A Graph Theory Approach to Clarifying Aging and Disease Related Changes in Cognitive Networks

Abstract

In accordance with the physiological networks that underlie it, human cognition is characterized by both the segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, can be conceptualized as a network of functions. A network approach to cognition has previously revealed topological differences in cognitive profiles between healthy and disease populations. The present study, therefore, used graph theory to determine variation in cognitive profiles across healthy aging and cognitive impairment. A comprehensive neuropsychological test battery was administered to 415 participants. This included three groups of healthy adults aged 18–39 (n = 75), 40–64 (n = 75), and 65 and over (n = 70) and three patient groups with either amnestic (n = 75) or non-amnestic (n = 60) mild cognitive impairment or Alzheimer’s type dementia (n = 60). For each group, cognitive networks were created reflective of test-to-test covariance, in which nodes represented cognitive tests and edges reflected statistical inter-nodal significance (p < 0.05). Network metrics were derived using the Brain Connectivity Toolbox. Network-wide clustering, local efficiency and global efficiency of nodes showed linear differences across the stages of aging, being significantly higher among older adults when compared with younger groups. Among patients, these metrics were significantly higher again when compared with healthy older controls. Conversely, average betweenness centralities were highest in middle-aged participants and lower among older adults and patients. In particular, compared with controls, patients demonstrated a distinct lack of centrality in the domains of semantic processing and abstract reasoning. Network composition in the amnestic mild cognitive impairment group was similar to the network of Alzheimer’s dementia patients. Using graph theoretical methods, this study demonstrates that the composition of cognitive networks may be measurably altered by the aging process and differentially impacted by pathological cognitive impairment. Network alterations characteristic of Alzheimer’s disease in particular may occur early and be distinct from alterations associated with differing types of cognitive impairment. A shift in centrality between domains may be particularly relevant in identifying cognitive profiles indicative of underlying disease. Such techniques may contribute to the future development of more sophisticated diagnostic tools for neurodegenerative disease.

The resting-state topological organization damage of language-related brain regions in post-stroke cognitive impairment

“…Studies have demonstrated that the human brain's network has small-world properties, re ecting the optimal balance of e ciency and cost (Farahani et al, 2019). However, the pathological injury may affect network organizations differently, resulting in cognitive impairment (delEtoile & Adeli, 2017; Wright et al, 2021). Some studies have used graph-theoretic analysis to characterize the topological changes of neural networks in cognitive disorderrelated disorders.…”

Network neuropsychology: The map and the territory

“…"Cognitive landmarks" then can refer to cognitive functions and/or changes in them over time, such as maturation or decline. Studies in Table 1 sought to investigate both the arrangement of interrelationships amongst variables (e.g., Ferguson for the Alzheimer's Disease Neuroimaging Initiative, 2021; Jonker et al, 2019;Garcia-Ramos et al, 2021;Kellermann et al, 2015Kellermann et al, , 2016Nevado et al, 2021a;Massa et al, 2015;Tosi et al, 2020) and developmental changes in these (e.g., Garcia-Ramos et al, 2015;Wright et al, 2021).…”

“…It may also be assumed that cognitive networks display patterns of segregation and integration (Garcia-Ramos et al, 2015Kellermann et al, 2016;Tosi et al, 2020;Wright et al, 2021). For example, Garcia-Ramos et al (2015) observed that patients with paediatric epilepsy displayed both lower integration and segregation across neuropsychological tests.…”

“…However, this pattern should not be over-interpreted. It is likely to change across the lifespan (Kan et al, 2019); reflecting, for example, the progressive specialisation of cognition in childhood (D'Souza and Filippi, 2017) and neural dedifferentiation in older age (Goh, 2011;Koen and Rugg, 2019;Wright et al, 2021).…”

“…The disorders studied with these methods include adult and paediatric epilepsies (Garcia-Ramos et al, 2021Kellermann et al, 2016Kellermann et al, , 2015, mild cognitive impairment (Ferguson for the Alzheimer's Disease Neuroimaging Initiative, 2021), Alzheimer's disease (Ferguson for the Alzheimer's Disease Neuroimaging Initiative, 2021; Nevado et al, 2021a;Tosi et al, 2020;Wright et al, 2021), vascular encephalopathy (Tosi et al, 2020), stroke (Massa et al, 2015), and frontal and non-frontal lesions arising from various aetiologies (Jonker et al, 2019). Comparable studies have created network models of more specific aspects of cognition, including intelligence (Kan et al, 2019;Schmank et al, 2019;van der Maas et al, 2017), reading (Angelelli et al, 2021;Goring et al, 2021;Zoccolotti et al, 2021), and semantic fluency (Bertola et al, 2014;De Marco et al, 2021;Goni et al, 2011˜ ;Lerner et al, 2009;Nevado et al, 2021b), or developmental processes like ageing (Garcia-Cabello et al, 2021;Konigs et al, 2021¨ ), in clinical and non-clinical groups.…”