Dynamic reconfiguration of frontal brain networks during executive cognition in humans

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of “dynamic network neuroscience” to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the “n-back” task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes “network flexibility,” employs transient and heterogeneous connectivity between frontal systems, which we refer to as “integration.” Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia.The era of human brain mapping has demonstrated the power of associating brain regions to specific cognitive functions. However, emerging evidence indicates that many so-called “domain-general” areas engage in multiple functions, differing from “domain-specific” areas such as primary visual cortex that perform a very specific function (1, 2). Such broad engagement is enabled by two fundamental features of brain function: time and interconnectivity. Brain areas and associated circuits or networks may be engaged in tasks differently over time: some transiently and some consistently (2, 3). A fundamental understanding of cognition in general and executive cognition in particular should therefore address the dynamic, interconnected nature of brain function.Here, we use and extend emerging tools from “dynamic network neuroscience,” a field of neuroscientific inquiry that embraces the inherently evolving, interconnected nature of neurophysiological phenomena underlying human cognition (3, 4). Building on the formalism of network science (5), this approach treats the patterns of communication between brain regions as evolving networks and links this evolution to behavioral outcomes. Conceptually, this approach is particularly useful in examining the consistent or transient engagement of neural (or cognitive) circuits or putative functional modules (Fig. 1). We define a network module to be a set of brain regions that are strongly connected to each other and weakly connected to the rest of the network. Using dynamic network-based clustering techniques (6), we seek to observe the flexible recruitment and integration of neural circuits underlying executive function in the form of working memory.Open in a separate windowFig. 1.Network reconfiguration during executive function. (A) We use a numerical n-back task consisting of 0-back and 2-back conditions. (B) We define 270 cortical and subcortical regions of interest (36), and (C) extract the mean time course from each region. (D) A sliding window comprising 15 volumes with no gap was applied to regional mean time courses, and for each window we estimated the functional connectivity between pairs of regions using coherence. This procedure resulted in a sequence of 114 time-ordered adjacency matrices. (E) Using a dynamic community detection algorithm (part 1 in panel), we identified network modules in each time window and tracked their evolution over time. (F) By estimating the probability that a brain region changes its allegiance to modules between any two consecutive time windows (part 2 in panel), we observed that whole-brain flexibility oscillated between unitask (2-back or 0-back only) and dual-task (2-back and 0-back in same time window) conditions.Working memory lies at the interface of perception and action (7) and requires the integration of large-scale neural circuits (8–11). Theoretical frameworks for working memory call on the interplay of distinct components (12) and their integration in broader cognitive circuits (1). The empirical neuroimaging literature has bolstered these conceptualizations by identifying several distinct sets of brain areas underlying working-memory performance (13–17). Nevertheless, a fundamental understanding of the flexible integration and recruitment of these circuits remains incomplete.In the present study, we characterize the time-dependent interactions between putative neural circuits [network modules (3)] underlying working-memory performance in humans as elicited by an n-back task performed during the acquisition of functional MRI (fMRI) data (Fig. 1 A and B). By deploying a sliding time window analysis (18, 19), we capture brain network dynamics during working-memory function (2-back), during a baseline condition (0-back), and in transitions between baseline and task (Fig. 1 C and D). We identify putative functional modules in each time window and track how brain regions change their engagement in these modules over time (Fig. 1 E and F). We quantify those changes over time by flexibility, which measures how often a particular brain region changes its modular allegiance. Based on the cognitive load of the 2-back condition (20–22), we hypothesize that the brain transiently reorganizes functional modules during task performance in comparison with baseline. Furthermore, we hypothesize that this reconfiguration is driven by higher order cognitive control systems, particularly in frontal cortex (2), which are known to play a role in task switching. Finally, based on prior evidence linking network reconfiguration to behavioral adaptation (3), we hypothesize that individuals who display more flexible network structures will perform better than individuals with more rigid network structures.     

Simple models of human brain functional networks

Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.     

