Abstract
BACKGROUND AND PURPOSE: Automated cortical thickness (CT) measurements are often used to assess gray matter changes in the healthy and diseased human brain. The FreeSurfer software is frequently applied for this type of analysis. The computational anatomy toolbox (CAT12) for SPM, which offers a fast and easy-to-use alternative approach, was recently made available. METHODS: In this study, we compared region of interest (ROI)-wise CT estimations of the surface-based FreeSurfer 6 (FS6) software and the volume-based CAT12 toolbox for SPM using 44 elderly healthy female control subjects (HC). In addition, these 44 HCs from the cross-sectional analysis and 34 age- and sex-matched patients with Alzheimer's disease (AD) were used to assess the potential of detecting group differences for each method. Finally, a test-retest analysis was conducted using 19 HC subjects. All data were taken from the OASIS database and MRI scans were recorded at 1.5 Tesla. RESULTS: A strong correlation was observed between both methods in terms of ROI mean CT estimates (R 2 =.83). However, CAT12 delivered significantly higher CT estimations in 32 of the 34 ROIs, indicating a systematic difference between both approaches. Furthermore, both methods were able to reliably detect atrophic brain areas in AD subjects, with the highest decreases in temporal areas. Finally, FS6 as well as CAT12 showed excellent test-retest variability scores. CONCLUSION: Although CT estimations were systematically higher for CAT12, this study provides evidence that this new toolbox delivers accurate and robust CT estimates and can be considered a fast and reliable alternative to FreeSurfer.
Original language | English |
---|---|
Pages (from-to) | 515-523 |
Number of pages | 9 |
Journal | Journal of Neuroimaging |
Volume | 28 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1 Sept 2018 |
Externally published | Yes |
Keywords
- Alzheimer's disease
- CAT12
- Cortical thickness
- FreeSurfer
- SPM
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Clinical Neurology