Laurent Younes

About

Laurent Younes is a professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University and a member of the JHU Center for Imaging Science. His research focuses on the statistical properties of image analysis, deformation analysis, and shape recognition, with particular emphasis on Markov Random Fields as a mathematical tool for modeling and making inferences about image data. His pioneering shape analysis algorithms enable researchers and clinicians to better interpret medical imaging data, advancing the detection and treatment of diseases such as Parkinson’s disease, Alzheimer’s disease, cardiac disease, and mental illness. Younes leads research teams studying the statistical properties of image analysis and shape recognition, and his work is central to the field of computational anatomy, especially in analyzing how diseases affect organ shapes. He has contributed significantly to understanding early-stage brain disease, network neurodegeneration during Alzheimer’s, and the creation of algorithms to examine cortical atrophy in mild cognitive impairment. Younes authored the book 'Shapes and Diffeomorphisms,' published by Springer, which is considered a definitive text on the mathematical analysis of shapes and their transformations. He has received recognition for his contributions, including being named a Fellow of the Institute for Mathematical Statistics in 2015 and a Fellow of the Society for Industrial and Applied Mathematics in 2023. Younes…

Research topics

• Medicine
• Pathology
• Psychology
• Internal medicine
• Psychiatry
• Artificial Intelligence
• Endocrinology
• Computer Science
• Neuroscience
• Mathematics

Selected publications

• Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease

  Frontiers in Neuroscience · 2020 · 49 citations

  This study examines the atrophy patterns in the entorhinal and transentorhinal cortices of subjects that converted from normal cognition to mild cognitive impairment. The regions were manually segmented from 3T MRI, then corrected for variability in boundary definition over time using an automated approach called longitudinal diffeomorphometry. Cortical thickness was calculated by deforming the gray matter-white matter boundary surface to the pial surface using an approach called normal geodesic…

  DOI

• Longitudinal changes in brain metabolites in healthy controls and patients with first episode psychosis: a 7-Tesla MRS study

  Molecular Psychiatry · 2023 · 38 citations

  DOI

• Longitudinal changes in brain metabolites in healthy subjects and patients with first episode psychosis (FEP): a 7-Tesla MRS study

  bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 8 citations

  Abstract Objective 7 Tesla (T) longitudinal magnetic resonance spectroscopy (MRS) offers a precise measurment of metabolic levels in human brain via a non-invasive approach. Studying longitudinal changes in neurometabolites could help identify trait and state markers for diseases and understand inconsistent findings from different researchers due to differences in the age of study participants and duration of illness. This study is the first to report novel longitudinal patterns in young adultho…

  DOI

• Susceptibility MRI Helps Predict Mild Cognitive Impairment Onset and Cognitive Decline in Cognitively Unimpaired Older Adults

  Radiology · 2025-09-01 · 7 citations

  articleOpen access

  Tissue magnetic susceptibility elevations measured at MRI in the entorhinal cortex and putamen were significant predictors of onset of mild cognitive impairment and cognitive decline in cognitively unimpaired older adults, especially those with amyloid neuropathologic abnormalities.

  PublisherOA PDFDOI

• Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections

  Nature Communications · 2024-04-25 · 7 citations

  articleOpen access

  This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encod…

  PublisherOA PDFDOI

Recent grants

• Numerical Computation of Geodesics in the Framework of Metamorphosis

  NSF · $275k · 2010–2013

• FRG: The Geometry, Mechanics and Statistics of the Infinite-dimensional Manifold of Shapes

  NSF · $800k · 2005–2009

Frequent coauthors

• Michael I. Miller

  Discovery Institute

  112 shared

• Alain Trouvé

  École Normale Supérieure Paris-Saclay

  53 shared

• J. Tilak Ratnanather

  Johns Hopkins University

  31 shared

• Donald Geman

  Johns Hopkins University

  31 shared

• Susumu Mori

  Johns Hopkins University

  29 shared

• Sylvain Arguillère

  29 shared

• Nicolas Charon

  University of Houston

  21 shared

• Luigi Marchionni

  21 shared

Awards & honors

• Fellow of the Institute for Mathematical Statistics (2015)
• Fellow of the Society for Industrial and Applied Mathematics…

Similar researchers at Johns Hopkins University

• Alan Yuilleh-134
• Adam Charlesh-126
• Rama Chellappah-124