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Musicianship has been associated with auditory processing benefits. It is unclear, however, whether pitch processing experience in nonmusical contexts, namely, speaking a tone language, has comparable associations with auditory processing. Studies comparing the auditory processing of musicians and tone language speakers have shown varying degrees of between-group similarity with regard to perceptual processing benefits and, particularly, nonlinguistic pitch processing. To test whether the auditory abilities honed by musicianship or speaking a tone language differentially impact the neural networks supporting nonlinguistic pitch processing (relative to timbral processing), we employed a novel application of brain signal variability (BSV) analysis. BSV is a metric of information processing capacity and holds great potential for understanding the neural underpinnings of experience-dependent plasticity. Here, we measured BSV in electroencephalograms of musicians, tone language-speaking nonmusicians, and English-speaking nonmusicians (controls) during passive listening of music and speech sound contrasts. Although musicians showed greater BSV across the board, each group showed a unique spatiotemporal distribution in neural network engagement: Controls had greater BSV for speech than music; tone language-speaking nonmusicians showed the opposite effect; musicians showed similar BSV for both domains. Collectively, results suggest that musical and tone language pitch experience differentially affect auditory processing capacity within the cerebral cortex. However, information processing capacity is graded: More experience with pitch is associated with greater BSV when processing this cue. Higher BSV in musicians may suggest increased information integration within the brain networks subserving speech and music, which may be related to their well-documented advantages on a wide variety of speech-related tasks.

Visual behavior is guided by memories from prior experience and knowledge of the visual scene. The hippocampal system (HC), in particular, has been implicated in the guidance of saccades: Amnesic patients, following damage to the HC, exhibit selective deficits in their gaze patterns. However, the neural circuitry by which mnemonic representations influence the oculomotor system remains unknown. We used a data-driven, network-based approach on directed anatomical connectivity from the macaque brain to reveal an extensive set of polysnaptic pathways spanning the extrastriate, posterior parietal and prefrontal cortices that potentially mediate the exchange of information between the memory and visuo-oculomotor systems. We additionally show how the potential for directed information flow from the hippocampus to oculomotor control areas is exceptionally high. In particular, the dorsolateral pFC and FEF-regions known to be responsible for the cognitive control of saccades-are topologically well positioned to receive information from the hippocampus. Together with neuropsychological evidence of altered gaze patterns following damage to the hippocampus, our findings suggest that a reconsideration of hippocampal involvement in oculomotor guidance is needed.

Current neuroscientific research has shown that the brain reconfigures its functional interactions at multiple timescales. Here, we sought to link transient changes in functional brain networks to individual differences in behavioral and cognitive performance by using an active learning paradigm. Participants learned associations between pairs of unrelated visual stimuli by using feedback. Interindividual behavioral variability was quantified with a learning rate measure. By using a multivariate statistical framework (partial least squares), we identified patterns of network organization across multiple temporal scales (within a trial, millisecond; across a learning session, minute) and linked these to the rate of change in behavioral performance (fast and slow). Results indicated that posterior network connectivity was present early in the trial for fast, and later in the trial for slow performers. In contrast, connectivity in an associative memory network (frontal, striatal, and medial temporal regions) occurred later in the trial for fast, and earlier for slow performers. Time-dependent changes in the posterior network were correlated with visual/spatial scores obtained from independent neuropsychological assessments, with fast learners performing better on visual/spatial subtests. No relationship was found between functional connectivity dynamics in the memory network and visual/spatial test scores indicative of cognitive skill. By using a comprehensive set of measures (behavioral, cognitive, and neurophysiological), we report that individual variations in learning-related performance change are supported by differences in cognitive ability and time-sensitive connectivity in functional neural networks. Hum Brain Mapp 37:3911-3928, 2016. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear. Whole-brain models based on long-range axonal connections, for example, can partly describe measured functional connectivity dynamics at rest. Here, we study the effect of SC on the network response to stimulation. We use a human whole-brain network model comprising long- and short-range connections. We systematically activate each cortical or thalamic area, and investigate the network response as a function of its short- and long-range SC. We show that when the brain is operating at the edge of criticality, stimulation causes a cascade of network recruitments, collapsing onto a smaller space that is partly constrained by SC. We found both short- and long-range SC essential to reproduce experimental results. In particular, the stimulation of specific areas results in the activation of one or more resting-state networks. We suggest that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks. We provide a lookup table linking stimulation targets and functional network activations, which potentially can be useful in diagnostics and treatments with brain stimulation.

Because smaller habitats dry more frequently and severely during droughts, habitat size is likely a key driver of survival in populations during climate change and associated increased extreme drought frequency. Here, we show that survival in populations during droughts is a threshold function of habitat size driven by an interaction with population density in metapopulations of the forest pool dwelling fish, Neochanna apoda. A mark-recapture study involving 830 N. apoda individuals during a one-in-seventy-year extreme drought revealed that survival during droughts was high for populations occupying pools deeper than 139 mm, but declined steeply in shallower pools. This threshold was caused by an interaction between increasing population density and drought magnitude associated with decreasing habitat size, which acted synergistically to increase physiological stress and mortality. This confirmed two long-held hypotheses, firstly concerning the interactive role of population density and physiological stress, herein driven by habitat size, and secondly, the occurrence of drought survival thresholds. Our results demonstrate how survival in populations during droughts will depend strongly on habitat size and highlight that minimum habitat size thresholds will likely be required to maximize survival as the frequency and intensity of droughts are projected to increase as a result of global climate change., (© 2016 John Wiley & Sons Ltd.)

Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudinal study of monolingual English-speaking 4- to 6-year-old children randomly selected to receive daily music or French language training, excluding weekends. Children completed passive EEG music note and French vowel auditory oddball detection tasks before and after training. Brain signal complexity was measured on source waveforms at multiple temporal scales as an index of neural information processing and network communication load. Comparing pretraining with posttraining, musical training was associated with increased EEG complexity at coarse temporal scales during the music and French vowel tasks in widely distributed cortical regions. Conversely, very minimal decreases in complexity at fine scales and trends toward coarse-scale increases were displayed after French training during the tasks. Spectral analysis failed to distinguish between training types and found overall theta (3.5-7.5 Hz) power increases after all training forms, with spatially fewer decreases in power at higher frequencies (>10 Hz). These findings demonstrate that musical training increased diversity of brain network states to support domain-specific music skill acquisition and music-to-language transfer effects.

