Name
Abstract | ||
|---|---|---|
Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s40708-016-0060-4 | Brain Informatics |
Keywords | Field | DocType |
Cortical structure,Cortical thickness,Fractal dimensionality,Gyrification,Reliability,Structural complexity,Subcortical | Gyrification,Computer science,Fractal,Brain morphometry,Curse of dimensionality,Artificial intelligence,Health informatics,Machine learning | Journal |
Volume | Issue | ISSN |
4 | 2 | 2198-4026 |
Citations | PageRank | References |
2 | 0.36 | 33 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher R. Madan | 1 | 31 | 4.93 |
| Elizabeth A. Kensinger | 2 | 50 | 8.33 |