A Brain-Wide Study of Age-Related Changes in Functional Connectivity.

By Linda Geerligs, Remco J. Renken, Emi Saliasi, Natasha M. Maurits, Monicque M. Lorist. Cerebral Cortex, 2015

753 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f7482fc0252fb57df2507d1a7e34fa54f3388c67

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Changes in structural and functional connectivity among resting-state networks across the human lifespan (2014, methodology): “, 2007; Tomasi and Volkow, 2012), control (Geerlings et al., 2014), and visual (Yan et al.”
Building a Science of Individual Differences from fMRI. (2016, background): “There is interest in examining individual differences in relation to healthy aging [4,5], personality [6], intelligence [7,8], mood [9] and genetic polymorphism [10].”
Metric learning with spectral graph convolutions on brain connectivity networks (2018, background): “, 2014) and age- (Geerligs et al., 2014) related differences in functional connectivity networks have also been reported.”
Modular Brain Networks (2015, result): “Corroborating these results, Geerligs et al. (2015) compared populations of young and old participants, reporting a decrease in modularity, Q , driven by decreased internal connectivity within control, default mode, and somatomotor modules along with increased intermodule connections.”
Decreased segregation of brain systems across the healthy adult lifespan (2014, result): “Conversely, comparisons of younger and older adults have revealed differences in brainwide modularity in a direction consistent with the present observations (30, 32, 33, 36).”
Effects of aging on functional and structural brain connectivity☆ (2017, background): “Two other studies (Cao et al., 2014; Geerligs et al., 2015) applied whole brain graph theoretical approaches to determine modularity and global and local efficiency.”
Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review (2024, background): “Compared with the well-organized brain networks of young healthy adults with good neural differentiation and network segregation, which have a high degree of modularity, older adults have a tendency towards neural inefficiency due to the loss of network segregation with low modularity [31,170–172].”