Aging is associated with decreased resting-state functional connectivity (RSFC) within the default mode network (DMN), but most functional imaging studies have restricted the analysis to specific brain regions or networks, a strategy not appropriate to describe system-wide changes. Moreover, few investigations have employed operational psychiatric interviewing procedures to select participants; this is an important limitation since mental disorders are prevalent and underdiagnosed and can be associated with RSFC abnormalities. In this study, resting-state fMRI was acquired from 59 adults free of cognitive and psychiatric disorders according to standardized criteria and based on extensive neuropsychological and clinical assessments. We tested for associations between age and whole-brain RSFC using Partial Least Squares, a multivariate technique. We found that normal aging is not only characterized by decreased RSFC within the DMN but also by ubiquitous increases in internetwork positive correlations and focal internetwork losses of anticorrelations (involving mainly connections between the DMN and the attentional networks). Our results reinforce the notion that the aging brain undergoes a dedifferentiation processes with loss of functional diversity. These findings advance the characterization of healthy aging effects on RSFC and highlight the importance of adopting a broad, system-wide perspective to analyze brain connectivity., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Stimulus repetition speeds behavioral responding (behavioral priming) and is accompanied by suppressed neural responses (repetition suppression; RS) that have been observed up to three days after initial exposure. While some proposals have suggested the two phenomena are linked, behavioral priming has been observed many years after initial exposure, whereas RS is widely considered a transitory phenomenon. This raises the question: what is the true upper limit of RS persistence? To answer this question, we scanned healthy, English-native adults with fMRI as they viewed novel (Asian) proverbs, recently repeated (Asian) proverbs, and previously known (English) proverbs that were matched on various dimensions. We then estimated RS by comparing repeated or previously known proverbs against novel ones. Multivariate analyses linked previously known and repeated proverbs with statistically indistinguishable RS in a broad visual-linguistic network. In each suppressed region, prior knowledge and repetition also induced a common shift in functional connectivity, further underscoring the similarity of the RS phenomenon induced by these conditions. By contrast, activated regions readily distinguished prior knowledge and repetition conditions in a manner consistent with engagement of semantic and episodic memory systems, respectively. Our results illustrate that regardless of whether RS is understood in terms of its magnitude, spatial extent or functional connectivity profile, typical RS effects can be elicited even under conditions where recently triggered biological processes or episodic memory are unlikely to play a prominent role. These results provide important new evidence that RS (of the kind observed after an interval of at least several minutes) reflects the facilitation of perceptual and comprehension processes by any type of information retrieved from long-term memory., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Functional interactions in the brain are constrained by the underlying anatomical architecture, and structural and functional networks share network features such as modularity. Accordingly, age-related changes of structural connectivity (SC) may be paralleled by changes in functional connectivity (FC). We provide a detailed qualitative and quantitative characterization of the SC-FC coupling in human aging as inferred from resting-state blood oxygen-level dependent functional magnetic resonance imaging and diffusion-weighted imaging in a sample of 47 adults with an age range of 18-82. We revealed that SC and FC decrease with age across most parts of the brain and there is a distinct age-dependency of regionwise SC-FC coupling and network-level SC-FC relations. A specific pattern of SC-FC coupling predicts age more reliably than does regionwise SC or FC alone (r = 0.73, 95% CI = [0.7093, 0.8522]). Hence, our data propose that regionwise SC-FC coupling can be used to characterize brain changes in aging. Hum Brain Mapp 37:2645-2661, 2016. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

The dynamics of spontaneous fluctuations in neural activity are shaped by underlying patterns of anatomical connectivity. While numerous studies have demonstrated edge-wise correspondence between structural and functional connections, much less is known about how large-scale coherent functional network patterns emerge from the topology of structural networks. In the present study, we deploy a multivariate statistical technique, partial least squares, to investigate the association between spatially extended structural networks and functional networks. We find multiple statistically robust patterns, reflecting reliable combinations of structural and functional subnetworks that are optimally associated with one another. Importantly, these patterns generally do not show a one-to-one correspondence between structural and functional edges, but are instead distributed and heterogeneous, with many functional relationships arising from nonoverlapping sets of anatomical connections. We also find that structural connections between high-degree hubs are disproportionately represented, suggesting that these connections are particularly important in establishing coherent functional networks. Altogether, these results demonstrate that the network organization of the cerebral cortex supports the emergence of diverse functional network configurations that often diverge from the underlying anatomical substrate., (© The Author 2016. Published by Oxford University Press.)

Recent empirical work suggests that, during healthy aging, the variability of network dynamics changes during task performance. Such variability appears to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into resting-state dynamics. We recorded EEG in young, middle-aged, and older adults during a "rest-task-rest" design and investigated if aging modifies the interaction between resting-state activity and external stimulus-induced activity. Using multiscale entropy as our measure of variability, we found that, with increasing age, resting-state dynamics shifts from distributed to more local neural processing, especially at posterior sources. In the young group, resting-state dynamics also changed from pre- to post-task, where fine-scale entropy increased in task-positive regions and coarse-scale entropy increased in the posterior cingulate, a key region associated with the default mode network. Lastly, pre- and post-task resting-state dynamics were linked to performance on the intervening task for all age groups, but this relationship became weaker with increasing age. Our results suggest that age-related changes in resting-state dynamics occur across different spatial and temporal scales and have consequences for information processing capacity.

