DOI: 10.3897/rio.2.e8834

Authors: Arno Klein

Abstract: There is a great need to gather data from vast populations to better understand the distribution of Parkinson’s disease and the variation in its symptoms, ultimately to aid in its diagnosis, determination of symptom severity, and prediction of disease progression in individuals. Toward these ends, we propose to take the most commonly administered Parkinson’s questionnaire and make it universally intelligible across languages, cultures, and education levels, as well as intuitive and engaging, by creating and evaluating a “Visual Parkinson’s Disease Rating Scale” (VPDRS). To administer a consistent and widely accessible implementation of the VPDRS to Parkinson’s patients in the U.S. and in India, we will deploy it on a mobile phone application, and store the data on server technology that we have developed and will establish in India for managing large-scale data collection from mobile devices in future studies.

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DOI: 10.3897/rio.2.e8848

Authors: Arno Klein

Abstract: Mobile phones provide a new way of collecting behavioral medical research data at a scale never before possible – Sage Bionetworks’ mPower Parkinson research app, launched at Apple’s March 9, 2015 ResearchKit announcement, is currently collecting data related to Parkinson symptoms, such as voice recordings, from thousands of registered study participants. Before making such voice data available to any qualified researcher in the world, they need to undergo quality control and editing, which is currently something only a human can do well. To achieve this goal and the required scale, we will crowdsource these tasks through Amazon's Mechanical Turk.

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DOI: 10.3897/rio.2.e8835

Authors: Arno Klein, Satrajit Ghosh

Abstract: The proposed research develops new computational tools to identify, diagnose, and predict treatment outcome for different mental illnesses. The research will be applied first to major depressive disorder, which affects millions of Americans, but is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain structure, activity, or connectivity between any two populations.

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DOI: 10.3897/rio.2.e8817

Authors: Arno Klein

Abstract: We will analyze variations in brain anatomy and create the first integrated software environment to extract patterns from brains and target differences related to inter-individual variability, pathology, development, or degeneration. We will evaluate how well these differences can help diagnose and predict treatment outcome for major depressive disorder, which affects millions of Americans, but our work is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain anatomy between any two populations.

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DOI: 10.3897/rio.2.e8816

Authors: Arno Klein

Abstract: The BigBrain, a high-resolution 3-D model of a human brain at nearly cellular resolution, is the best brain imaging data set in the world to establish a canonical space at both microscopic and macroscopic resolutions. However, for the cell-stained microstructural data to be truly useful, it needs to be segmented into cytoarchitectonic regions, a challenge no single lab could undertake. The principal aim of this proposal is to crowdsource the segmentation of cytoarchitectonic regions by means of a computer game, to transform an arduous, isolated task performed by experts into an engaging, collective activity of non-experts.

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DOI: 10.3897/rio.2.e8815

Authors: Arno Klein, Steven Ellis

Abstract: The proposed research develops new computational tools to identify, diagnose, and predict treatment response for different mental illnesses. The research will first be applied to publicly available resting state fMRI BOLD data from patients with attentiondeficit hyperactivity disorder and autism. It will also be applied to existing clinical and biological data concerning suicidality in the context of major depressive disorder. These disorders affect millions of Americans, but these tools can be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain, clinical, and biological data between any two populations.

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