Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluation of surface roughness of titanium implants on human fibroblast cells.

Scientific reports·2026
Same author

Assessment of the effects of ethnicity, home language, and socioeconomic status on neurodevelopmental performance in a multiethnic population.

BMJ paediatrics open·2026
Same author

Socioeconomic inequity in extreme outcomes within very pre-term and/or very low birthweight infants: evidence from multi-national cohorts.

Frontiers in public health·2026
Same author

A deep learning algorithm for automatic 3D segmentation and classification of the sheep placenta in magnetic resonance images.

Physiological reports·2026
Same author

The effect of very preterm birth on the Five-Factor Model of personality traits: A meta-analysis of individual participant data.

European journal of personality·2026
Same author

The Performance of Selective Screening Ultrasound to Detect the Small-for-Gestational-Age Foetus: A Prospective Cohort Study Nested Within the DESiGN Randomised Control Trial.

BJOG : an international journal of obstetrics and gynaecology·2026

Related Experiment Video

Updated: Jul 9, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.0K

Multi-source multi-modal markers for Bayesian Networks: Application to the extremely preterm born brain.

Hassna Irzan1, Michael Hütel2, Helen O'Reilly3

  • 1School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE17EU, UK; Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E6BT, UK.

Medical Image Analysis
|December 6, 2023
PubMed
Summary

A new Bayesian Network (BN) method, Modified PC-HC (MPC-HC), accurately models brain and cognitive outcomes in extremely preterm (EP) individuals. MPC-HC improves prediction and structure learning, identifying MRI effects on EP cognitive scores.

Keywords:
Bayesian networksBrainCognitive measurementsDiffusion MRIGraphical modelsMRINeuroimagingPreterm birthStructure learningfMRI

More Related Videos

Preterm EEG: A Multimodal Neurophysiological Protocol
19:32

Preterm EEG: A Multimodal Neurophysiological Protocol

Published on: February 18, 2012

28.5K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.2K

Related Experiment Videos

Last Updated: Jul 9, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.0K
Preterm EEG: A Multimodal Neurophysiological Protocol
19:32

Preterm EEG: A Multimodal Neurophysiological Protocol

Published on: February 18, 2012

28.5K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.2K

Area of Science:

  • Neuroscience
  • Computational Biology
  • Biostatistics

Background:

  • The preterm phenotype involves complex brain and cognitive interactions.
  • Accurate characterization of these interactions can identify prematurity markers.
  • Bayesian Networks (BNs) are effective for analyzing complex relationships and mitigating confounding factors.

Purpose of the Study:

  • Introduce and validate a novel Bayesian Network (BN) structural learning algorithm, Modified PC-HC (MPC-HC).
  • Investigate predictive relationships and mutual influences between brain structure (MRI) and cognitive performance in extremely preterm (EP) young adults.
  • Demonstrate the utility of BNs in modeling complex phenotypes like preterm birth.

Main Methods:

  • Developed Modified PC-HC (MPC-HC), a BN structural learning algorithm using statistical testing and search-and-score techniques.
  • Applied MPC-HC to estimate BNs in extremely preterm (EP) young adults and full-term controls using MRI and cognitive data.
  • Validated MPC-HC through bootstrapping for structure confidence and benchmark BN recovery, comparing its performance against PC, MMHC, and HC algorithms.

Main Results:

  • MPC-HC achieved higher average prediction accuracy (72.5%) compared to PC (62.5%), MMHC (64.5%), and HC (71.5%).
  • MPC-HC outperformed PC, MMHC, and HC in reconstructing benchmark BN structures.
  • Sensitivity analysis indicated that MRI measurements significantly influence cognitive scores in EP individuals.

Conclusions:

  • The novel MPC-HC algorithm offers improved performance in BN structure learning and prediction.
  • BNs are valuable tools for modeling complex phenotypes, predicting outcomes, and understanding variable interdependencies.
  • Findings highlight the impact of brain structure on cognitive outcomes in extremely preterm individuals, consistent with existing literature.