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Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort.
Argiris, Georgette; Stern, Yaakov; Lee, Seonjoo; Ryu, Hyunnam; Habeck, Christian.
Afiliación
  • Argiris G; Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States.
  • Stern Y; Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States.
  • Lee S; Mental Health Data Science, New York State Psychiatric Institute, New York, NY, United States; Department of Biostatistics, Mailman School of Public Health, New York, NY, United States; Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.
  • Ryu H; Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States; Taub Institute, Columbia University, New York, NY, United States; Mental Health Data Science, New York State Psychiatric Institute
  • Habeck C; Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States; Taub Institute, Columbia University, New York, NY, United States. Electronic address: gla2116@cumc.columbia.edu.
Neuroimage ; 277: 120237, 2023 08 15.
Article en En | MEDLINE | ID: mdl-37343735
Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Cognición Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Aged80 / Humans / Middle aged Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Cognición Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Aged80 / Humans / Middle aged Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos