scholarly journals Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease

2017 ◽  
Vol 15 ◽  
pp. 587-593 ◽  
Author(s):  
Laura J. de Schipper ◽  
Jeroen van der Grond ◽  
Johan Marinus ◽  
Johanna M.L. Henselmans ◽  
Jacobus J. van Hilten
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Structural Covariance

Anxiety in Parkinson's disease is associated with reduced structural covariance of the striatum

2018 ◽  
Vol 240 ◽  
pp. 113-120 ◽  
Author(s):  
Caroline S. Oosterwijk ◽  
Chris Vriend ◽  
Henk W. Berendse ◽  
Ysbrand D. van der Werf ◽  
Odile A. van den Heuvel
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Structural Covariance

Extraction of large-scale structural covariance networks from grey matter volume for Parkinson’s disease classification

2018 ◽  
Vol 28 (8) ◽  
pp. 3296-3305 ◽  
Author(s):  
Pei-Lin Lee ◽  
Kun-Hsien Chou ◽  
Cheng-Hsien Lu ◽  
Hsiu-Ling Chen ◽  
Nai-Wen Tsai ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Large Scale ◽  
Grey Matter ◽  
Disease Classification ◽  
Grey Matter Volume ◽  
Structural Covariance ◽  
Matter Volume

scholarly journals Structural covariance networks of striatum subdivision in patients with Parkinson's disease

2014 ◽  
Vol 36 (4) ◽  
pp. 1567-1584 ◽  
Author(s):  
Kun-Hsien Chou ◽  
Wei-Che Lin ◽  
Pei-Lin Lee ◽  
Nai-Wen Tsai ◽  
Yung-Cheng Huang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Structural Covariance

scholarly journals The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yueh-Sheng Chen ◽  
Hsiu-Ling Chen ◽  
Cheng-Hsien Lu ◽  
Chih-Ying Lee ◽  
Kun-Hsien Chou ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Gray Matter ◽  
Neuropsychological Testing ◽  
Whole Body ◽  
Adult Intelligence Scale ◽  
Structural Covariance ◽  
Normal Cognition

AbstractStructural covariance assesses similarities in gray matter between brain regions and can be applied to study networks of the brain. In this study, we explored correlations between structural covariance networks (SCNs) and cognitive impairment in Parkinson’s disease patients. 101 PD patients and 58 age- and sex-matched healthy controls were enrolled in the study. For each participant, comprehensive neuropsychological testing using the Wechsler Adult Intelligence Scale-III and Cognitive Ability Screening Instrument were conducted. Structural brain MR images were acquired using a 3.0T whole body GE Signa MRI system. T1 structural images were preprocessed and analyzed using Statistical Parametric Mapping software (SPM12) running on Matlab R2016a for voxel-based morphometric analysis and SCN analysis. PD patients with normal cognition received follow-up neuropsychological testing at 1-year interval. Cognitive impairment in PD is associated with degeneration of the amygdala/hippocampus SCN. PD patients with dementia exhibited increased covariance over the prefrontal cortex compared to PD patients with normal cognition (PDN). PDN patients who had developed cognitive impairment at follow-up exhibited decreased gray matter volume of the amygdala/hippocampus SCN in the initial MRI. Our results support a neural network-based mechanism for cognitive impairment in PD patients. SCN analysis may reveal vulnerable networks that can be used to early predict cognitive decline in PD patients.

scholarly journals Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Erlei Wang ◽  
Yujing Jia ◽  
Yang Ya ◽  
Jin Xu ◽  
Chengjie Mao ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Magnetic Resonance Images ◽  
Clustering Coefficient ◽  
Distribution Analysis ◽  
Structural Covariance ◽  
Theoretical Approaches ◽  
Topological Organization ◽  
And Gender ◽  
Sulcal Depth

Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.

scholarly journals Effects of a Highly Challenging Balance Training Program on Motor Function and Brain Structure in Parkinson’s Disease

2021 ◽  
Vol 11 (4) ◽  
pp. 2057-2071
Author(s):  
Franziska Albrecht ◽  
Joana B. Pereira ◽  
Mite Mijalkov ◽  
Malin Freidle ◽  
Hanna Johansson ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Training Program ◽  
Brain Structure ◽  
Repeated Measures ◽  
Brain Volume ◽  
Controlled Trial ◽  
Gait Training ◽  
Structural Covariance ◽  
Active Controls

Background: Parkinson’s disease (PD) is characterized by motor deficits and brain alterations having a detrimental impact on balance, gait, and cognition. Intensive physical exercise can induce changes in the neural system, potentially counteracting neurodegeneration in PD and improving clinical symptoms. Objective: This randomized controlled trial investigated effects of a highly challenging, cognitively demanding, balance and gait training (HiBalance) program in participants with PD on brain structure. Methods: 95 participants were assigned to either the HiBalance or an active control speech training program. The group-based interventions were performed in 1-hour sessions, twice per week over a 10-week period. Participants underwent balance, gait, cognitive function, and structural magnetic resonance imaging assessments before and after the interventions. Voxel-based morphometry was analyzed in 34 HiBalance and 31 active controls. Additionally, structural covariance networks were assessed. Results: There was no significant time by group interaction between the HiBalance and control training in balance, gait, or brain volume. Within-HiBalance-group analyses showed higher left putamen volumes post-training. In repeated measures correlation a positive linear, non-significant relationship between gait speed and putamen volume was revealed. In the HiBalance group we found community structure changes and stronger thalamic-cerebellar connectivity in structural covariance networks. Neither brain volume changes nor topology changes were found for the active controls after the training. Conclusion: Thus, subtle structural brain changes occur after balance and gait training. Future studies need to determine whether training modifications or other assessment methods lead to stronger effects.

scholarly journals Abnormalities in Structural Covariance of Cortical Gyrification in Parkinson's Disease

2017 ◽  
Vol 11 ◽  
Author(s):  
Jinping Xu ◽  
Jiuquan Zhang ◽  
Jinlei Zhang ◽  
Yue Wang ◽  
Yanling Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Structural Covariance
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