Association of olfaction dysfunction with brain microstructure in prodromal Parkinson disease

Moghaddam, Hossein Sanjari; Dolatshahi, Mahsa; Salardini, Elaheh; Aarabi, Mohammad Hadi

doi:10.1007/s10072-018-3629-2

Cited by 20 publications

(25 citation statements)

References 57 publications

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“…Diffusion tensor imaging can discover microstructural changes in the brain white matter. Previous studies found abnormal white matter fiber characteristic (mainly located in the right arcuate fasciculus and bilateral middle cerebellar peduncles) in prodromal early stage of PD (Sanjari Moghaddam et al., 2019). Another microstructure difference was located in the bilateral white matter fiber of the mediodorsal thalamic regions between the DPD and NDPD groups, but the sample size was relatively small and the clinical score only included the Hamilton depression rating scale (HAMD)(Li et al., 2010).…”

Section: Introductionmentioning

confidence: 88%

Aberrant functional connectivity and activity in Parkinson’s disease and comorbidity with depression based on radiomic analysis

et al. 2021

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Introduction The current diagnosis of Parkinson's disease (PD) comorbidity with depression (DPD) largely depends on clinical evaluation. However, the modality may tend to lack precision in detecting PD with depression. A radiomic approach that combines functional connectivity and activity with clinical scores has the potential to achieve accurate and differential diagnosis between PD and DPD. Methods In this study, we aimed to employ the radiomic approach to extract large‐scale features of functional connectivity and activity for differentiating among DPD, PD with no depression (NDPD), and healthy controls (HC). We extracted 6,557 features of five types from all subjects including clinical characteristics, resting‐state functional connectivity (RSFC), amplitude of low‐frequency fluctuation (ALFF), regional homogeneity (ReHo), and voxel‐mirrored homotopic connectivity (VMHC). Lasso, random forest, and support vector machine (SVM) were implemented for feature selection and dimension reduction based on the training sets, and the prediction performance for different methods in the testing sets was compared. Results The results showed that nineteen features were selected for the group of DPD versus HC, 34 features were selected for the group of NDPD versus HC, and 17 features were retained for the group of DPD versus NDPD. In the testing sets, Lasso prediction achieved the accuracies of 0.95, 0.96, and 0.85 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. Random forest achieved the accuracies of 0.90, 0.82, and 0.90 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively, while SVM yielded the accuracies of 1, 0.86 and 0.65 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. Conclusions By identifying aberrant functional connectivity and activity as potential biomarkers, the radiomic approach facilitates a deeper understanding and provides new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.

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Section: Introductionmentioning

confidence: 88%

Aberrant functional connectivity and activity in Parkinson’s disease and comorbidity with depression based on radiomic analysis

et al. 2021

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“…Additionally, PD patients with hyposmia were shown to exhibit significantly reduced global and local efficiency, as well as a disrupted connection between the right medial orbitofrontal cortex and left rectus, with poorer frontal-related cognitive functioning. Utilizing connectome analysis in 18 early-stage PD and 17 prodromal PD patients, Sanjari Moghaddam et al (76) investigated the microstructural association of olfaction in prodromal PD as compared to earlystage drug-naive PD patients. Their studies suggested that individuals with prodromal PD have a significantly higher QA as compared to PD patients in bilateral middle cerebellar peduncles and right arcuate fasciculus.…”

Section: Qsdr In Early-stage Pdmentioning

confidence: 99%

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

et al. 2020

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“…A brain microstructual study found decreased white matter fiber features in the right arcuate fasciculus and bilateral middle cerebellar peduncles. The study also detected increased network connectivity in prodromal early PD, which might indicate the neural compensation (Sanjari et al, 2019). The right accumben as one of selected GM features in the model is an interesting sign.…”

Section: Discussionmentioning

confidence: 55%

A Radiomics Approach to Predicting Parkinson’s Disease by Incorporating Whole-Brain Functional Activity and Gray Matter Structure

et al. 2020

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Parkinson's disease (PD) is a progressive, chronic, and neurodegenerative disorder that is primarily diagnosed by clinical examinations and magnetic resonance imaging (MRI). In this study, we proposed a machine learning based radiomics method to predict PD. Fifty healthy controls (HC) along with 70 PD patients underwent restingstate magnetic resonance imaging (rs-fMRI). For all subjects, we extracted five types of 6664 features, including mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), resting-state functional connectivity (RSFC), voxelmirrored homotopic connectivity (VMHC) and gray matter (GM) volume. After conducting dimension reduction utilizing Least absolute shrinkage and selection operator (LASSO), fifty-three radiomic features including 46 RSFCs, 1 mALFF, 3 mReHos, 1 VMHC, 2 GM volumes and 1 clinical factor were retained. The selected features also indicated the most discriminative regions for PD. We further conducted model fitting procedure for classifying subjects in the training set employing random forest and support volume machine (SVM) to evaluate the performance of the two methods. After cross-validation, both methods achieved 100% accuracy and area under curve (AUC) for distinguishing between PD and HC in the training set. In the testing set, SVM performed better than random forest with the accuracy, true positive rate (TPR) and AUC being 85%, 1 and 0.97, respectively. These findings demonstrate the radiomics technique has the potential to support radiological diagnosis and to achieve high classification accuracy for clinical diagnostic systems for patients with PD.

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Association of olfaction dysfunction with brain microstructure in prodromal Parkinson disease

Cited by 20 publications

References 57 publications

Aberrant functional connectivity and activity in Parkinson’s disease and comorbidity with depression based on radiomic analysis

Aberrant functional connectivity and activity in Parkinson’s disease and comorbidity with depression based on radiomic analysis

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

A Radiomics Approach to Predicting Parkinson’s Disease by Incorporating Whole-Brain Functional Activity and Gray Matter Structure

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