Characterizing different cognitive and neurobiological profiles in a community sample of children using a non-parametric approach: An fMRI study

Victoria Khalfin Fekson, Tomer Michaeli, Keri S. Rosch, Bradley L. Schlaggar, Tzipi Horowitz-Kraus

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Executive Functions (EF) is an umbrella term for a set of mental processes geared towards goal-directed behavior supporting academic skills such as reading abilities. One of the brain's functional networks implicated in EF is the Default Mode Network (DMN). The current study uses measures of inhibitory control, a main sub-function of EF, to create cognitive and neurobiological “inhibitory control profiles” and relate them to reading abilities in a large sample (N = 5055) of adolescents aged 9–10 from the Adolescent Brain Cognitive Development (ABCD) study. Using a Latent Profile Analysis (LPA) approach, data related to inhibitory control was divided into four inhibition classes. For each class, functional connectivity within the DMN was calculated from resting-state data, using a non-parametric algorithm for detecting group similarities. These inhibitory control profiles were then related to reading abilities. The four inhibitory control groups showed significantly different reading abilities, with neurobiologically different DMN segregation profiles for each class versus controls. The current study demonstrates that a community sample of children is not entirely homogeneous and is composed of different subgroups that can be differentiated both behaviorally/cognitively and neurobiologically, by focusing on inhibitory control and the DMN. Educational implications relating these results to reading abilities are noted.

Original languageEnglish
Article number101198
JournalDevelopmental Cognitive Neuroscience
Volume60
DOIs
StatePublished - Apr 2023

Keywords

  • Default Mode Network
  • Inhibitory control
  • Latent Profiles
  • Nonparametric approach
  • Reading abilities

ASJC Scopus subject areas

  • Cognitive Neuroscience

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