Laboratory for Deep Neurophenotyping

Overview

We are a group of neurologists and computer scientists who share the passion for using technology to precisely understand how certain neurologic diseases impact human motor and cognitive function. We believe that capturing high-definition behavior at scale will lead to the discovery of fundamental characteristics of disease that will inform new approaches to therapy development, diagnosis, and management. Our initial focus is on cerebellar ataxia, Parkinson’s disease, and neurodegenerative disease in general.

We are deeply thankful to the patients and patient advocates who donate their time to our research program during their visit to Massachusetts General Hospital. Likewise, this work would not be possible without the generous support from the Ataxia-Telangiectasia Children’s Project (A-TCP), Biogen Inc., the Broad Institute, CureDRPLA, Friedreich's Ataxia Research Alliance (FARA), NINDS, the Massachusetts Life Sciences Center, the Orphan Disease Center of UPenn and the Mass General Department of Neurology.

We have recently launched a new open source platform for large scale behavioral phenotyping called Neurobooth. Learn more about our first clinical study using Neurobooth in the Mass General Neurology clinic here: https://neurobooth.mgh.harvard.edu.

Group Members

Anoopum Gupta, MD, PhD
Principal Investigator
Dr. Gupta received his undergraduate degree in Electrical Engineering from Georgia Tech, his medical degree from the University of Pittsburgh School of Medicine, and his PhD in Robotics and the Center for the Neural Basis of Cognition from Carnegie Mellon University’s School of Computer Science. He completed his residency in neurology at the Mass General Brigham/Harvard Medical School Residency Program. Dr. Gupta’s previous research utilized methods in machine learning and signal processing to understand how large populations of neurons in the hippocampus support learning, memory, and decision making.

Siddharth Patel, PhD
Post-Doctoral Fellow
Siddharth received his PhD in Biophysics from Indian Institute of Science, Bangalore where his research focused on understanding the sequence-structure-function relationships in proteins. He translated this experience, analyzing Antibody structures at Pfizer, where he applied machine learning techniques to predict antibody properties. As a Data Scientist at MindMics, a health tech startup, he designed and implemented algorithms on wearable sensor time series data to monitor cardiovascular health. He is interested in characterizing movement impairment and quantifying progression of neurodegeneration by applying and interpreting deep learning models on wearable sensor data.

Divya Kulkarni, PhD
Post-Doctoral Fellow
Divya Kulkarni obtained her Ph.D. in Computer Science and Engineering from the Indian Institute of Technology (IIT) Guwahati, India. Her doctoral research explored the fusion of bio-inspired techniques and machine learning, specifically focusing on lifelong learning in robots. She has also worked on transfer learning in deep neural networks as part of her doctoral thesis. Apart from her thesis, she worked on two open-source mobile agent tools for robots and cyber-physical systems, namely Tartarus and Tarpy, during her Ph.D. As a Machine Learning Scientist at Eli Lilly, she worked mainly on computer vision to tackle challenging tasks in image segmentation, specifically, to understand drug penetration in the brain tissues.

Nancy N. Soja, PhD
Lab Manager | Senior Scientist
Nancy received her undergraduate degree from the University of Rochester in Psychology and her PhD from Massachusetts Institute of Technology in Cognitive Science. Her research has focused on language acquisition and conceptual development. She is also deeply committed to maintaining a collaborative and empowered research environment that enables all members to thrive.

Anna Luddy
Clinical Research Assistant
Anna graduated with a Bachelor of Science degree in Neuroscience from Trinity College in 2020. She hopes to pursue a career in neuropsychology, and is especially interested in health equity, health literacy and working with pediatric populations.

Faye Yang
Clinical Research Assistant
Faye graduated from the University of Illinois at Urbana Champaign with a Bachelor of Science degree in Interdisciplinary Health Sciences in Spring 2022. She hopes to attend medical school with a focus on conducting research and promoting cultural competency in healthcare.

Lawrence White
Software Engineer 
Lawrence received an MBA from New York University specializing in data science and information technology. He has held engineering and management roles in a number of tech companies ranging from early-stage startups to Google. Recently, his work was focused on the intersection of software and cancer genomics at Foundation Medicine. His interests include the use of sensor data to quantify the severity and progression of neurological disease.

Rohin Manohar
Clinical Research Assistant 
Rohin graduated from Tufts University with a Bachelor of Science degree in Clinical Psychology in 2024. He hopes to attend graduate school and pursue a career in neuroscience, focusing on psychopharmacology and clinical trial work for neurodegenerative diseases.

Carrie Lu
Clinical Research Assistant
Carrie graduated with a Bachelor of Arts degree in Psychology from Barnard College of Columbia University in 2023. She gained research experience in college and as a post-baccalaureate research assistant in the Clinical Psychology department at Harvard University, where she contributed to studies on anxiety disorders (OCD/PTSD/Bipolar). Carrie also worked as a Mental Health Specialist at McLean Hospital, focusing on acute trauma management at an inpatient Trauma Unit. She is passionate about advancing the understanding of brain function, behavior, and cognition to contribute to the field of clinical research and diagnosis/treatment precision.

