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

Modularity Measures of Functional Brain Networks Predict Individual Differences in Long-Term Memory

Long-term memory (LTM) is crucial to daily functioning, and individuals show a wide range in LTM capacity. In this study, we ask: How does the brain's functional organization explain individual differences in LTM? We focused on two important, widely studied forms of LTM, general recognition and recollection memory. Inspired by recent work on graph theory and modularity of the brain, we explored how modularity measures of brain activity during encoding could predict individual differences in later LTM performance. Specifically, we examined two modularity measures that describe distinct aspects of network functioning: diversity-the extent a node connects with different modules-and locality-the extent a node has more connections within its own modules. Combining modularity measures and connectome-predictive modeling (CPM), a powerful framework for predicting individual differences in behavior from brain functional connectivity, we found that diversity and locality measures together significantly predicted individual differences in both general recognition and recollection memory. Modularity-based predictions were less strong than CPM models using only connectivity features. With regard to predictive neuroanatomy, we found that the default mode network was the most consistently selected brain network across our models. Our findings extend previous work on how the modularity of the brain is related to cognition and demonstrate that successful LTM is supported by critical connector hubs coordinating between and within networks during encoding. More broadly, they demonstrate the utility of a graph-based approach to reveal how modularity of brain networks relates to individual differences in LTM.

Network Segregation and Integration Changes in Healthy Aging: Evidence From EEG Subbands During the Visual Short-Term Memory Binding Task

Working memory, which tends to be the most vulnerable cognitive domain to aging, is thought to depend on a functional brain network for efficient communication. The dynamic communication within this network is represented by segregation and integration. This study aimed to investigate healthy aging by examining age effect on outcomes of graph theory analysis during the visual short-term memory binding (VSTMB) task. VSTMB tasks rely on the integration of visual features and are less sensitive to semantic and verbal strategies. Effects of age on neuropsychological test scores, along with the EEG graph-theoretical integration, segregation and global organization metrics in frequencies from delta to gamma band were investigated. Neuropsychological assessment showed low sensitivity as a measure of age-related changes. EEG results indicated that network architecture changed more effectively during middle age, while this effectiveness appears to vanish or show compensatory mechanisms in the elderly. These differences were further found to be related to cognitive domain scores. This study is the first to demonstrate differences in working memory network architecture across a broad age range.

High-altitude exposure leads to increased modularity of brain functional network with the increased occupation of attention resources in early processing of visual working memory

Working memory is a complex cognitive system that temporarily maintains purpose-relevant information during human cognition performance. Working memory performance has also been found to be sensitive to high-altitude exposure. This study used a multilevel change detection task combined with Electroencephalogram data to explore the mechanism of working memory change from high-altitude exposure. When compared with the sea-level population, the performance of the change detection task with 5 memory load levels was measured in the Han population living in high-altitude areas, using the event-related potential analysis and task-related connectivity network analysis. The topological analysis of the brain functional network showed that the normalized modularity of the high-altitude group was higher in the memory maintenance phase. Event-related Potential analysis showed that the peak latencies of P1 and N1 components of the high-altitude group were significantly shorter in the occipital region, which represents a greater attentional bias in visual early processing. Under the condition of high memory loads, the high-altitude group had a larger negative peak in N2 amplitude compared to the low-altitude group, which may imply more conscious processing in visual working memory. The above results revealed that the visual working memory change from high-altitude exposure might be derived from the attentional bias and the more conscious processing in the early processing stage of visual input, which is accompanied by the increase of the modularity of the brain functional network. This may imply that the attentional bias in the early processing stages have been influenced by the increased modularity of the functional brain networks induced by high-altitude exposure.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10091-3.

EEG alpha band functional brain network correlates of cognitive performance in children after perinatal stroke

Mechanisms underlying cognitive impairment after perinatal stroke could be explained through brain network alterations. With aim to explore this connection, we conducted a matched test-control study to find a correlation between functional brain network properties and cognitive functions in children after perinatal stroke. First, we analyzed resting-state functional connectomes in the alpha frequency band from a 64-channel resting state EEG in 24 children with a history of perinatal stroke (12 with neonatal arterial ischemic stroke and 12 with neonatal hemorrhagic stroke) and compared them to the functional connectomes of 24 healthy controls. Next, all participants underwent cognitive evaluation. We analyzed the differences in functional brain network properties and cognitive abilities between groups and studied the correlation between network characteristics and specific cognitive functions. Functional brain networks after perinatal stroke had lower modularity, higher clustering coefficient, higher interhemispheric strength, higher characteristic path length and higher small world index. Modularity correlated positively with the IQ and processing speed, while clustering coefficient correlated negatively with IQ. Graph metrics, reflecting network segregation (clustering coefficient and small world index) correlated positively with a tendency to impulsive decision making, which also correlated positively with graph metrics, reflecting stronger functional connectivity (characteristic path length and interhemispheric strength). Our study suggests that specific cognitive functions correlate with different brain network properties and that functional network characteristics after perinatal stroke reflect poorer cognitive functioning.

