MRI classification of progressive supranuclear palsy, Parkinson disease and controls using deep learning and machine learning algorithms for the identification of regions and tracts of interest as potential biomarkers

Background: Quantitative magnetic resonance imaging (MRI) analysis has shown promise in differentiating neurodegenerative Parkinsonian syndromes and has significantly advanced our understanding of diseases like progressive supranuclear palsy (PSP) in recent years.

Objective: The aim of this study was to develop, implement and compare MRI analysis algorithms based on artificial intelligence (AI) that can differentiate PSP not only from healthy controls but also from Parkinson disease (PD), by analyzing changes in brain structure and microstructure. Specifically, this study focused on identifying regions of interest (ROIs) and tracts of interest (TOIs) that are crucial for the algorithms to provide clinically relevant performance indices for the distinction between disease variants.

Methods: MR data comprised diffusion tensor imaging (DTI - tractwise fractional anisotropy statistics (TFAS)) and T1-weighted (T1-w) data (texture analysis of the corpus callosum (CC)). One subject sample with 74 PSP patients and 63 controls was recorded at 3.0T at multiple sites. The other sample came from a single site, consisting of 66 PSP patients, 66 PD patients, and 44 controls, recorded at 1.5T. Four different machine learning algorithms (ML) and a deep learning (DL) neural network approach using Tensor Flow were implemented for the study. The training of the algorithms was performed on 80 % of the data, which included the entire single-site data and parts of the multiple-site data. The validation process was conducted on the remaining data, thereby consistently separating training and validation data.

Results: A random forest algorithm and a DL neural network classified PSP and healthy controls with accuracies of 92 % and 95 %, respectively. Particularly, DTI derived measures for the pons, midbrain tegmentum, superior cerebral peduncle, putamen, and CC contributed to high accuracies. Furthermore, DL neural network classification of PSP and PD with 86 % accuracy showed the importance of 19 structures. The four most important features were DTI derived measures for prefrontal white matter, the fasciculus frontooccipitalis, the midbrain tegmentum, and the CC area II. This DL network achieved a sensitivity of 88 % and specificity of 85 %, resulting in a Youden-index of 0.72.

Conclusion: The primary goal of the present study was to compare multiple ML-methods and a DL approach to identify the least necessary set of brain structures to classify PSP vs. controls and PSP vs. PD by ranking them in a hierarchical order of importance. That way, this study demonstrated the potential of AI approaches to MRI as possible diagnostic and scientific tools to differentiate variants of neurodegenerative Parkinsonism.