DeepBrainNet

DeepBrainNet predicts brain age from MRI scans using a convolutional neural network to provide a quantitative biomarker of brain health across diverse scanners, ages, ethnicities, and geographic locations.

Key Features:

• Convolutional neural network architecture: Implements a deep convolutional neural network for brain-age estimation from MRI scans.
• Training dataset: Trained on a heterogeneous dataset of 11,729 MRI scans drawn from multiple studies, scanners, ages, ethnicities, and geographic locations.
• No specialized image preparation required: Generates robust brain-age estimates without requiring specialized image preparation or processing steps.
• Validation and replication: Performance validated via rigorous cross-validation and a separate replication cohort of 2,739 individuals.
• Moderately fitting brain-age models: Produces moderately fitting models shown to be effective at distinguishing typical aging patterns from neuropathological conditions, in contrast to tightly- or loosely-fitting models.
• Transfer learning for classification: Architecture facilitates transfer learning to enable classification of brain diseases such as schizophrenia and Alzheimer's disease.
• Comparison to generic pretraining: Demonstrated more accurate disease classification relative to approaches that rely on patient-versus-control datasets or generic imaging databases such as ImageNet.
• Domain-specific pretraining: Uses domain-specific deep networks to reduce the necessity for application-specific adaptations and improve generalizability.
• Generalizability across systems and populations: Designed to address reproducibility and generalizability limitations across different MRI systems and populations.

Scientific Applications:

• Brain-age biomarker: Quantifies brain age as a biomarker of brain health across the lifespan.
• Distinguishing neuropathological aging: Differentiates disease-associated aging patterns from typical aging.
• Disease classification: Supports classification of schizophrenia and Alzheimer's disease via transfer learning.
• Multisite and population studies: Applicable to multisite cohorts and diverse population studies to improve reproducibility and generalizability of neuroimaging biomarkers.

Methodology:

Trains a convolutional neural network on 11,729 MRI scans, validates performance with rigorous cross-validation and a separate replication cohort of 2,739 individuals, produces brain-age estimates without specialized image preparation, and applies transfer learning for disease classification.