scholarly journals Deep learning and automated Cell Painting reveal Parkinson’s disease-specific signatures in primary patient fibroblasts

2020 ◽  
Author(s):  
Lauren Schiff ◽  
Bianca Migliori ◽  
Ye Chen ◽  
Deidre Carter ◽  
Caitlyn Bonilla ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Specific Cell ◽  
High Fidelity ◽  
Healthy Controls ◽  
Cell Models ◽  
Disease Phenotypes ◽  
Primary Fibroblasts ◽  
Disease Specific

Drug discovery for Parkinson’s disease (PD) is impeded by the lack of screenable phenotypes in scalable cell models. Here we present a novel unbiased phenotypic profiling platform that combines automation, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 PD patients and carefully matched healthy controls, generating the largest publicly available Cell Painting dataset to date. Using fixed weights from a convolutional deep neural network trained on ImageNet, we generated unbiased deep embeddings from each image, and applied these to train machine learning models to detect morphological disease phenotypes. Interestingly, our models captured individual variation by identifying specific cell lines within the cohort with high fidelity, even across different batches and plate layouts, demonstrating platform robustness and sensitivity. Importantly, our models were able to confidently separate LRRK2 and sporadic PD lines from healthy controls (ROC AUC 0.79 (0.08 standard deviation (SD))) supporting the capacity of this platform for PD modeling and drug screening applications.

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

2021 ◽  
Author(s):  
Koichiro Yasaka ◽  
Koji Kamagata ◽  
Takashi Ogawa ◽  
Taku Hatano ◽  
Haruka Takeshige-Amano ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Basal Ganglia ◽  
Neural Circuit ◽  
Characteristic Curve ◽  
Diffusion Kurtosis Imaging ◽  
Healthy Controls ◽  
Structural Connectome ◽  
Learning Technique

Abstract 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.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Global efficiency of the motor network is decreased in Parkinson's disease in comparison with essential tremor and healthy controls

2021 ◽  
Author(s):  
Natalia Pelizari Novaes ◽  
Joana Bisol Balardin ◽  
Fabiana Campos Hirata ◽  
Luciano Melo ◽  
Edson Amaro ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Healthy Controls ◽  
Global Efficiency ◽  
Motor Network

Deep learning‐based scheme to diagnose Parkinson's disease

2021 ◽  
Author(s):  
Tarjni Vyas ◽  
Raj Yadav ◽  
Chitra Solanki ◽  
Rutvi Darji ◽  
Shivani Desai ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning

Vestibular semicircular canal function as detected by video Head Impulse Test (vHIT) is essentially unchanged in people with Parkinson’s disease compared to healthy controls

2021 ◽  
pp. 1-9
Author(s):  
Kim E. Hawkins ◽  
Elodie Chiarovano ◽  
Serene S. Paul ◽  
Ann M Burgess ◽  
Hamish G. MacDougall ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Semicircular Canal ◽  
Neurodegenerative Disorder ◽  
Vestibular Function ◽  
Head Impulse Test ◽  
Healthy Controls ◽  
Video Head Impulse Test ◽  
Head Impulse ◽  
Vestibulo Ocular Reflex

BACKGROUND: Parkinson’s disease (PD) is a common multi-system neurodegenerative disorder with possible vestibular system dysfunction, but prior vestibular function test findings are equivocal. OBJECTIVE: To report and compare vestibulo-ocular reflex (VOR) gain as measured by the video head impulse test (vHIT) in participants with PD, including tremor dominant and postural instability/gait dysfunction phenotypes, with healthy controls (HC). METHODS: Forty participants with PD and 40 age- and gender-matched HC had their vestibular function assessed. Lateral and vertical semicircular canal VOR gains were measured with vHIT. VOR canal gains between PD participants and HC were compared with independent samples t-tests. Two distinct PD phenotypes were compared to HC using Tukey’s ANOVA. The relationship of VOR gain with PD duration, phenotype, severity and age were investigated using logistic regression. RESULTS: There were no significant differences between groups in vHIT VOR gain for lateral or vertical canals. There was no evidence of an effect of PD severity, phenotype or age on VOR gains in the PD group. CONCLUSION: The impulsive angular VOR pathways are not significantly affected by the pathophysiological changes associated with mild to moderate PD.

