Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity

Abstract

Objective: Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson’s disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI). Methods: Whole-brain rs-fMRI (EPI, TR = 2.2 s, TE = 30 ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1 mm, acquisition time ≈ 11 min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance. Results: Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 – 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 – 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 – 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks. Conclusion: A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting. Advances in knowledge: Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson’s disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

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

“…Radiomics can extract a large number of quantitative features; however, its data dimensionality is too high when compared with the sample size of most studies, making it is easy to fall into a “curse of dimensionality,” thus causing the model to overfit. Hence, the features must be selected for dimensionality reduction to obtain the most valuable features in order to improve the reliability and accuracy of the model (Gu et al, 2016 ; Péran et al, 2018 ; Rubbert et al, 2019 ; Wang et al, 2020 ). In our study, we performed a two-way independent samples t -test on the standardized data to select the features that were significantly different between groups for subsequent analysis.…”

“…Early 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, 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 ). However, 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 ; Szewczyk-Krolikowski et al, 2014 ; Peng et al, 2017 ; Amoroso et al, 2018 ).…”

Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics

“…To address this issue investigators have actively modeled a range of network parameters (e.g., distinct brain parcellation approaches, distinct edge definitions) and data processing steps as part of prediction modeling to simultaneously advance the methods and isolate the role of investigator data processing decisions on model performance (Abraham et al, 2017;Badea et al, 2017;J. Chen et al, 2021;Rubbert et al, 2019).…”

Within- and across-network alterations of the sensorimotor network in Parkinson’s disease

“…Parcellations resulting from group-level ICA on such data almost exclusively entail components with neuronal signal and no noise components [ 20 , 72 ], and the use of 15 to 30 independent components is quite common in such a setting. Additionally, in a recent machine learning-based classification approach between PD and HC, we could show that models using parcellations of 25 independent components perform better than models using 15, 50, 100, or 200 components, which might give a hint to the neurobiological most meaningful granularity to be within this range [ 73 ]. Twenty components were the best match to isolate most of the established intrinsic connectivity networks as single components and not splitting them into several sub-networks or having them merged into a single component.…”