Group-level comparison of brain connectivity networks

Background Functional connectivity (FC) studies are often performed to discern different patterns of brain connectivity networks between healthy and patient groups. Since many neuropsychiatric disorders are related to the change in these patterns, accurate modelling of FC data can provide useful inf...

Full description

Saved in:
Bibliographic Details
Published in:BMC medical research methodology 2022-10, Vol.22 (1), p.1-273, Article 273
Main Authors: Pourmotahari, Fatemeh, Doosti, Hassan, Borumandnia, Nasrin, Tabatabaei, Seyyed Mohammad, Alavi Majd, Hamid
Format: Article
Language:English
Subjects:
Alzheimer's disease
Brain
Brain research
Connectivity
Diagnosis
fMRI
Magnetic resonance imaging
Medical research
Mental illness
Methods
Nervous system diseases
Neural circuitry
Optimization techniques
Probability
Statistical power
Subject heterogeneity
Type I error rate
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background Functional connectivity (FC) studies are often performed to discern different patterns of brain connectivity networks between healthy and patient groups. Since many neuropsychiatric disorders are related to the change in these patterns, accurate modelling of FC data can provide useful information about disease pathologies. However, analysing functional connectivity data faces several challenges, including the correlations of the connectivity edges associated with network topological characteristics, the large number of parameters in the covariance matrix, and taking into account the heterogeneity across subjects. Methods This study provides a new statistical approach to compare the FC networks between subgroups that consider the network topological structure of brain regions and subject heterogeneity. Results The power based on the heterogeneity structure of identity scaled in a sample size of 25 exhibited values greater than 0.90 without influencing the degree of correlation, heterogeneity, and the number of regions. This index had values above 0.80 in the small sample size and high correlation. In most scenarios, the type I error was close to 0.05. Moreover, the application of this model on real data related to autism was also investigated, which indicated no significant difference in FC networks between healthy and patient individuals. Conclusions The results from simulation data indicated that the proposed model has high power and near-nominal type I error rates in most scenarios. Keywords: Connectivity, Subject heterogeneity, fMRI, Statistical power, Type I error rate
ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-022-01712-8