Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole‐brain white matter

2020 ◽  
Vol 85 (3) ◽  
pp. 1611-1624
Author(s):  
Zhen‐Yu Shu ◽  
Si‐Jia Cui ◽  
Xiao Wu ◽  
Yuyun Xu ◽  
Peiyu Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Whole Brain ◽  
Conventional Mri ◽  
Brain White Matter

scholarly journals Microstructure of Strategic White Matter Tracts and Cognition in Memory Clinic Patients with Vascular Brain Injury

2017 ◽  
Vol 44 (5-6) ◽  
pp. 268-282 ◽  
Author(s):  
J. Matthijs Biesbroek ◽  
Alexander Leemans ◽  
Hanna den Bakker ◽  
Marco Duering ◽  
Benno Gesierich ◽  
...  
Keyword(s):  
Brain Injury ◽  
White Matter ◽  
Executive Functioning ◽  
Diffusion Tensor ◽  
Added Value ◽  
Memory Clinic ◽  
Whole Brain ◽  
White Matter Tracts ◽  
Conventional Mri ◽  
Brain White Matter

Background: White matter injury is an important factor for cognitive impairment in memory clinic patients. We determined the added value of diffusion tensor imaging (DTI) of strategic white matter tracts in explaining variance in cognition in memory clinic patients with vascular brain injury. Methods: We included 159 patients. Conventional MRI markers (white matter hyperintensity volume, lacunes, nonlacunar infarcts, brain atrophy, and microbleeds), and fractional anisotropy and mean diffusivity (MD) of the whole brain white matter and of 18 white matter tracts were related to cognition using linear regression and Bayesian network analysis. Results: On top of all conventional MRI markers combined, MD of the whole brain white matter explained an additional 3.4% (p = 0.014), 7.8% (p < 0.001), and 1.2% (p = 0.119) variance in executive functioning, speed, and memory, respectively. The Bayesian analyses of regional DTI measures identified strategic tracts for executive functioning (right superior longitudinal fasciculus), speed (left corticospinal tract), and memory (left uncinate fasciculus). MD within these tracts explained an additional 3.4% (p = 0.012), 3.8% (p = 0.007), and 2.1% (p = 0.041) variance in executive functioning, speed, and memory, respectively, on top of all conventional MRI and global DTI markers combined. Conclusion: In memory clinic patients with vascular brain injury, DTI of strategic white matter tracts has a significant added value in explaining variance in cognitive functioning.

scholarly journals White matter microstructural characteristics in newly diagnosed Parkinson’s disease: An unbiased whole-brain study

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Ming-Ching Wen ◽  
Hannah S. E. Heng ◽  
Samuel Y. E. Ng ◽  
Louis C. S. Tan ◽  
Ling Ling Chan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Newly Diagnosed ◽  
Microstructural Characteristics ◽  
Whole Brain ◽  
Brain Study

scholarly journals Mild cognitive impairment in Parkinson's disease is associated with a distributed pattern of brain white matter damage

2013 ◽  
Vol 35 (5) ◽  
pp. 1921-1929 ◽  
Author(s):  
Federica Agosta ◽  
Elisa Canu ◽  
Elka Stefanova ◽  
Lidia Sarro ◽  
Aleksandra Tomić ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
White Matter Damage ◽  
Brain White Matter

Alterations of mean diffusivity in brain white matter and deep gray matter in Parkinson's disease

2013 ◽  
Vol 550 ◽  
pp. 64-68 ◽  
Author(s):  
Hengjun J. Kim ◽  
Sang Joon Kim ◽  
Ho Sung Kim ◽  
Choong Gon Choi ◽  
Namkug Kim ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Gray Matter ◽  
Mean Diffusivity ◽  
Brain White Matter ◽  
Deep Gray Matter

scholarly journals Alterations of white matter abnormalities in Parkinson’s disease: a machine learning approach

2021 ◽  
Author(s):  
long qian ◽  
chaoyong xiao ◽  
Sidong Liu ◽  
zaixu cui ◽  
xiao hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Rating Scale ◽  
Mean Diffusivity ◽  
Learning Approach ◽  
Healthy Controls ◽  
White Matter Tracts ◽  
Machine Learning Approach

