Senseable Intelligence Group · MIT
We build frameworks, run extensive collaborations, and use brain imaging and speech communication to improve assessments and treatments for mental health and neurological disorders — with reproducibility at the core.
Research highlights
A snapshot of active work spanning data coordination, neurophysiology archives, knowledgebases, and speech health.
BBQS team (MIT, Penn State, UMass-Medical, JHU/APL, UCBerkeley/LBNL)
A data and AI coordination center for the BRAIN Initiative Brain Behavior Quantification and Synchronization (BBQS) program. Supported by NIMH U24MH136628 (PIs: Ghosh, Cabrera, Kennedy).
DANDI team (MIT, Dartmouth, Kitware)
A platform for data ingestion, search, and computing targeted towards cellular neurophysiology. Supported by NIMH R24MH117295 (PI: Ghosh and Halchenko).
BICAN team (Allen Institute for Brain Science, MIT)
Develop an extensible Brain Cell Knowledge Base (BCKB) to ingest and standardize c omprehensive cell type information from BICAN's development of a multimodal, multi-species brain cell atlas and disseminate that atlas as an open and interactive community resource for advancing knowledge of the brain. Supported by NIMH 1U24MH130918 (PI: Mufti, Hawrylycz, Ng, and Ghosh).
ReproNim team
A Center dedicated to improving reproducibility in neuroimaging through the development of products designed to enhance efficiency and provenance tracking within laboratories and in collaboration with external stakeholders such as data archives and publishers. Supported by NIBIB P41 EB019936 (PI: David Kennedy, UMass Medical School).
Isaac Bevers, Kaley Jenny, Rahul Brito, Jordan Wilke, Miles Silva, Fabio Catania
The goal of this project is to observe and analyze speech variations in across disorders by analyzing different types of data including ecological momentary assessments, recordings of clinical interviews, and social media posts. Key questions we hope to answer are: 1) Is speech a good longitudinal biomarker? 2) How much speech information is needed for stable tracking? 3) Can linguistic information augment voice models?