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A brain-based general measure of attention.
Yoo, Kwangsun; Rosenberg, Monica D; Kwon, Young Hye; Lin, Qi; Avery, Emily W; Sheinost, Dustin; Constable, R Todd; Chun, Marvin M.
Afiliación
  • Yoo K; Department of Psychology, Yale University, New Haven, CT, USA. kwangsun.yoo@yale.edu.
  • Rosenberg MD; Department of Psychology, Yale University, New Haven, CT, USA.
  • Kwon YH; Department of Psychology, University of Chicago, Chicago, IL, USA.
  • Lin Q; Department of Psychology, Yale University, New Haven, CT, USA.
  • Avery EW; Department of Psychology, Yale University, New Haven, CT, USA.
  • Sheinost D; Department of Psychology, Yale University, New Haven, CT, USA.
  • Constable RT; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
  • Chun MM; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
Nat Hum Behav ; 6(6): 782-795, 2022 06.
Article en En | MEDLINE | ID: mdl-35241793
Attention is central to many aspects of cognition, but there is no singular neural measure of a person's overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual's task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N = 495) of various attentional measures, suggesting broad utility for research and clinical applications.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Conectoma Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Nat Hum Behav Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Conectoma Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Nat Hum Behav Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido