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

Epileptogenic lesions in the Australian epilepsy project: A harmonized 3-T magnetic resonance imaging protocol and its diagnostic yield.

Epilepsia·2026
Same author

Global Socioeconomic Context and Brain Ageing in Epilepsy: an ENIGMA-Epilepsy study.

medRxiv : the preprint server for health sciences·2026
Same author

Evaluating the task-specificity model of verbal memory: Regional volumetric analyses in temporal lobe epilepsy with hippocampal sclerosis.

Epilepsia open·2026
Same author

Structural brain imaging biomarkers for predicting seizure recurrence after a first unprovoked seizure.

Epilepsia open·2026
Same author

Cognition in adults with bottom-of-sulcus dysplasia and the consequences of focal resection.

Epilepsia·2026
Same author

Structural brain differences in professional Australian rules footballers following mild traumatic brain injury: When head size matters.

Frontiers in neurology·2026

Related Experiment Video

Updated: May 20, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
09:57

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

Sample size estimates for well-powered cross-sectional cortical thickness studies.

Heath R Pardoe1, David F Abbott, Graeme D Jackson

  • 1Brain Research Institute, Florey Neuroscience Institutes, Melbourne Brain Centre, Austin Hospital, Heidelberg, Victoria, Australia; Department of Medicine, The University of Melbourne, Victoria, Australia.

Human Brain Mapping
|July 19, 2012
PubMed
Summary

Researchers can now determine optimal sample sizes for cortical thickness studies using a new predictive model. This tool helps ensure well-powered neuroimaging analyses by estimating subject numbers based on processing parameters.

Keywords:
MRIcortical thicknessmorphometryneuroimagingpower analysisstudy design

More Related Videos

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
11:25

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Published on: May 14, 2009

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
07:55

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Published on: July 9, 2017

Related Experiment Videos

Last Updated: May 20, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
09:57

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index

Published on: January 2, 2012

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
11:25

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Published on: May 14, 2009

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
07:55

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Published on: July 9, 2017

Area of Science:

  • Neuroimaging
  • Brain anatomy
  • Statistical analysis

Background:

  • Cortical thickness mapping is crucial for analyzing neuroanatomical differences.
  • Power analysis methods are applied to optimize cross-sectional study designs.

Purpose of the Study:

  • To develop a predictive model for calculating required sample sizes in cortical thickness studies.
  • To ensure well-powered neuroimaging research by optimizing subject recruitment.

Main Methods:

  • Utilized 0.9-mm isotropic T1-weighted 3D MPRAGE MRI scans from 98 controls.
  • Applied power analyses and genetic programming to derive a sample size model.
  • Validated the model on Alzheimer's Disease Neuroimaging Initiative control datasets.

Main Results:

  • Approximately 50 subjects per group are needed for 0.25-mm thickness differences; <10 for 1-mm differences.
  • The model accurately predicted sample sizes (2-6% error) on independent datasets.
  • Site-specific parameter fitting reduced estimation error to <2%.

Conclusions:

  • A validated model enables researchers to calculate necessary subject numbers for well-powered cortical thickness analyses.
  • This tool simplifies sample size determination for neuroimaging studies.