Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish

According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance.     

Large-scale cortical functional organization and speech perception across the lifespan

Aging is accompanied by substantial changes in brain function, including functional reorganization of large-scale brain networks. Such differences in network architecture have been reported both at rest and during cognitive task performance, but an open question is whether these age-related differences show task-dependent effects or represent only task-independent changes attributable to a common factor (i.e., underlying physiological decline). To address this question, we used graph theoretic analysis to construct weighted cortical functional networks from hemodynamic (functional MRI) responses in 12 younger and 12 older adults during a speech perception task performed in both quiet and noisy listening conditions. Functional networks were constructed for each subject and listening condition based on inter-regional correlations of the fMRI signal among 66 cortical regions, and network measures of global and local efficiency were computed. Across listening conditions, older adult networks showed significantly decreased global (but not local) efficiency relative to younger adults after normalizing measures to surrogate random networks. Although listening condition produced no main effects on whole-cortex network organization, a significant age group x listening condition interaction was observed. Additionally, an exploratory analysis of regional effects uncovered age-related declines in both global and local efficiency concentrated exclusively in auditory areas (bilateral superior and middle temporal cortex), further suggestive of specificity to the speech perception tasks. Global efficiency also correlated positively with mean cortical thickness across all subjects, establishing gross cortical atrophy as a task-independent contributor to age-related differences in functional organization. Together, our findings provide evidence of age-related disruptions in cortical functional network organization during speech perception tasks, and suggest that although task-independent effects such as cortical atrophy clearly underlie age-related changes in cortical functional organization, age-related differences also demonstrate sensitivity to task domains.     

Task-related effects on the temporal and spatial dynamics of resting-state functional connectivity in the default network

Recent evidence points to two potentially fundamental aspects of the default network (DN), which have been relatively understudied. One is the temporal nature of the functional interactions among nodes of the network in the resting-state, usually assumed to be static. The second is possible influences of previous brain states on the spatial patterns (i.e., the brain regions involved) of functional connectivity (FC) in the DN at rest. The goal of the current study was to investigate modulations in both the spatial and temporal domains. We compared the resting-state FC of the DN in two runs that were separated by a 45 minute interval containing cognitive task execution. We used partial least squares (PLS), which allowed us to identify FC spatiotemporal patterns in the two runs and to determine differences between them. Our results revealed two primary modes of FC, assessed using a posterior cingulate seed--a robust correlation among DN regions that is stable both spatially and temporally, and a second pattern that is reduced in spatial extent and more variable temporally after cognitive tasks, showing switching between connectivity with certain DN regions and connectivity with other areas, including some task-related regions. Therefore, the DN seems to exhibit two simultaneous FC dynamics at rest. The first is spatially invariant and insensitive to previous brain states, suggesting that the DN maintains some temporally stable functional connections. The second dynamic is more variable and is seen more strongly when the resting-state follows a period of task execution, suggesting an after-effect of the cognitive activity engaged during task that carries over into resting-state periods.     

Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.     

基于空间ICA和时间相关方法的人脑视觉皮层V5区的功能连通性研究

利用功能磁共振成像技术,将空间ICA和时间相关方法相结合来研究不同活动状态下人脑视觉皮层V5区的功能连通性。首先利用空间ICA处理组块视觉运动刺激的数据,定位V5区;然后分别计算静息和连续视觉运动刺激两种稳态下V5区与其它脑区低频振荡的时间相关,检测出该区的功能连通网络。实验结果表明,静息时V5区的功能连通网络更广泛,且与已知的解剖连通一致;当被试接受连续视觉运动刺激时,与V5区连通的脑区网络局限在视觉皮层,此时的网络特定于处理视觉运动这一任务。     

The frustrated brain: from dynamics on motifs to communities and networks

Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems—resonance pairs—promote stable zero-lag synchrony among the small motifs in which they are embedded, and whose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zero-lag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across diverse cognitive processes.     

Dynamics of the central bottleneck: dual-task and task uncertainty

Why is the human brain fundamentally limited when attempting to execute two tasks at the same time or in close succession? Two classical paradigms, psychological refractory period (PRP) and task switching, have independently approached this issue, making significant advances in our understanding of the architecture of cognition. Yet, there is an apparent contradiction between the conclusions derived from these two paradigms. The PRP paradigm, on the one hand, suggests that the simultaneous execution of two tasks is limited solely by a passive structural bottleneck in which the tasks are executed on a first-come, first-served basis. The task-switching paradigm, on the other hand, argues that switching back and forth between task configurations must be actively controlled by a central executive system (the system controlling voluntary, planned, and flexible action). Here we have explicitly designed an experiment mixing the essential ingredients of both paradigms: task uncertainty and task simultaneity. In addition to a central bottleneck, we obtain evidence for active processes of task setting (planning of the appropriate sequence of actions) and task disengaging (suppression of the plan set for the first task in order to proceed with the next one). Our results clarify the chronometric relations between these central components of dual-task processing, and in particular whether they operate serially or in parallel. On this basis, we propose a hierarchical model of cognitive architecture that provides a synthesis of task-switching and PRP paradigms.     

