Merikangas, K. R. | bioRxiv Science

bioRxiv2022

Similarity-Based Multimodal Regression

To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distancebased method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations in multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.

neuroscience↗

bioRxiv2020

Remote Digital Psychiatry: MindLogger for Mobile Mental Health Assessment and Therapy

BackgroundUniversal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There is a vast number of people without access to care because of economic, geographic, and cultural barriers as well as limited availability of clinical experts who could help advance our understanding of mental health. ObjectiveTo create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include: an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems, and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. MethodsWe conducted a search for tools that satisfy the above requirements. We designed and developed a new software platform called "MindLogger" that exceeds the above requirements. To demonstrate the tools configurability, we built multiple "applets" (collections of activities) within the MindLogger mobile application and deployed several, including a comprehensive set of assessments underway in a large-scale, longitudinal, mental health study. ResultsOf the hundreds of products we researched, we found 10 that met our primary requirements above with 4 that support end-to-end encryption, 2 that enable restricted access to individual users data, 1 that provides open source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities, privacy and security, and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser-based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. ConclusionsWe have demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study, and by building a variety of other mental health-related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing availability of applets designed to assess and administer interventions will facilitate access to health care in the general population.

neuroscience↗