Nico Dosenbach is associate professor of neurology at Washington University School of Medicine. His research as a systems neuroscientist is focused on pushing resting-state functional connectivity MRI (RSFC), functional MRI (fMRI) and diffusion tensor imaging (DTI) to the level of individual patients. To create and annotate the connectomes of individuals he is working to improve the signal-to-noise, spatial resolution and replicability of RSFC, DTI and fMRI data.
Nico Dosenbach
Associate professor of neurology
Washington University School of Medicine
From this contributor
Breaking down the winner’s curse: Lessons from brain-wide association studies
We found an issue with a specific type of brain imaging study and tried to share it with the field. Then the backlash began.
Breaking down the winner’s curse: Lessons from brain-wide association studies
By
Nico Dosenbach, Scott Marek
25 March 2024 | 6 min read
Explore more from The Transmitter
Links between tuberous sclerosis complex and autism, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 17 August.
Links between tuberous sclerosis complex and autism, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 17 August.
By
Jill Adams
18 August 2026 | 1 min read
The fun and flexibility of data science
Taylor Bolt spent his Ph.D. and postdoc digging through brain imaging data for clues to cognition. In industry, the datasets are different but the joy of answering questions with data remains.
The fun and flexibility of data science
Taylor Bolt spent his Ph.D. and postdoc digging through brain imaging data for clues to cognition. In industry, the datasets are different but the joy of answering questions with data remains.
By
Katie Moisse
18 August 2026 | 7 min read
Mind over metrics: How can we tell if two brains (or AI models) are alike?
The ability to record from large populations of neurons has triggered the development of myriad methods for comparing them. But we’re still grappling with how to convert measures of likeness into a better mechanistic understanding.
Mind over metrics: How can we tell if two brains (or AI models) are alike?
The ability to record from large populations of neurons has triggered the development of myriad methods for comparing them. But we’re still grappling with how to convert measures of likeness into a better mechanistic understanding.
By
Alex Williams
17 August 2026 | 7 min read