ArticleNeuroImage. Clinical2019
Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Genetic architecture of hippocampus subfields volumes in Alzheimer's disease.CNS neuroscience & therapeutics · 2024Article
- YAEL: Your Advanced Electrode Localizer.eNeuro · 2023Article
- Rapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging.Human brain mapping · 2022Article
- A Baboon Brain Atlas for Magnetic Resonance Imaging and Positron Emission Tomography Image Analysis.Frontiers in neuroanatomy · 2021Article
- Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma.Frontiers in neuroscience · 2020Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
objectiveTo compare the performance of different methods for determining hippocampal atrophy rates using longitudinal MRI scans in aging and Alzheimer's disease (AD).
backgroundQuantifying hippocampal atrophy caused by neurodegenerative diseases is important to follow the course of the disease. In dementia, the efficacy of new therapies can be partially assessed by measuring their effect on hippocampal atrophy. In radiotherapy, the quantification of radiation-induced hippocampal volume loss is of interest to quantify radiation damage. We evaluated plausibility, reproducibility and sensitivity of eight commonly used methods to determine hippocampal atrophy rates using test-retest scans. MATERIALS AND
methodsManual, FSL-FIRST, FreeSurfer, multi-atlas segmentation (MALF) and non-linear registration methods (Elastix, NiftyReg, ANTs and MIRTK) were used to determine hippocampal atrophy rates on longitudinal T1-weighted MRI from the ADNI database. Appropriate parameters for the non-linear registration methods were determined using a small training dataset (N = 16) in which two-year hippocampal atrophy was measured using test-retest scans of 8 subjects with low and 8 subjects with high atrophy rates. On a larger dataset of 20 controls, 40 mild cognitive impairment (MCI) and 20 AD patients, one-year hippocampal atrophy rates were measured. A repeated measures ANOVA analysis was performed to determine differences between controls, MCI and AD patients. For each method we calculated effect sizes and the required sample sizes to detect one-year volume change between controls and MCI (N
resultsExcept for MALF, all methods detected a significant group difference between CTRL and AD, but none could find a significant difference between the CTRL and MCI. FreeSurfer and MIRTK required the lowest sample sizes (FreeSurfer: N DISCUSSION AND
conclusionNon-linear registration methods were most consistent in determining hippocampal atrophy and because of their better reproducibility, methods, such as ANTs, Elastix and NiftyReg, are preferred for determining hippocampal atrophy rates on longitudinal MRI. Since performances of non-linear registration methods are well comparable, the preferred method would mostly depend on computational efficiency.
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