Matthew L. Stanley

Defining nodes in complex brain networks

Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel). The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10–20 millimeter diameter) spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the “correct” method to be used remains an open, possibly unsolvable question that deserves extensive debate and research, we argue that the best method available at the current time is the voxel-wise method.

Changes in global and regional modularity associated with increasing working memory load

Using graph theory measures common to complex network analyses of neuroimaging data, the objective of this study was to explore the effects of increasing working memory processing load on functional brain network topology in a cohort of young adults. Measures of modularity in complex brain networks quantify how well a network is organized into densely interconnected communities. We investigated changes in both the large-scale modular organization of the functional brain network as a whole and regional changes in modular organization as demands on working memory increased from n = 1 to n = 2 on the standard n-back task. We further investigated the relationship between modular properties across working memory load conditions and behavioral performance. Our results showed that regional modular organization within the default mode and working memory circuits significantly changed from 1-back to 2-back task conditions. However, the regional modular organization was not associated with behavioral performance. Global measures of modular organization did not change with working memory load but were associated with individual variability in behavioral performance. These findings indicate that regional and global network properties are modulated by different aspects of working memory under increasing load conditions. These findings highlight the importance of assessing multiple features of functional brain network topology at both global and regional scales rather than focusing on a single network property.

Hippocampal Contributions to the Large-Scale Episodic Memory Network Predict Vivid Visual Memories

Abstract A common approach in memory research is to isolate the function(s) of individual brain regions, such as the hippocampus, without addressing how those regions interact with the larger network. To investigate the properties of the hippocampus embedded within large‐scale networks, we used functional magnetic resonance imaging and graph theory to characterize complex hippocampal interactions during the active retrieval of vivid versus dim visual memories. The study yielded 4 main findings. First, the right hippocampus displayed greater communication efficiency with the network (shorter path length) and became a more convergent structure for information integration (higher centrality measures) for vivid than dim memories. Second, vivid minus dim differences in our graph theory measures of interest were greater in magnitude for the right hippocampus than for any other region in the 90‐region network. Moreover, the right hippocampus significantly reorganized its set of direct connections from dim to vivid memory retrieval. Finally, beyond the hippocampus, communication throughout the whole‐brain network was more efficient (shorter global path length) for vivid than dim memories. In sum, our findings illustrate how multivariate network analyses can be used to investigate the roles of specific regions within the large‐scale network, while also accounting for global network changes.

Changes in brain network efficiency and working memory performance in aging.

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory.

From hippocampus to whole‐brain: The role of integrative processing in episodic memory retrieval

Multivariate functional connectivity analyses of neuroimaging data have revealed the importance of complex, distributed interactions between disparate yet interdependent brain regions. Recent work has shown that topological properties of functional brain networks are associated with individual and group differences in cognitive performance, including in episodic memory. After constructing functional whole‐brain networks derived from an event‐related fMRI study of memory retrieval, we examined differences in functional brain network architecture between forgotten and remembered words. This study yielded three main findings. First, graph theory analyses showed that successfully remembering compared to forgetting was associated with significant changes in the connectivity profile of the left hippocampus and a corresponding increase in efficient communication with the rest of the brain. Second, bivariate functional connectivity analyses indicated stronger interactions between the left hippocampus and a retrieval assembly for remembered versus forgotten items. This assembly included the left precuneus, left caudate, bilateral supramarginal gyrus, and the bilateral dorsolateral superior frontal gyrus. Integrative properties of the retrieval assembly were greater for remembered than forgotten items. Third, whole‐brain modularity analyses revealed that successful memory retrieval was marginally significantly associated with a less segregated modular architecture in the network. The magnitude of the decreases in modularity between remembered and forgotten conditions was related to memory performance. These findings indicate that increases in integrative properties at the nodal, retrieval assembly, and whole‐brain topological levels facilitate memory retrieval, while also underscoring the potential of multivariate brain connectivity approaches for providing valuable new insights into the neural bases of memory processes. Hum Brain Mapp 38:2242–2259, 2017.

