The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks.

In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on 'brain age' prediction - a common benchmarking task of predicting a subject's chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs.

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs.

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks.

In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on 'brain age' prediction - a common benchmarking task of predicting a subject's chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.

Effects of ApoE4 and ApoE2 genotypes on subcortical magnetic susceptibility and microstructure in 27,535 participants from the UK Biobank.

Disrupted iron homeostasis is associated with several neurodegenerative diseases, including Alzheimer's disease (AD), and may be partially modulated by genetic risk factors. Here we evaluated whether subcortical iron deposition is associated with ApoE genotype, which substantially affects risk for late-onset AD. We evaluated differences in subcortical quantitative susceptibility mapping (QSM), a type of MRI sensitive to cerebral iron deposition, between either ApoE4 (E3E4+E4E4) or ApoE2 (E2E3+E2E2) carriers and E3 homozygotes (E3E3) in 27,535 participants from the UK Biobank (age: 45-82 years). We found that ApoE4 carriers had higher hippocampal (d=0.036; p=0.012) and amygdalar (d=0.035; p=0.013) magnetic susceptibility, particularly individuals aged 65 years or older, while those carrying ApoE2 (which protects against AD) had higher QSM only in the hippocampus (d=0.05; p=0.006), particularly those under age 65. Secondary diffusion MRI microstructural associations in these regions revealed greater diffusivity and less diffusion restriction in E4 carriers, however no differences were detected in E2 carriers. Disease risk conferred by ApoE4 may be linked with higher subcortical iron burden in conjunction with inflammation or neuronal loss in aging individuals, while ApoE2 associations may not necessarily reflect unhealthy iron deposits earlier in life.

REGION SPECIFIC AUTOMATIC QUALITY ASSURANCE FOR MRI-DERIVED CORTICAL SEGMENTATIONS.

Quality control (QC) is a vital step for all scientific data analyses and is critically important in the biomedical sciences. Image segmentation is a common task in medical image analysis, and automated tools to segment many regions from human brain MRIs are now well established. However, these methods do not always give anatomically correct labels. Traditional methods for QC tend to reject statistical outliers, which may not necessarily be inaccurate. Here, we make use of a large database of over 12,000 brain images that contain 68 parcellations of the human cortex, each of which was assessed for anatomical accuracy by a human rater. We trained three machine learning models to determine if a region was anatomically accurate (as 'pass', or 'fail') and tested the performance on an independent dataset. We found good performance for the majority of labeled regions. This work will facilitate more anatomically accurate large-scale multi-site research.