Neurocognitive and psychiatric disorders‐related axonal degeneration in Parkinson's disease

Andica, Christina; Kamagata, Koji; Hatano, Taku; Saito, Yuya; Uchida, Wataru; Ogawa, Takashi; Takeshige‐Amano, Haruka; Hagiwara, Akifumi; Murata, Syo; Oyama, Genko; Shimo, Yashushi; Umemura, Atsushi; Akashi, Toshiaki; Wada, Akihiko; Kumamaru, Kanako Kunishima; Hori, Masaaki; Aoki, Shigeki

doi:10.1002/jnr.24584

Cited by 17 publications

(27 citation statements)

References 67 publications

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“…In this study, only ICVF and AVF showed performance with AUC of over 0.80 among the NODDI and g-ratio analysis, respectively. This does not conflict with a previous study [25], which reported reduction of ICVF in white matter indicating axonal degeneration in PD.…”

Section: Discussioncontrasting

confidence: 57%

“…In this prospective study, which was approved by our institutional review board, MRI data of patients with PD and healthy controls were analyzed. There is subject overlap (64 out of 230) with previous studies [25,26], which used different analyzing methods (tract-of-interest, tract-based spatial statistics, and gray matter-based spatial statistics analyses) from the current study. Those studies did not use deep learning technique or connectome matrices in the evaluations of PD.…”

Section: Methodsmentioning

confidence: 99%

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Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation

et al. 2021

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Purpose To investigate whether Parkinson’s disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)–based structural connectome matrices calculated from diffusion-weighted MRI. Methods In this prospective study, 115 PD patients and 115 healthy controls were enrolled. NOS-based and parameter-weighted connectome matrices were calculated from MRI images obtained with a 3-T MRI unit. With 5-fold cross-validation, diagnostic performance of convolutional neural network (CNN) models using those connectome matrices in differentiating patients with PD from healthy controls was evaluated. To identify the important brain connections for diagnosing PD, gradient-weighted class activation mapping (Grad-CAM) was applied to the trained CNN models. Results CNN models based on some parameter-weighted structural matrices (diffusion kurtosis imaging (DKI)–weighted, neurite orientation dispersion and density imaging (NODDI)–weighted, and g-ratio-weighted connectome matrices) showed moderate performance (areas under the receiver operating characteristic curve (AUCs) = 0.895, 0.801, and 0.836, respectively) in discriminating PD patients from healthy controls. The DKI-weighted connectome matrix performed significantly better than the conventional NOS-based matrix (AUC = 0.761) (DeLong’s test, p < 0.0001). Alterations of neural connections between the basal ganglia and cerebellum were indicated by applying Grad-CAM to the NODDI- and g-ratio-weighted matrices. Conclusion Patients with PD can be differentiated from healthy controls by applying the deep learning technique to the parameter-weighted connectome matrices, and neural circuit disorders including those between the basal ganglia on one side and the cerebellum on the contralateral side were visualized.

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Section: Discussioncontrasting

confidence: 57%

Section: Methodsmentioning

confidence: 99%

Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation

et al. 2021

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“…However, towards the changes of white matter microstructure in PD, the alterations of FA and MD due to PD remain controversial. Most studies focusing on advanced-stage PD patients reported decreased FA and/or increased MD, indicating demyelination and axonal degeneration of white matter microstructure 51 . Other results like increased FA and decreased MD were defined as microstructural compensation in prodromal-stage and early-stage of PD, which was distinguished from pathological findings 52 .…”

Section: Discussionmentioning

confidence: 99%

The role of brain perivascular space burden in early-stage Parkinson’s disease

Shen

Yue

Zhao

et al. 2021

npj Parkinsons Dis.

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Perivascular space (PVS) is associated with neurodegenerative diseases, while its effect on Parkinson’s disease (PD) remains unclear. We aimed to investigate the clinical and neuroimaging significance of PVS in basal ganglia (BG) and midbrain in early-stage PD. We recruited 40 early-stage PD patients and 41 healthy controls (HCs). Both PVS number and volume were calculated to evaluate PVS burden on 7 T magnetic resonance imaging images. We compared PVS burden between PD and HC, and conducted partial correlation analysis between PVS burden and clinical and imaging features. PD patients had a significantly more serious PVS burden in BG and midbrain, and the PVS number in BG was significantly correlated to the PD disease severity and L-dopa equivalent dosage. The fractional anisotropy and mean diffusivity values of certain subcortical nuclei and white matter fibers within or nearby the BG and midbrain were significantly correlated with the ipsilateral PVS burden indexes. Regarding to the midbrain, the difference between bilateral PVS burden was, respectively, correlated to the difference between fiber counts of white fiber tract passing through bilateral substantia nigra in PD. Our study suggests that PVS burden indexes in BG are candidate biomarkers to evaluate PD motor symptom severity and aid in predicting medication dosage. And our findings also highlight the potential correlations between PVS burden and both grey and white matter microstructures.

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“…PD is associated with a wide variety of non-motor symptoms. Patients with PD who experience neurocognitive and psychiatric disorders (i.e., cognitive impairment, hallucinations, depression, anxiety, apathy, and dopamine dysregulation syndrome) exhibit broader axonal loss (indexed by ICVF), predominantly in the posterior regions of the WM, compared to those without these symptoms ( Figure 11 ) [ 127 ]. Using linked independent component analysis, Andica et al [ 127 ] demonstrated that ICVF was the highest contributor to diagnosis (59% weight) when compared to the DTI metrics (FA, MD, AD, and RD), ODI, and WM volume.…”

Section: Applications Of Advanced Diffusion Mri Techniques In Neurodegenerative Diseasesmentioning

confidence: 99%

“…Tracts obtained using the ICBM-DTI-81 white matter tractography atlas ( B ). Abbreviations: ATR, anterior thalamic radiation; FA, fractional anisotropy; HC, healthy control; ICVF, intracellular volume fraction; IFOF, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; MD, mean diffusivity; PD-woNCPs, Parkinson’s disease without neurocognitive and psychiatric symptoms; PD-wNCPs, Parkinson’s disease with neurocognitive and psychiatric symptoms; RD, radial diffusivity; SLF, superior longitudinal fasciculus; UF, uncinate fasciculus (adapted and reproduced with permission from Andica et al [ 127 ]).…”

Section: Figurementioning

confidence: 99%

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

Kamagata

Andica

Kato

et al. 2021

IJMS

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There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

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Neurocognitive and psychiatric disorders‐related axonal degeneration in Parkinson's disease

Cited by 17 publications

References 67 publications

Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation

Parkinson’s disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation

The role of brain perivascular space burden in early-stage Parkinson’s disease

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

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