Abstract
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.
Highlights
Parkinson’s disease (PD) is one of the most common clinically progressive neurodegenerative diseases worldwide, with prevalence second only to Alzheimer’s disease, and it affects more than 10 million people worldwide (Kim et al, 2017; Srivastav et al, 2017)
The data in this article were obtained from a public database1 (Hu et al, 2015) including 41 healthy controls (HCs) and 59 PD patients
We divided the subjects into a primary set (32 HCs and 48 PDs) for training the model and a validation set for testing the model according to the order in which they entered the group based on an 8:2 ratio
Summary
Parkinson’s disease (PD) is one of the most common clinically progressive neurodegenerative diseases worldwide, with prevalence second only to Alzheimer’s disease, and it affects more than 10 million people worldwide (Kim et al, 2017; Srivastav et al, 2017). Diagnosis and treatment of PD are crucial to stop its progression in the initial stages (Chen et al, 2014; Adeli et al, 2016; Heim et al, 2017). In the early stage of PD, fMRI in Parkinson’s Disease Diagnosis the main manifestations are non-motor symptoms, which are nonspecific and difficult to diagnose (Peng et al, 2017; Cigdem et al, 2018; Tuovinen et al, 2018; Rubbert et al, 2019). Advancements in neuroimaging and machine learning technologies have led to an increasing role of such technologies in the accurate diagnosis of PD (Chen et al, 2014; SzewczykKrolikowski et al, 2014; Peng et al, 2017; Amoroso et al, 2018). The amplitude of low-frequency fluctuation (ALFF; Zhang et al, 2017; Xu et al, 2019), which can detect the amplitude of spontaneous brain fluctuations, is one of the most commonly used fMRI measurements
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