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients’ networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.Schizophrenia is increasingly conceived as a brain dysconnectivity syndrome or disorder of brain network organization (1–4). Various methods have been used to demonstrate abnormal structural or functional connectivity between brain regions in patients with schizophrenia. Specifically, several recent studies have used graph theory to measure the topological pattern of connections (or edges) between regional nodes in large-scale networks derived from neuroimaging data (5–12).The results to date of graph theoretical studies of schizophrenia are not entirely consistent, but there is some convergence around the concept of topological randomization (9, 13). For example, human brain networks (and many other complex, real-life networks) generally have a small-world topology that can be understood as intermediate between the regular, highly clustered organization of a lattice and the globally efficient organization of a random graph. Three independent functional MRI (fMRI) studies have shown that the functional brain networks of patients with schizophrenia are relatively shifted toward the random end of this small-world spectrum, i.e., they have lower clustering coefficient and greater efficiency than healthy brain networks (5, 7, 8). Previous studies have also reported schizophrenia-related disruptions in the normal community structure of fMRI networks, such as increased connectivity between modules (5), and abnormal rich clubs (14), in patients with schizophrenia. There is also some evidence that the physical (geometric) distance of edges tends to be relatively increased in structural and functional brain graphs of schizophrenia (6, 15).There were three main objectives of this study. The first was to assess the replicability of the prior topological and geometric markers of network randomization in an independent sample of patients with schizophrenia. Specifically, we wanted to test the hypothesis that the network abnormalities most frequently reported in US or European studies of schizophrenia would also be evident in a Chinese population. Second, we tested the hypothesis that brain network randomization in patients with schizophrenia would be associated with greater resilience to targeted attack on network hubs in silico. Third, we aimed to test the hypothesis that brain network randomization/resilience is an endophenotype, or marker of familial risk for schizophrenia, that is expected to be abnormal in nonpsychotic first-degree relatives of patients as well as in the patients themselves.We therefore analyzed resting-state fMRI data from 25 patients with schizophrenia (Sz), 25 first-degree relatives (Rel), and 29 healthy volunteers (HV). For each individual image, we constructed a functional brain graph and estimated some key topological and geometric markers of randomization (clustering coefficient, efficiency, degree distribution, distance distribution), and resilience to targeted attack and random failure. We predicted that functional brain networks would be more randomized and resilient in both Sz and Rel, compared with HV.     

Predicting errors from reconfiguration patterns in human brain networks

Task preparation is a complex cognitive process that implements anticipatory adjustments to facilitate future task performance. Little is known about quantitative network parameters governing this process in humans. Using functional magnetic resonance imaging (fMRI) and functional connectivity measurements, we show that the large-scale topology of the brain network involved in task preparation shows a pattern of dynamic reconfigurations that guides optimal behavior. This network could be decomposed into two distinct topological structures, an error-resilient core acting as a major hub that integrates most of the network’s communication and a predominantly sensory periphery showing more flexible network adaptations. During task preparation, core–periphery interactions were dynamically adjusted. Task-relevant visual areas showed a higher topological proximity to the network core and an enhancement in their local centrality and interconnectivity. Failure to reconfigure the network topology was predictive for errors, indicating that anticipatory network reconfigurations are crucial for successful task performance. On the basis of a unique network decoding approach, we also develop a general framework for the identification of characteristic patterns in complex networks, which is applicable to other fields in neuroscience that relate dynamic network properties to behavior.     

Fundamental limits on persistent activity in networks of noisy neurons

Neural noise limits the fidelity of representations in the brain. This limitation has been extensively analyzed for sensory coding. However, in short-term memory and integrator networks, where noise accumulates and can play an even more prominent role, much less is known about how neural noise interacts with neural and network parameters to determine the accuracy of the computation. Here we analytically derive how the stored memory in continuous attractor networks of probabilistically spiking neurons will degrade over time through diffusion. By combining statistical and dynamical approaches, we establish a fundamental limit on the network’s ability to maintain a persistent state: The noise-induced drift of the memory state over time within the network is strictly lower-bounded by the accuracy of estimation of the network’s instantaneous memory state by an ideal external observer. This result takes the form of an information-diffusion inequality. We derive some unexpected consequences: Despite the persistence time of short-term memory networks, it does not pay to accumulate spikes for longer than the cellular time-constant to read out their contents. For certain neural transfer functions, the conditions for optimal sensory coding coincide with those for optimal storage, implying that short-term memory may be co-localized with sensory representation.     

Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection

Analyses of functional interactions between large-scale brain networks have identified two broad systems that operate in apparent competition or antagonism with each other. One system, termed the default mode network (DMN), is thought to support internally oriented processing. The other system acts as a generic external attention system (EAS) and mediates attention to exogenous stimuli. Reports that the DMN and EAS show anticorrelated activity across a range of experimental paradigms suggest that competition between these systems supports adaptive behavior. Here, we used functional MRI to characterize functional interactions between the DMN and different EAS components during performance of a recollection task known to coactivate regions of both networks. Using methods to isolate task-related, context-dependent changes in functional connectivity between these systems, we show that increased cooperation between the DMN and a specific right-lateralized frontoparietal component of the EAS is associated with more rapid memory recollection. We also show that these cooperative dynamics are facilitated by a dynamic reconfiguration of the functional architecture of the DMN into core and transitional modules, with the latter serving to enhance integration with frontoparietal regions. In particular, the right posterior cingulate cortex may act as a critical information-processing hub that provokes these context-dependent reconfigurations from an intrinsic or default state of antagonism. Our findings highlight the dynamic, context-dependent nature of large-scale brain dynamics and shed light on their contribution to individual differences in behavior.     

Energetic cost of brain functional connectivity

The brain''s functional connectivity is complex, has high energetic cost, and requires efficient use of glucose, the brain''s main energy source. It has been proposed that regions with a high degree of functional connectivity are energy efficient and can minimize consumption of glucose. However, the relationship between functional connectivity and energy consumption in the brain is poorly understood. To address this neglect, here we propose a simple model for the energy demands of brain functional connectivity, which we tested with positron emission tomography and MRI in 54 healthy volunteers at rest. Higher glucose metabolism was associated with proportionally larger MRI signal amplitudes, and a higher degree of connectivity was associated with nonlinear increases in metabolism, supporting our hypothesis for the energy efficiency of the connectivity hubs. Basal metabolism (in the absence of connectivity) accounted for 30% of brain glucose utilization, which suggests that the spontaneous brain activity accounts for 70% of the energy consumed by the brain. The energy efficiency of the connectivity hubs was higher for ventral precuneus, cerebellum, and subcortical hubs than for cortical hubs. The higher energy demands of brain communication that hinges upon higher connectivity could render brain hubs more vulnerable to deficits in energy delivery or utilization and help explain their sensitivity to neurodegenerative conditions, such as Alzheimer’s disease.     

Double dissociation of two cognitive control networks in patients with focal brain lesions

Neuroimaging studies of cognitive control have identified two distinct networks with dissociable resting state connectivity patterns. This study, in patients with heterogeneous damage to these networks, demonstrates network independence through a double dissociation of lesion location on two different measures of network integrity: functional correlations among network nodes and within-node graph theory network properties. The degree of network damage correlates with a decrease in functional connectivity within that network while sparing the nonlesioned network. Graph theory properties of intact nodes within the damaged network show evidence of dysfunction compared with the undamaged network. The effect of anatomical damage thus extends beyond the lesioned area, but remains within the bounds of the existing network connections. Together this evidence suggests that networks defined by their role in cognitive control processes exhibit independence in resting data.     

