Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging

Abstract

Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc.

Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan

“…That is to say, head motion changes systematically with age, which may reflect true neurobiological effects of aging. (Geerligs, Tsvetanov, & Henson, 2017).…”

Frequency‐specific age‐related decreased brain network diversity in cognitively healthy elderly: A whole‐brain data‐driven analysis

“…The Similar as the wavelet coherence/correlation analysis used in previous studies (Bassett et al, 2011;Bassett et al, 2015), the wavelet coefficients at each scale were used for the dynamic functional connectivity estimation. In this study, the distance correlation was used to evaluate the pairwise correlation since it could evaluate both the linear and nonlinear dependencies of two signals (Szekely, Rizzo, & Bakirov, 2007), and has been demonstrated to be an effective measure to evaluate both the static and dynamic functional connectivity Geerligs, Tsvetanov, & Henson, 2017;Rudas et al, 2015). In this study, the distance correlation was used to evaluate the pairwise correlation since it could evaluate both the linear and nonlinear dependencies of two signals (Szekely, Rizzo, & Bakirov, 2007), and has been demonstrated to be an effective measure to evaluate both the static and dynamic functional connectivity Geerligs, Tsvetanov, & Henson, 2017;Rudas et al, 2015).…”

“…First, although we try to reduce the effects of non-neuronal physiological dynamics carefully, it is still difficult to disentangle the neuronal origin and nonneuronal origin, such as vascular health effects (Geerligs et al, 2017;Tsvetanov et al, 2015), dynamics in the common spatial components. First, although we try to reduce the effects of non-neuronal physiological dynamics carefully, it is still difficult to disentangle the neuronal origin and nonneuronal origin, such as vascular health effects (Geerligs et al, 2017;Tsvetanov et al, 2015), dynamics in the common spatial components.…”

Brain functional connectivity in regions that exhibit age-related cortical thinning

“…The study of causality metrics on these data brings us to another realization: functional neuroimaging tends to exhibit high temporal autocorrelation (EKLUND et al, 2012;GEERLIGS et al, 2017), effectively reducing the degrees of freedom provided by the data even without any temporal processing in the study of BOLD connectivity (POWER; SCHLAGGAR; PETERSEN, 2014). If the past states of a timeseries are enough to reasonably describe its own states, then other timeseries cannot add enough information to the predictive problem.…”

“…clear what GSR encompass to the interpretation of the resulting connectivity, and in special the anti-correlations, but it is known the global signal is significantly coupled with neural activity (GEERLIGS et al, 2017;SCHÖLVINCK et al, 2010).…”