NeuroMAP Phase II - Data Management and Statistics Core

PROJECT SUMMARY: Data Management and Statistics Core A 3-year project for a Research Project Leader (RPL) to conduct experimental human subjects research with psychiatric target populations and to obtain pilot data for an R-level grant can be challenging from a design, data management and processing, and statistical analysis perspective. The Data Management and Statistics

(DMS) Core will ensure the highest rigor of study design, the implementation of community-standard data management and processing protocols, and the application of cutting-edge data science algorithms that maximize out-of-sample prediction performance and power for assessing mechanisms of action. The Core will

work with RPLs and pilot project investigators to facilitate identifying and validating disease-modifying processes (DMPs) that are relevant for mood and anxiety disorders. This will greatly enhance the utility of the research produced by RPLs for use in formulating aims and developing hypotheses based on these

preliminary data and for designing future studies, thereby making R01-level applications more likely to succeed as well as being more competitive and fundable. Services provided by this Core consist of: (1) consultations with expert data scientists who will work with investigators to develop and instantiate an operating environment

that optimizes data use and analytics; and (2) procedures and programs developed by this Core to accommodate users' stimulus presentation, data management and statistical needs. The data management component will be instrumental in guaranteeing that data are acquired and processed reliably and efficiently

using our scalable data management infrastructure. Services will begin at study setup and include implementation and configuration of behavioral paradigms, pipelines to convert raw data into standard (e.g., Brain Imaging Data Structure: BIDS) format, periodic auditing and sharing as needed. This Core will provide

standard pipelines to extract common data elements and quality metrics and to facilitate access and usage of the institute's computing infrastructure. The statistics component of the Core will focus on developing study designs and analytic procedures applicable to assessing unbiased effects and predictive performance of DMPs

(e.g., threat sensitivity, avoidance during aversive interoception, repetitive negative thinking) on mental health outcomes. As these DMPs will be examined on several levels of analysis (symptoms, behavior, physiology, circuits, and molecules), study designs and analyses will need to integrate complex multi-method associations

and will need to account for potential biases in associations, e.g., due to selection, measurement error, and/or confounding. This Core will focus on multilevel models, causal inference and machine learning prediction that account for sources of variation (e.g., nested data) and confounding (e.g., confounding bias) while providing

maximal explanatory and out-of-sample prediction performance. The products of this Core will be instrumental in developing follow-up R-level research leveraging data produced by NeuroMAP projects.