Time-resolved resting-state brain networks

Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.The coordination of brain activity between disparate neural populations is a dynamic and context-dependent process (1–3). Although dynamic patterns of neural synchronization may be evident in time-dependent measures of functional connectivity (4, 5), the temporal stability of high-level topological properties is unknown. The topology of large-scale cortical activity—such as its efficient network layout (6), community structure (7), network hubs (8), rich-club organization (9, 10), and small worldness (11, 12)—may reflect fundamental aspects of cortical computation. Temporal fluctuations in these graph-theoretic measures may hence speak to adaptive properties of neuronal information processing.With international connectome mapping consortia such as the Human Connectome Project (HCP) (13) and the developing Human Connectome Project in full swing, resting-state functional magnetic resonance imaging (rsfMRI) data of unprecedented temporal resolution are now available to map the time-resolved properties of functional brain networks. Imaging the brain at rest reveals spontaneous low-frequency fluctuations in brain activity that are temporally correlated between functionally related regions (14–17). Interregional correlations are referred to as functional connections, and they collectively form a complex network (18).Functional brain networks are typically mapped in a time-averaged sense, based on the assumption that functional connections remain relatively static (stationary) in the resting brain. However, recent investigations have furnished compelling evidence challenging the “static” conceptualization of resting-state functional connectivity (5). In particular, the application of time-resolved methodologies for analyzing time series data has consistently revealed fluctuations in resting-state functional connectivity at timescales ranging from tens of seconds to a few minutes (19–24). Furthermore, the modular organization of functional brain networks appears to be time-dependent in the resting state (25, 26) and modulated by learning (27) and cognitive effort (28, 29). It is therefore apparent that reducing fluctuations in functional connectivity to time averages has led to a very useful but static and possibly oversimplified characterization of the brain’s functional networks. For example, connections that toggle between correlated and anticorrelated states are reduced to zero in a time-averaged sense, assuming equal dwell times in each state.Conventionally, rsfMRI data are sampled at a resolution of 2 s or slower. Using multiband accelerated echo planar imaging, the HCP has acquired high-quality rsfMRI data at a subsecond resolution (30). This order of magnitude improvement in temporal resolution is highly advantageous to the feasibility of time-resolved functional connectomics. Faster sampling rates enable a richer temporal characterization of resting-state fluctuations, denser sampling of physiological confounds, and greater degrees of freedom (30).Using a sliding-window approach applied to HCP rsfMRI data, we mapped the evolution of functional brain networks over a continuous 15-min interval at a subsecond resolution. For each of 10 individuals, this yielded a time series of correlation matrices (regions × regions × time), where matrix elements quantified the functional connectivity at a given time instant between cortical and subcortical regions comprising established brain parcellation atlases. We developed a statistic to test the time-resolved connectome for evidence of nonstationary temporal dynamics and applied it to the 10 individuals as well as a replication data set and simulated rsfMRI data.Our main aim was to investigate the consequences of nonstationary fluctuations on the topological organization of functional brain networks. We hypothesized that dynamic behavior is coordinated across the brain so that transitions between distinct states are marked by reorganization of the brain’s functional topology. Evidence for this hypothesis is provided by the coordinated fluctuations in network measures, such as hub centrality (31), that have been observed in simulated rsfMRI data (32, 33).     

Multiple component networks support working memory in prefrontal cortex

Lateral prefrontal cortex (PFC) is regarded as the hub of the brain’s working memory (WM) system, but it remains unclear whether WM is supported by a single distributed network or multiple specialized network components in this region. To investigate this problem, we recorded from neurons in PFC while monkeys made delayed eye movements guided by memory or vision. We show that neuronal responses during these tasks map to three anatomically specific modes of persistent activity. The first two modes encode early and late forms of information storage, whereas the third mode encodes response preparation. Neurons that reflect these modes are concentrated at different anatomical locations in PFC and exhibit distinct patterns of coordinated firing rates and spike timing during WM, consistent with distinct networks. These findings support multiple component models of WM and consequently predict distinct failures that could contribute to neurologic dysfunction.High-level cognition depends on the ability to translate stored information about recent experience into a behaviorally appropriate response, an ability known as working memory (WM). WM relies on a storage process that actively maintains information and a control process that manipulates stored information to support the selection and preparation of a contingent response (1–3). The neural mechanisms that support WM involve networks that are broadly distributed throughout the brain (4–7) and rely heavily on the prefrontal cortex (PFC) for normal operation (6–9). However, the degree to which WM is supported by a single distributed network or multiple specialized network components in PFC remains unclear (6, 10, 11), hindering progress in the search for neurocognitive therapies to treat disorders of cognition (12).Persistent spiking activity is commonly thought to reflect the mechanistic basis of WM in PFC (13–16). This activity manifests in different ways, including time-varying neuronal responses that decay, ramp up, or are stable in time during memory delays. Although such a diversity of responses could reflect distinct modes of persistent activity, it has long been a standard practice to treat all persistently active neurons in PFC as representative of a single composite WM function that supports the maintenance and manipulation of information necessary for memory-guided behavior (14, 17–19). The implicit assumption that the representations of stored information and contingent responses overlap at the neural circuit level contrasts with an alternate view, which suggests that PFC primarily encodes the selection and preparation of responses (6, 10, 11). This difference highlights the need to directly investigate the circuit-level organization of storage and response preparation-related activity in PFC.We address this problem here, using a simple manipulation of WM in concert with large-scale recordings from neurons across lateral PFC of macaque monkeys. By mapping neural activity during memory and visual delays of the same oculomotor delayed response (ODR) task, we show that WM is composed of three anatomically specific modes of persistent activity. The first two modes specifically encode early and late forms of memory storage, and the third mode predicts behavioral variability after the delay, consistent with response preparation. We then offer multiple convergent lines of evidence that the neural populations that support these three modes are organized with distinct spatiotemporal profiles in PFC. These results suggest that information storage and the preparation of contingent responses are supported by functionally specialized networks in PFC.     

Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain

Human brain functional networks contain a few densely connected hubs that play a vital role in transferring information across regions during resting and task states. However, the relationship of these functional hubs to measures of brain physiology, such as regional cerebral blood flow (rCBF), remains incompletely understood. Here, we used functional MRI data of blood-oxygenation-level–dependent and arterial-spin–labeling perfusion contrasts to investigate the relationship between functional connectivity strength (FCS) and rCBF during resting and an N-back working-memory task. During resting state, functional brain hubs with higher FCS were identified, primarily in the default-mode, insula, and visual regions. The FCS showed a striking spatial correlation with rCBF, and the correlation was stronger in the default-mode network (DMN; including medial frontal-parietal cortices) and executive control network (ECN; including lateral frontal-parietal cortices) compared with visual and sensorimotor networks. Moreover, the relationship was connection–distance dependent; i.e., rCBF correlated stronger with long-range hubs than short-range ones. It is notable that several DMN and ECN regions exhibited higher rCBF per unit connectivity strength (rCBF/FCS ratio); whereas, this index was lower in posterior visual areas. During the working-memory experiment, both FCS–rCBF coupling and rCBF/FCS ratio were modulated by task load in the ECN and/or DMN regions. Finally, task-induced changes of FCS and rCBF in the lateral-parietal lobe positively correlated with behavioral performance. Together, our results indicate a tight coupling between blood supply and brain functional topology during rest and its modulation in response to task demands, which may shed light on the physiological basis of human brain functional connectome.     

Working memory and multi-tasking in paranoid schizophrenia with and without comorbid substance use disorder

AIMS: Addiction is a frequent comorbid disorder in schizophrenia and associated with poor outcome. The present study sought to determine whether addicted and non-addicted schizophrenic patients are impaired differentially on the executive abilities of working memory and multi-tasking which are relevant for maintaining abstinence. DESIGN: Comparisons of executive performance in clinical and control groups. SETTING: In-patient setting. PARTICIPANTS: The cognitive profile of schizophrenic patients with and without comorbid substance abuse disorder was compared with that of patients suffering from major depression or alcoholism and healthy participants. MEASUREMENTS: A range of cognitive tasks was used to assess: (i) the ability to update continuously context information in working memory and to use it for action selection; and (ii) the capacity to divide attention between different sensory input channels and to coordinate verbal and manual responses. FINDINGS: Single-diagnosis schizophrenic patients showed pronounced impairments on measures of online maintenance and use of context information. Their ability to coordinate different sensory input channels (divided attention) was also impaired. Addicted schizophrenics showed evidence of impaired sensory input management and of reduced context sensitivity, when age differences were controlled. CONCLUSIONS: The present study indicates severe working memory and multi-tasking deficits in schizophrenia which are, however, not exacerbated by comorbid addiction.     

From the Cover: Cognitive control in media multitaskers

Chronic media multitasking is quickly becoming ubiquitous, although processing multiple incoming streams of information is considered a challenge for human cognition. A series of experiments addressed whether there are systematic differences in information processing styles between chronically heavy and light media multitaskers. A trait media multitasking index was developed to identify groups of heavy and light media multitaskers. These two groups were then compared along established cognitive control dimensions. Results showed that heavy media multitaskers are more susceptible to interference from irrelevant environmental stimuli and from irrelevant representations in memory. This led to the surprising result that heavy media multitaskers performed worse on a test of task-switching ability, likely due to reduced ability to filter out interference from the irrelevant task set. These results demonstrate that media multitasking, a rapidly growing societal trend, is associated with a distinct approach to fundamental information processing.     

