R Todd Constable
Yale University, CT·Neuroscience
Brain function emerges from networks of connected regions. A central question is how the strength of those connections relates to an individual's thinking, behavior and symptoms.
- Fields
Neuroscience
Biomedical Engineering
Medicine & Clinical Research
- Topics
functional MRI
fMRI
connectome
- Grants
- 3 known grants, through Oct 2029
What they work on
- Identify brain circuit changes linked to cognitive deficits and mood symptoms in people with neurological long COVID (neuro-PASC), using connectome-based predictive modeling and machine learning applied to brain imaging.
- Determine possible compensatory brain mechanisms in each thinking domain, and the distinct contribution of mood alterations, in neuro-PASC patients compared with controls without long COVID.
- Develop Bayesian models that jointly define brain regions and link brain connectivity to behavior, removing dependence on an arbitrarily chosen brain atlas.
- Validate the new models on Human Connectome Project data and a 450-person Yale sample spanning healthy and psychiatric participants, and share normative models for 16 behaviors and 6 clinical scores.
- Improve image resolution of an open, low-field MRI with an uneven magnetic field, using new radiofrequency coil designs, stop-motion imaging and gradient encoding.
- Test three fat–water contrast pulse sequences on the low-cost open MRI device to assess fatty liver disease in lean and obese subjects.
More about the research
The research builds methods that read these networks from MRI scans and link them to behavior and disease. It applies them to problems such as long COVID. A related project works on making MRI scanners themselves cheaper and more accessible.
Why it matters
Long COVID with brain symptoms burdens millions of people, and its brain basis is unclear. Mapping the affected circuits could point toward treatment targets. More reliable brain–behavior models could help classify patients with psychiatric illness, choose treatments and monitor response. A low-cost, open MRI scanner for doctors' offices could make MRI far more accessible for screening and monitoring chronic diseases such as fatty liver disease.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Alan C Seifert
Icahn School of Medicine at Mount Sinai, NY·Neuroscience
Test whether neuroPASC, lasting neurological symptoms after COVID-19, involves vascular injury and inflammation, by comparing ex vivo MRI and histology from half-brains of 25 neuroPASC and 15 control decedents.
Krishna Nayak
University of Southern California, CA·Biomedical Engineering
Develop and technically validate volumetric real-time MRI data acquisition on a 0.55 Tesla scanner to image the moving heart and airway in 3D.
Negar Omidvari
University of California-Davis, CA·Biomedical Engineering
Develop and validate a kinetic model of the T-cell–targeting tracer 18F-AraG across organs and tissue types, using dynamic total-body PET scans for more accurate measurement than standard static images.
The school
Yale University: 73 labs on PI Pages, $1.5B a year on research, 438 research doctorates a year.
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