Altered global modular organization of intrinsic functional connectivity in autism arises from atypical node-level processing

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by restricted interests and repetitive behaviors as well as social-communication deficits. These traits are associated with atypicality of functional brain networks. Modular organization in the brain plays a crucial role in network stability and adaptability for neurodevelopment. Previous neuroimaging research demonstrates discrepancies in studies of functional brain modular organization in ASD. These discrepancies result from the examination of mixed age groups. Furthermore, recent findings suggest that while much attention has been given to deriving atlases and measuring the connections between nodes, within node information may also be crucial in determining altered modular organization in ASD compared with typical development (TD). However, altered modular organization originating from systematic nodal changes are yet to be explored in younger children with ASD. Here, we used graph-theoretical measures to fill this knowledge gap. To this end, we utilized multicenter resting-state fMRI data collected from 5 to 10-year-old children-34 ASD and 40 TD obtained from the Autism Brain Image Data Exchange (ABIDE) I and II. We demonstrate that alterations in topological roles and modular cohesiveness are the two key properties of brain regions anchored in default mode, sensorimotor, and salience networks, and primarily relate to social and sensory deficits in children with ASD. These results demonstrate that atypical global network organization in children with ASD arises from nodal role changes, and contribute to the growing body of literature suggesting that there is interesting information within nodes providing critical markers of functional brain networks in autistic children.

Cortico-subcortical interactions in overlapping communities of edge functional connectivity

Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.

Emerging neurodevelopmental perspectives on mathematical learning

Strong foundational skills in mathematical problem solving, acquired in early childhood, are critical not only for success in the science, technology, engineering, and mathematical (STEM) fields but also for quantitative reasoning in everyday life. The acquisition of mathematical skills relies on protracted interactive specialization of functional brain networks across development. Using a systems neuroscience approach, this review synthesizes emerging perspectives on neurodevelopmental pathways of mathematical learning, highlighting the functional brain architecture that supports these processes and sources of heterogeneity in mathematical skill acquisition. We identify the core neural building blocks of numerical cognition, anchored in the posterior parietal and ventral temporal-occipital cortices, and describe how memory and cognitive control systems, anchored in the medial temporal lobe and prefrontal cortex, help scaffold mathematical skill development. We highlight how interactive specialization of functional circuits influences mathematical learning across different stages of development. Functional and structural brain integrity and plasticity associated with math learning can be examined using an individual differences approach to better understand sources of heterogeneity in learning, including cognitive, affective, motivational, and sociocultural factors. Our review emphasizes the dynamic role of neurodevelopmental processes in mathematical learning and cognitive development more generally.

Neurocognitive modeling of latent memory processes reveals reorganization of hippocampal-cortical circuits underlying learning and efficient strategies

Efficient memory-based problem-solving strategies are a cardinal feature of expertise across a wide range of cognitive domains in childhood. However, little is known about the neurocognitive mechanisms that underlie the acquisition of efficient memory-based problem-solving strategies. Here we develop, to the best of our knowledge, a novel neurocognitive process model of latent memory processes to investigate how cognitive training designed to improve children's problem-solving skills alters brain network organization and leads to increased use and efficiency of memory retrieval-based strategies. We found that training increased both the use and efficiency of memory retrieval. Functional brain network analysis revealed training-induced changes in modular network organization, characterized by increase in network modules and reorganization of hippocampal-cortical circuits. Critically, training-related changes in modular network organization predicted performance gains, with emergent hippocampal, rather than parietal cortex, circuitry driving gains in efficiency of memory retrieval. Our findings elucidate a neurocognitive process model of brain network mechanisms that drive learning and gains in children's efficient problem-solving strategies.

Event-related network changes unfold the dynamics of cortical integration during face processing

Face perception arises from a collective activation of brain regions in the occipital, parietal and temporal cortices. Despite the wide acknowledgment that these regions act in an intertwined network, the network behavior itself is poorly understood. Here we present a study in which time-varying connectivity estimated from EEG activity elicited by facial expressions presentation was characterized using graph-theoretical measures of node centrality and global network topology. Results revealed that face perception results from a dynamic reshaping of the network architecture, characterized by the emergence of hubs located in the occipital and temporal regions of the scalp. The importance of these nodes can be observed from the early stages of visual processing and reaches a climax in the same time-window in which the face-sensitive N170 is observed. Furthermore, using Granger causality, we found that the time-evolving centrality of these nodes is associated with ERP amplitude, providing a direct link between the network state and local neural response. Additionally, investigating global network topology by means of small-worldness and modularity, we found that face processing requires a functional network with a strong small-world organization that maximizes integration, at the cost of segregated subdivisions. Interestingly, we found that this architecture is not static, but instead, it is implemented by the network from stimulus onset to ~200 ms. Altogether, this study reveals the event-related changes underlying face processing at the network level, suggesting that a distributed processing mechanism operates through dynamically weighting the contribution of the cortical regions involved.

Contextual experience modifies functional connectome indices of topological strength and efficiency

Stimuli presented at short temporal delays before functional magnetic resonance imaging (fMRI) can have a robust impact on the organization of synchronous activity in resting state networks. This presents an opportunity to investigate how sensory, affective and cognitive stimuli alter functional connectivity in rodent models. In the present study we assessed the effect on functional connectivity of a familiar contextual stimulus presented 10 min prior to sedation for imaging. A subset of animals were co-presented with an unfamiliar social stimulus in the same environment to further investigate the effect of familiarity on network topology. Rats were imaged at 11.1 T and graph theory analysis was applied to matrices generated from seed-based functional connectivity data sets with 144 brain regions (nodes) and 10,152 pairwise correlations (after excluding 144 diagonal edges). Our results show substantial changes in network topology in response to the familiar (context). Presentation of the familiar context, both in the absence and presence of the social stimulus, strongly reduced network strength, global efficiency, and altered the location of the highest eigenvector centrality nodes from cortex to the hypothalamus. We did not observe changes in modular organization, nodal cartographic assignments, assortative mixing, rich club organization, and network resilience. We propose that experiential factors, perhaps involving associative or episodic memory, can exert a dramatic effect on functional network strength and efficiency when presented at a short temporal delay before imaging.

