCI develops. This reading is consistent with prior Parkinson’s disease imaging studies that linked cognitive impairment to posterior white matter abnormalities and with FBA work showing that visual dysfunction and low visual performance are associated with callosal and posterior thalamic degeneration before overt dementia. [4,25,32,33] It also accords with the present anatomical emphasis on optic radiations and thalamo-occipital pathways, which suggests that attentional difficulty in PD-MCI may emerge from broader posterior-sensory network disruption rather than from frontal dysfunction alone. [4,19,25,32,33] Executive function Executive measures showed a related but not identical pattern. Category Switching displayed stronger positive FD–performance coupling in PD-NC than PD-MCI, with overlapping FDC effects, predominantly within superior thalamic radiations, thalamo-precentral and thalamo-postcentral tracts, corticospinal fibers, parieto-occipital pontine pathways, and callosal connections. This topology is anatomically plausible for executive control. Basal ganglia models emphasize parallel thalamocortical circuits linking association cortex, basal ganglia, and thalamus, whereas systems neuroscience models of attention and control place strong weight on integrated cortical-subcortical loops for response selection, set-shifting, and conflict resolution. [19,21,36] The corticospinal component is also noteworthy because reaction-time and response-efficiency measures have been associated with corticospinal microstructure in humans, suggesting that executive tasks with strong speeded or switching demands may draw on sensorimotor pathway integrity as well as cognitive control circuitry. [37] An important nuance was that Category Fluency showed a significant positive FD relationship within PD-MCI rather than a stronger PD-NC > PD-MCI contrast. Because this was a within-group association, it may indicate that once executive-language vulnerability is established, performance becomes increasingly constrained by the residual integrity of a narrower tract set. That interpretation should remain tentative. No direct citation was found for an FBA-specific Category Fluency pattern of this exact type in PD-MCI, and the result is best treated as hypothesis-generating. Even so, the broader executive findings remain consistent with the well-established view that executive dysfunction is an early and frequent component of cognitive impairment in PD. [1,17,18,25] Learning and memory Learning and memory yielded the most extensive white matter–cognition pattern in the present cohort. DRS Memory scores showed stronger positive relationships in PD-NC than PD-MCI for FD, with convergent FC and FDC effects in overlapping fixels. Involved pathways included callosal fibers, optic radiations, thalamo-occipital and striato-occipital projections, striato-frontal/orbital connections, and the inferior longitudinal fasciculus. This distribution implicates a broad posterior–temporal–frontal scaffold for memory rather than a narrowly medial temporal account. Such an interpretation is consistent with distributed network models of memory and with tractography atlases showing that long association pathways support integration among visual, temporal, limbic, and frontal systems. [20,22] It is also consistent with Parkinson’s disease imaging studies showing that cognition and memory relate to callosal and posterior white matter integrity, and with broader FBA work in neurodegeneration indicating that microstructural fiber density can track cognition even when mean group effects are subtle. [4,12,25,30,38] The opposite-direction BVMT Delayed Recall effect deserves separate treatment. Here, FC showed a stronger positive relationship with performance in PD-MCI than in PD-NC. Because the effect emerged in FC rather than FD, one possibility is that visuospatial memory in PD-MCI is influenced more strongly by bundle morphology than by microstructural fiber density alone. This could reflect tract-specific reorganization, selective macrostructural vulnerability, or compensatory reliance on preserved bundle geometry. However, no direct citation was found for an FC-dominant BVMT delayed recall effect of this exact type in PD-MCI. The most appropriate stance is therefore caution: the finding is intriguing, potentially important, and worthy of replication, but it should not be overinterpreted beyond the present data. [10,14,15] Visuospatial function Visuospatial performance, indexed by DRS Construction, followed the broader pattern of stronger white matter–cognition coupling in PD-NC than PD-MCI across FD, FC, and FDC. This is directionally consistent with the wider PD literature, in which visuospatial dysfunction often signals more posterior network involvement and greater risk of future dementia. [17,18,33] Recent FBA studies have similarly implicated callosal, posterior thalamic, and occipito-association pathways in visual dysfunction and related cognitive vulnerability. [32,33] In the present cohort, however, interpretation should remain cautious because the DRS Construction distribution showed a ceiling effect in PD-NC, which reduces the reliability of slope estimates. The result is therefore best treated as supportive of the broader posterior-network story rather than as a central independent effect. Biological interpretation of FD, FC, and FDC Across domains, the same classes of pathways reappeared: commissural fibers, posterior thalamic and striatal projection systems, and long-range association tracts linking frontal, temporal, and occipital cortex. This recurrence argues against a single-tract explanation of PD-MCI and instead favors a distributed structural disconnection