Neuronal activity associated with visual processing of an unfamiliar face gradually diminishes when it is viewed repeatedly. This process, known as repetition suppression (RS), is involved in the acquisition of familiarity. Current models suggest that RS results from interactions between visual information processing areas located in the occipito-temporal cortex and higher order areas, such as the dorsolateral prefrontal cortex (DLPFC). Brain signal complexity, which reflects information dynamics of cortical networks, has been shown to increase as unfamiliar faces become familiar. However, the complementarity of RS and increases in brain signal complexity have yet to be demonstrated within the same measurements. We hypothesized that RS and brain signal complexity increase occur simultaneously during learning of unfamiliar faces. Further, we expected alteration of DLPFC function by transcranial direct current stimulation (tDCS) to modulate RS and brain signal complexity over the occipito-temporal cortex. Participants underwent three tDCS conditions in random order: right anodal/left cathodal, right cathodal/left anodal and sham. Following tDCS, participants learned unfamiliar faces, while an electroencephalogram (EEG) was recorded. Results revealed RS over occipito-temporal electrode sites during learning, reflected by a decrease in signal energy, a measure of amplitude. Simultaneously, as signal energy decreased, brain signal complexity, as estimated with multiscale entropy (MSE), increased. In addition, prefrontal tDCS modulated brain signal complexity over the right occipito-temporal cortex during the first presentation of faces. These results suggest that although RS may reflect a brain mechanism essential to learning, complementary processes reflected by increases in brain signal complexity, may be instrumental in the acquisition of novel visual information. Such processes likely involve long-range coordinated activity between prefrontal and lower order visual areas., (Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.)

Around the world, artificially drained agricultural lands are significant sources of reactive nitrogen to stream ecosystems, creating substantial stream health problems. One management strategy is the deployment of denitrification enhancement tools. Here, we evaluate the factors affecting the potential of denitrifying bioreactors to improve stream health and ecosystem services. The performance of bioreactors and the structure and functioning of stream biotic communities are linked by environmental parameters like dissolved oxygen and nitrate-nitrogen concentrations, dissolved organic carbon availability, flow and temperature regimes, and fine sediment accumulations. However, evidence of bioreactors' ability to improve waterway health and ecosystem services is lacking. To improve the potential of bioreactors to enhance desirable stream ecosystem functioning, future assessments of field-scale bioreactors should evaluate the influences of bioreactor performance on ecological indicators such as primary production, organic matter processing, stream metabolism, and invertebrate and fish assemblage structure and function. These stream health impact assessments should be conducted at ecologically relevant spatial and temporal scales. Bioreactors have great potential to make significant contributions to improving water quality, stream health, and ecosystem services if they are tailored to site-specific conditions and implemented strategically with land-based and stream-based mitigation tools within watersheds. This will involve combining economic, logistical, and ecological information in their implementation., (Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.)

We have seen important strides in our understanding of mechanisms underlying stroke recovery, yet effective translational links between basic and applied sciences, as well as from big data to individualized therapies, are needed to truly develop a cure for stroke. We present such an approach using The Virtual Brain (TVB), a neuroinformatics platform that uses empirical neuroimaging data to create dynamic models of an individual's human brain; specifically, we simulate fMRI signals by modeling parameters associated with brain dynamics after stroke. In 20 individuals with stroke and 11 controls, we obtained rest fMRI, T1w, and diffusion tensor imaging (DTI) data. Motor performance was assessed pre-therapy, post-therapy, and 6-12 months post-therapy. Based on individual structural connectomes derived from DTI, the following steps were performed in the TVB platform: (1) optimization of local and global parameters (conduction velocity, global coupling); (2) simulation of BOLD signal using optimized parameter values; (3) validation of simulated time series by comparing frequency, amplitude, and phase of the simulated signal with empirical time series; and (4) multivariate linear regression of model parameters with clinical phenotype. Compared with controls, individuals with stroke demonstrated a consistent reduction in conduction velocity, increased local dynamics, and reduced local inhibitory coupling. A negative relationship between local excitation and motor recovery, and a positive correlation between local dynamics and motor recovery were seen. TVB reveals a disrupted post-stroke system favoring excitation-over-inhibition and local-over-global dynamics, consistent with existing mammal literature on stroke mechanisms. Our results point to the potential of TVB to determine individualized biomarkers of stroke recovery.

Dementia affects a high proportion of Parkinson's disease (PD) patients and poses a burden on caregivers and healthcare services. Electroencephalography (EEG) is a common nonevasive and nonexpensive technique that can easily be used in clinical settings to identify brain functional abnormalities. Only few studies had identified EEG abnormalities that can predict PD patients at higher risk for dementia. Brain connectivity EEG measures, such as multiscale entropy (MSE) and phase-locking value (PLV) analyses, may be more informative and sensitive to brain alterations leading to dementia than previously used methods. This study followed 62 dementia-free PD patients for a mean of 3.4 years to identify cerebral alterations that are associated with dementia. Baseline resting state EEG of patients who developed dementia (N = 18) was compared to those of patients who remained dementia-free (N = 44) and of 37 healthy subjects. MSE and PLV analyses were performed. Partial least squares statistical analysis revealed group differences associated with the development of dementia. Patients who developed dementia showed higher signal complexity and lower PLVs in low frequencies (mainly in delta frequency) than patients who remained dementia-free and controls. Conversely, both patient groups showed lower signal variability and higher PLVs in high frequencies (mainly in gamma frequency) compared to controls, with the strongest effect in patients who developed dementia. These findings suggest that specific disruptions of brain communication can be measured before PD patients develop dementia, providing a new potential marker to identify patients at highest risk of developing dementia and who are the best candidates for neuroprotective trials.

We investigated the neural correlates of remote traumatic reexperiencing in survivors of a life-threatening incident: the near crash of Air Transat (AT) Flight 236. Survivors' brain activity was monitored during video-cued recollection of the AT disaster, September 11 th , 2001 (9/11), and a comparatively non-emotional (neutral) event. Passengers showed a robust memory enhancement effect for the AT incident relative to the 9/11 and neutral events. This traumatic memory enhancement was associated with activation in the amygdala, medial temporal lobe, anterior and posterior midline, and visual cortex in passengers. This brain-behavior relationship also held in relation to 9/11, which had elevated significance for passengers given its temporal proximity to the AT disaster. This pattern was not observed in a comparison group of non-traumatized individuals who were also scanned. These findings suggest that remote, traumatic memory is mediated by amygdalar activity, which likely enhances vividness via influences on hippocampal and ventral visual systems.