Older adults benefit from more widespread brain network integration during working memory

Neuroimaging evidence suggests that the aging brain relies on a more distributed set of cortical regions than younger adults in order to maintain successful levels of performance during demanding cognitive tasks. However, it remains unclear how task demands give rise to this age-related expansion in cortical networks. To investigate this issue, functional magnetic resonance imaging was used to measure univariate activity, network connectivity, and cognitive performance in younger and older adults during a working memory (WM) task. Here, individuals performed a WM task in which they held letters online while reordering them alphabetically. WM load was titrated to obtain four individualized difficulty levels with different set sizes. Network integration-defined as the ratio of within-versus between-network connectivity-was linked to individual differences in WM capacity. The study yielded three main findings. First, as task difficulty increased, network integration decreased in younger adults, whereas it increased in older adults. Second, age-related increases in network integration were driven by increases in right hemisphere connectivity to both left and right cortical regions, a finding that helps to reconcile existing theories of compensatory recruitment in aging. Lastly, older adults with higher WM capacity demonstrated higher levels of network integration in the most difficult task condition. These results shed light on the mechanisms of age-related network reorganization by demonstrating that changes in network connectivity may act as an adaptive form of compensation, with older adults recruiting a more distributed cortical network as task demands increase.

Functional Connectivity within and beyond the Face Network Is Related to Reduced Discrimination of Degraded Faces in Young and Older Adults

Degrading face stimuli reduces face discrimination in both young and older adults, but the brain correlates of this decline in performance are not fully understood. We used functional magnetic resonance imaging to examine the effects of degraded face stimuli on face and nonface brain networks and tested whether these changes would predict the linear declines seen in performance. We found decreased activity in the face network (FN) and a decrease in the similarity of functional connectivity (FC) in the FN across conditions as degradation increased but no effect of age. FC in whole-brain networks also changed with increasing degradation, including increasing FC between the visual network and cognitive control networks. Older adults showed reduced modulation of this whole-brain FC pattern. The strongest predictors of within-participant decline in accuracy were changes in whole-brain network FC and FC similarity of the FN. There was no influence of age on these brain-behavior relations. These results suggest that a systems-level approach beyond the FN is required to understand the brain correlates of performance decline when faces are obscured with noise. In addition, the association between brain and behavior changes was maintained into older age, despite the dampened FC response to face degradation seen in older adults.

Reducing the influence of intramodular connectivity in participation coefficient

Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coefficient (PC) is typically used to measure this attribute, although its value also depends on the size and connectedness of the module it belongs to and may lead to nonintuitive identification of highly connected nodes. Here, we develop a normalized PC that reduces the influence of intramodular connectivity compared with the conventional PC. Using brain, C. elegans, airport, and simulated networks, we show that our measure of participation is not influenced by the size or connectedness of modules, while preserving conceptual and mathematical properties, of the classic formulation of PC. Unlike the conventional PC, we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules.

Subjective Uncontrollability over Aversive Events Reduces Working Memory Performance and Related Large-Scale Network Interactions

Lack of control over significant events may induce a state of learned helplessness that is characterized by cognitive, motivational, and affective deficits. Although highly relevant in the pathogenesis of several mental disorders, the extent of the cognitive deficits induced by experiences of uncontrollability and the neural mechanisms underlying such deficits in humans remain poorly understood. Using functional magnetic resonance imaging (fMRI), we tested here whether uncontrollability over aversive events impairs subsequent working memory performance and, if so, which neural processes are involved in such deficits. We assessed working memory and the involved neurocircuitry in the MRI scanner before and after participants underwent a task in which they could either learn to avoid electric shocks or had no instrumental control over shocks. Our results show that subjective, but not objective, uncontrollability over aversive events impaired working memory performance. This impact of subjective uncontrollability was linked to altered prefrontal and parahippocampal activities and connectivity as well as decreased crosstalk between frontoparietal executive and salience networks. Our findings show that the perceived uncontrollability over aversive events, rather than the aversive events themselves or the actual, objective control over them, disrupts subsequent working memory processes, most likely through altered crosstalk between prefrontal, temporal, and parietal areas.

Brain network modularity predicts cognitive training-related gains in young adults

The brain operates via networked activity in separable groups of regions called modules. The quantification of modularity compares the number of connections within and between modules, with high modularity indicating greater segregation, or dense connections within sub-networks and sparse connections between sub-networks. Previous work has demonstrated that baseline brain network modularity predicts executive function outcomes in older adults and patients with traumatic brain injury after cognitive and exercise interventions. In healthy young adults, however, the functional significance of brain modularity in predicting training-related cognitive improvements is not fully understood. Here, we quantified brain network modularity in young adults who underwent cognitive training with casual video games that engaged working memory and reasoning processes. Network modularity assessed at baseline was positively correlated with training-related improvements on untrained tasks. The relationship between baseline modularity and training gain was especially evident in initially lower performing individuals and was not present in a group of control participants that did not show training-related gains. These results suggest that a more modular brain network organization may allow for greater training responsiveness. On a broader scale, these findings suggest that, particularly in low-performing individuals, global network properties can capture aspects of brain function that are important in understanding individual differences in learning.

Resting-state and Vocabulary Tasks Distinctively Inform On Age-Related Differences in the Functional Brain Connectome

Most of the current knowledge about age-related differences in brain neurofunctional organization stems from neuroimaging studies using either a "resting state" paradigm, or cognitive tasks for which performance decreases with age. However, it remains to be known if comparable age-related differences are found when participants engage in cognitive activities for which performance is maintained with age, such as vocabulary knowledge tasks. A functional connectivity analysis was performed on 286 adults ranging from 18 to 80 years old, based either on a resting state paradigm or when engaged in vocabulary tasks. Notable increases in connectivity of regions of the language network were observed during task completion. Conversely, only age-related decreases were observed across the whole connectome during resting-state. While vocabulary accuracy increased with age, no interaction was found between functional connectivity, age and task accuracy or proxies of cognitive reserve, suggesting that older individuals typically benefits from semantic knowledge accumulated throughout one's life trajectory, without the need for compensatory mechanisms.

Posterior parietal influences on visual network specialization during development: An fMRI study of functional connectivity in children ages 9 to 12

Visual processing in the primate brain is highly organized along the ventral visual pathway, although it is still unclear how categorical selectivity emerges in this system. While many theories have attempted to explain the pattern of visual specialization within the ventral occipital and temporal areas, the biased connectivity hypothesis provides a framework which postulates extrinsic connectivity as a potential mechanism in shaping the development of category selectivity. As the posterior parietal cortex plays a central role in visual attention, we examined whether the pattern of parietal connectivity with the face and scene processing regions is closely linked with the functional properties of these two visually selective networks in a cohort of 60 children ages 9 to 12. Functionally localized face and scene selective regions were used in deriving each visual network's resting-state functional connectivity. The children's face and scene processing networks appeared to show a weak network segregation during resting state, which was confirmed when compared to that of a group of gender and handedness matched adults. Parietal regions of these children showed differential connectivity with the face and scene networks, and the extent of this differential parietal-visual connectivity predicted individual differences in the degree of segregation between the two visual networks, which in turn predicted individual differences in visual perception performance. Finally, the pattern of parietal connectivity with the face processing network also predicted the foci of face-related activation in the right fusiform gyrus across children. These findings provide evidence that extrinsic connectivity with regions such as the posterior parietal cortex may have important implications in the development of specialized visual processing networks.

Time-evolving dynamics in brain networks forecast responses to health messaging

Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly.

Stereotype-based stressors facilitate emotional memory neural network connectivity and encoding of negative information to degrade math self-perceptions among women

Stress engendered by stereotype threatening situations may facilitate encoding of negative, stereotype confirming feedback received during a performance among women in science, technology, engineering and mathematics (STEM). It is unclear, however, whether this process is comprised of the same neurophysiological mechanisms evident in any emotional memory encoding context, or if this encoding bias directly undermines positive self-perceptions in the stigmatized domain. A total of 160 men and women completed a math test that provided veridical positive and negative feedback, a memory test for feedback, and math self-enhancing and valuing measures in a stereotype threatening or neutral context while continuous electroencephalography activity and startle probe responses to positive and negative feedback was recorded. Indexing amygdala activity to feedback via startle responses and emotional memory network connectivity elicited during accurate recognition of positive and negative feedback via graph analyses, only stereotype threatened women encoded negative feedback better when they exhibited increased amygdala activity and emotional memory network connectivity in response to said feedback. Emotional memory biases, in turn, predicted decreases in women’s self-enhancing, math valuing and performance. Findings provide an emotional memory encoding-based mechanism for well-established findings indicating that women have more negative math self-perceptions compared with men regardless of actual performance.

Multisensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis

In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealed-not significant, but very consistent-that MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing.

Dynamic Neural Network Reconfiguration During the Generation and Reinstatement of Mnemonic Representations

Mnemonic representations allow humans to re-experience the past or simulate future scenarios by integrating episodic features from memory. Theoretical models posit that mnemonic representations require dynamic processing between neural indexes in the hippocampus and areas of the cortex providing specialized information processing. However, it remains unknown whether global and local network topology varies as information is encoded into a mnemonic representation and subsequently reinstated. Here, we investigated the dynamic nature of memory networks while a representation of a virtual city is generated and reinstated during mental simulations. We find that the brain reconfigures from a state of heightened integration when encoding demands are highest, to a state of localized processing once representations are formed. This reconfiguration is associated with changes in hippocampal centrality at the intra- and inter-module level, decreasing its role as a connector hub between modules and within a hippocampal neighborhood as encoding demands lessen. During mental simulations, we found increased levels of hippocampal centrality within its local neighborhood coupled with decreased functional interactions between other regions of the neighborhood during highly vivid simulations, suggesting that information flow vis-à-vis the hippocampus is critical for high fidelity recapitulation of mnemonic representations.

Working Memory and Reasoning Benefit from Different Modes of Large-scale Brain Dynamics in Healthy Older Adults

Researchers have proposed that solving complex reasoning problems, a key indicator of fluid intelligence, involves the same cognitive processes as solving working memory tasks. This proposal is supported by an overlap of the functional brain activations associated with the two types of tasks and by high correlations between interindividual differences in performance. We replicated these findings in 53 older participants but also showed that solving reasoning and working memory problems benefits from different configurations of the functional connectome and that this dissimilarity increases with a higher difficulty load. Specifically, superior performance in a typical working memory paradigm ( n-back) was associated with upregulation of modularity (increased between-network segregation), whereas performance in the reasoning task was associated with effective downregulation of modularity. We also showed that working memory training promotes task-invariant increases in modularity. Because superior reasoning performance is associated with downregulation of modular dynamics, training may thus have fostered an inefficient way of solving the reasoning tasks. This could help explain why working memory training does little to promote complex reasoning performance. The study concludes that complex reasoning abilities cannot be reduced to working memory and suggests the need to reconsider the feasibility of using working memory training interventions to attempt to achieve effects that transfer to broader cognition.

Functional networks underlying item and source memory: shared and distinct network components and age-related differences

Although the medial temporal lobes (MTLs) are critical for both item memory (IM) and source memory (SM), the lateral prefrontal cortex and posterior parietal cortex play a greater role during SM than IM. It is unclear, however, how these differences translate into shared and distinct IM versus SM network components and how these network components vary with age. Within a sample of younger adults (YAs; n = 15, M_age = 19.5 years) and older adults (OAs; n = 40, M_age = 68.6 years), we investigated the functional networks underlying IM and SM. Before functional MRI scanning, participants encoded nouns while making either pleasantness or size judgments. During functional MRI scanning, participants completed IM and SM retrieval tasks. We found that MTL nodes were similarly interconnected among each other during both IM and SM (shared network components) but maintained more intermodule connections during SM (distinct network components). Also, during SM, OAs (compared to YAs) had MTL nodes with more widespread connections. These findings provide a novel viewpoint on neural mechanism differences underlying IM versus SM in YAs and OAs.

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

Mapping the structural and functional network architecture of the medial temporal lobe using 7T MRI

Medial temporal lobe (MTL) subregions play integral roles in memory function and are differentially affected in various neurological and psychiatric disorders. The ability to structurally and functionally characterize these subregions may be important to understanding MTL physiology and diagnosing diseases involving the MTL. In this study, we characterized network architecture of the MTL in healthy subjects (n = 31) using both resting state functional MRI and MTL-focused T2-weighted structural MRI at 7 tesla. Ten MTL subregions per hemisphere, including hippocampal subfields and cortical regions of the parahippocampal gyrus, were segmented for each subject using a multi-atlas algorithm. Both structural covariance matrices from correlations of subregion volumes across subjects, and functional connectivity matrices from correlations between subregion BOLD time series were generated. We found a moderate structural and strong functional inter-hemispheric symmetry. Several bilateral hippocampal subregions (CA1, dentate gyrus, and subiculum) emerged as functional network hubs. We also observed that the structural and functional networks naturally separated into two modules closely corresponding to (a) bilateral hippocampal formations, and (b) bilateral extra-hippocampal structures. Finally, we found a significant correlation in structural and functional connectivity (r = 0.25). Our findings represent a comprehensive analysis of network topology of the MTL at the subregion level. We share our data, methods, and findings as a reference for imaging methods and disease-based research.

Task-related changes in degree centrality and local coherence of the posterior cingulate cortex after major cardiac surgery in older adults

OBJECTIVES

Older adults often display postoperative cognitive decline (POCD) after surgery, yet it is unclear to what extent functional connectivity (FC) alterations may underlie these deficits. We examined for postoperative voxel-wise FC changes in response to increased working memory load demands in cardiac surgery patients and nonsurgical controls.

EXPERIMENTAL DESIGN

Older cardiac surgery patients (n = 25) completed a verbal N-back working memory task during MRI scanning and cognitive testing before and 6 weeks after surgery; nonsurgical controls with cardiac disease (n = 26) underwent these assessments at identical time intervals. We measured postoperative changes in degree centrality, the number of edges attached to a brain node, and local coherence, the temporal homogeneity of regional functional correlations, using voxel-wise graph theory-based FC metrics. Group × time differences were evaluated in these FC metrics associated with increased N-back working memory load (2-back > 1-back), using a two-stage partitioned variance, mixed ANCOVA.

PRINCIPAL OBSERVATIONS

Cardiac surgery patients demonstrated postoperative working memory load-related degree centrality increases in the left dorsal posterior cingulate cortex (dPCC; p < .001, cluster p-FWE < .05). The dPCC also showed a postoperative increase in working memory load-associated local coherence (p < .001, cluster p-FWE < .05). dPCC degree centrality and local coherence increases were inversely associated with global cognitive change in surgery patients (p < .01), but not in controls.

CONCLUSIONS

Cardiac surgery patients showed postoperative increases in working memory load-associated degree centrality and local coherence of the dPCC that were inversely associated with postoperative global cognitive outcomes and independent of perioperative cerebrovascular damage.

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

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.

Brain Network Modularity Predicts Exercise-Related Executive Function Gains in Older Adults

Recent work suggests that the brain can be conceptualized as a network comprised of groups of sub-networks or modules. The extent of segregation between modules can be quantified with a modularity metric, where networks with high modularity have dense connections within modules and sparser connections between modules. Previous work has shown that higher modularity predicts greater improvements after cognitive training in patients with traumatic brain injury and in healthy older and young adults. It is not known, however, whether modularity can also predict cognitive gains after a physical exercise intervention. Here, we quantified modularity in older adults (N = 128, mean age = 64.74) who underwent one of the following interventions for 6 months (NCT01472744 on ClinicalTrials.gov): (1) aerobic exercise in the form of brisk walking (Walk), (2) aerobic exercise in the form of brisk walking plus nutritional supplement (Walk+), (3) stretching, strengthening and stability (SSS), or (4) dance instruction. After the intervention, the Walk, Walk+ and SSS groups showed gains in cardiorespiratory fitness (CRF), with larger effects in both walking groups compared to the SSS and Dance groups. The Walk, Walk+ and SSS groups also improved in executive function (EF) as measured by reasoning, working memory, and task-switching tests. In the Walk, Walk+, and SSS groups that improved in EF, higher baseline modularity was positively related to EF gains, even after controlling for age, in-scanner motion and baseline EF. No relationship between modularity and EF gains was observed in the Dance group, which did not show training-related gains in CRF or EF control. These results are consistent with previous studies demonstrating that individuals with a more modular brain network organization are more responsive to cognitive training. These findings suggest that the predictive power of modularity may be generalizable across interventions aimed to enhance aspects of cognition and that, especially in low-performing individuals, global network properties can capture individual differences in neuroplasticity.

