Longitudinal brain connectivity changes and clinical evolution in Parkinson’s disease

Filippi, Massimo; Basaia, Silvia; Sarasso, Elisabetta; Stojković, Tanja; Stanković, Iva; Fontana, Andrea; Tomić, Aleksandra; Piramide, Noemi; Stefanova, Elka; Marković, Vladana; Kostić, Vladimir

doi:10.1038/s41380-020-0770-0

Cited by 47 publications

(42 citation statements)

References 36 publications

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“…Early/mild PD patients showed the lowest motor and non‐motor burden, whereas early/severe PD patients displayed the presence of additional non‐motor features, including mainly autonomic and sleep disturbances. This finding parallels other studies in both drug‐naïve and cross‐stages PD samples 5,7,48‐50 . Indeed, although implementing a different number of clusters as well as more clinical classifiers, the presence of a very mild and a “diffuse‐malignant” patient subtype have been consistently identified over the clusters' spectrum 5,7,48,49 .…”

Section: Discussionsupporting

confidence: 84%

“…Moreover, more severe brain atrophy was found to parallel the different burden of motor disability among progressively impaired PD clusters, even early in the disease 5,6 . However, there is still little evidence of cluster‐driven differences in brain functional connectivity in PD patients 7 …”

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confidence: 99%

“…Only a few RS fMRI studies using graph theory analysis have been conducted to identify connectivity patterns in patients with PD 7,13‐17 . There has been a degree of inconsistency between studies, likely related to the small number of subjects 15,17 or inclusion of patients in different stages of PD 7,13,14 .…”

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confidence: 99%

“…5,6 However, there is still little evidence of cluster-driven differences in brain functional connectivity in PD patients. 7 Resting-state functional magnetic resonance imaging (RS fMRI) provides consistent evidence to categorize PD patients from controls and can help to unravel neural correlates of motor and non-motor symptoms. 8,9 In recent years, graph theory measures and techniques have been used to understand the global topological organization of brain networks.…”

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confidence: 99%

“…7,[13][14][15][16][17] There has been a degree of inconsistency between studies, likely related to the small number of subjects 15,17 or inclusion of patients in different stages of PD. 7,13,14 In addition, most previous studies have been performed in PD patients under chronic dopamine replacement therapy, 7,13,14 which has been shown to variably influence functional connectivity. 18,19 Following these observations, we sought to determine the whole-brain network topologic organization of the functional connectome in a large cohort of drug-naïve early PD patients.…”

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confidence: 99%

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Functional Connectomics and Disease Progression in Drug‐Naïve Parkinson's Disease Patients

et al. 2021

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Background: Functional brain connectivity alterations may be detectable even before the occurrence of brain atrophy, indicating their potential as early markers of pathological processes. Objective: We aimed to determine the whole-brain network topologic organization of the functional connectome in a large cohort of drug-naïve Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging and to explore whether baseline connectivity changes may predict clinical progression. Methods: One hundred and forty-seven drug-naïve, cognitively unimpaired PD patients were enrolled in the study at baseline and compared to 38 age-and gendermatched controls. Non-hierarchical cluster analysis using motor and non-motor data was applied to stratify PD patients into two subtypes: 77 early/mild and 70 early/severe. Graph theory analysis and connectomics were used to assess global and local topological network properties and regional functional connectivity at baseline. Stepwise multivariate regression analysis investigated whether baseline functional imaging data were predictors of clinical progression over 2 years. Results: At baseline, widespread functional connectivity abnormalities were detected in the basal ganglia, sensorimotor, frontal, and occipital networks in PD patients compared to controls. Decreased regional functional connectivity involving mostly striato-frontal, temporal, occipital, and limbic connections differentiated early/mild from early/severe PD patients. Connectivity changes were found to be independent predictors of cognitive progression at 2-year follow-up. Conclusions: Our findings revealed that functional reorganization of the brain connectome occurs early in PD and underlies crucial involvement of striatal projections. Connectomic measures may be helpful to identify a specific PD patient subtype, characterized by severe motor and non-motor clinical burden as well as widespread functional connectivity abnormalities.

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confidence: 84%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Functional Connectomics and Disease Progression in Drug‐Naïve Parkinson's Disease Patients

et al. 2021

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Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

Hajebrahimi,

Budak,

Saricaoglu

et al. 2024

Brain and Behavior

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IntroductionCognitive impairment (CI) is a significant non‐motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD‐normal cognition [NC]) to stages with Mild CI (PD‐MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting‐state functional connectivity (FC) among three CI stages of PD.MethodsWe enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra‐ and inter‐network FC analysis in conjunction with comprehensive clinical cognitive assessment.ResultsOur findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter–intra‐network FC in differentiating between PD‐MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD‐NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD.ConclusionWe propose that resting‐state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD‐NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.

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Motor progression marker for newly diagnosed drug‐naïve patients with Parkinson's disease: A resting‐state functional MRI study

Hou

Zhang

et al. 2022

Human Brain Mapping

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The effective early prediction of clinical outcomes of Parkinson's disease (PD) is of great significance in the implementation of appropriate interventions. We aimed to propose a method based on the use of baseline resting‐state functional characteristics (i.e., fractional amplitude of low‐frequency fluctuations, fALFF) to predict motor progression in PD patients. Resting‐state functional magnetic resonance imaging was performed on 48 newly‐diagnosed drug‐naïve PD patients and 27 age‐ and sex‐ matched healthy controls (HCs). Two PD subgroups were defined with different annual increase of Unified PD Rating Scale Part III motor scores. Least absolute shrinkage and selection operator regression analysis was performed to explore the baseline region‐functional indicators for PD discrimination as well as the predictors for future motor deficits. Two significant models composed of baseline fALFF values from cerebral subregions were proposed. The classification model that distinguished PD patients from HCs (area under the curve [AUC] = 0.897) showed the most significant imaging characteristics in the putamen and precentral gyrus. The other prediction model that evaluated the degree of future deterioration of motor symptoms in PD patients (AUC = 0.916) showed the most significant imaging characteristics in the superior occipital gyrus and caudate nucleus. Furthermore, the increased regional function in bilateral caudate nuclei was correlated with the lower annual increase in motor deficits in all PD patients. The caudate nucleus might be the core region responsible for future motor deficits in newly‐diagnosed PD patients, which may aid the development of disease progression preventive strategies in clinical practice.

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Longitudinal brain connectivity changes and clinical evolution in Parkinson’s disease

Cited by 47 publications

References 36 publications

Functional Connectomics and Disease Progression in Drug‐Naïve Parkinson's Disease Patients

Functional Connectomics and Disease Progression in Drug‐Naïve Parkinson's Disease Patients

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment

Motor progression marker for newly diagnosed drug‐naïve patients with Parkinson's disease: A resting‐state functional MRI study

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