Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which of these imaging sequences are essential for accurate detection. In this study we aimed to find the optimal combination of magnetic resonance imaging (MRI) sequences for deep learning-based detection of enlarged perivascular spaces (ePVS). To this end, we implemented an effective light-weight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from susceptibility weighted imaging (SWI), fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1w) and T2-weighted (T2w) MRI sequences. We conclude that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network could make insignificant improvements in accuracy.

Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning

Neuroimaging biomarkers that distinguish between typical brain aging and Alzheimer's disease (AD) are valuable for determining how much each contributes to cognitive decline. Machine learning models can derive multi-variate brain change patterns related to the two processes, including the SPARE-AD (Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease) and SPARE-BA (of Brain Aging) investigated herein. However, substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology toward disentangling the two. T1-weighted MRI images of 4,054 participants (48-95 years) with AD, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. First, a subset of AD patients and CN adults were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus AD). Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2: amyloid-positive (A+) AD continuum group (consisting of A+AD, A+MCI, and A+ and tau-positive CN individuals) and amyloid-negative (A-) CN group. Finally, the combined group of the AD continuum and A-/CN individuals was used to train SPARE-BA3, with the intention to estimate brain age regardless of AD-related brain changes. Disentangled SPARE models derived brain patterns that were more specific to the two types of the brain changes. Correlation between the SPARE-BA and SPARE-AD was significantly reduced. Correlation of disentangled SPARE-AD was non-inferior to the molecular measurements and to the number of APOE4 alleles, but was less to AD-related psychometric test scores, suggesting contribution of advanced brain aging to these scores.

Disentangling brain heterogeneity via semi-supervised deep-learning and MRI: dimensional representations of Alzheimer's Disease

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a novel semi-supervised deep-clustering method, which dissects neuroanatomical heterogeneity, enabling identification of disease subtypes via their imaging signatures relative to controls. When applied to MRIs (2 studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified 4 neurodegenerative patterns/axes: P1, normal anatomy and highest cognitive performance; P2, mild/diffuse atrophy and more prominent executive dysfunction; P3, focal medial temporal atrophy and relatively greater memory impairment; P4, advanced neurodegeneration. Further application to longitudinal data revealed two distinct progression pathways: P1$\rightarrow$P2$\rightarrow$P4 and P1$\rightarrow$P3$\rightarrow$P4. Baseline expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered better yet complementary performance in predicting clinical progression, compared to amyloid/tau. These deep-learning derived biomarkers offer promise for precision diagnostics and targeted clinical trial recruitment.

Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging

Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine. However, these methods have yet to see widespread clinical adoption, in part due to limited generalization performance across various imaging devices, acquisition protocols, and patient populations. In this work, we propose a new paradigm in which data from a diverse range of acquisition conditions are "harmonized" to a common reference domain, where accurate model learning and prediction can take place. By learning an unsupervised image to image canonical mapping from diverse datasets to a reference domain using generative deep learning models, we aim to reduce confounding data variation while preserving semantic information, thereby rendering the learning task easier in the reference domain. We test this approach on two example problems, namely MRI-based brain age prediction and classification of schizophrenia, leveraging pooled cohorts of neuroimaging MRI data spanning 9 sites and 9701 subjects. Our results indicate a substantial improvement in these tasks in out-of-sample data, even when training is restricted to a single site.

DEEPMIR: A DEEP convolutional neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Background: Cerebral microbleeds (CMBs) and non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Recent advances using quantitative susceptibility mapping (QSM) make it possible to differentiate iron content from mineralization in-vivo using magnetic resonance imaging (MRI). However, automated detection of such lesions is still challenging, making quantification in large cohort bases studies rather limited. Purpose: Development of a fully automated method using deep learning for detecting CMBs and basal ganglia iron deposits using multimodal MRI. Materials and Methods: We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia, which resulted in defining the gold standard. This gold standard was then used to train a deep convolution neural network (CNN) model. Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI which are used in cohort-based studies. The detection performance was then evaluated using the cross-validation principle in order to ensure generalization of the results. Results: With multi-class CNN models, we achieved an average sensitivity and precision of about 0.8 and 0.6, respectively for detecting CMBs. The same framework detected non-hemorrhage iron deposits reaching an average sensitivity and precision of about 0.8. Conclusions: Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data such as QSM can improve the detection of CMB and non-hemorrhage iron deposits.