Research CV, (Last Updated: March, 2024)

Ph.D. Research Topic: Digital Biomarkers for Neurodegenerative Disorders: Identifying Associations Between Neural Circuitry from MRIs and Human Movement Disturbances from Videos

Advisory Committee: Ehsan Adeli, Ph.D.*, Kilian M. Pohl, Ph.D.*, Dorsa Sadigh, Ph.D., John Pauly, Ph.D. | @ Stanford Computational Neuroscience Lab

I am interested in using AI to discover digital biomarkers for neurodegenerative disorders that could be non-invasively and frequently measured with trackable trajectories. Ultimately, this work lays the foundation for the discovery of trackable digital biomarkers of progression and differentiation of neurodegenerative disorders of aging.  It will help improve the mechanistic understanding, diagnosis, and treatment of neuropsychiatric disorders from neuroimages. My work is divided into three aims:

  1. Develop novel machine learning methods to objectively identify dynamic patterns of brain functional and structural connectivity associated with PD, AD, and MCI;

  2. Correlate those patterns with movement-linked disturbances (e.g., tremor, postural instability, gait difficulty, stooped posture while walking) acquired from video recordings of patients while performing clinical tests (such as Short Physical Performance Battery tests) defining digital biomarkers;

  3. Use digital biomarkers and neural correlates to define different motor subtypes of neurodegenerative disorders.

Highlighted Works:

Digital Biomarkers for Neurodegenerative Disorders of Aging (@Stanford)

1.     Nerrise, F., Zhao, Q., Poston, K.L., Pohl, K.M., Adeli, E. An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment. (2023 Medical Image Computing and Computer Assisted Interventions conference).

2.     Nerrise, F., Pohl, K.M., Adeli, E. The Emergence of Digital Biomarkers for Neurodegenerative Disorders. (In preparation).

Novelty Detection for In-Situ Mars Exploration (@NASA JPL)

1.     Nerrise, F., Kerner, H. R., Wagstaff, K., et al. (2020, December). Evaluation of Machine Learning Methodologies for Novelty-based Target Selection in Planetary Imaging Data Sets: Examples from the Mars Science Laboratory Mission. In AGU Fall Meeting Abstracts (Vol. 2020, pp. P004-0007).

2. Wissler, S., Verma, V., Rebbapragada, U., Phillips, C., Nerrise, F., Lu, S., ... & Wagstaff, K. (2020). Machine Learning for Space and Planetary Exploration. NASA Jet Propulsion Laboratory Open Repository.

Research Labs

  • Graduate Research Assistant, Computational Neuroscience Lab

    Deriving AI-driven, digital biomarkers to predict movement-linked disturbances of people with neurodegenerative diseases, under the mentorship of Kilian Pohl, Ph.D. and Ehsan Adeli, Ph.D.

    More on the CNS Lab.

  • AI Ph.D. Resident —Quantum Sensing, SandboxAQ

    Developing an alternate navigation system to GPS using Earth's magnetic field and quantum sensing techniques, under the mentorship of Patrick Neary and Kimee Moore, Ph.D.

    More on SandboxAQ.