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

Cao, Xuan; Wang, Xiao; Xue, Chen; Zhang, Shaojun; Huang, Qingling; Liu, Weiguo

doi:10.3389/fnins.2020.00751

Cited by 51 publications

(47 citation statements)

References 52 publications

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“…Another study used the Harvard–Oxford atlas to extract the mean ReHo, ALFF, VMHC, gray matter volume, and FC features. The authors found that both random forest (accuracy = 0.8261, AUC = 0.9015) and support vector machine (accuracy = 0.8483, AUC = 0.9697) achieved the perfect accuracy and AUC for distinguishing between PD and HC subjects (Cao et al, 2020 ). Our results are better than those of these studies, which may indicate that our histogram analysis method can extract more information in the ROIs (Lambin et al, 2012 ; Gillies et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson’s disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

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Section: Discussionmentioning

confidence: 99%

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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“…We used the same imaging data from the same recruited subjects as in our previously published issue (Cao et al., 2020). The only difference is that we further stratify the PD patients into two groups of DPD and NDPD to examine the aberrant functional connectivity and activity in DPD and to build machine learning models for predicting DPD and NDPD.…”

Section: Methodsmentioning

confidence: 99%

“…A radiomic study based on PET/CT images extracted high‐order features and trained a SVM model to classify PD and HC subjects, and the results demonstrated that the radiomic method combined with SVM could distinguish PD from HC (Wu et al., 2019). Cao et al leveraging rs‐fMRI radiomic features showed that machine learning methods including Lasso and SVM could significantly improve diagnostic accuracy of PD (Cao et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

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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“…Moreover, even the main neural and pathophysiological mechanisms of PD are the degeneration of the nigrostriatal dopaminergic system; it cannot fully explain the heterogeneity of symptoms ( Tuovinen et al, 2018 ; Sheng et al, 2021 ). The exact mechanism of PD is still not well understood ( Tuovinen et al, 2018 ; Cao et al, 2020 ; Lin et al, 2020 ; Sheng et al, 2021 ). Therefore, quantifiable biomarkers are urgently needed for a more comprehensive understanding of the physiological mechanism of PD and improving the diagnosis accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…It can detect the amplitude of spontaneous, low-frequency oscillations of blood oxygen level-dependent signals to reflect the regularity and physiological state of neuron autonomous activity in different brain regions ( Qian et al, 2020 ). This approach provides a reliable and sensitive measurement to characterize the spontaneous neural activity and has been widely used in PD ( Cao et al, 2020 ; Tian et al, 2020 ; Pang et al, 2021 ; Shi et al, 2021b ).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

Shi

Zhang

Wang

et al. 2022

Front. Aging Neurosci.

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Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

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

Cited by 51 publications

References 52 publications

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

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

Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

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