Exploration of functional brain networks in neurodegenerative disease

Neurodegenerative diseases target specific anatomical and functional brain networks. A number of intrinsic functional brain networks can be identified in individuals at rest, that correspond to networks found in task-based functional MRI studies. However, the impact of pathological changes and relation to disease severity remains unclear.We examined three networks of interest in patients with progressive supranuclear palsy (PSP) and the neurodegenerative corticobasal syndrome (CBS). These two diseases share features of cognitive decline and a movement disorder, although they have important phenotypic differences. They are both associated with accumulation of tau protein in neuronal and glial cells. We examined the default mode network, which is deactivated during tasks and has been consistently implicated in Alzheimer's disease; the salience network, often activated during tasks and affected in frontotemporal dementia; and the basal ganglia network, since both PSP and CBS pathology affects the basal ganglia.Using resting state functional MRI scanning, we applied independent component analysis and template matching to identify networks of interest. Spatiotemporal group differences in network architecture were identified with dual regression to extract spatial maps for each network in individuals and perform group-wise t tests. In addition, clinical test scores were added as covariates to group comparisons.Increased functional connectivity was seen within all three networks in disease groups. Decreased connectivity was seen between the basal ganglia network and cortical regions in PSP. Network changes correlated with worse scores on clinical measures of disease.Increased connectivity in relevant functional brain networks identify neurodegenerative diseases and mirror clinical disease features.FundingUK Medical Research Council.     

Resting-brain functional connectivity predicted by analytic measures of network communication

The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures—search information and path transitivity—which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.The topology and dynamics of brain networks are a central focus of the emerging field of connectomics (1). A growing number of studies of human brain networks carried out with modern noninvasive neuroimaging methods have begun to characterize the architecture of structural networks (2–4), as well as spatially distributed components (5–7) and time-varying dynamics (8) of functional networks. Although structural connectivity (SC) is inferred from diffusion imaging and tractography, functional connectivity (FC) is generally derived from pairwise correlations of time series recorded during “resting” brain activity, measured with functional magnetic resonance imaging (fMRI). Both networks define a multiplex system (9) in which the SC level shapes or imposes constraints on the FC level. Indeed, mounting evidence indicates that SC and FC are robustly related. Numerous studies have documented strong and significant correlations between the strengths of structural and functional connections at whole-brain (2, 10–13) and mesoscopic scales (14), as well as acute changes in FC after perturbation of SC (15).Although there is ample evidence documenting statistical relationships between SC and FC, the causal role of SC in shaping whole-brain patterns of FC is still only incompletely understood. There are numerous reports of strong FC among brain regions that are not directly structurally connected, an effect that has been ascribed to signal propagation along one or more indirect structural paths (11), or to network-wide contextual influence (16). The present paper builds on two interrelated premises. First, if SC plays a major causal role in shaping resting-state FC, then appropriately configured generative models that incorporate SC topology should be able to predict, at least to some extent, FC patterns. To this end, a number of models have been proposed, including large-scale neural mass models generating synthetic fMRI time series (11, 17, 18) as well as analytic models based on distance and topological measures (19) or attractor dynamics (20, 21). Second, the extent to which the resting-state time courses of two brain regions become temporally aligned (i.e., highly functionally correlated) should be at least partially related to the ease with which mutual dynamic influences or perturbations can spread within the underlying structural brain network.Both premises imply that the strength of FC is related to measures of network communication. The principal communication measure applied previously in studies of brain networks is the efficiency (22), computed as the averaged inverse of the lengths of the shortest paths between node pairs. The use of this measure is based on the assumption that short paths are dynamically favored, as they allow more direct (faster, less noisy) transmission of neural signals. However, relying on path length as the sole measure of communication does not take into account how these paths are embedded in the rest of the network, which may further modulate the dynamic interactions of neuronal populations. For example, along a given path, branch points may lead to signal dispersion and hence attenuate FC, whereas local detours may offer alternative routes that amplify FC.Here, we present an approach toward predicting FC from SC based on several analytic measures of network communication. We used sets of high-resolution SC and FC maps of the cerebral cortex, obtained from three separate cohorts of participants and acquired using different scanners and imaging protocols. First, the relationship of FC to spatial embedding and path length was explored. Next, we attempted to predict FC from SC by implementing both linear and nonlinear computational models. We then examined the capacity of several analytic measures of network communication along shortest paths to predict FC from SC, singly and in the simple form of a joint multilinear model. Our results demonstrate that analytic measures that take into account the structural embedding of short paths are indeed capable of predicting a large portion of the variance observed in long-time averages of resting-brain FC.     

