Assessing White Matter Pathology in Early-Stage Parkinson Disease Using Diffusion MRI: A Systematic Review

Bergamino, Maurizio; Keeling, Elizabeth; Mishra, Virendra; Stokes, Ashley M.; Walsh, Ryan R.

doi:10.3389/fneur.2020.00314

Cited by 30 publications

(29 citation statements)

References 135 publications

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“…Directionality refers to a specific route or angle of the nerve fasciculus. In normal WM, nerve fibers are properly oriented and regulated; nevertheless, when myelin is damaged, the neural structure may be disrupted (Yu et al, 2004), consistent with the white matter damage observed even in patients with early-stage PD in previous studies using advanced MRI technologies, such as DTI (Bergamino et al, 2020;Sanjari Moghaddam et al, 2020), showing that structural changes in the WM may underlie the clinical and pathologic heterogeneity of PD and cause relative cognitive impairment. Therefore, WM is a promising brain tissue to provide new insights that will be important for the early diagnosis of PD.…”

Section: Discussionsupporting

confidence: 82%

An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI

Shu

Pang

Xiao

et al. 2020

Front. Aging Neurosci.

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Purpose: To develop and validate an integrative nomogram based on white matter (WM) radiomics biomarkers and nonmotor symptoms for the identification of early-stage Parkinson's disease (PD).Methods: The brain magnetic resonance imaging (MRI) and clinical characteristics of 336 subjects, including 168 patients with PD, were collected from the Parkinson's Progress Markers Initiative (PPMI) database. All subjects were randomly divided into training and test sets. According to the baseline MRI scans of patients in the training set, the WM was segmented to extract the radiomic features of each patient and develop radiomics biomarkers, which were then combined with nonmotor symptoms to build an integrative nomogram using machine learning. Finally, the diagnostic accuracy and reliability of the nomogram were evaluated using a receiver operating characteristic curve and test data, respectively. In addition, we investigated 58 patients with atypical PD who had imaging scans without evidence of dopaminergic deficit (SWEDD) to verify whether the nomogram was able to distinguish patients with typical PD from patients with SWEDD. A decision curve analysis was also performed to validate the clinical practicality of the nomogram.Results: The area under the curve values of the integrative nomogram for the training, testing and verification sets were 0.937, 0.922, and 0.836, respectively; the specificity values were 83.8, 88.2, and 91.38%, respectively; and the sensitivity values were 84.6, 82.4, and 70.69%, respectively. A significant difference in the number of patients with PD was observed between the high-risk group and the low-risk group based on the nomogram (P < 0.05).Conclusion: This integrative nomogram is a new potential method to identify patients with early-stage PD.

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Section: Discussionsupporting

confidence: 82%

An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI

Shu

Pang

Xiao

et al. 2020

Front. Aging Neurosci.

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“…The limited scan time results in problems such as low resolution, noise, artifacts, distortion and crossing-fibers, and reduces qualities of the DTI image and its quantitative measures. Technical developments and limitations of DTI studies in clinical PD have been well-documented in recent review papers ( 107 , 205 ). New developments of diffusion imaging techniques, such as high angular resolution diffusion imaging (HARDI) ( 206 ) and diffusion spectrum imaging (DSI) ( 207 ), have shown improvements including increased signal sensitivity and resolution, gaining details of intravoxel directions and allowing a better differentiation of the crossing fibers or the joining fibers.…”

Section: Developments In Dti Techniquesmentioning

confidence: 99%

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Zhang

Burock

2020

Front. Neurol.

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“…Additionally, to account for unequal sample sizes across acquisitions, we provide the main results as effect sizes, which reflect the magnitude of the differences. Finally, this study only analyzed FA, which is the most commonly used DTI index [15]. Other DTI related metrics, such as radial, axial, and mean diffusivity, may also be of interest, and work is ongoing to assess the impact of different fitting methods on those parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Methodological differences can lead to varying results, even within the same cohort of subjects [13]. This is true across neurodegenerative disease states, including Parkinson's disease [15].…”

Section: Introductionmentioning

confidence: 99%

Systematic Assessment of the Impact of DTI Methodology on Fractional Anisotropy Measures in Alzheimer’s Disease

et al. 2021

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White matter microstructural changes in Alzheimer’s disease (AD) are often assessed using fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI). FA depends on the acquisition and analysis methods, including the fitting algorithm. In this study, we compared FA maps from different acquisitions and fitting algorithms in AD, mild cognitive impairment (MCI), and healthy controls (HCs) using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Three acquisitions from two vendors were compared (Siemens 30, GE 48, and Siemens 54 directions). DTI data were fit using nine fitting algorithms (four linear least squares (LLS), two weighted LLS (WLLS), and three non-linear LLS (NLLS) from four software tools (FSL, DSI-Studio, CAMINO, and AFNI). Different cluster volumes and effect-sizes were observed across acquisitions and fits, but higher consistency was observed as the number of diffusion directions increased. Significant differences were observed between HC and AD groups for all acquisitions, while significant differences between HC and MCI groups were only observed for GE48 and SI54. Using the intraclass correlation coefficient, AFNI–LLS and CAMINO–RESTORE were the least consistent with the other algorithms. By combining data across all three acquisitions and nine fits, differences between AD and HC/MCI groups were observed in the fornix and corpus callosum, indicating FA differences in these regions may be robust DTI-based biomarkers. This study demonstrates that comparisons of FA across aging populations could be confounded by variability in acquisitions and fit methodologies and that identifying the most robust DTI methodology is critical to provide more reliable DTI-based neuroimaging biomarkers for assessing microstructural changes in AD.

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Assessing White Matter Pathology in Early-Stage Parkinson Disease Using Diffusion MRI: A Systematic Review

Cited by 30 publications

References 135 publications

An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI

An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Systematic Assessment of the Impact of DTI Methodology on Fractional Anisotropy Measures in Alzheimer’s Disease

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