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Considerations on brain age predictions from repeatedly sampled data across time.

Max KorbmacherMeng-Yun WangRune EikelandRalph BuchertOle Andreas AndreassenThomas EspesethEsten LeonardsenLars Tjelta WestlyeIvan I MaximovKarsten Specht
Published in: Brain and behavior (2023)
The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.
Keyphrases
  • white matter
  • resting state
  • electronic health record
  • functional connectivity
  • risk assessment
  • computed tomography
  • artificial intelligence
  • deep learning
  • brain injury
  • data analysis