scholarly journals Brain Functional Specialization and Cooperation in Parkinson’s Disease

Author(s):  
Jinmei Sun ◽  
Xiaoran Gao ◽  
Qiang Hua ◽  
Rongrong Du ◽  
Pingping Liu ◽  
...  

Abstract Background: Cerebral specialization and inter-hemispheric cooperation are two of the most prominent functional architectures of the human brain. Their dysfunctions may be related to pathophysiological changes in patients with Parkinson’s disease (PD), who are characterized by unbalanced onset and progression of motor symptoms. Objective: This study aimed to characterize the two intrinsic architectures of hemispheric functions in PD using resting-state functional magnetic resonance imaging.Methods: Seventy idiopathic PD patients and 70 age-, sex-, and education-matched healthy subjects were recruited. All participants underwent magnetic resonance image scanning and clinical evaluations. The cerebral specialization (Autonomy index, AI) and inter-hemispheric cooperation (Connectivity between Functionally Homotopic voxels, CFH) were calculated and compared between groups. Results: Compared with healthy controls, PD patients showed stronger AI in the left angular gyrus. Specifically, this difference in specialization resulted from increased functional connectivity (FC) of the ipsilateral areas (e.g., the left prefrontal area), and decreased FC in the contralateral area (e.g., the right supramarginal gyrus). Imaging-cognitive correlation analysis indicated that these connectivity were positively related to the score of Montreal Cognitive Assessment in PD patients. CFH between the bilateral sensorimotor regions was significantly decreased in PD patients compared with controls. No significant correlation between CFH and cognitive scores was found in PD patients.Conclusions: This study illustrated a strong leftward specialization but weak inter-hemispheric coordination in PD patients. It provided new insights to further clarify the pathological mechanism of PD.

2021 ◽  
Vol 13 ◽  
Author(s):  
Song’an Shang ◽  
Hongying Zhang ◽  
Yuan Feng ◽  
Jingtao Wu ◽  
Weiqiang Dou ◽  
...  

Background: Cognitive deficits are prominent non-motor symptoms in Parkinson’s disease (PD) and have been shown to involve the neurovascular unit (NVU). However, there is a lack of sufficient neuroimaging research on the associated modulating mechanisms. The objective of this study was to identify the contribution of neurovascular decoupling to the pathogenesis of cognitive decline in PD.Methods: Regional homogeneity (ReHo), a measure of neuronal activity, and cerebral blood flow (CBF), a measure of vascular responses, were obtained from patients with PD with mild cognitive impairment (MCI) and normal cognition (NC) as well as matched healthy controls (HCs). Imaging metrics of neurovascular coupling (global and regional CBF-ReHo correlation coefficients and CBF-ReHo ratios) were compared among the groups.Results: Neurovascular coupling was impaired in patients with PD-MCI with a decreased global CBF-ReHo correlation coefficient relative to HC subjects (P < 0.05). Regional dysregulation was specific to the PD-MCI group and localized to the right middle frontal gyrus, right middle cingulate cortex, right middle occipital gyrus, right inferior parietal gyrus, right supramarginal gyrus, and right angular gyrus (P < 0.05). Compared with HC subjects, patients with PD-MCI showed higher CBF-ReHo ratios in the bilateral lingual gyri (LG), bilateral putamen, and left postcentral gyrus and lower CBF-ReHo ratios in the right superior temporal gyrus, bilateral middle temporal gyri, bilateral parahippocampal gyri, and right inferior frontal gyrus. Relative to the HC and PD-NC groups, the PD-MCI group showed an increased CBF-ReHo ratio in the left LG, which was correlated with poor visual–spatial performance (r = −0.36 and P = 0.014).Conclusion: The involvement of neurovascular decoupling in cognitive impairment in PD is regionally specific and most prominent in the visual–spatial cortices, which could potentially provide a complementary understanding of the pathophysiological mechanisms underlying cognitive deficits in PD.


