Journal article
Investigating systematic bias in brain age estimation with application to post‐traumatic stress disorders
Human brain mapping, v 40(11), pp 3143-3152
01 Aug 2019
PMID: 30924225
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Brain age prediction using machine‐learning techniques has recently attracted growing attention, as it has the potential to serve as a biomarker for characterizing the typical brain development and neuropsychiatric disorders. Yet one long‐standing problem is that the predicted brain age is overestimated in younger subjects and underestimated in older. There is a plethora of claims as to the bias origins, both methodologically and in data itself. With a large neuroanatomical dataset (N = 2,026; 6–89 years of age) from multiple shared datasets, we show this bias is neither data‐dependent nor specific to particular method including deep neural network. We present an alternative account that offers a statistical explanation for the bias and describe a simple, yet efficient, method using general linear model to adjust the bias. We demonstrate the effectiveness of bias adjustment with a large multi‐modal neuroimaging data (N = 804; 8–21 years of age) for both healthy controls and post‐traumatic stress disorders patients obtained from the Philadelphia Neurodevelopmental Cohort.
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Details
- Title
- Investigating systematic bias in brain age estimation with application to post‐traumatic stress disorders
- Creators
- Hualou Liang - Drexel UniversityFengqing Zhang - Drexel UniversityXin Niu - Drexel University
- Publication Details
- Human brain mapping, v 40(11), pp 3143-3152
- Publisher
- John Wiley & Sons, Inc
- Number of pages
- 10
- Grant note
- Philadelphia Neurodevelopmental Cohort (RC2MH089924; RC2MH089983)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology); School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000472785400001
- Scopus ID
- 2-s2.0-85063667334
- Other Identifier
- 991019168593704721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Neuroimaging
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging