Your browser doesn't support javascript.
loading
Structural and Functional MRI Data Differentially Predict Chronological Age and Behavioral Memory Performance.
Soch, Joram; Richter, Anni; Kizilirmak, Jasmin M; Schütze, Hartmut; Feldhoff, Hannah; Fischer, Larissa; Knopf, Lea; Raschick, Matthias; Schult, Annika; Düzel, Emrah; Schott, Björn H.
Afiliação
  • Soch J; German Center for Neurodegenerative Diseases (DZNE), 37075 Göttingen, Germany joram.soch@dzne.de bschott@lin-magdeburg.de.
  • Richter A; Bernstein Center for Computational Neuroscience (BCCN), 10115 Berlin, Germany.
  • Kizilirmak JM; Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany.
  • Schütze H; German Center for Neurodegenerative Diseases (DZNE), 37075 Göttingen, Germany.
  • Feldhoff H; German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany.
  • Fischer L; Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany.
  • Knopf L; Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany.
  • Raschick M; Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany.
  • Schult A; Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany.
  • Düzel E; Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany.
  • Schott BH; Leibniz Institute for Neurobiology (LIN), 39118 Magdeburg, Germany.
eNeuro ; 9(6)2022.
Article em En | MEDLINE | ID: mdl-36376083
Human cognitive abilities decline with increasing chronological age, with decreased explicit memory performance being most strongly affected. However, some older adults show "successful aging," that is, relatively preserved cognitive ability in old age. One explanation for this could be higher brain-structural integrity in these individuals. Alternatively, the brain might recruit existing resources more efficiently or employ compensatory cognitive strategies. Here, we approached this question by testing multiple candidate variables from structural and functional neuroimaging for their ability to predict chronological age and memory performance, respectively. Prediction was performed using support vector machine (SVM) classification and regression across and within two samples of young (N = 106) and older (N = 153) adults. The candidate variables were (1) behavioral response frequencies in an episodic memory test; (2) recently described functional magnetic resonance imaging (fMRI) scores reflecting preservation of functional memory networks; (3) whole-brain fMRI contrasts for novelty processing and subsequent memory; (4) resting-state fMRI maps quantifying voxel-wise signal fluctuation; and (5) gray matter volume estimated from structural MRIs. While age group could be reliably decoded from all variables, chronological age within young and older subjects was best predicted from gray matter volume. In contrast, memory performance was best predicted from task-based fMRI contrasts and particularly single-value fMRI scores, whereas gray matter volume has no predictive power with respect to memory performance in healthy adults. Our results suggest that superior memory performance in healthy older adults is better explained by efficient recruitment of memory networks rather than by preserved brain structure.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Memória Episódica Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Aged / Humans Idioma: En Revista: ENeuro Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Memória Episódica Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Aged / Humans Idioma: En Revista: ENeuro Ano de publicação: 2022 Tipo de documento: Article