Intelligence is associated with the modular structure of intrinsic brain networks

General intelligence is a psychological construct that captures in a single metric the overall level of behavioural and cognitive performance in an individual. While previous research has attempted to localise intelligence in circumscribed brain regions, more recent work focuses on functional interactions between regions. However, even though brain networks are characterised by substantial modularity, it is unclear whether and how the brain's modular organisation is associated with general intelligence. Modelling subject-specific brain network graphs from functional MRI resting-state data (N = 309), we found that intelligence was not associated with global modularity features (e.g., number or size of modules) or the whole-brain proportions of different node types (e.g., connector hubs or provincial hubs). In contrast, we observed characteristic associations between intelligence and node-specific measures of within- and between-module connectivity, particularly in frontal and parietal brain regions that have previously been linked to intelligence. We propose that the connectivity profile of these regions may shape intelligence-relevant aspects of information processing. Our data demonstrate that not only region-specific differences in brain structure and function, but also the network-topological embedding of fronto-parietal as well as other cortical and subcortical brain regions is related to individual differences in higher cognitive abilities, i.e., intelligence.

The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition

A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition.

SIGNIFICANCE STATEMENT

The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brain's ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.

Fluctuations between high- and low-modularity topology in time-resolved functional connectivity

Modularity is an important topological attribute for functional brain networks. Recent human fMRI studies have reported that modularity of functional networks varies not only across individuals being related to demographics and cognitive performance, but also within individuals co-occurring with fluctuations in network properties of functional connectivity, estimated over short time intervals. However, characteristics of these time-resolved functional networks during periods of high and low modularity have remained largely unexplored. In this study we investigate basic spatiotemporal properties of time-resolved networks in the high and low modularity periods during rest, with a particular focus on their spatial connectivity patterns, temporal homogeneity and test-retest reliability. We show that spatial connectivity patterns of time-resolved networks in the high and low modularity periods are represented by increased and decreased dissociation of the default mode network module from task-positive network modules, respectively. We also find that the instances of time-resolved functional connectivity sampled from within the high (respectively, low) modularity period are relatively homogeneous (respectively, heterogeneous) over time, indicating that during the low modularity period the default mode network interacts with other networks in a variable manner. We confirmed that the occurrence of the high and low modularity periods varies across individuals with moderate inter-session test-retest reliability and that it is correlated with previously-reported individual differences in the modularity of functional connectivity estimated over longer timescales. Our findings illustrate how time-resolved functional networks are spatiotemporally organized during periods of high and low modularity, allowing one to trace individual differences in long-timescale modularity to the variable occurrence of network configurations at shorter timescales.

Cohesive network reconfiguration accompanies extended training

Human behavior is supported by flexible neurophysiological processes that enable the fine‐scale manipulation of information across distributed neural circuits. Yet, approaches for understanding the dynamics of these circuit interactions have been limited. One promising avenue for quantifying and describing these dynamics lies in multilayer network models. Here, networks are composed of nodes (which represent brain regions) and time‐dependent edges (which represent statistical similarities in activity time series). We use this approach to examine functional connectivity measured by non‐invasive neuroimaging techniques. These multilayer network models facilitate the examination of changes in the pattern of statistical interactions between large‐scale brain regions that might facilitate behavior. In this study, we define and exercise two novel measures of network reconfiguration, and demonstrate their utility in neuroimaging data acquired as healthy adult human subjects learn a new motor skill. In particular, we identify putative functional modules in multilayer networks and characterize the degree to which nodes switch between modules. Next, we define cohesive switches, in which a set of nodes moves between modules together as a group, and we define disjoint switches, in which a single node moves between modules independently from other nodes. Together, these two concepts offer complementary yet distinct insights into the changes in functional connectivity that accompany motor learning. More generally, our work offers statistical tools that other researchers can use to better understand the reconfiguration patterns of functional connectivity over time. Hum Brain Mapp 38:4744–4759, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Transition of the functional brain network related to increasing cognitive demands

Network neuroscience provides tools that can easily be used to verify main assumptions of the global workspace theory (GWT), such as the existence of highly segregated information processing during effortless tasks performance, engagement of multiple distributed networks during effortful tasks and the critical role of long-range connections in workspace formation. A number of studies support the assumptions of GWT by showing the reorganization of the whole-brain functional network during cognitive task performance; however, the involvement of specific large scale networks in the formation of workspace is still not well-understood. The aims of our study were: (1) to examine changes in the whole-brain functional network under increased cognitive demands of working memory during an n-back task, and their relationship with behavioral outcomes; and (2) to provide a comprehensive description of local changes that may be involved in the formation of the global workspace, using hub detection and network-based statistic. Our results show that network modularity decreased with increasing cognitive demands, and this change allowed us to predict behavioral performance. The number of connector hubs increased, whereas the number of provincial hubs decreased when the task became more demanding. We also found that the default mode network (DMN) increased its connectivity to other networks while decreasing connectivity between its own regions. These results, apart from replicating previous findings, provide a valuable insight into the mechanisms of the formation of the global workspace, highlighting the role of the DMN in the processes of network integration. Hum Brain Mapp 38:3659-3674, 2017. © 2017 Wiley Periodicals, Inc.

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. © 2017 Wiley Periodicals, Inc.

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity-a measure of network segregation-is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN.SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control network and default mode network strengthen their interaction with one another during episodic retrieval. Such across-network communication likely facilitates effective access to internally generated representations of past event knowledge.

