Machine Learning Models for Diagnosis of Parkinson’s Disease Using Multiple Structural Magnetic Resonance Imaging Features

Yang, Yong; Ji, Lirong; Jia, Yujing; Zou, Nan; Jiang, Zhen; Yin, Hongkun; Mao, Cheng‐Jie; Luo, Weifeng; Wang, Erlei; Fan, Guohua

doi:10.3389/fnagi.2022.808520

Cited by 7 publications

(4 citation statements)

References 61 publications

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“…The larger the ball, the higher the frequency. a statistical modeling technique that estimates the probability of a dependent variable relating to a set of independent variables through the sigmoid function (Ya et al, 2022), which has the advantages of simple implementation, good model interpretability, high numerical stability, and it is not easy to overfit. Among them, the penalty = L1, also called Lasso regularization, plays a key role by resetting the non-significant feature coefficients to zero.…”

Section: Discussionmentioning

confidence: 99%

“…In our study, four common classifiers were used to construct models and obtained excellent results in both training and testing sets, especially the model constructed by LR (penalty = L1) was considered the best model with the highest mean AUC of 0.924 in the testing set. The LR classifier is a statistical modeling technique that estimates the probability of a dependent variable relating to a set of independent variables through the sigmoid function ( Ya et al, 2022 ), which has the advantages of simple implementation, good model interpretability, high numerical stability, and it is not easy to overfit. Among them, the penalty = L1, also called Lasso regularization, plays a key role by resetting the non-significant feature coefficients to zero.…”

Section: Discussionmentioning

confidence: 99%

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Combined brain network topological metrics with machine learning algorithms to identify essential tremor

Li²,

Pan³

et al. 2022

Front. Neurosci.

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Background and objectiveEssential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET.MethodsResting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics.ResultsAll classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity.ConclusionThese results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Combined brain network topological metrics with machine learning algorithms to identify essential tremor

Li²,

Pan³

et al. 2022

Front. Neurosci.

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“…The authors in [9] have focused on Mini-Mental State Examination with classical machine learning methods to extract long-term predictions. Different studies [10,11] have used specific tools to extract the cerebellar and subcortical features from MRI, before applying some classical machine learning algorithms to obtain multiple indicators to assist the clinical diagnosis.…”

Section: Parkinson Dedicated Studiesmentioning

confidence: 99%

Long-term cognitive decline prediction based on multi-modal data using Multimodal3DSiameseNet: transfer learning from Alzheimer’s disease to Parkinson’s disease

et al. 2023

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Purpose Monitoring and predicting the cognitive state of subjects with neurodegenerative disorders is crucial to provide appropriate treatment as soon as possible. In this work, we present a machine learning approach using multimodal data (brain MRI and clinical) from two early medical visits, to predict the longer-term cognitive decline of patients. Using transfer learning, our model can be successfully transferred from one neurodegenerative disease (Alzheimer’s) to another (Parkinson’s). Methods Our model is a Deep Neural Network with siamese sub-modules dedicated to extracting features from each modality. We pre-train it with data from ADNI (Alzheimer’s disease), then transfer it on the smaller PPMI dataset (Parkinson’s disease). We show that, even when we do not fine-tune the filters learnt from the ADNI MRIs, the transferred model’s results are satisfying on PPMI. Results The first main result is that our model provides satisfying long-term predictions of cognitive decline from any pair of early visits, with no fixed time delay between these visits (provided the potential decline has started at the second visit). The second main result is that the prediction performance on Parkinson’s dataset (PPMI) reaches an AUC of 0.81 on PPMI after transfer learning from Alzheimer’s dataset (ADNI), without even having to re-train the image filters, versus an AUC of 0.72 for the model trained from scratch on PPMI. Conclusions First, our model is effective for predicting long-term cognitive decline from only two visits, even with irregular intervals of time. When dealing with neurodegenerative diseases, where patients often miss some control visits, this is an important finding. Second, our model is able to transfer the knowledge learnt from one neurodegenerative disease (Alzheimer’s) to another (Parkinson’s), when using the same imaging modalities (brain MRI) and different clinical variables. This makes it usable even for diseases that are rare or under-studied.

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“…The structure of the human brain has been vastly studied in the recent past by using sMRI to diagnose PD with increased accuracy [9], and also has a promising ability to differentially diagnose the disease [10]. Structural MRI has been exploited in machine learning models, such as Logistic Regression classifier [11] for diagnosis of PD. Classification performances using multiple machine learning approaches have been compared, separately on men and women, based on Regional GM features [12].…”

Section: Introductionmentioning

confidence: 99%

Brain connectivity for subtypes of parkinson’s disease using structural MRI

Samantaray,

Saini,

Pal

et al. 2024

Biomed. Phys. Eng. Express

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Objective: Delineating Parkinson’s disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises of two major objectives - firstly, subtyping Parkinson’s patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step. Approach: Source-based-morphometry decomposition was performed on 131 Parkinson’s patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics. Main results: Two distinct Parkinson’s subtypes (namely A and B) were detected employing loadings of two component satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p<0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree. Significance: MRI-based data-driven subtypes show frontal and temporal lobe playing key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson’s datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson's disease may provide subtype-specific treatment.

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Machine Learning Models for Diagnosis of Parkinson’s Disease Using Multiple Structural Magnetic Resonance Imaging Features

Cited by 7 publications

References 61 publications

Combined brain network topological metrics with machine learning algorithms to identify essential tremor

Combined brain network topological metrics with machine learning algorithms to identify essential tremor

Long-term cognitive decline prediction based on multi-modal data using Multimodal3DSiameseNet: transfer learning from Alzheimer’s disease to Parkinson’s disease

Brain connectivity for subtypes of parkinson’s disease using structural MRI

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