Using Structural Equation Modeling to Assess Functional Connectivity in the Brain
Power and Sample Size Considerations
Bibliographic Data
| ID | 20286010 |
|---|---|
| Authors | Georgios D Sideridis (0000-0002-4393-5995, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA), Panagiotis Simos (University of Crete, Herakleion, Greece), Panagiotis G Simos (0000-0002-0116-8564, University of Crete), Andrew C Papanicolaou (0000-0002-5543-1268, University of Tennessee Health Science Center), Andrew Papanicolaou (The University of Tennessee Health Science Center, Memphis, TN, USA), Jack Μ Fletcher (0000-0002-1181-6721, University of Houston) |
| Year | 2014 |
| Volume | 74 |
| Issue | 5 |
| Pages | 733-758 |
| Publication date | 2014-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164414525397 |
| PMID | 25435589 |
| PMCID | PMC4245025 |
| OpenAlex | W2028563875 |
| Language | EN |
| Citations received | 44 |
| References cited | 56 |
The present study assessed the impact of sample size on the power and fit of structural equation modeling applied to functional brain connectivity hypotheses. The data consisted of time-constrained minimum norm estimates of regional brain activity during performance of a reading task obtained with magnetoencephalography. Power analysis was first conducted for an autoregressive model with 5 latent variables (brain regions), each defined by 3 indicators (successive activity time bins). A series of simulations were then run by generating data from an existing pool of 51 typical readers (aged 7.5-12.5 years). Sample sizes ranged between 20 and 1,000 participants and for each sample size 1,000 replications were run. Results were evaluated using chi-square Type I errors, model convergence, mean RMSEA (root mean square error of approximation) values, confidence intervals of the RMSEA, structural path stability, and Δ-Fit index values. Results suggested that 70 to 80 participants were adequate to model relationships reflecting close to not so close fit as per MacCallum et al.’s recommendations. Sample sizes of 50 participants were associated with satisfactory fit. It is concluded that structural equation modeling is a viable methodology to model complex regional interdependencies in brain activation in pediatric populations
Autoregressive model · Confidence interval · Path analysis (statistics) · Sample (material) · Sample size determination · Statistics · Structural equation modeling · EEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies · Mathematics · Neural and Behavioral Psychology Studies · Psychology
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| Unique citing works | 44 |
|---|---|
| Citations per year | 4 |
| Citation span | 2015 - 2026 (12) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 42 |