Collaborators

• Steven Arnold, MD, Massachusetts General Hospital and Harvard Medical School
• James Berry, MD, Massachusetts General Hospital and Harvard Medical School
• Katey Burke, PT, DPT, NCS, Massachusetts General Hospital Institute of Health Professions
• Zhuoqing (George) Chang, PhD, Electrical and Computer Engineering, Duke University
• Jean-Francois Daneault, PhD, Department of Kinesiology and Health, Rutgers University
• Krzysztof Gajos, PhD, Harvard Paulson School of Engineering and Applied Sciences, Harvard University
• Amanda Guidon, MD, Massachusetts General Hospital and Harvard Medical School
• Albert Hung, MD, PhD, Neurology, Massachusetts General Hospital and Harvard Medical School
• Maia Jacobs, PhD, Harvard Paulson School of Engineering and Applied Sciences, Harvard University
• Sheraz Khan, PhD, Massachusetts General Hospital and Harvard Medical School
• Sunghoon Ivan Lee, PhD, College of Information and Computer Sciences, University of Massachusetts Amherst
• Vineet Pandey, PhD, Harvard Paulson School of Engineering and Applied Sciences, Harvard University
• Guillermo Sapiro, PhD, Electrical and Computer Engineering, Duke University
• Jeremy Schmahmann, MD, Neurology, Massachusetts General Hospital and Harvard Medical School
• Christopher Stephen, MD, Massachusetts General Hospital and Harvard Medical School

Alumni

• Winnie Ching, Clinical Research Assistant, subsequently a medical student at University of Pikeville-Kentucky College of Osteopathic Medicine
• Mary Donovan, Clinical Research Coordinator, subsequently a medical student at Georgetown University
• Nicole Eklund, Behavioral Neuroscience PhD Student at Boston University
• Mainak Jas, Postdoctoral Fellow, Martinos Center
• Nergis Khan, Clinical Research Coordinator, subsequently a medical student at Stanford University
• Karin Knudson, Post-Doctoral Fellow, subsequently a Senior Data Scientist at Tufts University
• Adonay Nunes, Senior Research Scientist, Biosensics
• Brandon Oubre, Post-Doctoral Fellow, subsequently Assistant Professor of Computer Science, University of Alabama at Birmingham
• Jessey Ouillin, Clinical Trial Associate, OM Pharma, Meyrin, Switzerland
• Akansha Pandey, Medical Student, Brown University
• Kyriakos Vattis, Data Scientist, Biofourmis
• Andrew Chang, AI Lead, Series B Startup Golden
  
  

Selected Publications

Recent publications include:

Eklund NM, Ouillon J, Pandey V, Stephen CD, Schmahmann JD, Edgerton J, Gajos KZ, Gupta AS. Real-life ankle submovements and computer mouse use reflect patient-reported function in adult ataxias. Brain Commun. 2023 Mar 13;5(2):fcad064.

Gupta AS, Patel S, Premasiri A, Vieira F. At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis. Nat Commun. 2023 Aug 21;14(1):5080.

Gupta AS, Luddy AC, Khan NC, Reiling S, Thornton JK. Real-life Wrist Movement Patterns Capture Motor Impairment in Individuals with Ataxia-Telangiectasia. Cerebellum. 2022. doi:10.1007/s12311-022-01385-5

Gupta AS (2022). Digital phenotyping in clinical neurology. Seminars in Neurology. doi: 10.1055/s-0041-1741495.

Khan NC, Pandey V, Gajos KZ, Gupta AS (2021) Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia. Cerebellum. https://doi.org/10.1007/s12311-021-01306-y 

Oubre B., Daneault JF., Whritenour K., Khan NC, Stephen CD, Schmahmann JD, Lee SI, Gupta AS (2021). Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia. Cerebellum. https://doi.org/10.1007/s12311-021-01247-6

Chang Z, Chen Z, Stephen CD, Schmahmann JD, Wu H-T, Sapiro G, Gupta AS (2020): Accurate Detection of Cerebellar Smooth Pursuit Eye Movement Abnormalities via Mobile Phone Video and Machine Learning. Scientific Reports 10, 18641 (2020). doi.org/10.1038/s41598-020-75661-x

Azami H, Arnold SE, Sanei S, Chang Z, Sapiro G, Escudero J, Gupta AS (2019): Multiscale Fluctuation-based Dispersion Entropy and its Applications to Neurological Diseases. IEEE Access. 2019 7:68718-68733. doi:10.1109/ACCESS.2019.2918560.

Jacobs M, Gheihman G, Gajos KZ, Gupta AS (2019): “I think we know more than our doctors:” How online health communities help caregivers manage care teams with limited disease-related expertise. Proceedings of the ACM on Human-Computer Interaction 3, CSCW, Article 159 (November 2019), 22 pages. Summary published at Medium.

Gajos KZ, Reinecke K, Donovan M, Stephen CD, Hung AY, Schmahmann JD, Gupta AS (2019): Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection. Movement Disorders. doi:10.1002/mds.27915

A complete list of our publications can be found here >