Alterations in white matter fiber in Parkinson disease across different cognitive stages

Sang, Tian; He, Jianzhong; Wang, Jingqiang; Zhang, Chengzhe; Zhou, Wenyang; Zeng, Qingrun; yuan, Yuan; Yu, Lihua; Feng, Yuanjing

doi:10.1016/j.neulet.2021.136424

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…In the early stages of PD, the patient’s cognitive function has already experienced certain damage. Due to different cognitive functions, there may also be certain differences in the structural damage of WM fibers caused by PD ( 51 ). In this case, MoCA results could indeed reflect cognitive dysfunction itself, and therefore can serve as an important factor in identifying early suspected PD patients.…”

Section: Discussionmentioning

confidence: 99%

White matter biomarker for predicting de novo Parkinson’s disease using tract-based spatial statistics: a machine learning-based model

Zhang,

Wang,

Shi

et al. 2024

Quant Imaging Med Surg

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Background Parkinson’s disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods This study selected 227 cases from the Parkinson’s Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case’s fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. Results In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

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Section: Discussionmentioning

confidence: 99%

White matter biomarker for predicting de novo Parkinson’s disease using tract-based spatial statistics: a machine learning-based model

Zhang,

Wang,

Shi

et al. 2024

Quant Imaging Med Surg

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“…This extracellular signal captured at lower b -values weakens the interpretability of FD specifically by providing superfluous information can be erroneously included in FD calculations ( Dhollander et al, 2021 ). Despite this, crossing fibres can still be resolved at the b = 1000 s/mm 2 level ( Tournier et al, 2007 , Tournier et al, 2008 ), and studies have successfully used values in the range of b = 1000 s/mm 2 for FBA ( Bishop et al, 2018 , Sang et al, 2022 ), especially with the aid of multi-tissue CSD techniques ( Choy et al, 2020 , Grazioplene et al, 2018 , Luo et al, 2021 , Verhelst et al, 2019 ). Multi-tissue CSD is particularly valuable for mitigating the problems introduced by extracellular signal – because it can be categorized with the CSF- and GM-like data rather than the WM – and has been shown to have good test–retest reliability ( Newman et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

White matter microstructure predicts measures of clinical symptoms in chronic back pain patients

Robertson

Aristi

Hashmi

2023

NeuroImage: Clinical

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“…Thinning of the corpus callosum was correlated with the thickness of the left orbitofrontal cortex in PD-MCI [ 218 ]. The corpus callosum and cingulum bundle showed the same trend to decline with cognitive dysfunction [ 240 ]. MCI in early PD stages was associated with poorer olfactory function and disordered WM integrity in the anterior olfactory structures [ 183 ].…”

Section: Neuroimaging Findings In CI In Pd (See Supplement ...mentioning

confidence: 99%

“…The strongest of DTI changes occurred in the most anterior and posterior callosal segments, reflecting fronto-striatal and posterior cortical type cognitive deficits [ 265 ]. The corpus callosum, cingulum bundle and corticospinal tract contributed to decline of cognitive function [ 240 ]. The PDD group showed decreased FA and/or mean diffusivity increase in bilateral cingulate tract [ 266 , 267 ], in genu of corpus callosum [ 268 ] and hippocampus [ 269 ].…”

Section: Neuroimaging Findings In CI In Pd (See Supplement ...mentioning

confidence: 99%

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Jellinger

2023

IJMS

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Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

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Alterations in white matter fiber in Parkinson disease across different cognitive stages

Cited by 13 publications

References 34 publications

White matter biomarker for predicting de novo Parkinson’s disease using tract-based spatial statistics: a machine learning-based model

White matter biomarker for predicting de novo Parkinson’s disease using tract-based spatial statistics: a machine learning-based model

White matter microstructure predicts measures of clinical symptoms in chronic back pain patients

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

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