What light have resting state fMRI studies shed on cognition and mood in Parkinson’s disease?

Background: Movement execution is impaired in patients with Parkinson’s disease. Evolving neurodegeneration leads to altered connectivity between distinct regions of the brain and altered activity at interconnected areas. How connectivity alterations influence complex movements like drawing spirals in Parkinson’s disease patients remains largely unexplored. Objective: We investigated whether deteriorations in interregional connectivity relate to impaired execution of drawing. Methods: Twenty-nine Patients and 31 age-matched healthy control participants drew spirals with both hands on a digital graphics tablet, and the regularity of drawing execution was evaluated by sample entropy. We recorded resting-state fMRI and task-related EEG, and calculated the time-resolved partial directed coherence to estimate effective connectivity for both imaging modalities to determine the extent and directionality of interregional interactions. Results: Movement performance in Parkinson’s disease patients was characterized by increased sample entropy, corresponding to enhanced irregularities in task execution. Effective connectivity between the motor cortices of both hemispheres, derived from resting-state fMRI, was significantly reduced in PD patients in comparison to controls. The connectivity strength in the nondominant to dominant hemisphere direction in both modalities was inversely correlated with irregularities during drawing, but not with the clinical state. Conclusion: Our findings suggest that interhemispheric connections are affected both at rest and during drawing movements by Parkinson’s disease. This provides novel evidence that disruptions of interhemispheric information exchange play a pivotal role for impairments of complex movement execution in Parkinson’s disease patients.

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Frequency-dependent Alterations in Complexity in Parkinson’s Disease and Prodromal Parkinson’s Disease: A Resting-State fMRI Study

10.21203/rs.3.rs-1063373/v1 ◽

2021 ◽

Author(s):

Jie Xiang ◽

Xueting Cheng ◽

Chen Cheng ◽

Yuxiang Guo ◽

Bin Wang ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Information Processing ◽

Gray Matter ◽

Resting State ◽

Resting State Fmri ◽

Fuzzy Entropy ◽

Precentral Gyrus ◽

Frequency Bands ◽

Normal Controls

Abstract Parkinson’s disease manifests principally as resting tremor, rigidity, akinesia and postural instability and exhibits deficits in information-processing tasks and abnormalities in the striatum. Human brain is one of the most complex information processing systems and resting-state fMRI signals, which possess complex nonlinear dynamic properties, have been extensively applied to study changes in brain function. However, it remains unclear whether patients with Parkinson’s disease and prodromal Parkinson’s disease have an abnormal complexity in resting-state fMRI signals and whether the abnormalities are frequency band dependent. Therefore, we investigated the complexity of signals in 47 patients with Parkinson’s disease, 26 patients with prodromal Parkinson’s disease and 21 normal controls within four frequency bands with Fuzzy Entropy. After preprocessing, entropy maps of the whole brain were extracted within four different frequency bands. Then we performed a one-way analysis of variance and results in slow-2 and slow-3 bands revealed that Parkinson’s disease patients exhibited higher complexity than those with prodromal Parkinson’s disease and normal controls. Prodromal Parkinson’s disease patients exhibited lower complexity than normal controls. Significant differences were observed mainly in the precentral gyrus , precuneus, caudate, thalamus and superior frontal gyrus. Significant correlations were found between the Fuzzy Entropy and clinical characteristics, regional homogeneity, gray matter volume and gray matter density. The results indicated that Parkinson’s disease and prodromal Parkinson’s disease patients had abnormal intrinsic neural oscillations, mainly in slow-3 and slow-2 bands, depending on frequency bands. Complexity analysis of resting-state fMRI signals in multiple bands can help probe brain activity and pathophysiology of neurodegenerative diseases.

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