Deep learning and multiplex networks for accurate modeling of brain age

Nicola Amoroso, Marianna La Rocca, Loredana Bellantuono, Domenico Diacono, Annarita Fanizzi, Eufemia Lella, Angela Lombardi, Tommaso Maggipinto, Alfonso Monaco, Sabina Tangaro, Roberto Bellotti

Research output: Contribution to journalArticlepeer-review


Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.

Original languageEnglish
Article number115
JournalFrontiers in Aging Neuroscience
Issue number5
Publication statusPublished - Jan 1 2019


  • Age prediction
  • Aging
  • Brain
  • Deep learning
  • Lifespan
  • Machine learning
  • Multiplex networks
  • Structural MRI

ASJC Scopus subject areas

  • Ageing
  • Cognitive Neuroscience


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