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Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects.Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods.Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone.Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.

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Introduction Mild behavioral impairment (MBI) is a high‐risk state for incident dementia and comprises five core domains including affective dysregulation, impulse dyscontrol, social inappropriateness, psychotic symptoms, and apathy. Apathy is among the most common neuropsychiatric symptoms (NPS) in dementia but can also develop in persons with normal cognition (NC) or mild cognitive impairment (MCI). The later‐life emergence and persistence of apathy as part of the MBI syndrome may be a driving factor for dementia risk. Therefore, we investigated MBI‐apathy–associated progression to dementia, and effect modification by sex, race, cognitive diagnosis, and apolipoprotein E ( APOE ) genotype. Methods Dementia‐free National Alzheimer's Coordinating Center participants were stratified by persistent apathy status, based on Neuropsychiatric Inventory (NPI)–Questionnaire scores at two consecutive visits. Hazard ratios (HRs) for incident dementia for MBI‐apathy and NPI‐apathy relative to no NPS, and MBI‐apathy relative to no apathy, were determined using Cox proportional hazards regressions, adjusted for baseline age, sex, years of education, race, cognitive diagnosis, and APOE genotype. Interactions with relevant model covariates were explored. Results Of the 3932 participants (3247 with NC), 354 had MBI‐apathy. Of all analytic groups, MBI‐apathy had the greatest dementia incidence (HR = 2.69, 95% confidence interval [CI]: 2.15–3.36, P < 0.001). Interaction effects were observed between cognitive diagnosis and APOE genotype with the NPS group. The contribution of apathy to dementia risk was greater in NC (HR = 5.91, 95% CI: 3.91–8.93) than in MCI (HR = 2.16, 95% CI: 1.69–2.77, interaction P < 0.001) and in all APOE genotypes, was greatest in APOE ɛ3 (HR = 4.25, 95% CI: 3.1–5.82, interaction P < 0.001). Discussion Individuals with MBI‐apathy have a markedly elevated risk for future dementia, especially when symptoms emerge in those with NC. Both cognitive status and APOE genotype are important moderators in the relationship between MBI‐apathy and incident dementia. MBI‐apathy may represent a group in whom apathy is a preclinical or prodromal manifestation of dementia and identify a precision medicine target for preventative interventions.