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Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor

Geneviève Richard1,2,3, Knut Kolskår1,2,3, Anne-Marthe Sanders1,2,3

  • 1NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

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Brain age prediction using multimodal imaging reveals tissue-specific differences. Deviations from predicted brain age correlate with cognitive performance, offering insights into brain health and disorders.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Multimodal brain imaging offers sensitive brain architecture and integrity measures.
  • High-dimensional imaging data presents analytical challenges.
  • Multivariate age prediction simplifies data into a single, biologically relevant measure.

Purpose of the Study:

  • To explore differential brain age models using tissue-specific classifiers.
  • To disentangle independent sources of heterogeneity in brain biology.
  • To assess the cognitive sensitivity of distinct brain tissue classes.

Main Methods:

  • Trained machine learning models to estimate brain age using FreeSurfer morphometry and diffusion tensor imaging (DTI) indices.
  • Utilized data from 612 healthy controls (aged 18-87 years).
  • Validated 11 models in an independent sample of 265 individuals (aged 20-88 years).

Main Results:

  • Comprehensive morphometry and white matter microstructure models showed high accuracy in age prediction (r=0.83 and r=0.79, respectively).
  • Deviations from chronological age were associated with cognitive performance on tests like spatial Stroop and symbol coding.
  • Individuals with an over-estimated brain age exhibited poorer cognitive performance.

Conclusions:

  • Tissue-specific brain age models provide sensitive measures of brain integrity.
  • These models have implications for studying various brain disorders.
  • Differential brain age trajectories and their cognitive correlates are characterized.