Brain Age: A State-Of-Mind? On the Stability of Functional Connectivity across Behavioral States

Abstract

The study of functional connectivity (FC) has become a major branch of functional MRI (fMRI) research. Biswal et al. (1995)'s seminal discovery, that voxels in the sensorimotor cortex exhibited highly correlated activity at rest, seeded the field; however, it took at least 10 more years for it to gain widespread interest (Cordes et al., 2000; Greicius et al., 2003; Fox et al., 2005; Smith et al., 2009). There is currently much research into using FC as a biomarker for clinical diagnosis (Greicius, 2008; Linden, 2012) and, more generally, to gain insight into individual differences in brain function (Smith et al., 2013). Most studies investigate FC in the so-called "resting state": subjects in the scanner are instructed to "lie still and think of nothing in particular," with eyes closed, or open and fixating (Patriat et al., 2013); however, FC can also be computed from task fMRI data, usually after regressing out stimulus-evoked activity (Fair et al., 2007). Cole et al. (2014) showed that, on average across subjects, a reliable intrinsic network structure is preserved through all tasks and rest. Additionally, ∼40% of the connections show mild but significant changes that are task- (equivalently, state-) dependent. The variability of FC in individual subjects is now well recognized; functional network structure actually moves through several states within the span of a single resting-state run (Hutchison et al., 2013; Allen et al., 2014). While some authors have used the dynamic nature of individual network structure to their advantage, e.g., Damaraju et al. (2014), there is growing concern that this variability could impede our ability to use FC as a stable, trait-like measure of individual subjects. A recent study in The Journal of Neuroscience (Geerligs et al., 2015) reinforces this concern. Geerligs et al. (2015)'s study is among the first published outputs of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) cohort study, a large-scale (N = ∼700), multimodal (MRI, MEG, and behavioral), cross-sectional, population-based adult lifespan (18–87 years old) investigation of the neural underpinnings of successful cognitive aging (Shafto et al., 2014; Taylor et al., 2015). Geerligs et al. (2015) used state-of-the-art imaging and preprocessing techniques, notably with respect to motion correction, which has been a thorny issue in the functional connectivity literature (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012; Tyszka et al., 2014), and is especially problematic in aging studies (older people tend to move more, as confirmed in this study). Geerligs et al. (2015)'s study boasts a final sample size of 587 subjects (∼100 per decade of life), all of whom completed three different tasks in the scanner: an 8 min, 40 s eyes-closed resting-state run (REST state), an 8 min, 40 s sensorimotor task (detection of brief auditory tones and/or visual checkerboard flashes; TASK state), and an 8 min, 13 s movie-watching run (the movie being a shortened version of Alfred Hitchcock's television episode "Bang, you're dead!," as described in Hasson et al. (2010); MOVIE state). Whole-brain FC was assessed among 748 nodes from a published functional parcellation (Craddock et al., 2012) (Fig. 1e), in each of the three states (REST, TASK, MOVIE), yielding a 748 × 748 FC matrix for each subject and each state (Fig. 1a). First, the authors performed the same analysis as Cole et al. (2014): they averaged FC matrices across subjects, then quantified the similarity of the average FC matrices for each pair of states using the Pearson correlation coefficient r (Fig. 1b). As in Cole et al. (2014), they found a high similarity between the REST and TASK FC matrices [variance explained r^2 = 87% of total variance (TV)]. Crucially, Geerligs et al. (2015) also quantified the reliability of the average FC matrix in each state using a (conservative) split-half procedure: the explainable variance (EV) was high (99%TV), because of the large number of subjects. The variance attributable to state effects was thus 99%TV − 87%TV = 12%TV; i.e., 12%TV/99%TV = 11.9%EV, for the REST–TASK comparison.

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