RESUMEN
BACKGROUND: Recognition memory is an essential ability for functioning in everyday life. Establishing robust brain networks linked to recognition memory performance can help to understand the neural basis of recognition memory itself and the interindividual differences in recognition memory performance. METHODS: We analysed behavioural and whole-brain fMRI data from 1'410 healthy young adults during the testing phase of a picture-recognition task. Using independent component analysis (ICA), we decomposed the fMRI contrast for previously seen vs. new (old-new) pictures into networks of brain activity. This was done in two independent samples (training sample: N = 645, replication sample: N = 665). Next, we investigated the relationship between the identified brain networks and interindividual differences in recognition memory performance by conducting a prediction analysis. We estimated the prediction accuracy in a third independent sample (test sample: N = 100). RESULTS: We identified 12 robust and replicable brain networks using two independent samples. Based on the activity of those networks we could successfully estimate interindividual differences in recognition memory performance with high accuracy in a third independent sample (r = 0.5, p = 1.29 × 10-07). CONCLUSION: Given the robustness of the ICA decomposition as well as the high prediction estimate, the identified brain networks may be considered as potential biomarkers of recognition memory performance in healthy young adults and can be further investigated in the context of health and disease.
Asunto(s)
Mapeo Encefálico , Encéfalo/fisiología , Red Nerviosa/fisiología , Reconocimiento Visual de Modelos/fisiología , Reconocimiento en Psicología/fisiología , Adulto , Encéfalo/diagnóstico por imagen , Femenino , Humanos , Imagen por Resonancia Magnética , Masculino , Red Nerviosa/diagnóstico por imagen , Adulto JovenRESUMEN
Depressive symptoms exist on a continuum, the far end of which is found in depressive disorders. Utilizing the continuous spectrum of depressive symptoms may therefore contribute to the understanding of the biological underpinnings of depression. Gene set enrichment analysis (GSEA) is an important tool for the identification of gene groups linked to complex traits, and was applied in the present study on genome-wide association study (GWAS) data of depression scores and their brain-level structural correlates in healthy young individuals. On symptom level (i.e. depression scores), robust enrichment was identified for two gene sets: NCAM1 Interactions and Collagen Formation. Depression scores were also associated with decreased fractional anisotropy (FA) - a brain white matter property - within the forceps minor and the left superior temporal longitudinal fasciculus. Within each of these tracts, mean FA value of depression score-associated voxels was used as a phenotype in a subsequent GSEA. The NCAM1 Interactions gene set was significantly enriched in these tracts. By linking the NCAM1 Interactions gene set to depression scores and their structural brain correlates in healthy participants, the current study contributes to the understanding of the molecular underpinnings of depressive symptomatology.