In systems neuroscience, the term "connectivity" has been defined in numerous ways, according to the particular empirical modality from which it is derived. Due to large differences in the phenomena measured by these modalities, the assumptions necessary to make inferences about axonal connections, and the limitations accompanying each, brain connectivity remains an elusive concept. Despite this, only a handful of studies have directly compared connectivity as inferred from multiple modalities, and there remains much ambiguity over what the term is actually referring to as a biological construct. Here, we perform a direct comparison based on the high-resolution and high-contrast Enhanced Nathan Klein Institute (NKI) Rockland Sample neuroimaging data set, and the CoCoMac database of tract tracing studies. We compare four types of commonly-used primate connectivity analyses: tract tracing experiments, compiled in CoCoMac; group-wise correlation of cortical thickness; tractographic networks computed from diffusion-weighted MRI (DWI); and correlational networks obtained from resting-state BOLD (fMRI). We find generally poor correspondence between all four modalities, in terms of correlated edge weights, binarized comparisons of thresholded networks, and clustering patterns. fMRI and DWI had the best agreement, followed by DWI and CoCoMac, while other comparisons showed striking divergence. Networks had the best correspondence for local ipsilateral and homotopic contralateral connections, and the worst correspondence for long-range and heterotopic contralateral connections. k-Means clustering highlighted the lowest cross-modal and cross-species consensus in lateral and medial temporal lobes, anterior cingulate, and the temporoparietal junction. Comparing the NKI results to those of the lower resolution/contrast International Consortium for Brain Imaging (ICBM) dataset, we find that the relative pattern of intermodal relationships is preserved, but the correspondence between human imaging connectomes is substantially better for NKI. These findings caution against using "connectivity" as an umbrella term for results derived from single empirical modalities, and suggest that any interpretation of these results should account for (and ideally help explain) the lack of multimodal correspondence., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Post-traumatic stress disorder (PTSD) is an anxiety disorder arising from exposure to a traumatic event. Although primarily defined in terms of behavioral symptoms, the global neurophysiological effects of traumatic stress are increasingly recognized as a critical facet of the human PTSD phenotype. Here we use magnetoencephalographic recordings to investigate two aspects of information processing: inter-regional communication (measured by functional connectivity) and the dynamic range of neural activity (measured in terms of local signal variability). We find that both measures differentiate soldiers diagnosed with PTSD from soldiers without PTSD, from healthy civilians, and from civilians with mild traumatic brain injury, which is commonly comorbid with PTSD. Specifically, soldiers with PTSD display inter-regional hypersynchrony at high frequencies (80-150 Hz), as well as a concomitant decrease in signal variability. The two patterns are spatially correlated and most pronounced in a left temporal subnetwork, including the hippocampus and amygdala. We hypothesize that the observed hypersynchrony may effectively constrain the expression of local dynamics, resulting in less variable activity and a reduced dynamic repertoire. Thus, the re-experiencing phenomena and affective sequelae in combat-related PTSD may result from functional networks becoming "stuck" in configurations reflecting memories, emotions, and thoughts originating from the traumatizing experience., Significance Statement: The present study investigates the effects of post-traumatic stress disorder (PTSD) in combat-exposed soldiers. We find that soldiers with PTSD exhibit hypersynchrony in a circuit of temporal lobe areas associated with learning and memory function. This rigid functional architecture is associated with a decrease in signal variability in the same areas, suggesting that the observed hypersynchrony may constrain the expression of local dynamics, resulting in a reduced dynamic range. Our findings suggest that the re-experiencing of traumatic events in PTSD may result from functional networks becoming locked in configurations that reflect memories, emotions, and thoughts associated with the traumatic experience., (Copyright © 2016 the authors 0270-6474/16/360419-13$15.00/0.)

The emphasis of modern neurobiological theories has recently shifted from the independent function of brain areas to their interactions in the context of whole-brain networks. As a result, neuroimaging methods and analyses have also increasingly focused on network discovery. Magnetoencephalography (MEG) is a neuroimaging modality that captures neural activity with a high degree of temporal specificity, providing detailed, time varying maps of neural activity. Partial least squares (PLS) analysis is a multivariate framework that can be used to isolate distributed spatiotemporal patterns of neural activity that differentiate groups or cognitive tasks, to relate neural activity to behavior, and to capture large-scale network interactions. Here we introduce [MEG]PLS, a MATLAB-based platform that streamlines MEG data preprocessing, source reconstruction and PLS analysis in a single unified framework. [MEG]PLS facilitates MRI preprocessing, including segmentation and coregistration, MEG preprocessing, including filtering, epoching, and artifact correction, MEG sensor analysis, in both time and frequency domains, MEG source analysis, including multiple head models and beamforming algorithms, and combines these with a suite of PLS analyses. The pipeline is open-source and modular, utilizing functions from FieldTrip (Donders, NL), AFNI (NIMH, USA), SPM8 (UCL, UK) and PLScmd (Baycrest, CAN), which are extensively supported and continually developed by their respective communities. [MEG]PLS is flexible, providing both a graphical user interface and command-line options, depending on the needs of the user. A visualization suite allows multiple types of data and analyses to be displayed and includes 4-D montage functionality. [MEG]PLS is freely available under the GNU public license (http://meg-pls.weebly.com)., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The Virtual Brain (TVB; thevirtualbrain.org) is a neuroinformatics platform for full brain network simulation based on individual anatomical connectivity data. The framework addresses clinical and neuroscientific questions by simulating multi-scale neural dynamics that range from local population activity to large-scale brain function and related macroscopic signals like electroencephalography and functional magnetic resonance imaging. TVB is equipped with a graphical and a command-line interface to create models that capture the characteristic biological variability to predict the brain activity of individual subjects. To enable researchers from various backgrounds a quick start into TVB and brain network modeling in general, we developed an educational module: TVB-EduPack. EduPack offers two educational functionalities that seamlessly integrate into TVB's graphical user interface (GUI): (i) interactive tutorials introduce GUI elements, guide through the basic mechanics of software usage and develop complex use-case scenarios; animations, videos and textual descriptions transport essential principles of computational neuroscience and brain modeling; (ii) an automatic script generator records model parameters and produces input files for TVB's Python programming interface; thereby, simulation configurations can be exported as scripts that allow flexible customization of the modeling process and self-defined batch- and post-processing applications while benefitting from the full power of the Python language and its toolboxes. This article covers the implementation of TVB-EduPack and its integration into TVB architecture. Like TVB, EduPack is an open source community project that lives from the participation and contribution of its users. TVB-EduPack can be obtained as part of TVB from thevirtualbrain.org.

Learning impairment is a core deficit in schizophrenia that impacts on real-world functioning and yet, elucidating its underlying neural basis remains a challenge. A key issue when interpreting learning-task experiments is that task-independent changes may confound interpretation of task-related signal changes in neuroimaging studies. The nature of these task-independent changes in schizophrenia is unknown. Therefore, we examined task-independent "time effects" in a group of participants with schizophrenia contrasted with healthy participants in a longitudinal fMRI learning-experiment designed to allow for examination of non-specific effects of time. Flanking the learning portions of the experiment with a task-of-no-interest allowed us to extract task-independent BOLD changes. Task-independent effects occurred in both groups, but were more robust in the schizophrenia group. There was a significant interaction effect between group and time in a distributed activity pattern that included inferior and superior temporal regions, frontal areas (left anterior insula and superior medial gyri), and parietal areas (posterior cingulate cortices and precuneus). This pattern showed task-independent linear decrease in BOLD amplitude over the two scanning sessions for the schizophrenia group, but showed either opposite effect or no activity changes for the control group. There was a trend towards a correlation between task-independent effects and the presence of more negative symptoms in the schizophrenia group. The strong interaction between group and time suggests that both the scanning experience as a whole and the transition between task-types evokes a different response in persons with schizophrenia and may confound interpretation of learning-related longitudinal imaging experiments if not explicitly considered.

There currently remains considerable variability in stroke survivor recovery. To address this, developing individualized treatment has become an important goal in stroke treatment. As a first step, it is necessary to determine brain dynamics associated with stroke and recovery. While recent methods have made strides in this direction, we still lack physiological biomarkers. The Virtual Brain (TVB) is a novel application for modeling brain dynamics that simulates an individual's brain activity by integrating their own neuroimaging data with local biophysical models. Here, we give a detailed description of the TVB modeling process and explore model parameters associated with stroke. In order to establish a parallel between this new type of modeling and those currently in use, in this work we establish an association between a specific TVB parameter (long-range coupling) that increases after stroke with metrics derived from graph analysis. We used TVB to simulate the individual BOLD signals for 20 patients with stroke and 10 healthy controls. We performed graph analysis on their structural connectivity matrices calculating degree centrality, betweenness centrality, and global efficiency. Linear regression analysis demonstrated that long-range coupling is negatively correlated with global efficiency (P = 0.038), but is not correlated with degree centrality or betweenness centrality. Our results suggest that the larger influence of local dynamics seen through the long-range coupling parameter is closely associated with a decreased efficiency of the system. We thus propose that the increase in the long-range parameter in TVB (indicating a bias toward local over global dynamics) is deleterious because it reduces communication as suggested by the decrease in efficiency. The new model platform TVB hence provides a novel perspective to understanding biophysical parameters responsible for global brain dynamics after stroke, allowing the design of focused therapeutic interventions.

Slowness of thought is not necessarily a handicap but could be a signature of optimal brain function. Emerging evidence shows that neuroanatomical and dynamical constraints of the human brain shape its functionality in optimal ways, characterized by slowness during task-based cognition in the context of spontaneous resting-state activity. This activity can be described mechanistically by whole-brain computational modeling that relates directly to optimality in the context of theories arguing for metastability in the brain. We discuss the role for optimal processing of information in the context of cognitive, task-related activity, and propose that combining multi-modal neuroimaging and explicit whole-brain models focused on the timing of functional dynamics can help to uncover fundamental rules of brain function in health and disease., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The influence of capture interval on trap shyness, and temperature, rainfall and drought on capture probability (p) in 827 brown mudfish Neochanna apoda was quantified using mark-recapture models. In particular, it was hypothesized that the loss of trapping memory in marked N. apoda would lead to a capture-interval threshold required to minimize trap shyness. Neochanna apoda trap shyness approximated a threshold response to capture interval, declining rapidly with increasing capture intervals up to 16.5 days, after which p remained constant. Tests for detecting trap-dependent capture probability in Cormack-Jolly-Seber models failed to detect trap shyness in N. apoda capture histories with capture intervals averaging 16 days. This confirmed the applicability of the 16 day capture-interval threshold for mark-recapture studies. Instead, N. apoda p was positively influenced by water temperature and rainfall during capture. These results imply that a threshold capture interval is required to minimize the trade-off between the competing assumptions of population closure and p homogeneity between capture occasions in closed mark-recapture models. Moreover, environmental factors that influence behaviour could potentially confound abundance indices, and consequently abundance trends should be interpreted with caution in the face of long-term climate change, such as with global warming., (© 2015 The Fisheries Society of the British Isles.)

Autism spectrum disorder (ASD) includes deficits in social cognition, communication, and executive function. Recent neuroimaging studies suggest that ASD disrupts the structural and functional organization of brain networks and, presumably, how they generate information. Here, we relate deficits in an aspect of cognitive control to network-level disturbances in information processing. We recorded magnetoencephalography while children with ASD and typically developing controls performed a set-shifting task designed to test mental flexibility. We used multiscale entropy (MSE) to estimate the rate at which information was generated in a set of sources distributed across the brain. Multivariate partial least-squares analysis revealed 2 distributed networks, operating at fast and slow time scales, that respond completely differently to set shifting in ASD compared with control children, indicating disrupted temporal organization within these networks. Moreover, when typically developing children engaged these networks, they achieved faster reaction times. When children with ASD engaged these networks, there was no improvement in performance, suggesting that the networks were ineffective in children with ASD. Our data demonstrate that the coordination and temporal organization of large-scale neural assemblies during the performance of cognitive control tasks is disrupted in children with ASD, contributing to executive function deficits in this group., (© The Author 2014. Published by Oxford University Press.)

Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

While human brains are specialized for complex and variable real world tasks, most neuroscience studies reduce environmental complexity, which limits the range of behaviours that can be explored. Motivated to overcome this limitation, we conducted a large-scale experiment with electroencephalography (EEG) based brain-computer interface (BCI) technology as part of an immersive multi-media science-art installation. Data from 523 participants were collected in a single night. The exploratory experiment was designed as a collective computer game where players manipulated mental states of relaxation and concentration with neurofeedback targeting modulation of relative spectral power in alpha and beta frequency ranges. Besides validating robust time-of-night effects, gender differences and distinct spectral power patterns for the two mental states, our results also show differences in neurofeedback learning outcome. The unusually large sample size allowed us to detect unprecedented speed of learning changes in the power spectrum (~ 1 min). Moreover, we found that participants' baseline brain activity predicted subsequent neurofeedback beta training, indicating state-dependent learning. Besides revealing these training effects, which are relevant for BCI applications, our results validate a novel platform engaging art and science and fostering the understanding of brains under natural conditions.

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere "take over" their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children., (Copyright © 2015 the authors 0270-6474/15/358914-11$15.00/0.)

The functional interaction between the brain's two hemispheres includes a unique set of connections between corresponding regions in opposite hemispheres (i.e., homotopic regions) that are consistently reported to be exceptionally strong compared with other interhemispheric (i.e., heterotopic) connections. The strength of homotopic functional connectivity (FC) is thought to be mediated by the regions' shared functional roles and their structural connectivity. Recently, homotopic FC was reported to be stable over time despite the presence of dynamic FC across both intrahemispheric and heterotopic connections. Here we build on this work by considering whether homotopic FC is also stable across conditions. We additionally test the hypothesis that strong and stable homotopic FC is supported by the underlying structural connectivity. Consistent with previous findings, interhemispheric FC between homotopic regions were significantly stronger in both humans and macaques. Across conditions, homotopic FC was most resistant to change and therefore was more stable than heterotopic or intrahemispheric connections. Across time, homotopic FC had significantly greater temporal stability than other types of connections. Temporal stability of homotopic FC was facilitated by direct anatomical projections. Importantly, temporal stability varied with the change in conductive properties of callosal axons along the anterior-posterior axis. Taken together, these findings suggest a notable role for the corpus callosum in maintaining stable functional communication between hemispheres.

The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture., (Copyright © 2015 the authors 0270-6474/15/355579-10$15.00/0.)

The human brain undergoes marked structural changes with age including cortical thinning and reduced connectivity because of the degradation of myelin. Although these changes can compromise cognitive function, the brain is able to functionally reorganize to compensate for some of this structural loss. However, there are interesting individual differences in outcome: When comparing individuals of similar age, those who engage in regular physical activity are less affected by the typical age-related decline in cognitive function. This study used multiscale entropy to reveal a shift in the way the brain processes information in older adults that is related to physical activity. Specifically, older adults who were more physically active engaged in more local neural information processing. Interestingly, this shift toward local information processing was also associated with improved executive function performance in older adults, suggesting that physical activity may help to improve aspects of cognitive function in older adults by biasing the neural system toward local information processing. In the face of age-related structural decline, the neural plasticity that is enhanced through physical activity may help older adults maintain cognitive health longer into their lifespan.

Music has always played a central role in human culture. The question of how musical sounds can have such profound emotional and rewarding effects has been a topic of interest throughout generations. At a fundamental level, listening to music involves tracking a series of sound events over time. Because humans are experts in pattern recognition, temporal predictions are constantly generated, creating a sense of anticipation. We summarize how complex cognitive abilities and cortical processes integrate with fundamental subcortical reward and motivation systems in the brain to give rise to musical pleasure. This work builds on previous theoretical models that emphasize the role of prediction in music appreciation by integrating these ideas with recent neuroscientific evidence., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Objective: Physical and sociocognitive lifestyle activities promote aspects of cognitive function in older adults. Very little is known about the relation between these lifestyle activities and cognitive function in young adults. One aspect of cognitive function that is critical for everyday function is episodic memory. The present study examined the relationship between lifestyle activities and episodic memory in younger and older adults., Method: Participants were 62 younger (mean age = 24 years) and older adults (mean age = 74 years). The augmented Victoria Longitudinal Study Activities Questionnaire was used to quantify level of engagement in physical activity, sociocognitive activity, and TV viewing. Episodic memory was assessed using the old-new face recognition paradigm in which memory for younger and older faces was tested., Results: Compared to younger adults, older adults reported being less physically and sociocognitively active while engaging in more passive behaviors such as TV viewing. A positive association was observed between physical activity and episodic memory for young adults but not for older adults. Interestingly, TV viewing was negatively associated with episodic memory in older adults but not younger adults. No relationship was found between sociocognitive activity and episodic memory for either younger or older adults. Although the own-age effect was observed for older adults, face age did not interact with lifestyle activities., Conclusion: The positive cognitive benefits of physical activity extend to younger adults; however, the interplay between physical activity and cognition may differ across the life span. Furthermore, TV viewing may be particularly detrimental to cognitive performance later in life.

We tested the hypothesis that active exploration of the visual environment is mediated not only by visual attention but also by visual working memory (VWM) by examining performance in both a visual search and a change detection task. Subjects rarely fixated previously examined distracters during visual search, suggesting that they successfully retained those items. Change detection accuracy decreased with increasing set size, suggesting that subjects had a limited VWM capacity. Crucially, performance in the change detection task predicted visual search efficiency: Higher VWM capacity was associated with faster and more accurate responses as well as lower probabilities of refixation. We found no temporal delay for return saccades, suggesting that active vision is primarily mediated by VWM rather than by a separate attentional disengagement mechanism commonly associated with the inhibition-of-return (IOR) effect. Taken together with evidence that visual attention, VWM, and the oculomotor system involve overlapping neural networks, these data suggest that there exists a general capacity for cognitive processing., (© 2014 ARVO.)

