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GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning
Authors:
Vishnu M. Bashyam,
Guray Erus,
Junhao Wen,
Pratik Chaudhari,
Randa Melhem,
Sindhuja Govindarajan Tirumalai,
Gareth Harman,
Yong Fan,
Colin L. Masters,
Paul Maruff,
Sterling C. Johnson,
Jurgen Fripp,
Duygu Tosun,
John C. Morris,
Daniel S. Marcus,
Pamela LaMontagne,
Tammie Benzinger,
Susan R. Heckbert,
Mark Espeland,
Marilyn S. Albert,
Andrew J. Saykin,
Paul M. Thompson,
Timothy J. Hohman,
Susan M. Resnick,
R. Nick Bryan
, et al. (7 additional authors not shown)
Abstract:
Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs. We trained this modular architecture on 49,246 individuals across 11 cohorts, using 17 diverse classification and regression tasks spann…
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Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs. We trained this modular architecture on 49,246 individuals across 11 cohorts, using 17 diverse classification and regression tasks spanning cognition, clinical, diagnosis, demographics, and biomarkers. This yields aggregated, focused feature sets that capture rich, clinically- and biologically-relevant brain representations. We developed a sequential learning approach where tasks progressively build on previously learned representations. Through an analysis of 5,000 task sequences, we identified an optimal sequence length of six tasks and introduced a Donor Score metric to quantify each task's contribution to downstream performance. This analysis revealed five consistently strong donor tasks (Age, AD/MCI, MMSE, Hypertension, Hyperlipidemia) that formed the base of our sequential model. We demonstrated the utility of our learned representation, in various tasks beyond those included in the training set, to serve as the foundation for specialized secondary predictors. We further showed that using the learned feature representation can substantially increase the sample efficiency of secondary deep learning training tasks and models, as well as improve their accuracy.
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Submitted 12 August, 2026;
originally announced August 2026.
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Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI
Authors:
Tanweer Rashid,
Hangfan Liu,
Jeffrey B. Ware,
Karl Li,
Jose Rafael Romero,
Elyas Fadaee,
Ilya M. Nasrallah,
Saima Hilal,
R. Nick Bryan,
Timothy M. Hughes,
Christos Davatzikos,
Lenore Launer,
Sudha Seshadri,
Susan R. Heckbert,
Mohamad Habes
Abstract:
BACKGROUND AND PURPOSE: 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 dete…
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BACKGROUND AND PURPOSE: 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 imaging sequences are crucial for precise detection. This study introduces a novel deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. MATERIALS AND METHODS: We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The training data included 21 participants, which were randomly selected from the MESA cohort. Participants had ePVS 683 lesions on average. For T1w, T2w, and FLAIR images, the MESA study collected 3D isotropic MRI scans at six different sites with Siemens scanners. Our training data included participants from all these sites and all the scanner models, and the proposed model was applied to the whole brain instead of selective regions. RESULTS: The experimental results showed 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 had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading.
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Submitted 14 October, 2022; v1 submitted 27 September, 2022;
originally announced September 2022.
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DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI
Authors:
Tanweer Rashid,
Ahmed Abdulkadir,
Ilya M. Nasrallah,
Jeffrey B. Ware,
Hangfan Liu,
Pascal Spincemaille,
J. Rafael Romero,
R. Nick Bryan,
Susan R. Heckbert,
Mohamad Habes
Abstract:
Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessar…
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Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. 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 two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). 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. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.
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Submitted 7 June, 2021; v1 submitted 30 September, 2020;
originally announced October 2020.