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Background: White matter (WM) tract alterations are early signs of cognitive impairment in Parkinson disease (PD) patients. Fixel-based analysis (FBA) has advantages over traditional diffusion tensor imaging in managing complex and crossing fibers. We used FBA to measure fiber-specific changes in patients with PD mild cognitive impairment (PD-MCI) and PD normal cognition (PD-NC)., Methods: Seventy-one patients with PD without dementia were included: 39 PD-MCI and 32 PD-NC. All underwent diffusion-weighted imaging, clinical examinations, and tests to evaluate their cognitive function globally and in five cognitive domains. FBA was used to investigate fiber-tract alterations and compare PD-MCI with PD-NC subjects. Correlations with each cognitive test were analyzed., Results: Patients with PD-MCI were significantly older (p = 0.044), had a higher male-to-female ratio (p = 0.006) and total Unified Parkinson's Disease Rating Scale score (p = 0.001). All fixel-based metrics were significantly reduced within the body of the corpus callosum and superior corona radiata in PD-MCI patients (family-wise error-corrected P value < 0.05) compared with PD-NC patients. The cingulum, superior longitudinal fasciculi, and thalamocortical circuit exhibited predominantly fiber-bundle cross-section (FC) changes. In regression analysis, reduced FC values in cerebellar circuits were associated with poor motor function in PD-MCI patients and poor picture-naming ability in PD-NC patients., Conclusions: PD-MCI patients have significant WM alterations compared with PD-NC patients. FBA revealed these changes in various bundle tracts, helping us to better understand specific WM changes that are functionally implicated in PD cognitive decline. FBA is potentially useful in detecting early cognitive decline in PD., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest., (© 2023 The Authors. Published by Elsevier B.V. on behalf of Chang Gung University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Objective: To explore the neuroimage change in Parkinson's disease (PD) patients with cognitive impairments, this study investigated the correlation between plasma biomarkers and morphological brain changes in patients with normal cognition and mild cognitive impairment. The objective was to identify the potential target deposition regions of the plasma biomarkers and to search for the relevant early neuroimaging biomarkers on the basis of different cognitive domains., Methods: Structural brain MRI and diffusion weighted images were analyzed from 49 eligible PD participants (male/female: 27/22; mean age: 73.4 ± 8.5 years) from a retrospective analysis. Plasma levels of α-synuclein, amyloid beta peptide, and total tau were collected. A comprehensive neuropsychological assessment of the general and specific cognitive domains was performed. Difference between PD patients with normal cognition and impairment was examined. Regression analysis was performed to evaluate the correlation between image-derived index and plasma biomarkers or neuropsychological assessments., Results: Significant correlation was found between plasma Aβ-42 level and fractional anisotropy of the middle occipital, angular, and middle temporal gyri of the left brain, as well as plasma T-tau level and the surface area of the isthmus or the average thickness of the posterior part of right cingulate gyrus. Visuospatial and executive function is positively correlated with axial diffusivity in bilateral cingulate gyri., Conclusion: In nondemented PD patients, the target regions for plasma deposition might be located in the cingulate, middle occipital, angular, and middle temporal gyri. Changes from multiple brain regions can be correlated to the performance of different cognitive domains., Clinical Relevance Statement: Cognitive impairment in Parkinson's disease is primarily linked to biomarkers associated with Alzheimer's disease rather than those related to Parkinson's disease and resembles the frontal variant of Alzheimer's disease, which may guide management strategies for cognitive impairment in Parkinson's disease., Key Points: • Fractional anisotropy, surface area, and thickness in the cingulate, middle occipital, angular, and middle temporal gyri can be significantly correlated with plasma Aβ-42 and T-tau level. • Axial diffusivity in the cingulate gyri was correlated with visuospatial and executive function. • The pattern of cognitive impairment in Parkinson's disease can be similar to the frontal variant than typical Alzheimer's disease., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated., Objective: To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group., Methods: Cerebellar parcellation was performed using a deep learning-based approach from 2487 people with PD and 1212 age and sex-matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age- and sex- matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated., Results: Overall, people with PD had a regionally smaller posterior lobe (d max  = -0.15). HY stage-specific analyses revealed a larger anterior lobule V bilaterally (d max  = 0.28) in people with PD in HY stage 1 compared to controls. In contrast, smaller bilateral lobule VII volume in the posterior lobe was observed in HY stages 3, 4, and 5 (d max  = -0.76), which was incrementally lower with higher disease stage. Within PD, cognitively impaired individuals had lower total cerebellar volume compared to cognitively normal individuals (d = -0.17)., Conclusions: We provide evidence of a dissociation between anterior "motor" lobe and posterior "non-motor" lobe cerebellar regions in PD. Whereas less severe stages of the disease are associated with larger motor lobe regions, more severe stages of the disease are marked by smaller non-motor regions. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Parkinson's disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer's disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification.Clinical Relevance- This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson's disease.

