Neurocognitive outcomes and neural mechanisms in children with traumatic brain injury Towards personalized prognosis

Open Access
Authors
  • C.C. Kooper
Supervisors
  • J. Oosterlaan
  • H. Bruining
Cosupervisors
  • M. Königs
  • M. Engelen
Award date 28-01-2026
ISBN
  • 9789465228365
Number of pages 285
Organisations
  • Faculty of Medicine (AMC-UvA)
Abstract
Traumatic brain injury (TBI) is the leading cause of disability in children and young adults worldwide, with highly heterogeneous outcomes affecting daily life, including neurocognitive, behavioral, and academic functioning. This thesis aims to improve the assessment and understanding of neurocognitive outcomes and underlying neural mechanisms in children with TBI, and to move towards personalized prognosis.
In Part I, comprehensive computerized neurocognitive assessments revealed that even after mild TBI, children are at risk for long-term deficits in domains such as information processing stability, and longitudinal school performance data revealed deficits in technical reading up to two years post-injury. Network analysis showed that mild to severe TBI disrupts the global organization of the neurocognitive network, which was related to intelligence and behavioral outcomes. Furthermore, cluster analysis identified distinct subgroups with diverging neurocognitive outcome profiles, highlighting the heterogeneity of TBI sequelae.
Part II investigated neural mechanisms of impairment using advanced neuroimaging techniques, including quantitative EEG and resting-state fMRI. The findings showed widespread disruptions in brain network dynamics and organization following TBI, which were associated with neurocognitive and behavioral outcomes, and provided additional information beyond conventional clinical characteristics.
Part III systematically reviewed and proposes prediction models for neurocognitive outcome, integrating demographic, premorbid, clinical, and neuroimaging data. Findings underscore the limited predictive value of injury-related characteristics alone and emphasize the need for multimodal, machine learning-based approaches to achieve individualized prognosis.
Overall, this thesis contributes to a better understanding of the complex interplay between injury, neurocognitive functioning, and daily life outcomes in children with TBI, and highlights the importance of early identification and tailored follow-up to optimize recovery and development.
Document type PhD thesis
Language English
Downloads
Thesis (complete) (Embargo up to 2028-01-28)
Chapter 5: Detrended fluctuation analysis as a potential quantitative EEG marker of paediatric traumatic brain injury (Embargo up to 2028-01-28)
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