scholarly journals Subtle anomaly detection in MRI brain scans: Application to biomarkers extraction in patients with 'de novo' Parkinson's disease

2021 ◽  
Author(s):  
Veronica Munoz-Ramirez ◽  
Virgilio Kmetzsch ◽  
Florence Forbes ◽  
Sara Meoni ◽  
Elena Moro ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Anomaly Detection ◽  
De Novo ◽  
Diffusion Tensor ◽  
Quantitative Mri ◽  
Subcortical Structures ◽  
Brain Scans ◽  
Mri Brain

With the advent of recent deep learning techniques, computerized methods for automatic lesion segmentation have reached performances comparable to those of medical practitioners. However, little attention has been paid to the detection of subtle physiological changes caused by evolutive pathologies such as neurodegenerative diseases. In this work, we investigated the ability of deep learning models to detect anomalies in magnetic resonance imaging (MRI) brain scans of recently diagnosed and untreated ('de novo') patients with Parkinson's disease (PD). We evaluated two families of auto-encoders, fully convolutional and variational auto-encoders. The models were trained with diffusion tensor imaging (DTI) parameter maps of healthy controls. Then, reconstruction errors computed by the models in different brain regions allowed to classify controls and patients with ROC AUC up to 0.81. Moreover, the white matter and the subcortical structures, particularly the substantia nigra, were identified as the regions the most impacted by the disease, in accordance with the physio-pathology of PD. Our results suggest that deep learning-based anomaly detection models, even trained on a moderate number of images, are promising tools for extracting robust neuroimaging biomarkers of PD. Interestingly, such models can be seamlessly extended with additional quantitative MRI parameters and could provide new knowledge about the physio-pathology of neuro-degenerative diseases.

scholarly journals Deep-learning-based target screening and similarity search for the predicted inhibitors of the pathways in Parkinson's disease

RSC Advances ◽  
2019 ◽  
Vol 9 (18) ◽  
pp. 10326-10339 ◽  
Author(s):  
Abbas Khan ◽  
Aman Chandra Kaushik ◽  
Syed Shujait Ali ◽  
Nisar Ahmad ◽  
Dong-Qing Wei
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Small Molecule ◽  
Similarity Search ◽  
De Novo ◽  
Learning Method ◽  
Small Molecule Library ◽  
Target Screening

Herein, a two-step de novo approach was developed for the prediction of piperine targets and another prediction of similar (piperine) compounds from a small molecule library using a deep-learning method.

scholarly journals Characterizing white matter alterations in drug-naïve de novo Parkinson’s disease with diffusion MRI

2021 ◽  
Author(s):  
Yiming Xiao ◽  
Terry M. Peters ◽  
Ali R. Khan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
De Novo ◽  
Neurodegenerative Disorder ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Motor Dysfunction ◽  
White Matter Integrity ◽  
Drug Naïve

AbstractParkinson’s disease (PD) is a progressive neurodegenerative disorder that is characterized by a range of motor and non-motor symptoms, often with the motor dysfunction initiated unilaterally. Knowledge regarding disease-related alterations in white matter pathways can effectively help improve the understanding of the disease and propose targeted treatment strategies. Microstructural imaging techniques, including diffusion tensor imaging (DTI), allows inspection of white matter integrity to study the pathogenesis of various neurological conditions. Previous voxel-based analyses with DTI measures, such as fractional anisotropy and mean diffusivity have uncovered changes in brain regions that are associated with PD, but the conclusions were inconsistent, partially due to small patient cohorts and the lack of consideration for clinical laterality onset, particularly in early PD. Fixel-based analysis (FBA) is a recent framework that offers tract-specific insights regarding white matter health, but very few FBA studies on PD exist. We present a study that reveals strengthened and weakened white matter integrity that is subject to symptom laterality in a large drug-naïve de novo PD cohort using complementary DTI and FBA measures. The findings suggest that the disease gives rise to both functional degeneration and the creation of compensatory networks in the early stage.

scholarly journals Differentiation of multiple system atrophy from Parkinson’s disease by structural connectivity derived from probabilistic tractography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Alexandra Abos ◽  
Hugo C. Baggio ◽  
Barbara Segura ◽  
Anna Campabadal ◽  
Carme Uribe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Multiple System Atrophy ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Discrimination Performance ◽  
Probabilistic Tractography ◽  
Subcortical Structures ◽  
Diffusion Weighted Images

Abstract Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson’s disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3 T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.

Association of the Non-Motor Burden with Patterns of Striatal Dopamine Loss in de novo Parkinson’s Disease

2020 ◽  
Vol 10 (4) ◽  
pp. 1541-1549
Author(s):  
Seok Jong Chung ◽  
Sangwon Lee ◽  
Han Soo Yoo ◽  
Yang Hyun Lee ◽  
Hye Sun Lee ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
De Novo ◽  
Early Stage ◽  
Striatal Dopamine ◽  
Motor Deficits ◽  
Dopamine Depletion ◽  
Severe Motor ◽  
Severe Group ◽  
Striatal Dopamine Depletion

Background: Striatal dopamine deficits play a key role in the pathogenesis of Parkinson’s disease (PD), and several non-motor symptoms (NMSs) have a dopaminergic component. Objective: To investigate the association between early NMS burden and the patterns of striatal dopamine depletion in patients with de novo PD. Methods: We consecutively recruited 255 patients with drug-naïve early-stage PD who underwent 18F-FP-CIT PET scans. The NMS burden of each patient was assessed using the NMS Questionnaire (NMSQuest), and patients were divided into the mild NMS burden (PDNMS-mild) (NMSQuest score <6; n = 91) and severe NMS burden groups (PDNMS-severe) (NMSQuest score >9; n = 90). We compared the striatal dopamine transporter (DAT) activity between the groups. Results: Patients in the PDNMS-severe group had more severe parkinsonian motor signs than those in the PDNMS-mild group, despite comparable DAT activity in the posterior putamen. DAT activity was more severely depleted in the PDNMS-severe group in the caudate and anterior putamen compared to that in the PDMNS-mild group. The inter-sub-regional ratio of the associative/limbic striatum to the sensorimotor striatum was lower in the PDNMS-severe group, although this value itself lacked fair accuracy for distinguishing between the patients with different NMS burdens. Conclusion: This study demonstrated that PD patients with severe NMS burden exhibited severe motor deficits and relatively diffuse dopamine depletion throughout the striatum. These findings suggest that the level of NMS burden could be associated with distinct patterns of striatal dopamine depletion, which could possibly indicate the overall pathological burden in PD.

The Superior Colliculus in De Novo Parkinson's Disease: A Prodromal Dysfunction?

2018 ◽  
Author(s):  
Elena Moro ◽  
Emmanuelle Bellot ◽  
Sara Meoni ◽  
Pierre Pelissier ◽  
Ruxandra Hera ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Superior Colliculus ◽  
De Novo

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

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.

Sign in / Sign up

Export Citation Format

Share Document