Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

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

Sort by

Same author

Insulin resistance and poorer treatment outcomes in depression: evidence from UK Biobank primary care data.

The British journal of psychiatry : the journal of mental science·2025

Same author

Shared genetics and causal relationship between sociability and the brain's default mode network.

Psychological medicine·2025

Same author

Cell type-specific methylome-wide association studies of childhood ADHD symptoms.

medRxiv : the preprint server for health sciences·2025

Same author

Local patterns of genetic sharing between neuropsychiatric and insulin resistance-related conditions.

Translational psychiatry·2025

Same author

Multiscale Heterogeneity of White Matter Morphometry in Psychiatric Disorders.

Biological psychiatry. Cognitive neuroscience and neuroimaging·2025

Same author

Deciphering the role of disturbed insulin signalling in brain disorders.

Neuroscience and biobehavioral reviews·2025

Search research articles

Related Experiment Video

Updated: Jul 2, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model

Ruiyang Ge¹, Yuetong Yu¹, Yi Xuan Qi¹

¹Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.

The Lancet. Digital Health

|February 23, 2024

Summary

This study benchmarks normative modeling algorithms for brain morphometric data, identifying multivariate fractional polynomial regression (MFPR) as optimal. The MFPR model accurately tracks age-related brain changes across the lifespan.

More Related Videos

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

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

Published on: June 9, 2018

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

Related Experiment Videos

Last Updated: Jul 2, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

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

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

Published on: June 9, 2018

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

Area of Science:

Neuroimaging
Computational Neuroscience
Biostatistics

Background:

Normative models are crucial for interpreting neuroimaging data in research and clinical settings.
Robustness and systematic comparison of modeling algorithms are essential but have been lacking.
Previous studies have not comprehensively evaluated algorithms for brain morphometric normative modeling.

Purpose of the Study:

To identify the optimal approach for normative modeling of brain morphometric data.
To systematically benchmark different algorithms and parameters for accuracy and performance.
To establish a reliable framework for assessing neuroanatomical variations.

Main Methods:

Comparative evaluation of eight algorithms using regional morphometric data from 37,407 healthy individuals.
Benchmarking included various covariate combinations (image acquisition, quality, software versions, global measures, longitudinal stability).
Multivariate fractional polynomial regression (MFPR) was identified as the preferred algorithm, optimized with non-linear age effects and global measures as covariates.

Main Results:

Multivariate fractional polynomial regression (MFPR) demonstrated superior accuracy across the lifespan and within age groups.
MFPR models exhibited excellent longitudinal stability over a 2-year period.
Optimal model performance was achieved with sample sizes exceeding 3000 participants.

Conclusions:

The optimized MFPR model provides a robust framework for normative modeling of brain morphometric data.
This approach can inform the biological and behavioral implications of deviations from typical neuroanatomical development.
The developed model and scripts are available to support future research and clinical study designs.