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Updated: Jun 13, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Spatiotemporal atlas estimation for developmental delay detection in longitudinal datasets.

Stanley Durrleman1, Xavier Pennec, Alain Trouvé

  • 1INRIA - Asclepios Team-Project, Sophia Antipolis, France.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 30, 2010
PubMed
Summary

This study introduces a novel method to analyze anatomical changes over time, considering both shape and evolutionary dynamics. The approach models individual growth and population-level evolution, applicable to diverse datasets like hominid skulls and brain development.

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Area of Science:

  • Biomedical Imaging
  • Developmental Biology
  • Anthropology

Background:

  • Longitudinal studies are crucial for understanding anatomical changes.
  • Existing methods often struggle with irregular sampling and dynamic evolution.
  • Quantifying anatomical variability across different developmental trajectories is challenging.

Purpose of the Study:

  • To develop a flexible methodology for analyzing anatomical variability in longitudinal data.
  • To account for both static 3D anatomical variations and dynamic changes in evolutionary patterns.
  • To enable analysis of populations with uneven scanning frequencies and ages.

Main Methods:

  • A regression model is employed to infer continuous shape evolution from individual observations.
  • Spatiotemporal registration is used to jointly deform 3D geometry and evolutionary dynamics via time change functions.

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Eye Tracking Young Children with Autism
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  • Population-level prototype scenarios and 4D variability are inferred.

    • Main Results:

    • The method successfully analyzes morphological evolution in 2D hominid skull profiles.
    • It is applied to study brain growth, specifically the amygdala, in children with autism, developmental delay, and controls.
    • The approach accommodates subjects scanned at different ages and frequencies.

    Conclusions:

    • The proposed methodology offers a robust framework for analyzing complex anatomical evolution in longitudinal datasets.
    • It provides insights into both typical and atypical developmental trajectories.
    • This technique has broad applicability in fields requiring the study of anatomical changes over time.