A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain

Funding Information: This work was supported by the Icelandic Centre for Research (RANNIS, https://en.rannis.is/) through grant 173942-051 (PI:Ellingsen). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study. The authors would like to thank Dr. Jerry Prince and Mr. Aaron Carass for providing pre-processed and manually delineated NPH data from Johns Hopkins University. Publisher Copyright: © 2022 Atlason et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Age-related changes in brain structure include atrophy of the brain parenchyma and white matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to atrophy or impaired cerebrospinal fluid (CSF) circulation. The co-occurrence of these changes in neurodegenerative diseases and in aging brains often requires investigators to take both into account when studying the brain, however, automated segmentation of enlarged ventricles and white matter hyperintensities (WMHs) can be a challenging task. Here, we present a hybrid multi-atlas segmentation and convolutional autoencoder approach for joint ventricle parcellation and WMH segmentation from magnetic resonance images (MRIs). Our fully automated approach uses a convolutional autoencoder to generate a standardized image of grey matter, white matter, CSF, and WMHs, which, in conjunction with labels generated by a multi-atlas segmentation approach, is then fed into a convolutional neural network to parcellate the ventricular system. Hence, our approach does not depend on manually delineated training data for new data sets. The segmentation pipeline was validated on both healthy elderly subjects and subjects with normal pressure hydrocephalus using ground truth manual labels and compared with state-of-the-art segmentation methods. We then applied the method to a cohort of 2401 elderly brains to investigate associations of ventricle volume and WMH load with various demographics and clinical biomarkers, using a multiple regression model. Our results indicate that the ventricle volume and WMH load are both highly variable in a cohort of elderly subjects and there is an independent association between the two, which highlights the importance of taking both the possibility of enlarged ventricles and WMHs into account when studying the aging brain.Peer reviewe

Brain age prediction using deep learning uncovers associated sequence variants

Publisher's version (útgefin grein).Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: N= 12378 , replication set: N= 4456) yielded two sequence variants, rs1452628-T (β= − 0.08 , P= 1.15 × 10 − 9) and rs2435204-G (β= 0.102 , P= 9.73 × 1 0 − 12). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).This research has been conducted using the UK Biobank Resource under Application Number 24898. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreements no. 115008 (NEWMEDS) and no. 115300 (EUAIMS), of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union’s Seventh Framework Programme (EU-FP7/2007-2013). The financial support from the European Commission to the NeuroPain project (FP7#HEALTH-2013-602891-2) is acknowledged. The authors are grateful to the participants, and we thank the research nurses and staff at the Recruitment centre (Þjónustumiðstöð rannsóknarverkefna).Peer Reviewe