The hippocampal formation is a key structure for memory function in the brain. The functional anatomy of the brain suggests that the hippocampus may be a convergence zone, as it receives polysensory input from distributed association areas throughout the neocortex. However, recent quantitative graph-theoretic analyses of the static large-scale connectome have failed to demonstrate the centrality of the hippocampus; in the context of the whole brain, the hippocampus is not among the most connected or reachable nodes. Here we show that when communication dynamics are taken into account, the hippocampus is a key hub in the connectome. Using a novel computational model, we demonstrate that large-scale brain network topology is organized to funnel and concentrate information flow in the hippocampus, supporting the long-standing hypothesis that this region acts as a critical convergence zone. Our results indicate that the functional capacity of the hippocampus is shaped by its embedding in the large-scale connectome.

Spontaneous brain activity, that is, activity in the absence of controlled stimulus input or an explicit active task, is topologically organized in multiple functional networks (FNs) maintaining a high degree of coherence. These "resting state networks" are constrained by the underlying anatomical connectivity between brain areas. They are also influenced by the history of task-related activation. The precise rules that link plastic changes and ongoing dynamics of resting-state functional connectivity (rs-FC) remain unclear. Using the framework of the open source neuroinformatics platform "The Virtual Brain," we identify potential computational mechanisms that alter the dynamical landscape, leading to reconfigurations of FNs. Using a spiking neuron model, we first demonstrate that network activity in the absence of plasticity is characterized by irregular oscillations between low-amplitude asynchronous states and high-amplitude synchronous states. We then demonstrate the capability of spike-timing-dependent plasticity (STDP) combined with intrinsic alpha (8-12 Hz) oscillations to efficiently influence learning. Further, we show how alpha-state-dependent STDP alters the local area dynamics from an irregular to a highly periodic alpha-like state. This is an important finding, as the cortical input from the thalamus is at the rate of alpha. We demonstrate how resulting rhythmic cortical output in this frequency range acts as a neuronal tuner and, hence, leads to synchronization or de-synchronization between brain areas. Finally, we demonstrate that locally restricted structural connectivity changes influence local as well as global dynamics and lead to altered rs-FC.

Major Depressive Disorder (MDD) is characterized by rumination. Prior research suggests that resting-state brain activation reflects rumination when depressed individuals are not task engaged. However, no study has directly tested this. Here we investigated whether resting-state epochs differ from induced ruminative states for healthy and depressed individuals. Most previous research on resting-state networks comes from seed-based analyses with the posterior cingulate cortex (PCC). By contrast, we examined resting state connectivity by using the complete multivariate connectivity profile (i.e., connections across all brain nodes) and by comparing these results to seeded analyses. We find that unconstrained resting-state intervals differ from active rumination states in strength of connectivity and that overall connectivity was higher for healthy vs. depressed individuals. Relationships between connectivity and subjective mood (i.e., behavior) were strongly observed during induced rumination epochs. Furthermore, connectivity patterns that related to subjective mood were strikingly different for MDD and healthy control (HC) groups suggesting different mood regulation mechanisms., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Moment-to-moment brain signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened signal variability can index and aid efficient neural function, but it is not known whether signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean brain signals. Using mixed modeling, we also linked parametric modulations in signal variability with modulations in task performance. We found that difficulty-related reductions in signal variability predicted reduced accuracy and longer reaction times within-person; mean signal changes were not predictive. We further probed the various differences between signal variance and signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that brain signal variability provides a systematic task-driven signal of interest from which we can understand the dynamic function of the human brain, and in a way that mean signals cannot capture., (© The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dynamic basis of these networks is inter-regional communication involving local and distal regions. It is assumed that inter-regional distances play a pivotal role in modulating network dynamics. Using three different neuroimaging modalities, 6 datasets were evaluated to determine whether experimental manipulations asymmetrically affect functional relationships based on the distance between brain regions in human participants. Contrary to previous assumptions, here we show that short- and long-range connections are equally likely to strengthen or weaken in response to task demands. Additionally, connections between homotopic areas are the most stable and less likely to change compared to any other type of connection. Our results point to a functional connectivity landscape characterized by fluid transitions between local specialization and global integration. This ability to mediate functional properties irrespective of spatial distance may engender a diverse repertoire of cognitive processes when faced with a dynamic environment.

Given their unique connectivity, a primary function of brain networks must be to transfer and integrate information. Therefore, the way in which information is integrated by individual nodes of the network may be an informative aspect of cognitive processing. Here we present a method inspired by telecommunications research that utilizes time-frequency fluctuations of neural activity to infer how information is integrated by individual nodes of the network. We use a queueing theoretical model to interpret empirical data in terms of information processing and integration. In particular, we demonstrate, in participants aged from 6 to 41 years, that the well-known face inversion phenomenon may be explained in terms of information integration. Our model suggests that inverted faces may be associated with shorter and more frequent neural integrative stages, indicating fractured processing and consistent with the notion that inverted faces are perceived by parts. Conversely, our model suggests that upright faces may be associated with a smaller number of sustained episodes of integration, indicating more involved processing, akin to holistic and configural processing. These differences in how upright and inverted faces are processed became more pronounced during development, indicating a gradual specialization for face perception. These effects were robustly expressed in the right fusiform gyrus (all groups), as well as right parahippocampal gyrus (children and adolescents only) and left inferior temporal cortex (adults only).

