Progressive infant brain connectivity evolution prediction from neonatal MRI using bidirectionally supervised sample selection

Olfa Ghribi, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The early postnatal developmental period is highly dynamic, where brain connections undergo both growth and pruning processes. Understanding typical brain connectivity evolution would enable us to spot abnormal connectional development patterns. However, this generally requires the acquisition of longitudinal neuroimaging datasets that densely cover the first years of postnatal development. This might not be easily investigated since neonatal follow-up scans are rarely acquired in a clinical setting. Furthermore, waiting for the acquisition of later brain scans would hinder early neurodevelopmental disorder diagnosis. To solve this problem, we unprecedentedly propose a bidirectionally supervised sample selection framework, while leveraging the time-dependency between consecutive observations, for predicting neonatal brain connectome evolution from a single structural magnetic resonance imaging (MRI) acquired around birth. Specifically, we propose to learn how to select the best training samples by supervisedly training a bidirectional ensemble of regressors from the space of pairwise neonatal connectome disparities to their expected prediction scores resulting from using one training connectome to predict another training connectome. The proposed supervised ensemble learning is time-dependent and has a recall memory anchored at the ground truth baseline observation, allowing to progressively pass over previous predictions through the connectome evolution trajectory. We then rank training samples at current timepoint ti-1 based on their expected prediction scores by the ensemble and average their connectomes at follow-up timepoint ti to predict the testing connectome at ti. Our framework significantly outperformed comparison methods in leave-one-out cross-validation.

Original languageEnglish
Title of host publicationPredictive Intelligence in Medicine - 2nd International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsIslem Rekik, Ehsan Adeli, Sang Hyun Park
PublisherSpringer
Pages63-72
Number of pages10
ISBN (Print)9783030322809
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11843 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ghribi, O., Li, G., Lin, W., Shen, D., & Rekik, I. (2019). Progressive infant brain connectivity evolution prediction from neonatal MRI using bidirectionally supervised sample selection. In I. Rekik, E. Adeli, & S. H. Park (Eds.), Predictive Intelligence in Medicine - 2nd International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 63-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11843 LNCS). Springer. https://doi.org/10.1007/978-3-030-32281-6_7