model. Such a model is compatible with classical network accounts of cognition, with basal ganglia–thalamocortical models of executive control, and with the Parkinson’s disease literature showing that white matter abnormalities can precede marked cortical atrophy. [5,17,20,21] In practical terms, the present results suggest that PD-MCI may involve weakening integration among interhemispheric transfer systems, posterior sensory-associative pathways, and control circuits that bridge striatal, thalamic, and frontal territories. The relative prominence of FD is also biologically coherent. FBA was designed specifically to distinguish microstructural fiber density from macrostructural bundle morphology in individual fiber populations within a voxel. FD is proportional to the intra-axonal volume of the relevant fixel and is therefore the most directly microstructural FBA metric; FC reflects bundle cross-section; and FDC captures the combined effects of both. [8,26,43] In clinically matched cohorts with subtle meanshisorrect

Methods Participants A total of 45 participants were enrolled through the center for Neurodegeneration and Translational Neuroscience (CNTN)] (www.nevadacntn.org).database comprising three group:15 healthy controls(HC),12 individuals with Parkinson’s disease and normal cognition(PD-NC),and 18 individuals with Parkinson’s disease and mild cognitive impairment (PD-MCI).Written informed consent was obtained from all participants prior to enrollment, in accordance with the Declaration of Helsinki, and all procedure were approved by Cleveland Clinic Institutional Review Board. Eligibility criteria required the absence of MRI contraindications including certain implants or metallic foreign bodies in the eye, as well as no prior history of stroke, brain tumor, psychiatric conditions, or neurological conditions other than PD. Clinical and Neuropsychological Assessment All participants underwent clinical evaluation according to Level II Movement Disorder Society (MDS) criteria(Dubois, et al. 2007). Collected demographics included age at recruitment, sex, years of education (YOE), and dominant hand. For individuals with PD, additional clinical variables were recorded including disease duration(DDX), Levodopa Equivalent Daily Dose(LEDD), Unified Parkinson’s Disease Rating Scale score in the OFF state(UPDRS-OFF), and affected side.A summary of demographic and clinical characteristics is presented in Table 1 and Table 2. A comprehensive neuropsychological battery was administered to evaluate cognitive function across five domains. Attention and working memory were assessed via the Wechsler Adult Intelligence Scale Fourth Edition(Ryan, et al. 2012) (WAIS-IV) Digit span Forward and Backward subtests, as well as the Dementia Rating Scale-2 (Matteau, et al. 2011)(DRS-2) Attention subscale. Executive function was evaluated through the Delis-Kaplan Executive Function System(Heled, et al. 2012) (D-KEFS) Letter Fluency, Category Fluency Total Raw Score, and Category Switching tests. Language function was assessed using the Boston Naming Test (BNT). Verbal memory was assessed with Rey Auditory Verbal Test (RAVLT)(Loring, et al. 2023) while visual memory was assessed via Brief Visuospatial Memory test (BVMT) Learning and Delayed Recall trials, Visuospatial memory was assessed via DRS-2 Construction subtest. A summary of neuropsychological performance across groups is provided in Table 3. Determination of Cognitive Status A panel of clinical experts reviewed all clinical and neuropsychological assessments to confirm the presence or absence of mild cognitive impairment. PD-MCI was classified in accordance with the Litvan Level II Movement Disorder Society (MDS) (Litvan, et al. 2012)diagnostic criteria, using an impairment threshold of ≥ 1.5 standard deviations below normative expectations on at least two tests within a cognitive domain. Based on these criteria, the study cohort was divided into three groups:18 participants with PD-MCI, 12 with PD-NC, and 15 with healthy controls (HC) MRI data acquisition All scans were acquired on a 32-channel transmit receive head coil using a 3T Siemens Skyra scanner. Diffusion MRI (dMRI) data were acquired using the following parameters: TR=5218 ms, TE=100 ms, flip angle=780, with 1.5×1.5×1.5 mm3 voxel resolution, in-plane acceleration=2, and multiband factor=3. Diffusion weighting comprised 25 b0 volumes interleaved among 213 diffusion weighted volumes across three shells (b=500,1000,2500 s/mm2). The phase-encoding direction was P>>A, with an effective echo spacing of 0.47 ms and a total readout time of 63.45ms. Opposite phase-encoding A>>P b0 images were additionally acquired to correct for susceptibility and eddy current distortions(Andersson and Sotiropoulos 2016). High-resolution T1 - weighted structural images were obtained using a magnetization-prepared rapid gradient echo (MPRAGE) sequence with 1×1×1 mm isotropic resolution and 176 sagittal slices (TR=2300 ms, TE=2.96 ms, inversion time =900 ms, flip angle=90). Total acquisition time was approximately 25 minutes. Data Processing All diffusion MRI data were processed using the MRtrix3 freeware. Noise reduction was first applied via the dwidenoise(Tournier, et al. 2019) command using a Marchenko-Pastur principal component analysis filter before further preprocessing(Veraart, et al. 2016a; Veraart, et al. 2016b). Head motion and eddy-current distortions were subsequently corrected using dwifslpreproc(Tournier, et al. 2019) command, applying FSL-based(Smith, et al. 2004) eddy-current correction via sub-voxel shifting and motion realignment, while additionally correcting for b=0 susceptibility distortion(Andersson and Sotiropoulos 2016). A