scholarly journals Oral Factors That Impact the Oral Microbiota in Parkinson’s Disease

2021 ◽  
Vol 9 (8) ◽  
pp. 1616
Author(s):  
Natalia S. Rozas ◽  
Gena D. Tribble ◽  
Cameron B. Jeter
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Oral Health ◽  
Soft Tissue ◽  
Oral Hygiene ◽  
Beta Diversity ◽  
Oral Microbiota ◽  
Healthy Controls ◽  
Microbiota Composition ◽  
Salivary Ph

Patients with Parkinson’s disease (PD) are at increased risk of aspiration pneumonia, their primary cause of death. Their oral microbiota differs from healthy controls, exacerbating this risk. Our goal was to explore if poor oral health, poor oral hygiene, and dysphagia status affect the oral microbiota composition of these patients. In this cross-sectional case-control study, the oral microbiota from hard and soft tissues of patients with PD (n = 30) and age-, gender-, and education-matched healthy controls (n = 30) was compared using 16S rRNA gene sequencing for bacterial identification. Study participants completed dietary, oral hygiene, drooling, and dysphagia questionnaires, and an oral health screening. Significant differences in soft tissue beta-diversity (p < 0.005) were found, and a higher abundance of opportunistic oral pathogens was detected in patients with PD. Factors that significantly influenced soft tissue beta-diversity and microbiota composition include dysphagia, drooling (both p < 0.05), and salivary pH (p < 0.005). Thus, patients with PD show significant differences in their oral microbiota compared to the controls, which may be due, in part, to dysphagia, drooling, and salivary pH. Understanding factors that alter their oral microbiota could lead to the development of diagnostic and treatment strategies that improve the quality of life and survivability of these patients.

Development of standardized regression-based formulas to assess meaningful cognitive change in early Parkinson’s disease

2020 ◽  
Author(s):  
Hannah L Combs ◽  
Kate A Wyman-Chick ◽  
Lauren O Erickson ◽  
Michele K York
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Test Scores ◽  
Prediction Models ◽  
Early Stage ◽  
Cognitive Change ◽  
Cognitive Test ◽  
Healthy Controls ◽  
Change Over Time ◽  
Over Time

Abstract Objective Longitudinal assessment of cognitive and emotional functioning in patients with Parkinson’s disease (PD) is helpful in tracking progression of the disease, developing treatment plans, evaluating outcomes, and educating patients and families. Determining whether change over time is meaningful in neurodegenerative conditions, such as PD, can be difficult as repeat assessment of neuropsychological functioning is impacted by factors outside of cognitive change. Regression-based prediction formulas are one method by which clinicians and researchers can determine whether an observed change is meaningful. The purpose of the current study was to develop and validate regression-based prediction models of cognitive and emotional test scores for participants with early-stage idiopathic PD and healthy controls (HC) enrolled in the Parkinson’s Progression Markers Initiative (PPMI). Methods Participants with de novo PD and HC were identified retrospectively from the PPMI archival database. Data from baseline testing and 12-month follow-up were utilized in this study. In total, 688 total participants were included in the present study (NPD = 508; NHC = 185). Subjects from both groups were randomly divided into development (70%) and validation (30%) subsets. Results Early-stage idiopathic PD patients and healthy controls were similar at baseline. Regression-based models were developed for all cognitive and self-report mood measures within both populations. Within the validation subset, the predicted and observed cognitive test scores did not significantly differ, except for semantic fluency. Conclusions The prediction models can serve as useful tools for researchers and clinicians to study clinically meaningful cognitive and mood change over time in PD.

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