Advanced hardware components embedded in modern smartphones have the potential to serve as widely available medical diagnostic devices, particularly when used in conjunction with custom software and tested algorithms. The goal of the present pilot study was to develop a smartphone application that could quantify the severity of Parkinson's disease (PD) motor symptoms, and in particular, bradykinesia. We developed an iPhone application that collected kinematic data from a small cohort of PD patients during guided movement tasks and extracted quantitative features using signal processing techniques. These features were used in a classification model trained to differentiate between overall motor impairment of greater and lesser severity using standard clinical scores provided by a trained neurologist. Using a support vector machine classifier, a classification accuracy of 0.945 was achieved under 6-fold cross validation, and several features were shown to be highly discriminatory between more severe and less severe motor impairment by area under the receiver operating characteristic curve (AUC > 0.85). Accurate classification for discriminating between more severe and less severe bradykinesia was not achieved with these methods. We discuss future directions of this work and suggest that this platform is a first step toward development of a smartphone application that has the potential to provide clinicians with a method for monitoring patients between clinical appointments.

Although many genetic markers are identified as being associated with Alzheimer's disease (AD), not much is known about their association with the structural changes that happen as the disease progresses. In this study, we investigate the genetic etiology of neurodegeneration in AD by associating genetic markers with atrophy profiles obtained using patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The atrophy profiles were quantified using a linear least-squares regression model over the span of patient enrollment, and used as imaging features throughout the analysis. A subset of the imaging features were selected for genetic association based on their ability to discriminate between healthy individuals and AD patients in a Support Vector Machines (SVM) classifier. Each imaging feature was associated with single-nucleotide polymorphisms (SNPs) using a linear model that included age and cognitive impairment scores as covariates to correct for normal disease progression. After false discovery rate correction, we observed 53 significant associations between SNPs and our imaging features, including associations of ventricular enlargement with SNPs on estrogen receptor 1 (ESR1) and sortilin-related VPS10 domain containing receptor 1 (SORCS1), hippocampal atrophy with SNPs on ESR1, and cerebral atrophy with SNPs on transferrin (TF) and amyloid beta precursor protein (APP). This study provides important insights into genetic predictors of specific types of neurodegeneration that could potentially be used to improve the efficacy of treatment strategies for the disease and allow the development of personalized treatment plans based on each patient's unique genetic profile.