MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide

Topic: Brain imaging, connectivity and assessment

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet.

Key findings

• Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases.
• Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing.
• Our results offer some experimental evidence against commonly pursued tightly-fitting models.
• We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested.
• Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet.

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Study details

• Participants2,739
• Evidence strengthModerate

Read the original

• DOI: 10.1093/brain/awaa160
• PubMed: 32591831
• Original source →

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@article{bashyam2020-32591831,
  author  = {Bashyam, V. M. and Erus, G. and Doshi, J. and Habes, M. and Nasrallah, I. and Truelove-Hill, M. and Srinivasan, D. and Mamourian, L. and others},
  title   = {{MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide}},
  journal = {Brain : a journal of neurology},
  year    = {2020},
  doi     = {10.1093/brain/awaa160},
  pmid    = {32591831},
  url     = {https://doi.org/10.1093/brain/awaa160}
}

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