brain mask was then computed using dwi2mask(Tournier, et al. 2019) and applied through dwibiascorrect(Tournier, et al. 2019) command with the ANTs N4 algorithm(Tustison, et al. 2010) to correct for bias field across all diffusion voulmes using b=0 data. Global intensity normalization was performed using the dwinormalize(Tournier, et al. 2019) command(CA. 2017; Dhollander, et al. 2021), from which a white matter mask and fractional anisotropy map were additionally obtained. A group averaged single-shell white matter response function was then estimated using the dwi2response(Tournier, et al. 2013; Tournier, et al. 2019) and response mean(Tournier, et al. 2019) functions. Finally, diffusion data were upsampled to an isotropic voxel size of 1.25 mm via mgrid(Tournier, et al. 2019), and a corresponding upsampled brain mask was generated using dwi2mask(Tournier, et al. 2019) prior to fiber orientation distribution estimation to optimize the accuracy of downstream analyses. Fixel-Based Analysis Pipeline Fiber orientation distributions (FODs) were estimated within each voxel using constrained spherical deconvolution (CSD)(Jeurissen, et al. 2013; Tournier, et al. 2007) with the group averaged white matter response function, restricted to the brain mask to avoid estimates in non-brain tissue(Tournier, et al. 2007). Diffusion weighted volumes were extracted via dwiextract(Tournier, et al. 2019) and input to dwi2fod csd(Tournier, et al. 2019) to generate voxel-wise FODs capturing the distribution and relative density of fiber orientations. All individual FOD images were registered to a common group template space via mrregister(Raffelt, et al. 2012a; Raffelt, et al. 2011) to construct a study-specific FOD template, and a corresponding template masks via mrtransform(Raffelt, et al. 2012a; Raffelt, et al. 2012b) after warping subject masks to the template space. FOD images were then segmented into fixels using fod2fixel (Tournier, et al. 2019) to determine the number and orientations of discrete fiber populations within each voxel, and fixel correspondence between the individual subjects and the group template was demonstrated via fixel correspondence(Tournier, et al. 2019), producing a fixel-wise data matrix of fiber density (FD) values for every subject. Fiber cross-section (FC) was computed within the template space using warp2metric(Raffelt, et al. 2017; Tournier, et al. 2019), capturing macrostructural bundle size not reflected by FD alone(Raffelt, et al. 2017), and the combined measure fiber density cross-section (FDC) was derived as the product of FD and FC via mrcal(Tournier, et al. 2019), enabling simultaneous quantification of microstructural and macrostructural white matter alterations. Whole brain probabilistic tractography was generated from the FOD template via tckgen-act(Smith, et al. 2012), and spherical-deconvolution informed filtering of tractograms(SIFT)(Smith, et al. 2013) was applied via tcksift(Tournier, et al. 2019) to reduce reconstruction biases in streamline densities. A fxel-fixel connectivity matrix was subsequently constructed from the filtered tractogram via fixelconncetivity(Tournier, et al. 2019), and connectivity-based smoothing was performed via fixelfilter-smooth(Tournier, et al. 2019) to improve sensitivity to white mater alterations that are spatially coherent along fiber tracts. Statistical analysis Fixel-based correlation analyses were performed using a general linear model to examine the relationship between FD, FC, and FDC and clinical scores. Age, sex, years of education, dominant hand, absolute motion and intracranial volumes were included as covariates of no interest across all models. For participants with PD, additional separate models were constructed to further account for affected side and Levodopa Equivalent Daily Dose (LEDD). Statistical significance was determined at a family-wise error corrected threshold of pcorr < 0.05 at the fixel level. Fixels surviving this threshold were visualized in mrview(Tournier J 2012; Tournier, et al. 2019) using the white matter FOD template as an underlay, and TractSeg(Wasserthal, et al. 2018) was subsequently applied to identify significant regions of interest from the MRtrix-derived tractography. Continuous demographic variables were compared across PD-MCI, PD-NC, and HC groups using one-way ANOVA, pairwise differences between PD-MCI and PD-NC were examined using the Mann-Whitney test, and categorical variables were compared across all three groups using the chi-square test. Results Demographics and Clinical Characteristics HC, PD-NC, and PD-MCI groups did not significantly differ across demographic variables, including age (p=0.40), sex (p=0.20), years of education (p=0.61) Table 1. Dominant hand significantly differed across groups (p=0.01), with pairwise showing significant differences between PD-MCI and HC (p<0.001), PD-NC and HC (p=0.040), and PD-MCI and PD-NC (p=0.025). Within the PD groups, disease duration (p=0.74), Levodopa Equivalent Daily Dose (p=0.43), UPDRS-OFF (p=0.70), and affected side (p=0.74) did not significantly differ between PD-MCI and PD-NC Table 2. No significant differences were observed between groups in MRI quality measures including absolute motion (p=0.75) and intracranial volume (p=0.97) Table 4. Significant group differences were observed across several neuropsychological measures, including Category Fluency Total Raw Score (p=0.001), with PD-MCI performing significantly worse than HC (p=0.0003); RAVLT Delayed Recall (p=0.003), with PD-MCI performing significantly worse than both HC (p=0.002) and PD-NC (p=0.04); DRS-2 Memory (p=0.012), with PD-MCI performing significantly worse than PD-NC (p=0.006); and BVMT Delayed Recall (p=0.04), with PD-MCI performing significantly worse than PD-NC (p=0.016). RAVLT Learning Over Trials showed a trend toward significance across groups (p=0.06), with PD-MCI performing significantly worse than HC (p=0.025), but no significant difference between PD-MCI and PD-NC (p=0.53). Across all significant neuropsychological measures, PD-MCI demonstrated the poorest performance compared to both PD-NC and HC groups. No significant group differences were observed across attention and working memory measures including WAIS-IV Digit Span Backward(p=0.53), WAIS-IV Digit Span Forward (p=0.35), and DRS-2 Attention (p=0.51), Executive function measures including D-KEFS Letter Fluency (p=0.30) and D-KEFS Category Switching (p=0.14), Language function as assessed by Boston Naming Test (p=0.45), BVMT Learning (p=0.42), Visuospatial function as assessed by the DRS-2 Construction subscale (p=0.27).Table 3 Fixel Based Analysis At the FWE-corrected threshold, the only group difference in fixel metrics was elevated FC in PD-MCI relative to HC (p=0.031±0.011, t=3.97±0.33; Figure 1), localized to left parieto-occipital pontine tract and corpus callosum. PD-MCI and PD-NC did not differ on FD, FC, or FDC. Neuropsychological Measures PD-NC vs PD-MCI I. Attention/Working Memory In the attention/working memory domain, DRS-2 Attention Scores showed a significantly stronger positive correlation with FD in the PD-NC group compared to the PD-MCI group(p=0.025±0.013; Figure).Significant fixels were localized predominantly to the right hemisphere, involving the bilateral optic radiations (OR), corpus callosum (CC_1, CC_3), left striato frontal/orbital tract (ST_FO L), left uncinate fasciculus (UF L), bilateral striato occipital tracts (ST_OCC), and bilateral thalamo occipital tracts (T_OCC). This effect was also significant across FC(p=0.038±0.009, t=3.47±0.52) and FDC (p=0.026±0.012, t=3.25±0.50) in overlapping fixels(Supplementary Figure X, Supplementary Table X). II. Executive Function Within the executive function domain, Category Switching showed a significantly stronger positive correlation with FD in PD-NC compared to PD-MCI(p= 0.028±0.011, t=3.06±0.39; Figure ).Significant fixels were predominantly left hemisphere and thalamo motor in distribution, involving the bilateral superior thalamic radiation (STR), bilateral thalamo postcentral tract (T_POSTC), corpus callosum (CC_7), left striato postcentral tract (ST_POSTC L), left corticospinal tract (CST L), left parieto occipital pontine tract (POPT L), left thalamo precentral tract (T_PREC L), and left thalamo parietal tract (T_PAR L). This same pattern PD-NC> PD-MCI was also significant across FDC( p=0.042±0.006, t=3.26±0.29; Supplementary figure ). In contrast, Category Fluency Raw Score, which showed a significant positive correlation with FD within PD-MCI(p=0.042±0.005, t=3.65±0.27; Supplementary figure ) a relationship not observed in PD-NC. III. Learning Memory Within the learning and memory domain, DRS-2 Memory Scores showed a significantly stronger positive correlation with FD in PD-NC compared to PD-MCI(p=0.023±0.015, t=3.51±0.62; Figure) . Significant fixels were widespread and predominantly right hemisphere, involving the bilateral optic radiations (OR), corpus callosum (CC_2, CC_3), bilateral thalamo occipital tract (T_OCC), bilateral striato occipital tract (ST_OCC), left striato frontal/orbital tract (ST_FO L), and right inferior longitudinal fasciculus (ILF R). This same pattern (PD NC > PD MCI) was also significant across FC (p=0.018±0.013, t=3.95±0.77; Supplementary Figure) and FDC (p=0.024±0.015, t=3.78±0.78; Supplementary Figure) in overlapping fixels . Notably, an opposite trend was observed for BVMT Delayed Recall, which showed a significantly positive correlation with FC in PD-MCI>PD-NC(p=0.043±0.004, t=3.70±0.14; Supplementary Figure) IV. Visuospatial Memory Within the visuospatial memory domain, DRS 2 Construction scores showed a significantly stronger positive correlation with FD in PD NC compared to PD MCI (p=0.033±0.011, t=3.10±0.51 ; Supplementary Figure 9a). This same pattern (PD NC greater than PD MCI) was also significant across FC (p=0.027±0.013, t=3.52±0.55 ; Supplementary Figure 9a) and FDC (p=0.037±0.008, t=3.51±0.55 ; Supplementary Figure 9a) in overlapping fixels. Interpretation of this contrast is limited by a ceiling effect in DRS 2 Construction performance, with minimal variance observed insiscussionectionorrect

Methods Participants A total of 45 participants were enrolled through the center for Neurodegeneration and Translational Neuroscience (CNTN)] (www.nevadacntn.org).database comprising three group:15 healthy controls(HC),12 individuals with Parkinson’s disease and normal cognition(PD-NC),and 18 individuals with Parkinson’s disease and mild cognitive impairment (PD-MCI).Written informed consent was obtained from all participants prior to enrollment, in accordance with the Declaration of Helsinki, and all procedure were approved by Cleveland Clinic Institutional Review Board. Eligibility criteria required the absence of MRI contraindications including certain implants or metallic foreign bodies in the eye, as well as no prior history of stroke, brain tumor, psychiatric conditions, or neurological conditions other than PD. Clinical and Neuropsychological Assessment All participants underwent clinical evaluation according to Level II Movement Disorder Society (MDS) criteria(Dubois, et al. 2007). Collected demographics included age at recruitment, sex, years of education (YOE), and dominant hand. For individuals with PD, additional clinical variables were recorded including disease duration(DDX), Levodopa Equivalent Daily Dose(LEDD), Unified Parkinson’s