Abstract
Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups.Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures.Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups.Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.
Highlights
IntroductionA common approach has been to focus on how a particular cognitive function changes over time or differs across age groups, using univariate methods for data analysis (West, 2001; Tisserand and Jolles, 2003; Lachman, 2004; Schroeder and Salthouse, 2004; Schaie, 2005; Salthouse, 2009, 2010, 2016; Harada et al, 2013; Ferreira et al, 2015; Reas et al, 2017; Oschwald et al, 2019)
Participants were selected according to the following criteria: (1) No dementia according to a Mini-Mental State Examination (MMSE) score ≥24, a Blessed Dementia Rating Scale (BDRS) score
We applied this criterion because some cognitive functions such as language abilities (Springer et al, 1999), visuospatial functions (Zaidel, 1990; Kong et al, 2018), or attention (Heilman, 1995) are lateralized and so, the ‘‘cognitive connectome’’ could be different in left-handed individuals; (4) No abnormal findings such as stroke, tumors, or hippocampal sclerosis on MRI according to an experienced neuroradiologist; and (5) No neurologic or psychiatric disorders, systemic diseases with neuropsychological consequences, or history of substance abuse
Summary
A common approach has been to focus on how a particular cognitive function changes over time or differs across age groups, using univariate methods for data analysis (West, 2001; Tisserand and Jolles, 2003; Lachman, 2004; Schroeder and Salthouse, 2004; Schaie, 2005; Salthouse, 2009, 2010, 2016; Harada et al, 2013; Ferreira et al, 2015; Reas et al, 2017; Oschwald et al, 2019) While this approach has provided important insight on age-related cognitive decline, cognitive functions are highly interrelated with each other through complex associations, possibly in a dynamic manner across ages, i.e., these inter-relations may differ across age groups. Within the connectome field of neuroscience (Bullmore and Sporns, 2021), graph theory has recently emerged as a promising technique to investigate complex associations in the data, both in normal and pathological aging
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
