The National Institute on Drug Abuse (NIDA) is seeking a GS-15 Data Scientist for the Office of the Director. The incumbent will serve as a Data Scientist and Senior Advisor to the NIDA Director and other leadership positions, on matters related to health informatics and data science, data management, emerging technologies, opportunities for collaboration, data standards and policy within the scope of the NIDA mission. Responsibilities include planning and carrying out quality control programs, selecting statistical methods for quality control analysis, ensuring the reliability and consistency of data, providing technical expertise and project leadership to advance strategic initiatives in data- and knowledge-driven methods, and developing collaborations with researchers and administrators in neuroscience.

In this talk, I will discuss the OpenNeuro Fitlins GLM package and provide an illustration of the analytic workflow. OpenNeuro FitLins GLM is a semi-automated pipeline that reduces barriers to analyzing task-based fMRI data from OpenNeuro's 600+ task datasets. Created for psychology, psychiatry and cognitive neuroscience researchers without extensive computational expertise, this tool automates what is largely a manual process and compilation of in-house scripts for data retrieval, validation, quality control, statistical modeling and reporting that, in some cases, may require weeks of effort. The workflow abides by open-science practices, enhancing reproducibility and incorporates community feedback for model improvement. The pipeline integrates BIDS-compliant datasets and fMRIPrep preprocessed derivatives, and dynamically creates BIDS Statistical Model specifications (with Fitlins) to perform common mass univariate [GLM] analyses. To enhance and standardize reporting, it generates comprehensive reports which includes design matrices, statistical maps and COBIDAS-aligned reporting that is fully reproducible from the model specifications and derivatives. OpenNeuro Fitlins GLM has been tested on over 30 datasets spanning 50+ unique fMRI tasks (e.g., working memory, social processing, emotion regulation, decision-making, motor paradigms), reducing analysis times from weeks to hours when using high-performance computers, thereby enabling researchers to conduct robust single-study, meta- and mega-analyses of task fMRI data with significantly improved accessibility, standardized reporting and reproducibility.

Protein quality control is essential for maintenance of a healthy and functional proteome that can attend the multiplicity of cellular functions. Failure of the systems that contribute to protein homeostasis, the so called proteostasis networks, have been identified in the pathogenesis of multiple neurodegenerative disorders and demonstrated to contribute to disease onset and progression. We are interested in autophagy, one of the components of the proteostasis network, and in the interplay of wo selective types of autophagy, chaperone-mediated autophagy (CMA) and endosomal microautophagy (eMI), with neurodegeneration. We have recently found that pathogenic proteins involved in common neurodegenerative conditions such as tauopathies or Parkinson’s disease, can exert a toxic effect in both types of selective types of autophagy compromising their functioning. We have now used mouse models with compromised CMA that support increased propagation of proteins such as tau and alpha-synuclein and an exacerbation of disease phenotype with aging. Conversely, genetic or chemical upregulation of CMA in this context of proteotoxicity slow down disease progression by facilitating effective intracellular removal of pathogenic proteins. Our findings highlight CMA and eMI as potential novel therapeutic targets against neurodegeneration.