Dynamic functional connectivity in the static connectome of Caenorhabditis elegans

A hallmark of adaptive behavior is the ability to flexibly respond to sensory cues. To understand how neural circuits implement this flexibility, it is critical to resolve how a static anatomical connectome can be modulated such that functional connectivity in the network can be dynamically regulated. Here, we review recent work in the roundworm Caenorhabditis elegans on this topic. EM studies have mapped anatomical connectomes of many C. elegans animals, highlighting the level of stereotypy in the anatomical network. Brain-wide calcium imaging and studies of specified neural circuits have uncovered striking flexibility in the functional coupling of neurons. The coupling between neurons is controlled by neuromodulators that act over long timescales. This gives rise to persistent behavioral states that animals switch between, allowing them to generate adaptive behavioral responses across environmental conditions. Thus, the dynamic coupling of neurons enables multiple behavioral states to be encoded in a physically stereotyped connectome.     

Task-induced deactivation from rest extends beyond the default mode brain network

Activity decreases, or deactivations, of midline and parietal cortical brain regions are routinely observed in human functional neuroimaging studies that compare periods of task-based cognitive performance with passive states, such as rest. It is now widely held that such task-induced deactivations index a highly organized 'default-mode network' (DMN): a large-scale brain system whose discovery has had broad implications in the study of human brain function and behavior. In this work, we show that common task-induced deactivations from rest also occur outside of the DMN as a function of increased task demand. Fifty healthy adult subjects performed two distinct functional magnetic resonance imaging tasks that were designed to reliably map deactivations from a resting baseline. As primary findings, increases in task demand consistently modulated the regional anatomy of DMN deactivation. At high levels of task demand, robust deactivation was observed in non-DMN regions, most notably, the posterior insular cortex. Deactivation of this region was directly implicated in a performance-based analysis of experienced task difficulty. Together, these findings suggest that task-induced deactivations from rest are not limited to the DMN and extend to brain regions typically associated with integrative sensory and interoceptive processes.     

Multivariate EEG spectral analysis evidences the functional link between motor and visual cortex during integrative sensorimotor tasks

The identification of the networks connecting brain areas and the understanding of their role in executing complex tasks is a crucial issue in cognitive neuroscience. In this study, specific visuomotor tasks were devised to reveal the functional network underlying the cooperation process between visual and motor regions. Electroencephalography (EEG) data were recorded from twelve healthy subjects during a combined visuomotor task, which integrated precise grip motor commands with sensory visual feedback (VM). This condition was compared with control tasks involving pure motor action (M), pure visual perception (V) and visuomotor performance without feedback (V + M). Multivariate parametric cross-spectral analysis was applied to ten EEG derivations in each subject to assess changes in the oscillatory activity of the involved cortical regions and quantify their coupling. Spectral decomposition was applied to precisely and objectively determine the power associated with each oscillatory component of the spectrum, while surrogate data analysis was performed to assess the statistical significance of estimated coherence values. A significant decrease of the alpha and/or beta power in EEG spectra with respect to rest values was assumed as indicative of specific cortical area activation during task execution. Indeed alpha band coherence increased in proximity of task-involved areas, while it was suppressed or remained unchanged in other regions, suggesting the activation of a specific network for each task. According to our coherence analysis, a direct link between visual and motor areas was activated during V + M and VM tasks. The effect of visual feedback was evident in the beta band, where the increase of coherence was observed only during the VM task. Multivariate analysis suggested the presence of a functional link between motor and visual cortex subserving sensorimotor integration. Furthermore, network activation was related to the sum of single task (M and V) local effects in the alpha band, and to the presence of visual feedback in the beta band.     

