Minimal Relationship between Local Gyrification and General Cognitive Ability in Humans. (9th February 2020)

• Record Type: Journal Article
• Authors: Mathias, Samuel R; Knowles, Emma E M; Mollon, Josephine; Rodrigue, Amanda; Koenis, Marinka M C; Alexander-Bloch, Aaron F; Winkler, Anderson M; Olvera, Rene L; Duggirala, Ravi; Göring, Harald H H; Curran, Joanne E; Fox, Peter T; Almasy, Laura; Blangero, John; Glahn, David C
• Is Part Of: Cerebral cortex. Volume 30:Number 6(2020)
• Page Start: 3439
• Page End: 3450
• Publication Date: 2020-02-09
• Subjects: cognition -- general intelligence -- gyrification; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhz319
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: Previous studies suggest that gyrification is associated with superior cognitive abilities in humans, but the strength of this relationship remains unclear. Here, in two samples of related individuals (total N = 2882), we calculated an index of local gyrification (LGI) at thousands of cortical surface points using structural brain images and an index of general cognitive ability ( g ) using performance on cognitive tests. Replicating previous studies, we found that phenotypic and genetic LGI– g correlations were positive and statistically significant in many cortical regions. However, all LGI– g correlations in both samples were extremely weak, regardless of whether they were significant or nonsignificant. For example, the median phenotypic LGI– g correlation was 0.05 in one sample and 0.10 in the other. These correlations were even weaker after adjusting for confounding neuroanatomical variables (intracranial volume and local cortical surface area). Furthermore, when all LGIs were considered together, at least 89% of the phenotypic variance of g remained unaccounted for. We conclude that the association between LGI and g is too weak to have profound implications for our understanding of the neurobiology of intelligence. This study highlights potential issues when focusing heavily on statistical significance rather than effect sizes in large-scale observational neuroimaging studies.