2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Juan Shen ◽  
Chao Xu

This paper uses resting-state functional magnetic resonance imaging to observe the changes in local consistency of brain activity in patients with Parkinson’s disease (PD). Both healthy volunteers and Parkinson’s disease patients were scanned for resting brain functional imaging, and the collected raw data were processed using resting functional magnetic resonance data processing toolkit software. This study adopted the use of Regional Homogeneity (ReHo). The postprocessing method of RS-fMRI is to study the spontaneous brain activity changes of patients with Parkinson’s disease and cognitive impairment and to explore the changes in the function of their brain regions in the hope of providing help for the treatment of Parkinson’s disease cognitive impairment. The results showed that, compared with the normal control group, the brain regions with increased ReHo values in the PD group were the right central anterior gyrus, the right lingual gyrus, the left middle occipital gyrus, and the bilateral anterior cuneiform lobes. The results show that PD patients have abnormal brain nerve activities in the resting state, and these abnormal brain nerve activities may be related to PD cognitive and behavioral dysfunction.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.


2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Sangwoo Kim ◽  
Youngjeon Lee ◽  
Chang-Yeop Jeon ◽  
Yeung Bae Jin ◽  
Sukhoon Oh ◽  
...  

Abstract Background Although the thalamus is known to modulate basal ganglia function related to motor control activity, the abnormal changes within the thalamus during distinct medical complications have been scarcely investigated. In order to explore the feasibility of assessing iron accumulation in the thalamus as an informative biomarker for Parkinson’s disease (PD), this study was designed to employ quantitative susceptibility mapping using a 7 T magnetic resonance imaging system in cynomolgus monkeys. A 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-injected cynomolgus monkey and a healthy control (HC) were examined by 7 T magnetic resonance imaging. Positron emission tomography with 18F-N-(3-fluoro propyl)-2ß-carboxymethoxy-3ß-(4-iodophenyl) nortropane was also employed to identify the relationship between iron deposits and dopamine depletion. All acquired values were averaged within the volume of interest of the nigrostriatal pathway. Findings Compared with the HC, the overall elevation of iron deposition within the thalamus in the Parkinson’s disease model (about 53.81% increase) was similar to that in the substantia nigra (54.81%) region. Substantial susceptibility changes were observed in the intralaminar part of the thalamus (about 70.78% increase). Additionally, we observed that in the Parkinson’s disease model, binding potential values obtained from positron emission tomography were considerably decreased in the thalamus (97.51%) and substantia nigra (92.48%). Conclusions The increased iron deposition in the thalamus showed negative correlation with dopaminergic activity in PD, supporting the idea that iron accumulation affects glutaminergic inputs and dopaminergic neurons. This investigation indicates that the remarkable susceptibility changes in the thalamus could be an initial major diagnostic biomarker for Parkinson’s disease-related motor symptoms.


2021 ◽  
Vol 10 (2) ◽  
pp. 205846012098809
Author(s):  
Byeong H Oh ◽  
Hyeong C Moon ◽  
Aryun Kim ◽  
Hyeon J Kim ◽  
Chae J Cheong ◽  
...  

Background The pathology of Parkinson’s disease leads to morphological changes in brain structure. Currently, the progressive changes in gray matter volume that occur with time and are specific to patients with Parkinson’s disease, compared to healthy controls, remain unclear. High-tesla magnetic resonance imaging might be useful in differentiating neurological disorders by brain cortical changes. Purpose We aimed to investigate patterns in gray matter changes in patients with Parkinson’s disease by using an automated segmentation method with 7-tesla magnetic resonance imaging. Material and Methods High-resolution T1-weighted 7 tesla magnetic resonance imaging volumes of 24 hemispheres were acquired from 12 Parkinson’s disease patients and 12 age- and sex-matched healthy controls with median ages of 64.5 (range, 41–82) years and 60.5 (range, 25–74) years, respectively. Subgroup analysis was performed according to whether axial motor symptoms were present in the Parkinson’s disease patients. Cortical volume, cortical thickness, and subcortical volume were measured using a high-resolution image processing technique based on the Desikan-Killiany-Tourville atlas and an automated segmentation method (FreeSurfer version 6.0). Results After cortical reconstruction, in 7 tesla magnetic resonance imaging volume segmental analysis, compared with the healthy controls, the Parkinson’s disease patients showed global cortical atrophy, mostly in the prefrontal area (rostral middle frontal, superior frontal, inferior parietal lobule, medial orbitofrontal, rostral anterior cingulate area), and subcortical volume atrophy in limbic/paralimbic areas (fusiform, hippocampus, amygdala). Conclusion We first demonstrated that 7 tesla magnetic resonance imaging detects structural abnormalities in Parkinson’s disease patients compared to healthy controls using an automated segmentation method. Compared with the healthy controls, the Parkinson’s disease patients showed global prefrontal cortical atrophy and hippocampal area atrophy.


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