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Objective: Our objective was to explore the patterns of resting-state network (RSN) connectivity alterations and investigate how the influences of individual-level network connections on cognition varied across clinical stages without assuming a constant relationship. Methods: 108 PD patients with continuum of cognitive decline (PD-NC = 46, PD-MCI = 43, PDD = 19) and 34 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological tests. Independent component analysis (ICA) and graph theory analyses (GTA) were employed to explore RSN connection changes. Additionally, stage-dependent differential impact of network communication on cognitive performance were examined using sparse varying coefficient modeling. Results: Compared to HCs, the dorsal attention network (DAN) and dorsal sensorimotor network (dSMN) were central networks with decreased connections in PD-NC and PD-MCI stage, while the lateral visual network (LVN) emerged as a central network in patients with dementia. Additionally, connectivity of the cerebellum network (CBN) increased in the PD-NC and PD-MCI stages. GTA demonstrated decreased nodal metrics for DAN and dSMN, coupled with an increase for CBN. Moreover, the degree centrality (DC) values of DAN and dSMN exhibited a stage-dependent differential impact on cognitive performance across the continuum of cognitive decline. Conclusion: Our findings suggest that across the progression of cognitive impairment, the LVN gradually transitions into a core node with reduced connectivity, while the enhancement of connections in CBN diminishes. Furthermore, the non-linear relationship between the DC values of RSNs and cognitive decline indicates the potential for tailored interventions targeting specific stages.

Objective: Freezing of gait (FOG), a specific survival-threatening gait impairment, needs to be urgently explored in patients with multiple system atrophy (MSA), which is characterized by rapid progression and death within 10 years of symptom onset. The objective of this study was to explore the topological organisation of both low- and high-order functional networks in patients with MAS and FOG. Method: Low-order functional connectivity (LOFC) and high-order functional connectivity FC (HOFC) networks were calculated and further analysed using the graph theory approach in 24 patients with MSA without FOG, 20 patients with FOG, and 25 healthy controls. The relationship between brain activity and the severity of freezing symptoms was investigated in patients with FOG. Results: Regarding global topological properties, patients with FOG exhibited alterations in the whole-brain network, dorsal attention network (DAN), frontoparietal network (FPN), and default network (DMN), compared with patients without FOG. At the node level, patients with FOG showed decreased nodal centralities in sensorimotor network (SMN), DAN, ventral attention network (VAN), FPN, limbic regions, hippocampal network and basal ganglia network (BG), and increased nodal centralities in the FPN, DMN, visual network (VIN) and, cerebellar network. The nodal centralities of the right inferior frontal sulcus, left lateral amygdala and left nucleus accumbens (NAC) were negatively correlated with the FOG severity. Conclusion: This study identified a disrupted topology of functional interactions at both low and high levels with extensive alterations in topological properties in MSA patients with FOG, especially those associated with damage to the FPN. These findings offer new insights into the dysfunctional mechanisms of complex networks and suggest potential neuroimaging biomarkers for FOG in patients with MSA.

Abstract Objectives This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson’s disease (PD) and multiple system atrophy (MSA). Methods A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. Results The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. Conclusions Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.