Prediction of longitudinal development of infant cortical surface shape using a 4D current-based learning framework

Islem Rekik, Gang Li, Weili Lin, Dinggang Shen

Research output: Contribution to journalArticle

Abstract

Understanding the early dynamics of the highly folded human cerebral cortex is still an actively evolving research field teeming with unanswered questions. Longitudinal neuroimaging analysis and modeling have become the new trend to advance research in this field. However, this is challenged by a limited number of acquisition timepoints and the absence of inter-subject matching between timepoints. In this paper, we propose a novel framework that unprecedentedly solves the problem of predicting the dynamic evolution of infant cortical surface shape solely from a single baseline shape based on a spatiotemporal (4D) current-based learning approach. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we first use the current-based shape regression model to set up the inter-subject cortical surface correspondences at baseline of all training subjects. We then estimate for each training subject the diffeomorphic temporal evolution trajectories of the cortical surface shape and build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we first warp all training subjects onto its baseline cortical surface. Second, we select the most appropriate learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints from its baseline cortical surface, based on closeness metrics between this baseline surface and the learnt baseline population average surface atlas. We used the proposed framework to predict the inner cortical surface shape at 3, 6 and 9 months from the cortical shape at birth in 9 healthy infants. Our method predicted with good accuracy the spatiotemporal dynamic change of the highly folded cortex.

Original languageEnglish
Pages (from-to)576-587
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9123
DOIs
Publication statusPublished - 2015
Externally publishedYes

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Prediction
Baseline
Atlas
Cortex
Framework
Learning
Trajectories
Trajectory
Neuroimaging
Predict
Longitudinal Data
Training
Regression Model
Correspondence
Metric
Modeling
Estimate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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abstract = "Understanding the early dynamics of the highly folded human cerebral cortex is still an actively evolving research field teeming with unanswered questions. Longitudinal neuroimaging analysis and modeling have become the new trend to advance research in this field. However, this is challenged by a limited number of acquisition timepoints and the absence of inter-subject matching between timepoints. In this paper, we propose a novel framework that unprecedentedly solves the problem of predicting the dynamic evolution of infant cortical surface shape solely from a single baseline shape based on a spatiotemporal (4D) current-based learning approach. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we first use the current-based shape regression model to set up the inter-subject cortical surface correspondences at baseline of all training subjects. We then estimate for each training subject the diffeomorphic temporal evolution trajectories of the cortical surface shape and build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we first warp all training subjects onto its baseline cortical surface. Second, we select the most appropriate learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints from its baseline cortical surface, based on closeness metrics between this baseline surface and the learnt baseline population average surface atlas. We used the proposed framework to predict the inner cortical surface shape at 3, 6 and 9 months from the cortical shape at birth in 9 healthy infants. Our method predicted with good accuracy the spatiotemporal dynamic change of the highly folded cortex.",
author = "Islem Rekik and Gang Li and Weili Lin and Dinggang Shen",
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