Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults.
Abdur Raquib RidwanMohammad Rakeen NiazYingjuan WuXiaoxiao QiShengwei ZhangMarinos KontzialisCarles Javierre-PetitMahir Tazwarnull nullDavid A BennettYongyi YangKonstantinos ArfanakisPublished in: Human brain mapping (2021)
Τhe accuracy of template-based neuroimaging investigations depends on the template's image quality and representativeness of the individuals under study. Yet a thorough, quantitative investigation of how available standardized and study-specific T1-weighted templates perform in studies on older adults has not been conducted. The purpose of this work was to construct a high-quality standardized T1-weighted template specifically designed for the older adult brain, and systematically compare the new template to several other standardized and study-specific templates in terms of image quality, performance in spatial normalization of older adult data and detection of small inter-group morphometric differences, and representativeness of the older adult brain. The new template was constructed with state-of-the-art spatial normalization of high-quality data from 222 older adults. It was shown that the new template (a) exhibited high image sharpness, (b) provided higher inter-subject spatial normalization accuracy and (c) allowed detection of smaller inter-group morphometric differences compared to other standardized templates, (d) had similar performance to that of study-specific templates constructed with the same methodology, and (e) was highly representative of the older adult brain.
Keyphrases
- physical activity
- image quality
- molecularly imprinted
- resting state
- white matter
- computed tomography
- machine learning
- magnetic resonance
- contrast enhanced
- magnetic resonance imaging
- deep learning
- cerebral ischemia
- middle aged
- wastewater treatment
- big data
- functional connectivity
- network analysis
- artificial intelligence
- real time pcr
- cross sectional
- sensitive detection