Disease Rating Scale score in the OFF state(UPDRS-OFF), and affected side.A summary of demographic and clinical characteristics is presented in Table 1 and Table 2. A comprehensive neuropsychological battery was administered to evaluate cognitive function across five domains. Attention and working memory were assessed via the Wechsler Adult Intelligence Scale Fourth Edition(Ryan, et al. 2012) (WAIS-IV) Digit span Forward and Backward subtests, as well as the Dementia Rating Scale-2 (Matteau, et al. 2011)(DRS-2) Attention subscale. Executive function was evaluated through the Delis-Kaplan Executive Function System(Heled, et al. 2012) (D-KEFS) Letter Fluency, Category Fluency Total Raw Score, and Category Switching tests. Language function was assessed using the Boston Naming Test (BNT). Verbal memory was assessed with Rey Auditory Verbal Test (RAVLT)(Loring, et al. 2023) while visual memory was assessed via Brief Visuospatial Memory test (BVMT) Learning and Delayed Recall trials, Visuospatial memory was assessed via DRS-2 Construction subtest. A summary of neuropsychological performance across groups is provided in Table 3. Determination of Cognitive Status A panel of clinical experts reviewed all clinical and neuropsychological assessments to confirm the presence or absence of mild cognitive impairment. PD-MCI was classified in accordance with the Litvan Level II Movement Disorder Society (MDS) (Litvan, et al. 2012)diagnostic criteria, using an impairment threshold of ≥ 1.5 standard deviations below normative expectations on at least two tests within a cognitive domain. Based on these criteria, the study cohort was divided into three groups:18 participants with PD-MCI, 12 with PD-NC, and 15 with healthy controls (HC) MRI data acquisition All scans were acquired on a 32-channel transmit receive head coil using a 3T Siemens Skyra scanner. Diffusion MRI (dMRI) data were acquired using the following parameters: TR=5218 ms, TE=100 ms, flip angle=780, with 1.5×1.5×1.5 mm3 voxel resolution, in-plane acceleration=2, and multiband factor=3. Diffusion weighting comprised 25 b0 volumes interleaved among 213 diffusion weighted volumes across three shells (b=500,1000,2500 s/mm2). The phase-encoding direction was P>>A, with an effective echo spacing of 0.47 ms and a total readout time of 63.45ms. Opposite phase-encoding A>>P b0 images were additionally acquired to correct for susceptibility and eddy current distortions(Andersson and Sotiropoulos 2016). High-resolution T1 - weighted structural images were obtained using a magnetization-prepared rapid gradient echo (MPRAGE) sequence with 1×1×1 mm isotropic resolution and 176 sagittal slices (TR=2300 ms, TE=2.96 ms, inversion time =900 ms, flip angle=90). Total acquisition time was approximately 25 minutes. Data Processing All diffusion MRI data were processed using the MRtrix3 freeware. Noise reduction was first applied via the dwidenoise(Tournier, et al. 2019) command using a Marchenko-Pastur principal component analysis filter before further preprocessing(Veraart, et al. 2016a; Veraart, et al. 2016b). Head motion and eddy-current distortions were subsequently corrected using dwifslpreproc(Tournier, et al. 2019) command, applying FSL-based(Smith, et al. 2004) eddy-current correction via sub-voxel shifting and motion realignment, while additionally correcting for b=0 susceptibility distortion(Andersson and Sotiropoulos 2016). A brain mask was then computed using dwi2mask(Tournier, et al. 2019) and applied through dwibiascorrect(Tournier, et al. 2019) command with the ANTs N4 algorithm(Tustison, et al. 2010) to correct for bias field across all diffusion voulmes using b=0 data. Global intensity normalization was performed using the dwinormalize(Tournier, et al. 2019) command(CA. 2017; Dhollander, et al. 2021), from which a white matter mask and fractional anisotropy map were additionally obtained. A group averaged single-shell white matter response function was then estimated using the dwi2response(Tournier, et al. 2013; Tournier, et al. 2019) and response mean(Tournier, et al. 2019) functions. Finally, diffusion data were upsampled to an isotropic voxel size of 1.25 mm via mgrid(Tournier, et al. 2019), and a corresponding upsampled brain mask was generated using dwi2mask(Tournier, et al. 2019) prior to fiber orientation distribution estimation to optimize the accuracy of downstream analyses. Fixel-Based Analysis Pipeline Fiber orientation distributions (FODs) were estimated within each voxel using constrained spherical deconvolution (CSD)(Jeurissen, et al. 2013; Tournier, et al. 2007) with the group averaged white matter response function, restricted to the brain mask to avoid estimates in non-brain tissue(Tournier, et al. 2007). Diffusion weighted volumes were extracted via dwiextract(Tournier, et al. 2019) and input to dwi2fod csd(Tournier, et al. 2019) to generate voxel-wise FODs capturing the distribution and relative density of fiber orientations. All individual FOD images were registered to a common group template space via mrregister(Raffelt, et al. 2012a; Raffelt, et al. 2011) to construct a study-specific FOD template, and a corresponding template masks via mrtransform(Raffelt, et al. 2012a; Raffelt, et al. 2012b) after warping subject masks to the template space. FOD images were then segmented into fixels using fod2fixel (Tournier, et al. 2019) to determine the number and orientations of discrete fiber populations within each voxel, and fixel correspondence between the individual subjects and the group template was demonstrated via fixel correspondence(Tournier, et al. 2019), producing a fixel-wise data matrix of fiber density (FD) values for every subject. Fiber cross-section (FC) was