Correspondence between evoked and intrinsic functional brain network configurations

Much of the literature exploring differences between intrinsic and task-evoked brain architectures has examined changes in functional connectivity patterns between specific brain regions. While informative, this approach overlooks important overall functional changes in hub organization and network topology that may provide insights about differences in integration between intrinsic and task-evoked states. Examination of changes in overall network organization, such as a change in the concentration of hub nodes or a quantitative change in network organization, is important for understanding the underlying processes that differ between intrinsic and task-evoked brain architectures. The present study used graph-theoretical techniques applied to publicly available neuroimaging data collected from a large sample of individuals (N = 202), and a within-subject design where resting-state and several task scans were collected from each participant as part of the Human Connectome Project. We demonstrate that differences between intrinsic and task-evoked brain networks are characterized by a task-general shift in high-connectivity hubs from primarily sensorimotor/auditory processing areas during the intrinsic state to executive control/salience network areas during task performance. In addition, we demonstrate that differences between intrinsic and task-evoked architectures are associated with changes in overall network organization, such as increases in network clustering, global efficiency and integration between modules. These findings offer a new perspective on the principles guiding functional brain organization by identifying unique and divergent properties of overall network organization between the resting-state and task performance. Hum Brain Mapp 38:1992-2007, 2017. © 2017 Wiley Periodicals, Inc.

Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults

Cognitive training interventions are a promising approach to mitigate cognitive deficits common in aging and, ultimately, to improve functioning in older adults. Baseline neural factors, such as properties of brain networks, may predict training outcomes and can be used to improve the effectiveness of interventions. Here, we investigated the relationship between baseline brain network modularity, a measure of the segregation of brain sub-networks, and training-related gains in cognition in older adults. We found that older adults with more segregated brain sub-networks (i.e., more modular networks) at baseline exhibited greater training improvements in the ability to synthesize complex information. Further, the relationship between modularity and training-related gains was more pronounced in sub-networks mediating “associative” functions compared with those involved in sensory-motor processing. These results suggest that assessments of brain networks can be used as a biomarker to guide the implementation of cognitive interventions and improve outcomes across individuals. More broadly, these findings also suggest that properties of brain networks may capture individual differences in learning and neuroplasticity.

Trail Registration: ClinicalTrials.gov, NCT#00977418

Stability of Network Communities as a Function of Task Complexity

The analysis of the community architecture in functional brain networks has revealed important relations between specific behavioral patterns and characteristic features of the associated functional organization. Numerous studies have assessed changes in functional communities during different states of awareness, learning, information processing, and various behavioral patterns. The robustness of detected communities within a network has been an often-discussed topic in complex systems research. However, our knowledge regarding the intersubject stability of functional communities in the human brain while performing different tasks is still lacking. In this study, we examined the variability of functional communities in weighted undirected graphs based on fMRI recordings of healthy participants across three conditions: the resting state, syllable production as a simple vocal motor task, and meaningful speech production representing a complex behavioral pattern with cognitive involvement. On the basis of the constructed empirical networks, we simulated a large cohort of artificial graphs and performed a leave-one-out stability analysis to assess the sensitivity of communities in the group-averaged networks with respect to perturbations in the averaging cohort. We found that the stability of partitions derived from group-averaged networks depended on task complexity. The determined community architecture in mean networks reflected within-behavior network stability and between-behavior flexibility of the human functional connectome. The sensitivity of functional communities increased from rest to syllable production to speaking, which suggests that the approximation quality of the community structure in the average network to reflect individual per-participant partitions depends on task complexity.

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 Anderson's (2014) research program.

Moderate-Heavy Alcohol Consumption Lifestyle in Older Adults Is Associated with Altered Central Executive Network Community Structure during Cognitive Task

Older adults today consume more alcohol than previous generations, the majority being social drinkers. The effects of heavy alcohol use on brain functioning closely resemble age-related changes, but it is not known if moderate-heavy alcohol consumption intensifies brain aging. Whether a lifestyle of moderate-heavy alcohol use in older adults increased age-related brain changes was examined. Forty-one older adults (65–80 years) that consumed light (< 2 drinks/week and ≥ 1 drink/month, n = 20) or moderate-heavy (7–21 drinks/week, non-bingers, n = 21) amounts of alcohol were enrolled. Twenty-two young adults (24–35 years) were also enrolled (light, n = 11 and moderate-heavy, n = 11). Functional brain networks based on magnetic resonance imaging data were generated for resting state and during a working memory task. Whole-brain, Central Executive Network (CEN), and Default Mode Network (DMN) connectivity were assessed in light and moderate-heavy alcohol consuming older adults with comparisons to young adults. The older adults had significantly lower whole brain connectivity (global efficiency) and lower regional connectivity (community structure) in the CEN during task and in the DMN at rest. Moderate-heavy older drinkers did not exhibit whole brain connectivity differences compared to the low drinkers. However, decreased CEN connectivity was observed during the task. There were no differences in the DMN connectivity between drinking groups. Taken together, a lifestyle including moderate-heavy alcohol consumption may be associated with further decreases in brain network connectivity within task-related networks in older adults. Further research is required to determine if this decrease is compensatory or an early sign of decline.

Graph-Theoretical Study of Functional Changes Associated with the Iowa Gambling Task

The primary aim of this study was to examine changes in functional brain network organization from rest to the Iowa Gambling Task (IGT) using a graph-theoretical approach. Although many functional neuroimaging studies have examined task-based activations in complex-decision making tasks, changes in functional network organization during this task remain unexplored. This study used a repeated-measures approach to examine changes in functional network organization across multiple sessions of resting-state and IGT scans. The results revealed that global network organization shifted from a local, clustered organization at rest to a more global, integrated organization during the IGT. In addition, network organization was stable across sessions of rest and the IGT. Regional analyses of the Default Mode Network (DMN) and Fronto-Parietal Network (FPN) revealed differential patterns of change in regional network organization from rest to the IGT. The results of this study reveal that global and regional network organization is significantly modulated across states and fairly stable over time, and that network changes in the FPN are particularly important in the decision-making processes necessary for successful IGT performance.

