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Embrace the Complexity: Agnostic Evaluation of Children’s Neuropsychological Performances Reveals Hidden Neurodevelopment Patterns

Elisa Cainelli
•
Luca Vedovelli
•
Dario Gregori
altro
Patrizia Bisiacchi
2022
  • journal article

Periodico
CHILDREN
Abstract
The most common adverse pre/perinatal events have a great impact on neurodevelopment, with avalanche effects on academic performance, occupational status, and quality of life. Although the injury process starts early, the effects may become evident much later, when life starts to pose more challenging demands. In the present work, we want to address the impact of early insults from an evolutionary perspective by performing unsupervised cluster analysis. We fed all available data, but not the group identification, into the algorithm for 114 children aged 5–10 years, with different adverse medical conditions: healthy (n = 30), premature (n = 28), neonatal hypoxic-ischemic encephalopathy (n = 28), and congenital heart disease (n = 28). We measured general intelligence and many neuropsychological domains (language, attention, memory, executive functions, and social skills). We found three emerging groups that identify children with multiple impairments (cluster 3), children with variable neuropsychological profiles but in the normal range (cluster 2), and children with adequate profiles and good performance in IQ and executive functions (cluster 1). Our analysis divided our patients by severity levels rather than by identifying specific neuropsychological phenotypes, suggesting different developmental trajectories that are characterized by good resilience to early stressful events with adequate development or by pervasive vulnerability to neurodevelopmental disorders.
DOI
10.3390/children9060775
WOS
WOS:000818248700001
Archivio
http://hdl.handle.net/11390/1231116
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85131522894
https://ricerca.unityfvg.it/handle/11390/1231116
Diritti
open access
Soggetti
  • cluster analysi

  • cognitive

  • congenital heart dise...

  • hypoxic-ischemic ence...

  • machine learning

  • preterm

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