computed within the template space using warp2metric(Raffelt, et al. 2017; Tournier, et al. 2019), capturing macrostructural bundle size not reflected by FD alone(Raffelt, et al. 2017), and the combined measure fiber density cross-section (FDC) was derived as the product of FD and FC via mrcal(Tournier, et al. 2019), enabling simultaneous quantification of microstructural and macrostructural white matter alterations. Whole brain probabilistic tractography was generated from the FOD template via tckgen-act(Smith, et al. 2012), and spherical-deconvolution informed filtering of tractograms(SIFT)(Smith, et al. 2013) was applied via tcksift(Tournier, et al. 2019) to reduce reconstruction biases in streamline densities. A fxel-fixel connectivity matrix was subsequently constructed from the filtered tractogram via fixelconncetivity(Tournier, et al. 2019), and connectivity-based smoothing was performed via fixelfilter-smooth(Tournier, et al. 2019) to improve sensitivity to white mater alterations that are spatially coherent along fiber tracts. Statistical analysis Fixel-based correlation analyses were performed using a general linear model to examine the relationship between FD, FC, and FDC and clinical scores. Age, sex, years of education, dominant hand, absolute motion and intracranial volumes were included as covariates of no interest across all models. For participants with PD, additional separate models were constructed to further account for affected side and Levodopa Equivalent Daily Dose (LEDD). Statistical significance was determined at a family-wise error corrected threshold of pcorr < 0.05 at the fixel level. Fixels surviving this threshold were visualized in mrview(Tournier J 2012; Tournier, et al. 2019) using the white matter FOD template as an underlay, and TractSeg(Wasserthal, et al. 2018) was subsequently applied to identify significant regions of interest from the MRtrix-derived tractography. Continuous demographic variables were compared across PD-MCI, PD-NC, and HC groups using one-way ANOVA, pairwise differences between PD-MCI and PD-NC were examined using the Mann-Whitney test, and categorical variables were compared across all three groups using the chi-square test. Results Demographics and Clinical Characteristics HC, PD-NC, and PD-MCI groups did not significantly differ across demographic variables, including age (p=0.40), sex (p=0.20), years of education (p=0.61) Table 1. Dominant hand significantly differed across groups (p=0.01), with pairwise showing significant differences between PD-MCI and HC (p<0.001), PD-NC and HC (p=0.040), and PD-MCI and PD-NC (p=0.025). Within the PD groups, disease duration (p=0.74), Levodopa Equivalent Daily Dose (p=0.43), UPDRS-OFF (p=0.70), and affected side (p=0.74) did not significantly differ between PD-MCI and PD-NC Table 2. No significant differences were observed between groups in MRI quality measures including absolute motion (p=0.75) and intracranial volume (p=0.97) Table 4. Significant group differences were observed across several neuropsychological measures, including Category Fluency Total Raw Score (p=0.001), with PD-MCI performing significantly worse than HC (p=0.0003); RAVLT Delayed Recall (p=0.003), with PD-MCI performing significantly worse than both HC (p=0.002) and PD-NC (p=0.04); DRS-2 Memory (p=0.012), with PD-MCI performing significantly worse than PD-NC (p=0.006); and BVMT Delayed Recall (p=0.04), with PD-MCI performing significantly worse than PD-NC (p=0.016). RAVLT Learning Over Trials showed a trend toward significance across groups (p=0.06), with PD-MCI performing significantly worse than HC (p=0.025), but no significant difference between PD-MCI and PD-NC (p=0.53). Across all significant neuropsychological measures, PD-MCI demonstrated the poorest performance compared to both PD-NC and HC groups. No significant group differences were observed across attention and working memory measures including WAIS-IV Digit Span Backward(p=0.53), WAIS-IV Digit Span Forward (p=0.35), and DRS-2 Attention (p=0.51), Executive function measures including D-KEFS Letter Fluency (p=0.30) and D-KEFS Category Switching (p=0.14), Language function as assessed by Boston Naming Test (p=0.45), BVMT Learning (p=0.42), Visuospatial function as assessed by the DRS-2 Construction subscale (p=0.27).Table 3 Fixel Based Analysis At the FWE-corrected threshold, the only group difference in fixel metrics was elevated FC in PD-MCI relative to HC (p=0.031±0.011, t=3.97±0.33; Figure 1), localized to left parieto-occipital pontine tract and corpus callosum. PD-MCI and PD-NC did not differ on FD, FC, or FDC. Neuropsychological Measures PD-NC vs PD-MCI I. Attention/Working Memory In the attention/working memory domain, DRS-2 Attention Scores showed a significantly stronger positive correlation with FD in the PD-NC group compared to the PD-MCI group(p=0.025±0.013; Figure).Significant fixels were localized predominantly to the right hemisphere, involving the bilateral optic radiations (OR), corpus callosum (CC_1, CC_3), left striato frontal/orbital tract (ST_FO L), left uncinate fasciculus (UF L), bilateral striato occipital tracts (ST_OCC), and bilateral thalamo occipital tracts (T_OCC). This effect was also significant across FC(p=0.038±0.009, t=3.47±0.52) and FDC (p=0.026±0.012, t=3.25±0.50) in overlapping fixels(Supplementary Figure X, Supplementary Table X). II. Executive Function Within the executive function domain, Category Switching showed a significantly stronger positive correlation with FD in PD-NC compared to PD-MCI(p= 0.028±0.011, t=3.06±0.39; Figure ).Significant fixels were predominantly left hemisphere and thalamo motor in distribution, involving the bilateral superior