Syed A. Rizvi

We introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate prediction of clinical variables like age, anxiety, and PTSD as well as forecasting of future brain states. Critically, the model generalizes well to entirely new external cohorts not seen during training. In zero-shot inference mode, BrainLM can identify intrinsic functional networks directly from raw fMRI data without any network-based supervision during training. The model also generates interpretable latent representations that reveal relationships between brain activity patterns and cognitive states. Overall, BrainLM offers a versatile and interpretable framework for elucidating the complex spatiotemporal dynamics of human brain activity. It serves as a powerful "lens" through which massive repositories of fMRI data can be analyzed in new ways, enabling more effective interpretation and utilization at scale. The work demonstrates the potential of foundation models to advance computational neuroscience research.

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into ”cell sentences,” C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex celltype annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

The COVID-19 pandemic affected countries across the globe, demanding drastic public health policiesto mitigate the spread of infection, which led to economic crises as a collateral damage. In this work,we investigate the impact of human mobility, described via international commercial flights, onCOVID-19 infection dynamics on a global scale. We developed a graph neural network (GNN)-basedframework called Dynamic Weighted GraphSAGE (DWSAGE), which operates over spatiotemporalgraphs and is well-suited for dynamically changing flight information updated daily. This architecture isdesigned to be structurally sensitive, capable of learning the relationships between edge features andnode features. To gain insights into the influence of air traffic on infection spread, we conducted localsensitivity analysis on our model through perturbation experiments. Our analyses identified WesternEurope, the Middle East, and North America as leading regions in fueling the pandemic due to thehigh volume of air traffic originating or transiting through these areas. We used these observationsto propose air traffic reduction strategies that can significantly impact controlling the pandemic withminimal disruption to human mobility. Our work provides a robust deep learning-based tool to studyglobal pandemics and is of key relevance to policymakers for making informed decisions regarding airtraffic restrictions during future outbreaks.