Dr. Lenglet’s research aims to understand the effects of neurodegeneration and discover novel imaging biomarkers of the central nervous system. His lab develops mathematical and computational techniques to map the connectivity of the brain using high-field MRI. He was a co-chair of the Human Connectome Project (HCP) operational team for the analysis of diffusion MRI data. He is currently involved in several international studies in ataxias, COVID, ALS and TBI dealing with MRI data standardization and analysis across sites. He is also an MPI of the BRAIN CONNECTS Center for Mesoscale Connectomics, where he co-leads the diffusion MRI analyses. His research interests include brain microstructure characterization, connectomics, optimization of diffusion MRI acquisition protocols, as well as machine learning and network science.

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Dr. Lenglet is a professor at the Center for Magnetic Resonance Research (CMRR) and a scholar of the Institute for Translational Neuroscience. After completing his undergraduate degree in computer science engineering, with a minor in philosophy and cognitive sciences, and a M.S. in applied mathematics, he received a Ph.D. in biomedical imaging and neuroscience. He then joined the Imaging and Visualization Department at Siemens Corporate Research as a research scientist. In 2008, he moved to the University of Minnesota as a research associate in the Department of Electrical and Computer Engineering and subsequently became an assistant professor (tenure track) at CMRR. He was a recipient of the University of Minnesota 2014-2016 McKnight Land-Grant Professorship.

Education

Selected Publications

Labounek R., Bondy M.T., Paulson A.L., […] Cohen-Adad J.*, Lenglet C.*, Nestrašil I.*; Body size and intracranial volume interact with the structure of the central nervous system: A multi-center in vivo neuroimaging study. Imaging Neuroscience (2025) 3, imag_a_00559

Farooq H, Pisharady P, Lenglet C. Biophysical modeling: Multicompartment biophysical models for brain tissue microstructure imaging. In: Computational and Network Modeling of Neuroimaging Data (Elsevier, 2024) 159-208

Adanyeguh I.*, Joers J.*, Deelchand D., Hutter D., Eberly L., Iltis I., Bushara K., Henry P.G^, Lenglet C.^. Brain MRI Detects Early-Stage Alterations and Disease Progression in Friedreich ataxia. Brain Communications (2023) 5(4), fcad196  

Pisharady PK, Eberly LE, Adanyegugh IM, Manousakis G, Guliani G, Walk D*, Lenglet C*. Multimodal MRI improves diagnostic accuracy and sensitivity to longitudinal change in amyotrophic lateral sclerosis. Commun Med (2023) 3(1), 84

Joers J.*, Adanyeguh I.*, Deelchand D., Hutter D., Eberly L., Iltis I., Bushara K., Lenglet C.^, Henry P.G^. Spinal Cord  Magnetic Resonance Imaging and Spectroscopy Detect Early-stage Alterations and Disease Progression in Friedreich Ataxia. Brain Communications (2022) 4(5), fcac246

Pisharady PK, Eberly LE, Cheong I, Manousakis G, Guliani G, Clark HB, Bathe M, Walk D*, Lenglet C*. Tract-specific Analysis Improves Sensitivity of Spinal Cord Diffusion MRI to Cross-sectional and Longitudinal Changes in Amyotrophic Lateral Sclerosis. Commun Biol (2020) 3, 370

Farooq H., Chen Y., Georgiou T., Tannebaum A., Lenglet C. Network Curvature as a Hallmark of Brain Structural Connectivity, Nat. Comms (2019) 10(1):1-11

Gulban OF, De Martino F, Vu AT, Yacoub E, Ugurbil K, Lenglet C. Cortical Fibers Orientation Mapping using in-vivo Whole Brain 7 Tesla Diffusion MRI. Neuroimage (2018) 178:104-118

*, ^: Authors contributed equally.