thalamic radiation (STR), bilateral thalamo postcentral tract (T_POSTC), corpus callosum (CC_7), left striato postcentral tract (ST_POSTC L), left corticospinal tract (CST L), left parieto occipital pontine tract (POPT L), left thalamo precentral tract (T_PREC L), and left thalamo parietal tract (T_PAR L). This same pattern PD-NC> PD-MCI was also significant across FDC( p=0.042±0.006, t=3.26±0.29; Supplementary figure ). In contrast, Category Fluency Raw Score, which showed a significant positive correlation with FD within PD-MCI(p=0.042±0.005, t=3.65±0.27; Supplementary figure ) a relationship not observed in PD-NC. III. Learning Memory Within the learning and memory domain, DRS-2 Memory Scores showed a significantly stronger positive correlation with FD in PD-NC compared to PD-MCI(p=0.023±0.015, t=3.51±0.62; Figure) . Significant fixels were widespread and predominantly right hemisphere, involving the bilateral optic radiations (OR), corpus callosum (CC_2, CC_3), bilateral thalamo occipital tract (T_OCC), bilateral striato occipital tract (ST_OCC), left striato frontal/orbital tract (ST_FO L), and right inferior longitudinal fasciculus (ILF R). This same pattern (PD NC > PD MCI) was also significant across FC (p=0.018±0.013, t=3.95±0.77; Supplementary Figure) and FDC (p=0.024±0.015, t=3.78±0.78; Supplementary Figure) in overlapping fixels . Notably, an opposite trend was observed for BVMT Delayed Recall, which showed a significantly positive correlation with FC in PD-MCI>PD-NC(p=0.043±0.004, t=3.70±0.14; Supplementary Figure) IV. Visuospatial Memory Within the visuospatial memory domain, DRS 2 Construction scores showed a significantly stronger positive correlation with FD in PD NC compared to PD MCI (p=0.033±0.011, t=3.10±0.51 ; Supplementary Figure 9a). This same pattern (PD NC greater than PD MCI) was also significant across FC (p=0.027±0.013, t=3.52±0.55 ; Supplementary Figure 9a) and FDC (p=0.037±0.008, t=3.51±0.55 ; Supplementary Figure 9a) in overlapping fixels. Interpretation of this contrast is limited by a ceiling effect in DRS 2 Construction performance, with minimal variance observed in

Methods Participants A total of 45 participants were enrolled through the center for Neurodegeneration and Translational Neuroscience (CNTN)] (www.nevadacntn.org).database comprising three group:15 healthy controls(HC),12 individuals with Parkinson’s disease and normal cognition(PD-NC),and 18 individuals with Parkinson’s disease and mild cognitive impairment (PD-MCI).Written informed consent was obtained from all participants prior to enrollment, in accordance with the Declaration of Helsinki, and all procedure were approved by Cleveland Clinic Institutional Review Board. Eligibility criteria required the absence of MRI contraindications including certain implants or metallic foreign bodies in the eye, as well as no prior history of stroke, brain tumor, psychiatric conditions, or neurological conditions other than PD. Clinical and Neuropsychological Assessment All participants underwent clinical evaluation according to Level II Movement Disorder Society (MDS) criteria(Dubois, et al. 2007). Collected demographics included age at recruitment, sex, years of education (YOE), and dominant hand. For individuals with PD, additional clinical variables were recorded including disease duration(DDX), Levodopa Equivalent Daily Dose(LEDD), Unified Parkinson’s Disease Rating Scale score in the OFF state(UPDRS-OFF), and affected side.A summary of demographic and clinical characteristics is presented in Table 1 and Table 2. A comprehensive neuropsychological battery was administered to evaluate cognitive function across five domains. Attention and working memory were assessed via the Wechsler Adult Intelligence Scale Fourth Edition(Ryan, et al. 2012) (WAIS-IV) Digit span Forward and Backward subtests, as well as the Dementia Rating Scale-2 (Matteau, et al. 2011)(DRS-2) Attention subscale. Executive function was evaluated through the Delis-Kaplan Executive Function System(Heled, et al. 2012) (D-KEFS) Letter Fluency, Category Fluency Total Raw Score, and Category Switching tests. Language function was assessed using the Boston Naming Test (BNT). Verbal memory was assessed with Rey Auditory Verbal Test (RAVLT)(Loring, et al. 2023) while visual memory was assessed via Brief Visuospatial Memory test (BVMT) Learning and Delayed Recall trials, Visuospatial memory was assessed via DRS-2 Construction subtest. A summary of neuropsychological performance across groups is provided in Table 3. Determination of Cognitive Status A panel of clinical experts reviewed all clinical and neuropsychological assessments to confirm the presence or absence of mild cognitive impairment. PD-MCI was classified in accordance with the Litvan Level II Movement Disorder Society (MDS) (Litvan, et al. 2012)diagnostic criteria, using an impairment threshold of ≥ 1.5 standard deviations below normative expectations on at least two tests within a cognitive domain. Based on these criteria, the study cohort was divided into three groups:18 participants with PD-MCI, 12 with PD-NC, and 15 with healthy controls (HC) MRI data acquisition All scans were acquired on a 32-channel transmit receive head coil using a 3T Siemens Skyra scanner. Diffusion MRI (dMRI) data were acquired using the following parameters: TR=5218 ms, TE=100 ms, flip angle=780, with 1.5×1.5×1.5 mm3 voxel resolution, in-plane acceleration=2, and multiband factor=3. Diffusion weighting comprised 25 b0 volumes interleaved among 213 diffusion weighted volumes across three shells (b=500,1000,2500 s/mm2). The phase-encoding direction was P>>A, with an effective echo spacing of 0.47 ms and a total readout time of 63.45ms. Opposite