ML4H: Brandon Oubre, Digital and Quantitative Behavioral Phenotyping in Neurologic Disease @broadinstitute

Uploaded January 2026 | Updated September 2026, 2 weeks ago

In this video, Brandon Oubre (HMS/MGH) presents 'Digital and Quantitative Behavioral Phenotyping in Neurologic Disease'.

Presentation Date: 02/07/24

Abstract: Semiquantitative clinical scales are the current gold standard used to assess motor signs in neurologic diseases. These scales have necessarily course granularity, are susceptible to floor and ceiling effects at the limits of human perception, are difficult to scale, and do not necessarily reflect disease impacts on natural behavior. Though patient reported outcome measures are easier to scale and can capture aspects of natural behavior, they are often highly subjective and sensitive to bias. This talk highlights two research threads aiming to develop digital measures to address these limitations. First, the Neurobooth project aims to collect a large corpus of high-quality, multimodal time-series data across several motor domains using an array of sensing devices, including eye tracking, audio, wearable inertial sensors, and video capture using both an iPhone and RGBD cameras. These information-rich time-series support deep modeling of constrained behaviors, including analysis of cross-domain motor coordination. Second, the talk will highlight efforts to capture and analyze natural, unconstrained behavior to develop ecologically valid measures of disease severity and progression, with a focus on modeling inertial data obtained from wearable devices in free-living environments.

Bio: Brandon Oubre is a research fellow at the Massachusetts General Hospital Department of Neurology and Harvard Medical School. He received his Ph.D. from the University of Massachusetts Amherst Donning College of Computer and Information Sciences with an Outstanding Dissertation Award. He is currently a member of the Laboratory for Deep Neurophenotyping (PI: Anoopum S. Gupta). His research interests include digital and mobile health, with a focus on quantitative analysis of behavior in neurologic disease. More specifically, this research aims to leverage wearable and ubiquitous technologies to support 1) development of more sensitive measures of disease progression, 2) identification of early disease signs, and 3) development of ecologically valid assessments of natural behavior. His work has been published in both clinical and engineering venues, including featured articles in IEEE Transactions on Biomedical Engineering and IEEE Transactions on Neural Systems and Rehabilitation Engineering.

For more information, visit: broadinstitute.org/ml4h

Copyright Broad Institute, 2025. All rights reserved.

ML4H: Brandon Oubre, Digital and Quantitative Behavioral Phenotyping in Neurologic Disease

Meet A Broad Scientist: Matt Johnson #science #sciencemuseum

Ladders to Cures Accelerator Symposium: Keynote - Jonathan Weissman

MPG Primer: Base Editing Triplet Repeats of Huntingtons Disease and Friedrichs Ataxia (2025)

How vision, creativity, scrappiness, struggle, and teamwork were key to Broad’s early success

EWSC - From Model Explanations to Discovery: Explainable AI in Cancer Precision Medicine (2026)

$MIA: Lorin Crawford, Rethinking scale in single-cell foundation models; Primer: Davide DAscenzo Models, Inference and Algorithms September 24, 2025 Broad Institute of MIT and Harvard Meeting: When more isn’t better: rethinking scale in single-cell foundation models Lorin Crawford Microsoft Research New England (MSR) The success of transformer-based foundation models on natural language and images has motivated their use in single-cell biology. Single-cell foundation models have been trained on increasingly larger transcriptomic datasets, scaling from initial studies with 1 million cells to newer atlases with over 100 million cells. In this talk, we will investigate the role of pre-training dataset size and diversity on the performance of single-cell foundation models on both zero-shot and fine-tuned tasks. In the first half, we use a large corpus of 22.2 million cells to pre-train a total of 400 models and evaluate over 6,400 experiments. We show that current methods tend to plateau in performance with pre-training datasets that are only a fraction of the size. This will lead us to the second half of the talk where we evaluate training data composition on model performance. Focusing on a tractable biological system (human hematopoiesis), we train and analyze deep generative models with a variety of training datasets, including cells from adult and developing tissues, disease states, and perturbation atlases. From the performance across these models, we observe that (1) deep generative models generalize poorly to unseen cell types, (2) addition of malignant cells to a healthy cell training corpus does not necessarily improve modeling of unseen malignant cells, and (3) inclusion of an embryonic stem cell transcription factor differentiation atlas in training data improves performance on out-of-distribution tasks. Our results highlight the distinct contributions of different training data types and point towards strategies for optimizing future single cell foundation models. Primer: Infrastructure and modeling challenges in single-cell omics Davide DAscenzo Polytechnic University of Turin Deep learning is expected to play a major role in advancing the analysis of single-cell omics data, but a number of practical challenges have so far limited its impact. In this talk, we will focus on two such challenges, one at the level of data infrastructure and one at the level of model design. With the rise of very large-scale single-cell experiments, we are now able to use datasets of hundreds of millions of cells. Model training on these datasets is often bottlenecked by the data-loading process of moving data from disk to GPU. To address this, we developed scDataset, a scalable and efficient data-loading solution for quasi-random sampling of single-cell data from disk [1]. We present block sampling and batched fetching strategies that balance I/O efficiency with memory consumption and minibatch diversity, demonstrating how to achieve increased throughput while maintaining sampling quality comparable to true random shuffling. Moving to biological tasks, we focus on cell type annotation. We find that both simple linear models and more complex transformer-based architectures struggle to generalize in out-of-distribution settings. To mitigate this, we introduce a hierarchical cross-entropy loss that incorporates the structure of cell type ontologies [2]. Across model classes, this leads to consistent improvements in performance, suggesting that structured biological priors may be more useful than scaling up model parameters.[1] Davide D’Ascenzo and Sebastiano Cultrera di Montesano. scDataset: Scalable data loading for deep learning on large-scale single-cell omics. arXiv, 2025. [2] Sebastiano Cultrera di Montesano, Davide D’Ascenzo, Srivatsan Raghavan, Ava P. Amini, Peter S. Winter, and Lorin Crawford. Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models. bioRxiv, 2025. For more information visit: https://www.broadinstitute.org/talks/spring-2025/mia Copyright Broad Institute, 2025. All rights reserved.$

MPG Primer: Beyond the Gene: A Not So Deep Dive into Bulk RNA-Seq, Isoforms & Alternative Splicing

ML4H: Collin Stultz: Self-Supervised Learning for Patient Phenotyping and Forecasting Adverse Events

MPG Primer: The Awesome Power of Genetic Linkage (2025)