Background: There are currently no specific tests for either idiopathic Parkinson's disease or Parkinson-plus syndromes. The study aimed to investigate the diagnostic performance of features extracted from the whole brain using diffusion tensor imaging concerning parkinsonian disorders., Methods: The retrospective data yielded 625 participants (average age: 61.4 ± 8.2, men/women: 313/312; healthy controls/idiopathic Parkinson's disease/multiple system atrophy/progressive supranuclear palsy: 219/286/51/69) between 2008 and 2017. Diffusion-weighted images were obtained using a 3T MR scanner. The 90th, 50th, and 10th percentiles of fractional anisotropy and mean/axial/radial diffusivity from each parcellated brain area were recorded. Statistical analysis was evaluated based on the features extracted from the whole brain, as determined using discriminant function analysis and support vector machine. 20% of the participants were used as an independent blind dataset with 5 times cross-verification. Diagnostic performance was evaluated by the sensitivity and the F1 score., Results: Diagnoses were accurate for distinguishing idiopathic Parkinson's disease from healthy control and Parkinson-plus syndromes (87.4 ± 2.1% and 82.5 ± 3.9%, respectively). Diagnostic F1 scores varied for Parkinson-plus syndromes with 67.2 ± 3.8% for multiple system atrophy and 71.6 ± 3.5% for progressive supranuclear palsy. For early and late detection of idiopathic Parkinson's disease, the diagnostic performance was 79.2 ± 7.4% and 84.4 ± 6.9%, respectively. The diagnostic performance was 68.8 ± 11.0% and 52.5 ± 8.9% in early and late detection to distinguish different Parkinson-plus syndromes., Conclusions: Features extracted from diffusion tensor imaging of the whole brain can provide objective evidence for the diagnosis of healthy control, idiopathic Parkinson's disease, and Parkinson-plus syndromes with fair to very good diagnostic performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Chang Gung University. Published by Elsevier B.V. All rights reserved.)

The diagnostic performance of a combined architecture on Parkinson's disease using diffusion tensor imaging was evaluated. A convolutional neural network was trained from multiple parcellated brain regions. A greedy algorithm was proposed to combine the models from individual regions into a complex one. Total 305 Parkinson's disease patients (aged 59.9±9.7 years old) and 227 healthy control subjects (aged 61.0±7.4 years old) were enrolled from 3 retrospective studies. The participants were divided into training with ten-fold cross-validation (N = 432) and an independent blind dataset (N = 100). Diffusion-weighted images were acquired from a 3T scanner. Fractional anisotropy and mean diffusivity were calculated and was subsequently parcellated into 90 cerebral regions of interest based on the Automatic Anatomic Labeling template. A convolutional neural network was implemented which contained three convolutional blocks and a fully connected layer. Each convolutional block consisted of a convolutional layer, activation layer, and pooling layer. This model was trained for each individual region. A greedy algorithm was implemented to combine multiple regions as the final prediction. The greedy algorithm predicted the area under curve of 94.1±3.2% from the combination of fractional anisotropy from 22 regions. The model performance analysis showed that the combination of 9 regions is equivalent. The best area under curve was 74.7±5.4% from the right postcentral gyrus. The current study proposed an architecture of convolutional neural network and a greedy algorithm to combine from multiple regions. With diffusion tensor imaging, the algorithm showed the potential to distinguish patients with Parkinson's disease from normal control with satisfactory performance., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: Disruption to white matter pathways is an important contributor to the pathogenesis of Parkinson's disease. Fixel-based analysis has recently emerged as a useful fiber-specific tool for examining white matter structure. In this longitudinal study, we used Fixel-based analysis to investigate white matter changes occurring over time in patients with Parkinson's disease., Methods: Fifty patients with idiopathic Parkinson's disease (27 men and 23 women; mean age: 61.8 ± 6.1 years), were enrolled. Diffusion-weighted imaging and clinical examinations were performed at three different time points (baseline, first follow-up [after a mean of 24±2 months], and second follow-up [after a mean of 40 ± 3 months]). Additional 76 healthy control subjects (38 men and 38 women; mean age: 62.3 ± 5.5 years) were examined at baseline. The following fixel-based metrics were obtained: fiber density (FD), fiber bundle cross-section (FC), and a combined measure of both (FDC). Paired comparisons of metrics between three different time points were performed in patients. Linear regression was implemented between longitudinal changes of fixel-based metrics and the corresponding modifications in clinical parameters. A family-wise error corrected p < 0.05 was considered statistically significant., Results and Discussions: Early degeneration in the splenium of corpus callosum was identified as a typical alteration of Parkinson's disease over time. At follow-up, we observed significant FDC reductions compared with baseline in white matter, noticeably in corpus callosum; tapetum; cingulum, posterior thalamic radiation, corona radiata, and sagittal stratum. We also identified significant FC decreases that reflected damage to white matter structures involved in Parkinson's disease -related pathways. Fixel-based metrics were found to relate with a deterioration of 39-item Parkinson's Disease Questionnaire, Unified Parkinson's Disease Rating Scale and activity of daily living. A Parkinson's disease -facilitated aging effect was observed in terms of white matter disruption., Conclusion: This study provides a thorough fixel-based profile of longitudinal white matter alterations occurring in patients with Parkinson's disease and new evidence of FC as an important role in white matter degeneration in this setting., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)