Title: Predicting early neural functional alterations in neurodegeneration disorders
Pedro Pinheiro-Chagas, Professor
UC San Francisco
Closed. This professor is continuing with Spring 2024 apprentices on this project; no new apprentices needed for Fall 2024.
Semantic dementia (SD) presents as a unique neurodegenerative disorder with focal atrophy of
the anterior temporal lobes (ATLs). It is comprised of a primarily left-lateralized language
syndrome and a right-lateralized behavioral disorder. One current challenge in this disease is in
accurately identifying the distant brain regions that are connected to the ATLs and therefore
potentially compromised. This project employs two advanced MR neuroimaging
techniques—namely, fixel-based analysis and functional network analysis—to evaluate these
remote areas. The first delves into white matter pathology and, unlike the tensor-derived
metrics, provides fiber tract-specific measures by utilizing high-order diffusion models. The
second technique uses BOLD signaling for a functional connectivity profile, aiding the
investigation of inter-network connectivity and the broader impact of ATL atrophy on network-
level architecture. By combining these modalities, we aim to enhance the understanding of
connectivity changes in relation to the loci of atrophy and to inform the development of targeted
interventions that are precisely tailored to the affected neural pathways. Analyses will utilize
advanced neuroimaging and statistical techniques to investigate early neural alterations in a
cohort of patients with SD. The precise localization and quantification of preserved structural
and functional networks offers a novel and potentially valuable biomarker for tracking disease
progression and treatment efficacy.
Role: Successful candidates will play a role in multiple aspects of the project. Responsibilities will
encompass data organization, integration, and data cleaning to ensure the dataset’s integrity.
You will also assist with the pre-processing of the data, developing models to define the
connectivity profile, and creating summary ad hoc reports. An important part of your role will be
ensuring the secure and ethical handling of sensitive patient data, adhering to the HIPAA
standards. This position offers a unique opportunity to leverage statistic and technology in
application of neuroimaging.
Qualifications: Ideal candidates should have a background in data science, computer science, biostatistics,
neuroscience, radiology, epidemiology, or a related field (open to other fields if the fit is right),
with a good understanding of Python, statistics, and some knowledge of machine learning
techniques. Most importantly, we value candidates who show enthusiasm and are up for a
hands-on approach to gain experience in all the steps of the research process.
Day-to-day supervisor for this project: Professor Maria Luisa Mandelli
Hours: to be negotiated
Biological & Health Sciences Digital Humanities and Data Science