Determination of Imaging Biomarkers to Decipher Disease Trajectories and Differential Diagnosis of Neurodegenerative Diseases (DIsease TreND)

[Display omitted] •An innovative framework to identify imaging biomarker and subject classification.•Identified biomarkers have been used to indicate trajectory of disease progression.•We report accuracy of ≥ 90% for differential diagnosis of AD, PD and related disorders.•Proposed method has been tested on 1316 MRIs obtained from ADNI and PPMI. Understanding disease progression of neurodegenerative diseases (NDs) is important for better prognosis and decisions on the appropriate course of treatment to slow down the disease progression. We present here an innovative machine learning framework capable of (1) indicating the trajectory of disease progression by identifying relevant imaging biomarkers and (2) automated disease diagnosis. Self-Organizing Maps (SOM) have been used for data dimensionality reduction and to reveal potentially useful disease-specific biomarkers, regions of interest (ROIs). These ROIs have been used for automated disease diagnosis using Least Square Support Vector Machines (LS-SVM) and to delineate disease progression. A multi-site, multi-scanner dataset containing 1316 MRIs was obtained from ADNI33ADNI: Alzheimer’s disease neuroimaging initiative and PPMI: Parkinson’s progression markersinitiative. and PPMI. Identified biomarkers have been used to decipher (1) trajectory of disease progression and (2) identify clinically relevant ROIs. Furthermore, we have obtained a classification accuracy of 94.29 ± 0.08% and 95.37 ± 0.02% for distinguishing AD and PD from HC subjects respectively. The goal of this study was fundamentally different from other machine learning based studies for automated disease diagnosis. We aimed to develop a method that has two-fold benefits (1) It can be used to understand pathology of neurodegenerative diseases and (2) It also achieves automated disease diagnosis. In the absence of established disease biomarkers, clinical diagnosis is heavily prone to misdiagnosis. Being clinically relevant and readily adaptable in the current clinical settings, the developed framework could be a stepping stone to make machine learning based Clinical Decision Support System (CDSS) for neurodegenerative disease diagnosis a reality.