phase-encoding A>>P b0 images were additionally acquired to correct for susceptibility and eddy current distortions(Andersson and Sotiropoulos 2016). High-resolution T1 - weighted structural images were obtained using a magnetization-prepared rapid gradient echo (MPRAGE) sequence with 1×1×1 mm isotropic resolution and 176 sagittal slices (TR=2300 ms, TE=2.96 ms, inversion time =900 ms, flip angle=90). Total acquisition time was approximately 25 minutes. Data Processing All diffusion MRI data were processed using the MRtrix3 freeware. Noise reduction was first applied via the dwidenoise(Tournier, et al. 2019) command using a Marchenko-Pastur principal component analysis filter before further preprocessing(Veraart, et al. 2016a; Veraart, et al. 2016b). Head motion and eddy-current distortions were subsequently corrected using dwifslpreproc(Tournier, et al. 2019) command, applying FSL-based(Smith, et al. 2004) eddy-current correction via sub-voxel shifting and motion realignment, while additionally correcting for b=0 susceptibility distortion(Andersson and Sotiropoulos 2016). A brain mask was then computed using dwi2mask(Tournier, et al. 2019) and applied through dwibiascorrect(Tournier, et al. 2019) command with the ANTs N4 algorithm(Tustison, et al. 2010) to correct for bias field across all diffusion voulmes using b=0 data. Global intensity normalization was performed using the dwinormalize(Tournier, et al. 2019) command(CA. 2017; Dhollander, et al. 2021), from which a white matter mask and fractional anisotropy map were additionally obtained. A group averaged single-shell white matter response function was then estimated using the dwi2response(Tournier, et al. 2013; Tournier, et al. 2019) and response mean(Tournier, et al. 2019) functions. Finally, diffusion data were upsampled to an isotropic voxel size of 1.25 mm via mgrid(Tournier, et al. 2019), and a corresponding upsampled brain mask was generated using dwi2mask(Tournier, et al. 2019) prior to fiber orientation distribution estimation to optimize the accuracy of downstream analyses. Fixel-Based Analysis Pipeline Fiber orientation distributions (FODs) were estimated within each voxel using constrained spherical deconvolution (CSD)(Jeurissen, et al. 2013; Tournier, et al. 2007) with the group averaged white matter response function, restricted to the brain mask to avoid estimates in non-brain tissue(Tournier, et al. 2007). Diffusion weighted volumes were extracted via dwiextract(Tournier, et al. 2019) and input to dwi2fod csd(Tournier, et al. 2019) to generate voxel-wise FODs capturing the distribution and relative density of fiber orientations. All individual FOD images were registered to a common group template space via mrregister(Raffelt, et al. 2012a; Raffelt, et al. 2011) to construct a study-specific FOD template, and a corresponding template masks via mrtransform(Raffelt, et al. 2012a; Raffelt, et al. 2012b) after warping subject masks to the template space. FOD images were then segmented into fixels using fod2fixel (Tournier, et al. 2019) to determine the number and orientations of discrete fiber populations within each voxel, and fixel correspondence between the individual subjects and the group template was demonstrated via fixel correspondence(Tournier, et al. 2019), producing a fixel-wise data matrix of fiber density (FD) values for every subject. Fiber cross-section (FC) was computed within the template space using warp2metric(Raffelt, et al. 2017; Tournier, et al. 2019), capturing macrostructural bundle size not reflected by FD alone(Raffelt, et al. 2017), and the combined measure fiber density cross-section (FDC) was derived as the product of FD and FC via mrcal(Tournier, et al. 2019), enabling simultaneous quantification of microstructural and macrostructural white matter alterations. Whole brain probabilistic tractography was generated from the FOD template via tckgen-act(Smith, et al. 2012), and spherical-deconvolution informed filtering of tractograms(SIFT)(Smith, et al. 2013) was applied via tcksift(Tournier, et al. 2019) to reduce reconstruction biases in streamline densities. A fxel-fixel connectivity matrix was subsequently constructed from the filtered tractogram via fixelconncetivity(Tournier, et al. 2019), and connectivity-based smoothing was performed via fixelfilter-smooth(Tournier, et al. 2019) to improve sensitivity to white mater alterations that are spatially coherent along fiber tracts. Statistical analysis Fixel-based correlation analyses were performed using a general linear model to examine the relationship between FD, FC, and FDC and clinical scores. Age, sex, years of education, dominant hand, absolute motion and intracranial volumes were included as covariates of no interest across all models. For participants with PD, additional separate models were constructed to further account for affected side and Levodopa Equivalent Daily Dose (LEDD). Statistical significance was determined at a family-wise error corrected threshold of pcorr < 0.05 at the fixel level. Fixels surviving this threshold were visualized in mrview(Tournier J 2012; Tournier, et al. 2019) using the white matter FOD template as an underlay, and TractSeg(Wasserthal, et al. 2018) was subsequently applied to identify significant regions of interest from the MRtrix-derived tractography. Continuous demographic variables were compared across PD-MCI, PD-NC, and HC groups using one-way ANOVA, pairwise differences between PD-MCI and PD-NC were examined using the Mann-Whitney test, and categorical variables were compared across all three groups using the chi-square test. this is methods

see check whther discussion and intro part is perfect only those 2 sections for now please