Age-dependent changes in task-based modular organization of the human brain
Introduction
Humans experience notable changes in cognitive ability and behavior as they age, often in situations involving memory encoding, memory retrieval, and executive control functions (Balota et al., 2000, Grady and Craik, 2000, Cepeda et al., 2001, West et al., 2002, Treitz et al., 2007). Over the past few decades, advances in brain imaging have made it possible to observe and quantify neural changes associated with advanced age. One of the most widely-reported phenomena associated with aging is the loss of segregation between neural systems: many networks become less internally coherent, while at the same time they become more similar to other networks. This result has been reported using a number of methodological approaches, including whole-brain ICA (Onoda et al., 2012), whole-brain parcel-based functional connectivity methods (Betzel et al., 2014, Chan et al., 2014, Song et al., 2014, Ferreira et al., 2015, Geerligs et al., 2015) as well as similar analyses confined to a subset of systems (Grady et al., 2016, Ng et al., 2016), whole brain voxel-wise analyses (Tomasi and Volkow, 2012), and seed-based methods (Zhang et al., 2014) (for reviews, see Dennis and Thompson, 2014, Contreras et al., 2015, Sala-Llonch et al., 2015). Moreover, these changes have been tracked longitudinally within participants (Ng et al., 2016), have been shown to affect various properties theoretically associated with the efficiency and efficacy of information processing in the brain (Sala-Llonch et al., 2014, Gomez-Ramirez et al., 2015), and have been associated with behavioral effects (Ng et al., 2016, Sala-Llonch et al., 2014).
Although the dominant change associated with aging is one of decreased intra-network connectivity and increased inter-network connectivity, this pattern varies across networks. The loss of intra-network connectivity is found most consistently in the default mode network (DMN), even among those studies that consider brainwide connectivity (Onoda et al., 2012, Tomasi and Volkow, 2012, Wang et al., 2012, Song et al., 2014, Ferreira et al., 2015, Geerligs et al., 2015, Ng et al., 2016). Some studies also report similar decreases in networks associated with higher cognitive functions (Onoda et al., 2012, Wang et al., 2012, Chan et al., 2014, Geerligs et al., 2015, Ng et al., 2016). However, other networks consistently show no change, or even an increase in intra-network connectivity, especially those associated with sensory functions (Tomasi and Volkow, 2012, Song et al., 2014, Geerligs et al., 2015). Similarly, connectivity between the DMN and other networks tends to increase (or, equivalently, the uniqueness of the networks decreases) (Ferreira et al., 2015, Ng et al., 2016).
In parallel with this line of research on how the brain's functional architecture changes with age, a largely separate effort has sought to extend connectivity methods by accounting for the fact that the brain is not static (for a review, see Calhoun et al. (2014)). To the contrary, this work has demonstrated that patterns of connectivity are quite variable (Gonzalez-Castillo et al., 2014), which can be characterized as constituting a series of transitions between fairly well-defined brain states (Hansen et al., 2015). It has been proposed that the greatest variability occurs in regions that serve to connect fairly well-segregated systems (Zalesky et al., 2014), and that a small set of networks may modulate the organization across a large number of others (Di and Biswal, 2015). The time-resolved approach adds yet another dimension for investigating age-related effects; for example, Qin et al. (2015) report increased variability in connectivity across networks including DMN and cerebellum, and decreased variability between those two and within the cingulo-opercular network, as a function of age.
Having established these aging-related changes in functional connectivity—along with some general principles of dynamic connectivity—in the resting state, an obvious next question is how the results differ during task performance. “Task-free” paradigms dominate studies of functional connectivity. Incorporating a task could affect connectivity, including its relationship with age and its dynamics, in a number of ways. For instance, compensatory strategies employed by older—but not younger—adults could drive the connectivity profiles of the two groups even further apart; alternatively, the presence of an extrinsic input could impose structure on the systems that have become homogenized in older adults. Indeed, Dubois (2016) demonstrated widespread changes in the relationship between age and connectivity across resting and task scans, with the largest effects being a weakening in the age–connectivity relationship during tasks compared with rest. Likewise, connectivity between and within networks could change as participants learn, change strategies, or even simply become fatigued.