Resting-state networks do not determine cognitive function networks: a commentary on Campbell and Schacter (2016)

Resting-state networks do not determine cognitive function networks: a commentary on Campbell and Schacter (2016) Simon W. Davis , Matthew L. Stanley, Morris Moscovitch and Roberto Cabeza Neurology, Duke University School of Medicine, Durham, NC, USA; Center for Cognitive Neuroscience, Duke Institute for Brain Sciences, Duke University, Durham, NC, USA; Psychology, University of Toronto, Toronto, Ontario, Canada

Emotional intensity in episodic autobiographical memory and counterfactual thinking

Episodic counterfactual thoughts-imagined alternative ways in which personal past events might have occurred-are frequently accompanied by intense emotions. Here, participants recollected positive and negative autobiographical memories and then generated better and worse episodic counterfactual events from those memories. Our results suggest that the projected emotional intensity during the simulated remembered/imagined event is significantly higher than but typically positively related to the emotional intensity while remembering/imagining the event. Furthermore, repeatedly simulating counterfactual events heightened the emotional intensity felt while simulating the counterfactual event. Finally, for both the emotional intensity accompanying the experience of remembering/imagining and the projected emotional intensity during the simulated remembered/imagined event, the emotional intensity of negative memories was greater than the emotional intensity of upward counterfactuals generated from them but lower than the emotional intensity of downward counterfactuals generated from them. These findings are discussed in relation to clinical work and functional theories of counterfactual thinking.

Counterfactual Plausibility and Comparative Similarity

Counterfactual thinking involves imagining hypothetical alternatives to reality. Philosopher David Lewis (1973, 1979) argued that people estimate the subjective plausibility that a counterfactual event might have occurred by comparing an imagined possible world in which the counterfactual statement is true against the current, actual world in which the counterfactual statement is false. Accordingly, counterfactuals considered to be true in possible worlds comparatively more similar to ours are judged as more plausible than counterfactuals deemed true in possible worlds comparatively less similar. Although Lewis did not originally develop his notion of comparative similarity to be investigated as a psychological construct, this study builds upon his idea to empirically investigate comparative similarity as a possible psychological strategy for evaluating the perceived plausibility of counterfactual events. More specifically, we evaluate judgments of comparative similarity between episodic memories and episodic counterfactual events as a factor influencing peoples judgments of plausibility in counterfactual simulations, and we also compare it against other factors thought to influence judgments of counterfactual plausibility, such as ease of simulation and prior simulation. Our results suggest that the greater the perceived similarity between the original memory and the episodic counterfactual event, the greater the perceived plausibility that the counterfactual event might have occurred. While similarity between actual and counterfactual events, ease of imagining, and prior simulation of the counterfactual event were all significantly related to counterfactual plausibility, comparative similarity best captured the variance in ratings of counterfactual plausibility. Implications for existing theories on the determinants of counterfactual plausibility are discussed.

I’m not the person I used to be: The self and autobiographical memories of immoral actions.

People maintain a positive identity in at least two ways: They evaluate themselves more favorably than other people, and they judge themselves to be better now than they were in the past. Both strategies rely on autobiographical memories. The authors investigate the role of autobiographical memories of lying and emotional harm in maintaining a positive identity. For memories of lying to or emotionally harming others, participants judge their own actions as less morally wrong and less negative than those in which other people lied to or emotionally harmed them. Furthermore, people judge those actions that happened further in the past to be more morally wrong than those that happened more recently. Finally, for periods of the past when they believed that they were very different people than they are now, participants judge their actions to be more morally wrong and more negative than those actions from periods of their pasts when they believed that they were very similar to who they are now. The authors discuss these findings in relation to theories about the function of autobiographical memory and moral cognition in constructing and perceiving the self over time.

Modularity in network neuroscience and neural reuse

Neural reuse allegedly stands in stark contrast against a modular view of the brain. However, the development of unique modularity algorithms in network science has provided the means to identify functionally cooperating, specialized subsystems in a way that remains consistent with the neural reuse view and offers a set of rigorous tools to fully engage in Andersons (2014) research program.