Corpus Callosum and Cingulum Tractography in Parkinson's Disease

Wiltshire, Katie; Concha, Luis; Gee, Myrlene; Bouchard, Thomas; Beaulieu, Christian; Camicioli, Richard

doi:10.1017/s0317167100010751

Cited by 49 publications

(31 citation statements)

References 29 publications

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“…It has been shown that the deterioration of the corpus callosum genu is linked to PD dementia (Kamagata et al, 2013b), as well as executive and attention dysfunctions (Zheng et al, 2014). DTI of the corpus callosum (in the body and the splenium) can help differentiating PD patients according to their cognitive status (normal, mild cognitive impairment, or dementia; Deng et al, 2013); for this purpose, MD demonstrated to be more accurate than FA (Wiltshire et al, 2010). In addition, structural alteration of the corpus callosum, shown using DTI, seems to be involved in predominant gait disorders (Chan et al, 2014) and impulse control disorders (Yoo et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis

Atkinson-Clément

Pinto

Eusebio

et al. 2017

NeuroImage: Clinical

237

190

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BackgroundNeuroimaging studies help us better understand the pathophysiology and symptoms of Parkinson's disease (PD). In several of these studies, diffusion tensor imaging (DTI) was used to investigate structural changes in cerebral tissue. Although data have been provided as regards to specific brain areas, a whole brain meta-analysis is still missing.MethodsWe compiled 39 studies in this meta-analysis: 14 used fractional anisotropy (FA), 1 used mean diffusivity (MD), and 24 used both indicators. These studies comprised 1855 individuals, 1087 with PD and 768 healthy controls. Regions of interest were classified anatomically (subcortical structures; white matter; cortical areas; cerebellum). Our statistical analysis considered the disease effect size (DES) as the main variable; the heterogeneity index (I2) and Pearson's correlations between the DES and co-variables (demographic, clinical and MRI parameters) were also calculated.ResultsOur results showed that FA-DES and MD-DES were able to distinguish between patients and healthy controls. Significant differences, indicating degenerations, were observed within the substantia nigra, the corpus callosum, and the cingulate and temporal cortices. Moreover, some findings (particularly in the corticospinal tract) suggested opposite brain changes associated with PD. In addition, our results demonstrated that MD-DES was particularly sensitive to clinical and MRI parameters, such as the number of DTI directions and the echo time within white matter.ConclusionsDespite some limitations, DTI appears as a sensitive method to study PD pathophysiology and severity. The association of DTI with other MRI methods should also be considered and could benefit the study of brain degenerations in PD.

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Section: Discussionmentioning

confidence: 99%

Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis

Atkinson-Clément

Pinto

Eusebio

et al. 2017

NeuroImage: Clinical

237

190

View full text Add to dashboard Cite

show abstract

“…The loss of dopaminergic neurons in the substantia nigra is considered the hallmark neuropathological finding of PD [9,10]. Yet it is evident that the white matter pathological changes go beyond the basal ganglia, as shown by significant white matter deterioration in the cingulum [11,12] corticospinal tract, corpus callosum [13], and projection fibers along the thalamus, putamen, temporal, and frontal cortices [10,14]. …”

Section: Introductionmentioning

confidence: 99%

“…However negative comparisons results between PD, PDD, and CON have also been reported in regions such as the cingulum and corpus callosum [12]. Additional DTI studies examined individuals with Lewy body dementia (LBD), which presents with early cognitive changes as well as parkinsonism and has a pathology similar to PDD [21].…”

Section: Introductionmentioning

confidence: 99%

A Comparative White Matter Study with Parkinson′s disease,Parkinson′s Disease with Dementia and Alzheimer′s Disease

Perea¹,

Rada

Wilson

et al. 2013

J Alzheimers Dis Parkinsonism

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Alzheimer's disease (AD) and Parkinson's disease (PD) are among the most common neurodegenerative disorders affecting older populations. AD is characterized by impaired memory and cognitive decline while the primary symptoms of PD include resting tremor, bradykinesia and rigidity. In PD, mild cognitive changes are frequently present, which could progress to dementia (PD dementia (PDD)). PDD and AD dementias are different in pathology although the difference in microstructural changes remains unknown. To further understand these diseases, it is essential to understand the distinct mechanism of their microstructural changes. We used diffusion tensor imaging (DTI) to investigate white matter tract differences between early stage individuals with AD (n=14), PD (n=12), PDD (n=9), and healthy non-demented controls (CON) (n=13). We used whole brain tract based spatial statistics (TBSS) and a region of interest (ROI) analysis focused on the substantia nigra (SN). We found that individuals with PDD had more widespread white matter degeneration compared to PD, AD, and CON. Individuals with AD had few regional abnormalities in the anterior and posterior projections of the corpus callosum while PD and CON did not appear to have significant white matter degeneration when compared to other groups. ROI analyses showed that PDD had the highest diffusivity in the SN and were significantly different from CON. There were no significant ROI differences between CON, PD, or AD. In conclusion, global white matter microstructural deterioration is evident in individuals with PDD, and DTI may provide a means with which to tease out pathological differences between AD and PD dementias.

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“…This important structure is the major fiber tract which connects the homologous cortical areas of the left and right hemisphere [49]. The three dimensional (3D) surface of the corpus callosum can be delineated with the powerful DTI, while it is difficult for the traditional MRI modalities.…”

Section: Resultsmentioning

confidence: 99%

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

et al. 2014

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High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

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Corpus Callosum and Cingulum Tractography in Parkinson's Disease

Cited by 49 publications

References 29 publications

Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis

Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis

A Comparative White Matter Study with Parkinson′s disease,Parkinson′s Disease with Dementia and Alzheimer′s Disease

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

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