For the present study, we used a memory task that incorporated a strong element of cognitive control. In particular, after studying a list of items, participants were presented with the studied items, along with novel (unstudied) items, and instructed to indicate whether each item was studied or not. Items occurred in one of two contexts: a “liberal” context indicating that each item in that context was likely to have been studied (70% of items were studied items) or a “conservative” context indicating that each item was unlikely to have been studied (30% of items studied). In the face of imperfect memory evidence, participants must exert cognitive control—adjusting the criterion they use to endorse an item as studied—in order to perform well on this task. Given that the domains of memory and cognitive control are fundamental in human cognition, and are associated with changes over the lifespan (Jacoby et al., 2005), this task is an appealing choice for studying how the brain's architecture changes with age when not at rest. Previous results with this task revealed wide individual differences in adaptability (Aminoff et al., 2012), and implicated a network of regions including lateral prefrontal and lateral posterior parietal cortex in performing this task (Aminoff et al., 2015).
Although the brain regions associated with the performance of this task are well documented, these results are derived from the standard mass-univariate GLM analysis of BOLD data, and therefore give little basis for predictions in terms of network-level dynamics. In fact, by definition, these existing results assume stationarity and consider each voxel as independent. Even results derived from methods that explicitly model the spatiotemporal nature of brain activity (e.g., ICA) would require a theoretical framework in order to define regions of interest in the context of how network dynamics relate to other factors, such as age. Thus, there remains a gap in understanding of the neural processes related to performance of this task on the level of dynamic interactions between large-scale brain regions and networks. Our current understanding of these processes, based on existing theories and results, is specified on a very different level from the target of our current investigation. Our goal in this work is to apply a data-driven analysis method to investigate the dynamics of these regions and networks, which allows us to uncover age-related changes at scales at which it is difficult to make specific hypotheses based upon existing literature.
We apply a dynamic community detection method to quantify several higher-order aspects of task-based functional connectivity and their dependence on age. This method and other network science approaches have proved successful in distilling the information in fMRI data into intuitive, descriptive, and predictive network characteristics (Bassett et al., 2012, Davison et al., 2015, Siebenhühner et al., 2013, Bullmore and Sporns, 2012, Bassett et al., 2009, Bassett et al., 2011, Bassett et al., 2015). While previous results suggest that static community structure will meaningfully differ on a group level between older and younger adults at rest (Meunier et al., 2008), we ask whether the dynamic changes in these communities are affected by age during task-based cognition, and how such effects vary across individual participants. We quantify the size and number of functional brain communities, the degree to which brain regions flexibly switch between communities, and the association of the community structure with known intrinsic functional connectivity networks or systems, in order to determine whether these systems are differentially involved in age-related changes.
Section snippets
Participants
126 participants were recruited from the UCSB and Santa Barbara communities and scanned at the UCSB Brain Imaging Center. 22 subjects were not included in this analysis due to technical issues, metal screening issues, claustrophobia, and attrition. The 104 participants assessed here came from three separate age groups: 35 adolescents (age 18, 18 female), 34 young adults (ages 25–33, mean age 28.5, 16 female), and 35 older adults (ages 60–75, mean age 67.2, 18 female). All subjects had a history
Results
In this section, we present the characteristics of dynamic community structure within individuals, and evaluate their correspondence with age and recognition memory performance.
For assessing correlations with age throughout this section, we use the Spearman rank correlation, due to the non-continuity and non-uniformity of the ages in our subject sample. However, we use the Pearson correlation for assessing correlations with all performance measures, which are continuously and approximately
Discussion
These findings relating functional community dynamics to age provide important insight into factors affecting the significant individual differences in community dynamics. The community structure appears to act as a signature of individual functional dynamics that is strongly influenced by age, indicating that cognitive organization during such a memory task differs across the lifespan of participants.
Interestingly, despite marked differences in community dynamics, we find no significant
Conclusion
Overall, this work confirms that the dynamics of functional community structure in the human brain during a memory task vary considerably with age. In particular, both whole-brain flexibility, which measures the tendency of brain regions to switch between communities over time, and the overall number of functional communities show notable individual differences and are strongly correlated with age, with older subjects demonstrating significantly higher flexibility and more fragmented functional
Acknowledgements
This work was supported by the National Science Foundation Graduate Research Fellowship under grant DGE-1144085, the Packard Foundation, and the Institute for Collaborative Biotechnologies through grant W911NF-09-0001 from the U.S. Army Research Office. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.
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