Condition-dependent functional connectivity: syntax networks in bilinguals

This paper introduces a method to study the variation of brain functional connectivity networks with respect to experimental conditions in fMRI data. It is related to the psychophysiological interaction technique introduced by Friston et al. and extends to networks of correlation modulation (CM networks). Extended networks containing several dozens of nodes are determined in which the links correspond to consistent correlation modulation across subjects. In addition, we assess inter-subject variability and determine networks in which the condition-dependent functional interactions can be explained by a subject-dependent variable. We applied the technique to data from a study on syntactical production in bilinguals and analysed functional interactions differentially across tasks (word reading or sentence production) and across languages. We find an extended network of consistent functional interaction modulation across tasks, whereas the network comparing languages shows fewer links. Interestingly, there is evidence for a specific network in which the differences in functional interaction across subjects can be explained by differences in the subjects' syntactical proficiency. Specifically, we find that regions, including ones that have previously been shown to be involved in syntax and in language production, such as the left inferior frontal gyrus, putamen, insula, precentral gyrus, as well as the supplementary motor area, are more functionally linked during sentence production in the second, compared with the first, language in syntactically more proficient bilinguals than in syntactically less proficient ones. Our approach extends conventional activation analyses to the notion of networks, emphasizing functional interactions between regions independently of whether or not they are activated. On the one hand, it gives rise to testable hypotheses and allows an interpretation of the results in terms of the previous literature, and on the other hand, it provides a basis for studying the structure of functional interactions as a whole, and hence represents a further step towards the notion of large-scale networks in functional imaging.     

Learning to juggle: on the assembly of functional subsystems into a task-specific dynamical organization

We examined the development of task-specific couplings among functional subsystems (i.e., ball circulation, respiration, and body sway) when learning to juggle a three-ball cascade, with a focus on learning-induced changes in the coupling between ball movements and respiration and the coupling between ball movements and body sway. Six novices practiced to juggle three balls in cascade fashion for one hour per day for twenty days. On specific days (7 in total), ball movements, center-of-pressure (CoP) trajectories and respiration traces were measured simultaneously. Discrete, time-continuous and spectral analyses revealed that the spatio-temporal variability of the juggling patterns decreased with practice and that the degree to which the task constraints were satisfied increased gradually. No conclusive evidence was found for ball movement-respiration coupling. In contrast, clear-cut evidence was found for the presence of 1:3 and 2:3 frequency locking between the vertical component of the ball trajectories and both the anterior-posterior and the medio-lateral components of the CoP. Incidence and expression of these mode locks varied across individuals and altered in the course of learning. Gradual changes in locking strength, appearances and disappearances of mode locks, as well as abrupt transitions between coupled states were observed. These results indicate that dissimilar learning dynamics may arise in the functional embedding of subsystems into a task-specific organization and that motor equivalence is an inherent property of such emerging task-specific organizations.     

Plant functional classifications: from general groups to specific groups based on response to disturbance

Predicting the effects of anthropogenic changes in climate, atmospheric composition and land use on vegetation patterns has been a central concern of recent ecological research. This aim has revived the search for classification schemes that can be to group plant species according to their response to specified environmental factors. One way forward is to adopt a hierarchical classification, where different sets of traits are examined depending on growth form. Also, at the level of interpretation, the environmental context and purpose of functional classifications need to be specified explicitly, so that global generalizations can be made by comparing across environments functional classifications derived from similar methodologies.     

Rule-guided executive control of response inhibition: functional topography of the inferior frontal cortex

Background

The human inferior frontal cortex (IFC) is a large heterogeneous structure with distinct cytoarchitectonic subdivisions and fiber connections. It has been found involved in a wide range of executive control processes from target detection, rule retrieval to response control. Since these processes are often being studied separately, the functional organization of executive control processes within the IFC remains unclear.

Methodology/Principal Findings

We conducted an fMRI study to examine the activities of the subdivisions of IFC during the presentation of a task cue (rule retrieval) and during the performance of a stop-signal task (requiring response generation and inhibition) in comparison to a not-stop task (requiring response generation but not inhibition). We utilized a mixed event-related and block design to separate brain activity in correspondence to transient control processes from rule-related and sustained control processes. We found differentiation in control processes within the IFC. Our findings reveal that the bilateral ventral-posterior IFC/anterior insula are more active on both successful and unsuccessful stop trials relative to not-stop trials, suggesting their potential role in the early stage of stopping such as triggering the stop process. Direct countermanding seems to be outside of the IFC. In contrast, the dorsal-posterior IFC/inferior frontal junction (IFJ) showed transient activity in correspondence to the infrequent presentation of the stop signal in both tasks and the left anterior IFC showed differential activity in response to the task cues. The IFC subdivisions also exhibited similar but distinct patterns of functional connectivity during response control.

Conclusions/Significance

Our findings suggest that executive control processes are distributed across the IFC and that the different subdivisions of IFC may support different control operations through parallel cortico-cortical and cortico-striatal circuits.     

Network Physiology: How Organ Systems Dynamically Interact

We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.     

Brain Network Adaptability across Task States

Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.     

