Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners

Autor/inn/en	Simpson-Kent, Ivan L.; Fried, Eiko I.; Akarca, Danyal; Mareva, Silvana; Bullmore, Edward T.; Kievit, Rogier A.
Titel	Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners
Quelle	In: Journal of Intelligence, 9 (2021), Artikel 32 (21 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Akarca, Danyal) ORCID (Mareva, Silvana) ORCID (Kievit, Rogier A.)
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2079-3200
Schlagwörter	Network Analysis; Brain Hemisphere Functions; Intelligence; Schemata (Cognition); Correlation; Cognitive Ability; Attribution Theory; Behavior Patterns; Learning Problems; Diagnostic Tests; Referral; Elementary School Students; Secondary School Students; Foreign Countries; Reading Difficulties; Attention Control; Mathematics; Language Skills; Memory; Models; United Kingdom (Cambridge) + Suchen Sie Ihr Suchwort? Netzplantechnik; Intelligenz; Klugheit; Cognition; Schema; Kognition; Korrelation; Denkfähigkeit; Lernproblem; Diagnostic test; Diagnostischer Test; Sekundarschüler; Ausland; Reading difficulty; Leseschwierigkeit; Aufmerksamkeitstest; Mathematik; Language skill; Sprachkompetenz; Gedächtnis; Analogiemodell
Abstract	Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies. [The CALM Team co-authored this article.] (As Provided).
Anmerkungen	MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: indexing@mdpi.com; e-mail: jintelligence@mdpi.com; Web site: https://www.mdpi.com/journal/jintelligence
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01