Cortical atrophy is associated with cognitive impairment in Parkinson’s disease: a combined analysis of cortical thickness and functional connectivity

Zhu, Yongyun; Yang, Baiyuan; Zhou, Chen; Gao, Chao; Hu, Yanfei; Yin, Wei; Yin, Kangfu; Zhu, Yangfan; Jia, Guanghong; Ren, Hui; Pang, Ailan; Yang, Xinglong

doi:10.1007/s11682-022-00714-w

Cited by 15 publications

(10 citation statements)

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“…Although the underlying mechanisms and resultant clinical symptoms are complex, the spreading of misfolded α-synuclein in cortices has been confirmed as the main pathophysiological mechanism of neuronal degradation (1,25). Accordingly, previous studies of PD have observed impaired structural networks of cortical atrophy by analyzing cortical thickness or volume (13,26,27). Relative to these findings based on macroscopic architecture, the present study further explored the impaired network topology based on the microstructure of the GM before morphological abnormalities and revealed alterations with aberrant global properties and disrupted nodal centralities.…”

Section: Discussionmentioning

confidence: 99%

Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease

Zhu,

Wang,

et al. 2023

Acta Radiol

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Background Parkinson's disease (PD) has been regarded as a disconnection syndrome with functional and structural disturbances. However, as the anatomic determinants, the structural disconnections in PD have yet to be fully elucidated. Purpose To non-invasively construct structural networks based on microstructural complexity and to further investigate their potential topological abnormalities in PD given the technical superiority of diffusion kurtosis imaging (DKI) to the quantification of microstructure. Material and Methods The microstructural data of gray matter in both the PD group and the healthy control (HC) group were acquired using DKI. The structural networks were constructed at the group level by a covariation approach, followed by the calculation of topological properties based on graph theory and statistical comparisons between groups. Results A total of 51 patients with PD and 50 HCs were enrolled. Individuals were matched between groups with respect to demographic characteristics ( P >0.05). The constructed structural networks in both the PD and HC groups featured small-world properties. In comparison with the HC group, the PD group exhibited significantly altered global properties, with higher normalized characteristic path lengths, clustering coefficients, local efficiency values, and characteristic path lengths and lower global efﬁciency values ( P <0.05). In terms of nodal centralities, extensive nodal disruptions were observed in patients with PD ( P <0.05); these disruptions were mainly distributed in the sensorimotor network, default mode network, frontal-parietal network, visual network, and subcortical network. Conclusion These findings contribute to the technical application of DKI and the elucidation of disconnection syndrome in PD.

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Section: Discussionmentioning

confidence: 99%

Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease

Zhu,

Wang,

et al. 2023

Acta Radiol

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“…PD-MCI patients showed reduced gray matter volume (GMV) in the temporal and parietal cortex, putamen, amygdala, hippocampus and cerebellum [ 166 ]. Global cognitive dysfunction (mainly non-memory related) was associated with atrophy in the frontal lobe, left middle and inferior temporal gyrus or in the left superior temporal, right fusiform and bilateral lingual regions [ 219 ]. A meta-analysis reported higher GM atrophy in the bilateral prefrontal cortex, left insula and angula gyrus, right supramarginal gyrus and midcingulate cortex in the PD-MCI cohort [ 164 ], while another meta-analysis reported GM atrophy in the left anterior insula, left inferior and orbital frontal gyrus [ 168 ].…”

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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“…Magnetic resonance imaging (MRI) is one of the most widely used neuroimaging technique that can provide insights into the structural changes occurring in the brain. Several studies have used MRI to investigate the alterations in brain volume, cortical thickness, and white matter integrity that can be linked to cognitive decline in PD ( Beyer et al, 2007 ; Song et al, 2011 ; Mak et al, 2014 ; Gao et al, 2017 ; Devignes et al, 2021 ; Li et al, 2022 ; Zhu et al, 2022 ). For example, PD-MCI patients have shown significant atrophy in the frontotemporal cortices, thalamus, nucleus accumbens, as well as caudate nucleus compared to the PD patients with healthy cognition (PD-HC) ( Zhou et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting the occurrence of mild cognitive impairment in Parkinson’s disease using structural MRI data

Beheshti,

2024

Front. Neurosci.

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IntroductionMild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson’s disease (PD) and a main risk factor for progressing to dementia. Our objective was to identify early anatomical brain changes that precede the transition from healthy cognition to MCI in PD.MethodsStructural T1-weighted magnetic resonance imaging data of PD patients with healthy cognition at baseline were downloaded from the Parkinson’s Progression Markers Initiative database. Patients were divided into two groups based on the annual cognitive assessments over a 5-year time span: (i) PD patients with unstable healthy cognition who developed MCI over a 5-year follow-up (PD-UHC, n = 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC, n = 52). These 52 PD-SHC were selected among 192 PD-SHC patients using propensity score matching method to have similar demographic and clinical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier.ResultsThe average accuracy of classifying PD-UHC vs. PD-SHC was 80.76%, with 82.05% sensitivity and 79.48% specificity using 10-fold cross-validation. The performance was similar in the hold-out test sets with all accuracy, sensitivity, and specificity at 76.92%. SHAP analysis showed that the most influential brain regions in the prediction model were located in the frontal, occipital, and cerebellar regions as well as midbrain.DiscussionOur machine learning-based analysis yielded promising results in identifying PD individuals who are at risk of cognitive decline from the earliest disease stage and revealed the brain regions which may be linked to the prospective cognitive decline in PD before clinical symptoms emerge.

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Cortical atrophy is associated with cognitive impairment in Parkinson’s disease: a combined analysis of cortical thickness and functional connectivity

Cited by 15 publications

References 60 publications

Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease

Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Predicting the occurrence of mild cognitive impairment in Parkinson’s disease using structural MRI data

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