Predicting disulfide connectivity patterns

Disulfide bonds play an important role in stabilizing protein structure and regulating protein function. Therefore, the ability to infer disulfide connectivity from protein sequences will be valuable in structural modeling and functional analysis. However, to predict disulfide connectivity directly from sequences presents a challenge to computational biologists due to the nonlocal nature of disulfide bonds, i.e., the close spatial proximity of the cysteine pair that forms the disulfide bond does not necessarily imply the short sequence separation of the cysteine residues. Recently, Chen and Hwang (Proteins 2005;61:507-512) treated this problem as a multiple class classification by defining each distinct disulfide pattern as a class. They used multiple support vector machines based on a variety of sequence features to predict the disulfide patterns. Their results compare favorably with those in the literature for a benchmark dataset sharing less than 30% sequence identity. However, since the number of disulfide patterns grows rapidly when the number of disulfide bonds increases, their method performs unsatisfactorily for the cases of large number of disulfide bonds. In this work, we propose a novel method to represent disulfide connectivity in terms of cysteine pairs, instead of disulfide patterns. Since the number of bonding states of the cysteine pairs is independent of that of disulfide bonds, the problem of class explosion is avoided. The bonding states of the cysteine pairs are predicted using the support vector machines together with the genetic algorithm optimization for feature selection. The complete disulfide patterns are then determined from the connectivity matrices that are constructed from the predicted bonding states of the cysteine pairs. Our approach outperforms the current approaches in the literature.     

Abdominal motor unit activity during respiratory and nonrespiratory tasks

Abdominalmuscles serve multiple roles, but the functional organization of theirmotoneurons remains unclear. To gain insight, we recorded single motorunit potentials from the internal oblique (IO) and transversusabdominis (TA) muscles of three standing subjects during quietbreathing, a leg lift, and an expiratory threshold load. Inspiratoryairflow, recorded from a pneumotachometer, provided tidal volumes andrespiratory cycle timing. Fine wires, implanted under ultrasonicimaging, detected single motor unit potentials that were visuallydistinguished by their spike morphology. From the number of spikes,firing profiles, times of occurrence in the respiratory cycle, andtheir onset, instantaneous, mean, and peak firing frequencies wededuced that 1) breathing patterns varied across tasks, 2) differentmotor units were recruited for each task with essentially no overlap,3) their firing displayed prominentexpiratory activity during each task, and4) the recruitment levels anddischarge patterns of IO and TA were different. We conclude that the IOand TA motor pools receive a strong central respiratory drive, yet eachpool receives its own distinct, task-dependent synapticinput.

     

Subject-specific thumb muscle activity during functional tasks of daily life

BackgroundThe trapeziometacarpal joint is subjected to high compressive forces during powerful pinch and grasp tasks due to muscle loading. In addition, muscle contraction is important for stability of the joint. The aim of the present study is to explore if different muscle activation patterns can be found between three functional tasks.MethodsIsometric forces and fine-wire electromyographic (fEMG) activity produced by three intrinsic and four extrinsic thumb muscles were measured in 10 healthy female volunteers. The participants performed isometric contractions in a lateral key pinch, a power grasp and a jar twist task. The tasks were executed with and without EMG recording to verify if electrode placement influenced force production.ResultsA subject-specific muscle recruitment was found which remained largely unchanged across tasks. Extrinsic thumb muscles were significantly more active than intrinsic muscles in all tasks. Insertion of the fEMG electrodes decreased force production significantly in all tasks.ConclusionThe thumb muscles display a high variability in muscle activity during functional tasks of daily life. The results of this study suggest that to produce a substantial amount of force, a well-integrated, but subject-specific, co-contraction between the intrinsic and extrinsic thumb muscles is necessary.     

Altered patterns of muscle activation during performance of four functional tasks in patients with shoulder disorders: interpretation from voluntary response index.

Altered motor control of the shoulder muscles during performance of a specific motor task in patients with shoulder disorders (SDs) has been an interesting subject to researchers. This study compared shoulder muscle activation patterns by surface electromyography (sEMG), including the upper trapezius (UT), lower trapezius (LT), and serratus anterior (SA) muscles, during four functional tasks in 25 patients with SDs and controls. A voluntary response index (VRI) was calculated, including magnitude and similarity index (SI), to quantify sEMG patterns during four functional tasks. Responsiveness and clinically meaningful levels of discrimination between patients and control for EMG magnitude and SI were determined. An altered pattern of motor control during four functional tasks was evident in the patients, in which greater EMG amplitude and abnormal EMG patterns were found. For SI among four functional tasks, normal subjects ranged from 0.80 to 1.00 while patients ranged from 0.70 to 0.99. High probabilities (97%) of discrimination between patients and normal subjects were found by SI method during an overhead height task (patients: 0.85-0.96, normal subjects: 0.95-1.00). Our results also suggest that an individual can be estimated to be abnormal when lower SI values are observed during the four functional tasks.