Reconfiguration of brain network architecture to support executive control in aging

Aging is accompanied by declines in executive control abilities and changes in underlying brain network architecture. Here, we examined brain networks in young and older adults during a task-free resting state and an N-back task and investigated age-related changes in the modular network organization of the brain. Compared with young adults, older adults showed larger changes in network organization between resting state and task. Although young adults exhibited increased connectivity between lateral frontal regions and other network modules during the most difficult task condition, older adults also exhibited this pattern of increased connectivity during less-demanding task conditions. Moreover, the increase in between-module connectivity in older adults was related to faster task performance and greater fractional anisotropy of the superior longitudinal fasciculus. These results demonstrate that older adults who exhibit more pronounced network changes between a resting state and task have better executive control performance and greater structural connectivity of a core frontal-posterior white matter pathway.

Default Mode Dynamics for Global Functional Integration

The default mode network (DMN) has been traditionally assumed to hinder behavioral performance in externally focused, goal-directed paradigms and to provide no active contribution to human cognition. However, recent evidence suggests greater DMN activity in an array of tasks, especially those that involve self-referential and memory-based processing. Although data that robustly demonstrate a comprehensive functional role for DMN remains relatively scarce, the global workspace framework, which implicates the DMN in global information integration for conscious processing, can potentially provide an explanation for the broad range of higher-order paradigms that report DMN involvement. We used graph theoretical measures to assess the contribution of the DMN to global functional connectivity dynamics in 22 healthy volunteers during an fMRI-based n-back working-memory paradigm with parametric increases in difficulty. Our predominant finding is that brain modularity decreases with greater task demands, thus adapting a more global workspace configuration, in direct relation to increases in reaction times to correct responses. Flexible default mode regions dynamically switch community memberships and display significant changes in their nodal participation coefficient and strength, which may reflect the observed whole-brain changes in functional connectivity architecture. These findings have important implications for our understanding of healthy brain function, as they suggest a central role for the DMN in higher cognitive processing.

SIGNIFICANCE STATEMENT

The default mode network (DMN) has been shown to increase its activity during the absence of external stimulation, and hence was historically assumed to disengage during goal-directed tasks. Recent evidence, however, implicates the DMN in self-referential and memory-based processing. We provide robust evidence for this network's active contribution to working memory by revealing dynamic reconfiguration in its interactions with other networks and offer an explanation within the global workspace theoretical framework. These promising findings may help redefine our understanding of the exact DMN role in human cognition.

Global features of functional brain networks change with contextual disorder

It is known that features of stimuli in the environment affect the strength of functional connectivity in the human brain. However, investigations to date have not converged in determining whether these also impact functional networks' global features, such as modularity strength, number of modules, partition structure, or degree distributions. We hypothesized that one environmental attribute that may strongly impact global features is the temporal regularity of the environment, as prior work indicates that differences in regularity impact regions involved in sensory, attentional and memory processes. We examined this with an fMRI study, in which participants passively listened to tonal series that had identical physical features and differed only in their regularity, as defined by the strength of transition structure between tones. We found that series-regularity induced systematic changes to global features of functional networks, including modularity strength, number of modules, partition structure, and degree distributions. In tandem, we used a novel node-level analysis to determine the extent to which brain regions maintained their within-module connectivity across experimental conditions. This analysis showed that primary sensory regions and those associated with default-mode processes are most likely to maintain their within-module connectivity across conditions, whereas prefrontal regions are least likely to do so. Our work documents a significant capacity for global-level brain network reorganization as a function of context. These findings suggest that modularity and other core, global features, while likely constrained by white-matter structural brain connections, are not completely determined by them.

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We examined global features of whole-brain functional connectivity to inputs with varying disorder.

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Modularity, module numbers, and partition similarity varied with input disorder.

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Default-mode and sensory brain regions were least impacted by the manipulation.

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.

Brain connectivity and prediction of relapse after cognitive-behavioral therapy in obsessive-compulsive disorder

BACKGROUND

Intensive cognitive-behavioral therapy (CBT) can effectively reduce symptoms in obsessive-compulsive disorder (OCD). However, many relapse after treatment. Few studies have investigated biological markers predictive of follow-up clinical status. The objective was to determine if brain network connectivity patterns prior to intensive CBT predict worsening of clinical symptoms during follow-up.

METHODS

We acquired resting-state functional magnetic resonance imaging data from 17 adults with OCD prior to and following 4 weeks of intensive CBT. Functional connectivity data were analyzed to yield graph-theory metrics. We examined the relationship between pre-treatment connectome properties and OCD clinical symptoms before and after treatment and during a 12-month follow-up period.

RESULTS

Mean OCD symptom decrease was 40.4 ± 16.4% pre- to post-treatment (64.7% responded; 58.8% remitted), but 35.3% experienced clinically significant worsening during follow-up. From pre- to post-treatment, small-worldness and clustering coefficient significantly increased. Decreases in modularity correlated with decreases in OCD symptoms. Higher pre-treatment small-world connectivity was significantly associated with worsening of OCD symptoms during the follow-up period. Psychometric and neurocognitive measures pre- and post-treatment were not significant predictors.

CONCLUSION

This is the first graph-theory connectivity study of the effects of CBT in OCD, and the first to test associations with follow-up clinical status. Results show functional network efficiency as a biomarker of CBT response and relapse in OCD. CBT increases network efficiency as it alleviates symptoms in most patients, but those entering therapy with already high network efficiency are at greater risk of relapse. Results have potential clinical implications for treatment selection.