Abstract The inter-tract/region dependencies of white-matter in Parkinson’s disease are usually ignored by standard statistical tests. Moreover, it remains unclear whether the disruption of white-matter tracts/regions suffices to identify Parkinson’s disease patients from healthy controls. A machine learning approach was applied to capture the interdependencies between white-matter tracts/regions and to differentiate PD patients from healthy controls. First, the mean regional white-matter profiles, including white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were extracted as features in Parkinson’s disease patients (N = 78) and in healthy controls (N = 91). Then, the feature selection and classification were performed using t-test and linear support vector machine, respectively. Last, the relationships between clinical variables and regional magnetic resonance indices were estimated. Our results showed the combined features (white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) had the best performance with an accuracy of 75.15% and area under curve of 0.8171, respectively. The most discriminative white-matter features were centered on the association fibers, commissural fibers, projection fibers, and striatal fibers. The discriminative regions of right anterior limb of internal capsule had positive association trends with the Unified Parkinson Disease Rating Scale III score; while the genu of corpus callosum and right retrolenticular part of internal capsule had positively association trends with the Hamilton Depression Rating Scale score. Our finding showed the multivariate machine learning approach is a promising tool to detect abnormal white-matter tracts/regions in Parkinson’s disease, and provides us a multidimensional means for neuroimaging classification.

scholarly journals Whole brain white matter connectivity analysis using machine learning: An application to autism

NeuroImage ◽  
2018 ◽  
Vol 172 ◽  
pp. 826-837 ◽  
Author(s):  
Fan Zhang ◽  
Peter Savadjiev ◽  
Weidong Cai ◽  
Yang Song ◽  
Yogesh Rathi ◽  
...  
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
Connectivity Analysis ◽  
Whole Brain ◽  
Brain White Matter ◽  
White Matter Connectivity

Assessment of White Matter Lesions in Parkinson's Disease: Voxel-based Analysis and Tract-based Spatial Statistics Analysis of Parkinson's Disease with Mild Cognitive Impairment

2020 ◽  
Vol 17 (4) ◽  
pp. 480-486
Author(s):  
Wei Pu ◽  
Xudong Shen ◽  
Mingming Huang ◽  
Zhiqian Li ◽  
Xianchun Zeng ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Corpus Callosum ◽  
Fractional Anisotropy ◽  
Spatial Statistics ◽  
Diffusion Tensor ◽  
Tract Based Spatial Statistics

Objective: Application of diffusion tensor imaging (DTI) to explore the changes of FA value in patients with Parkinson's disease (PD) with mild cognitive impairment. Methods: 27 patients with PD were divided into PD with mild cognitive impairment (PD-MCI) group (n = 7) and PD group (n = 20). The original images were processed using voxel-based analysis (VBA) and tract-based spatial statistics (TBSS). Results: The average age of pd-mci group was longer than that of PD group, and the course of disease was longer than that of PD group. Compared with PD group, the voxel based analysis-fractional anisotropy (VBA-FA) values of PD-MCI group decreased in the following areas: bilateral frontal lobe, bilateral temporal lobe, bilateral parietal lobe, bilateral subthalamic nucleus, corpus callosum, and gyrus cingula. Tract-based spatial statistics-fractional anisotropy (TBSS-FA) values in PD-MCI group decreased in bilateral corticospinal tract, anterior cingulum, posterior cingulum, fornix tract, bilateral superior thalamic radiation, corpus callosum(genu, body and splenium), bilateral uncinate fasciculus, bilateral inferior longitudinal fasciculus, bilateral superior longitudinal fasciculus, bilateral superior fronto-occipital fasciculus, bilateral inferior fronto-occipital fasciculus, and bilateral parietal-occipital tracts. The mean age of onset in the PD-MCI group was greater than that in the PD group, and the disease course was longer than that in the PD group. Conclusion: DTI-based VBA and TBSS post-processing methods can detect abnormalities in multiple brain areas and white matter fiber tracts in PD-MCI patients. Impairment of multiple cerebral cortex and white matter fiber pathways may be an important causes of cognitive dysfunction in PD-MCI.

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