Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease.

Journal: Medical image analysis
PMID: 40121807

Abstract

Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric and neurological disorders. Previous sulcal pattern analysis used spectral graph matching of sulcal pit-based graph structures to assess deviations from normative sulcal patterns. However, challenges exist, including the absence of a standard criterion for defining a typical reference set, time-consuming cost of graph matching, user-defined feature weight sets, and assumptions about uniform node distribution. We developed a deep learning-based sulcal pattern analysis to address these challenges by adapting prototype-based graph neural networks to sulcal pattern graphs. Additionally, we proposed a prototype inverse-projection for better interpretability. Unlike other prototype-based models, our approach inversely projects prototypes onto individual node representations to calculate the inverse-projection weights, enabling efficient visualization of prototypes and focusing the model on selective regions. We evaluated our method through a classification task between healthy controls (n = 174, age = 15.4 ±1.9 [mean ± standard deviation, years]) and patients with congenital heart disease (n = 345, age = 15.8 ±4.7) from four cohort studies and a public dataset. Our approach demonstrated superior classification performance compared to other state-of-the-art models, supported by extensive ablative studies. Furthermore, we visualized and examined the learned prototypes to enhance understanding. We believe our method has the potential to be a sensitive and understandable tool for sulcal pattern analysis.

Authors

  • Hyeokjin Kwon
    Department of Electronic Engineering, Hanyang University, Seoul, South Korea; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
  • Seungyeon Son
    Department of Artificial Intelligence, Hanyang University, Seoul, South Korea.
  • Sarah U Morton
    Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
  • David Wypij
    Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA.
  • John Cleveland
    Department of Surgery and Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Caitlin K Rollins
  • Hao Huang
    School of Information Science and Engineering, Xinjiang University, Shangli Road, Urumqi 830046, China.
  • Elizabeth Goldmuntz
    Division of Cardiology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Ashok Panigrahy
    Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA.
  • Nina H Thomas
    Department of Child and Adolescent Psychiatry and Behavioral Sciences and Center for Human Phenomic Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
  • Wendy K Chung
    6 Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY, USA.
  • Evdokia Anagnostou
    Department of Pediatrics, Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, Toronto, Ontario, Canada.
  • Ami Norris-Brilliant
    Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Bruce D Gelb
    Mindich Child Health and Development Institute, Departments of Pediatrics and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, USA.
  • Patrick McQuillen
    Department of Pediatrics and Department of Neurology, University of California, San Francisco, CA, USA.
  • George A Porter
    Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA.
  • Martin Tristani-Firouzi
    Division of Pediatric Cardiology (M.T.-F.), University of Utah School of Medicine, Salt Lake City.
  • Mark W Russell
    Department of Pediatrics, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, MI, USA.
  • Amy E Roberts
    Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.
  • Jane W Newburger
    Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA, USA.
  • P Ellen Grant
    Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
  • Jong-Min Lee
    Department of Obstetrics and Gynecology, Kyung Hee University Hospital at Gangdong, Kyung Hee University School of Medicine, Seoul, Republic of Korea.
  • Kiho Im
    Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA; Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA. Electronic address: Kiho.Im@childrens.harvard.edu.

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