Cognitive Training Program to Improve Working Memory in Older Adults with MCI

ABSTRACT

Objectives: Deficits in working memory (WM) are associated with age-related decline. We report findings from a clinical trial that examined the effectiveness of Cogmed, a computerized program that trains WM. We compare this program to a Sham condition in older adults with Mild Cognitive Impairment (MCI). Methods: Older adults (N = 68) living in the community were assessed. Participants reported memory impairment and met criteria for MCI, either by poor delayed memory or poor performance in other cognitive areas. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS, Delayed Memory Index) and the Clinical Dementia Rating scale (CDR) were utilized. All presented with normal Mini Mental State Exams (MMSE) and activities of daily living (ADLs). Participants were randomized to Cogmed or a Sham computer program. Twenty-five sessions were completed over five to seven weeks. Pre, post, and follow-up measures included a battery of cognitive measures (three WM tests), a subjective memory scale, and a functional measure.Results: Both intervention groups improved over time. Cogmed significantly outperformed Sham on Span Board and exceeded in subjective memory reports at follow-up as assessed by the Cognitive Failures Questionnaire (CFQ). The Cogmed group demonstrated better performance on the Functional Activities Questionnaire (FAQ), a measure of adjustment and far transfer, at follow-up. Both groups, especially Cogmed, enjoyed the intervention.Conclusions: Results suggest that WM was enhanced in both groups of older adults with MCI. Cogmed was better on one core WM measure and had higher ratings of satisfaction. The Sham condition declined on adjustment.     

Adaptive reconfiguration of fractal small-world human brain functional networks

Brain function depends on adaptive self-organization of large-scale neural assemblies, but little is known about quantitative network parameters governing these processes in humans. Here, we describe the topology and synchronizability of frequency-specific brain functional networks using wavelet decomposition of magnetoencephalographic time series, followed by construction and analysis of undirected graphs. Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a finger-tapping task, whereas the other half were studied at rest. We found that brain functional networks were characterized by small-world properties at all six wavelet scales considered, corresponding approximately to classical delta (low and high), , alpha, beta, and gamma frequency bands. Global topological parameters (path length, clustering) were conserved across scales, most consistently in the frequency range 2-37 Hz, implying a scale-invariant or fractal small-world organization. Dynamical analysis showed that networks were located close to the threshold of order/disorder transition in all frequency bands. The highest-frequency gamma network had greater synchronizability, greater clustering of connections, and shorter path length than networks in the scaling regime of (lower) frequencies. Behavioral state did not strongly influence global topology or synchronizability; however, motor task performance was associated with emergence of long-range connections in both beta and gamma networks. Long-range connectivity, e.g., between frontal and parietal cortex, at high frequencies during a motor task may facilitate sensorimotor binding. Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters.     

fMRI measurement of brain dysfunction in alcohol-dependent young women

BACKGROUND: Studies of brain functioning in alcohol-dependent adults have produced varied results but generally suggest that alcohol affects brain functioning and that relatively short durations of heavy drinking may adversely affect women. It remains unclear when in the course of alcohol dependency and at which developmental stage these brain changes emerge. Our neuropsychological studies have indicated that drinking-related neurocognitive effects occur as early as adolescence (Brown et al., 2000; Tapert & Brown, 1999). This study seeks to characterize brain regions that subserve the affected neurocognitive functions. METHODS: Alcohol-dependent young women (n = 10) were recruited from a longitudinal study of alcohol- and drug-abusing youth, all of whom met criteria for alcohol dependence. Control participants (n = 10) had no history of alcohol or drug problems and were comparable with alcohol-dependent participants on age (18-25 years), family history of alcohol use disorders, and education. After a minimum of 72 hr of abstinence, functional magnetic resonance imaging, neuropsychological, alcohol/drug involvement, and mood data were collected. Participants performed spatial working memory and vigilance tasks during functional magnetic resonance imaging acquisition to probe brain response. RESULTS: Alcohol-dependent women demonstrated significantly less blood oxygen level-dependent response than controls during the spatial working memory task in the right superior and inferior parietal, right middle frontal, right postcentral, and left superior frontal cortex, after controlling for the baseline vigilance response. CONCLUSIONS: Working memory produces a larger neuronal response in some cortical regions than vigilance. Alcohol-dependent women showed less differential response to working memory than controls in frontal and parietal regions, especially in the right hemisphere. Heavy, chronic drinking appears to produce adverse neural effects that are detectable by functional magnetic resonance imaging.