Primate sensory systems subserve complex neurocomputational functions. Consequently, these systems are organised anatomically in a distributed fashion, commonly linking areas to form specialised processing streams. Each stream is related to a specific function, as evidenced from studies of the visual cortex, which features rather prominent segregation into spatial and non-spatial domains. It has been hypothesised that other sensory systems, including auditory, are organised in a similar way on the cortical level. Recent studies offer rich qualitative evidence for the dual stream hypothesis. Here we provide a new paradigm to quantitatively uncover these patterns in the auditory system, based on an analysis of multiple anatomical studies using multivariate techniques. As a test case, we also apply our assessment techniques to more ubiquitously-explored visual system. Importantly, the introduced framework opens the possibility for these techniques to be applied to other neural systems featuring a dichotomised organisation, such as language or music perception., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18-72) and one magnetoencephalography (n = 31, ages 20-75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence., (© The Authors 2013. Published by Oxford University Press.)

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions., (Copyright © 2014 the authors 0270-6474/14/347910-07$15.00/0.)

Food webs are strongly size-structured so will be vulnerable to changes in environmental factors that affect large predators. However, mechanistic understanding of environmental controls of top predator size is poorly developed. We used streams to investigate how predator body size is altered by three fundamental climate change stressors: reductions in habitat size, increases in disturbance and warmer temperatures. Using new survey data from 74 streams, we showed that habitat size and disturbance were the most important stressors influencing predator body size. A synergistic interaction between that habitat size and disturbance due to flooding meant the sizes of predatory fishes peaked in large, benign habitats and their body size decreased as habitats became either smaller or harsher. These patterns were supported by experiments indicating that habitat-size reductions and increased flood disturbance decreased both the abundance and biomass of large predators. This research indicates that interacting climate change stressors can influence predator body size, resulting in smaller predators than would be predicted from examining an environmental factor in isolation. Thus, climate-induced changes to key interacting environmental factors are likely to have synergistic impacts on predator body size which, because of their influence on the strength of biological interactions, will have far-reaching effects on food-web responses to global environmental change., (© 2013 John Wiley & Sons Ltd.)

TheVirtualBrain (TVB) is a neuroinformatics Python package representing the convergence of clinical, systems, and theoretical neuroscience in the analysis, visualization and modeling of neural and neuroimaging dynamics. TVB is composed of a flexible simulator for neural dynamics measured across scales from local populations to large-scale dynamics measured by electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and core analytic and visualization functions, all accessible through a web browser user interface. A datatype system modeling neuroscientific data ties together these pieces with persistent data storage, based on a combination of SQL and HDF5. These datatypes combine with adapters allowing TVB to integrate other algorithms or computational systems. TVB provides infrastructure for multiple projects and multiple users, possibly participating under multiple roles. For example, a clinician might import patient data to identify several potential lesion points in the patient's connectome. A modeler, working on the same project, tests these points for viability through whole brain simulation, based on the patient's connectome, and subsequent analysis of dynamical features. TVB also drives research forward: the simulator itself represents the culmination of several simulation frameworks in the modeling literature. The availability of the numerical methods, set of neural mass models and forward solutions allows for the construction of a wide range of brain-scale simulation scenarios. This paper briefly outlines the history and motivation for TVB, describing the framework and simulator, giving usage examples in the web UI and Python scripting.

Episodic memory and semantic memory produce very different subjective experiences yet rely on overlapping networks of brain regions for processing. Traditional approaches for characterizing functional brain networks emphasize static states of function and thus are blind to the dynamic information processing within and across brain regions. This study used information theoretic measures of entropy to quantify changes in the complexity of the brain's response as measured by magnetoencephalography while participants listened to audio recordings describing past personal episodic and general semantic events. Personal episodic recordings evoked richer subjective mnemonic experiences and more complex brain responses than general semantic recordings. Critically, we observed a trade-off between the relative contribution of local versus distributed entropy, such that personal episodic recordings produced relatively more local entropy whereas general semantic recordings produced relatively more distributed entropy. Changes in the relative contributions of local and distributed entropy to the total complexity of the system provides a potential mechanism that allows the same network of brain regions to represent cognitive information as either specific episodes or more general semantic knowledge.

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a "rich club" of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

With the increasing availability of advanced imaging technologies, we are entering a new era of neuroscience. Detailed descriptions of the complex brain network enable us to map out a structural connectome, characterize it with graph theoretical methods, and compare it to the functional networks with increasing detail. To link these two aspects and understand how dynamics and structure interact to form functional brain networks in task and in the resting state, we use theoretical models. The advantage of using theoretical models is that by recreating functional connectivity and time series explicitly from structure and pre-defined dynamics, we can extract critical mechanisms by linking structure and function in ways not directly accessible in the real brain. Recently, resting-state models with varying local dynamics have reproduced empirical functional connectivity patterns, and given support to the view that the brain works at a critical point at the edge of a bifurcation of the system. Here, we present an overview of a modeling approach of the resting brain network and give an application of a neural mass model in the study of complexity changes in aging., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Beamformers are one of the most common inverse models currently used in the estimation of source activity from magnetoencephelography (MEG) data. They rely on a minimization of total power while constraining the gain in the voxel of interest, resulting in the suppression of background noise. Nonetheless, in cases where background noise is strong compared to the source of interest, or when many sources are present, the ability of the beamformer to detect and accurately localize weak sources is reduced. In visual paradigms, two main background sources can substantially impact an accurate estimation of weaker sources. Ocular artifacts are orders of magnitude higher than neural sources making it difficult for the beamformer to effectively suppress them. Primary visual activations also result in strong signals that can impede localization of weak sources. In this paper, we systematically evaluated how neural (visual) and non-neural (eye, heart) sources affect the localization accuracy of frontal and medial temporal sources in visual tasks. These sources are of tremendous interest in learning and memory studies as well as in clinical settings (Alzheimer's/epilepsy) and are typically difficult to localize robustly in MEG. Empirical data from two tasks - active learning and control - were used to evaluate our analysis techniques. Global field power calculations showed multiple time periods where active learning was significantly different from response selection with dominant sources converging to the eyes. Extensive leakage of eye activity into frontal and visual that evoked responses into parietal cortices was also observed. Contributions from ocular activity to the reconstructed time series were indiscernible from task-based recruitment of frontal sources in the original data. Removing artifacts (eye movements, cardiac, and muscular) by means of independent component analysis (ICA) led to a significant improvement in detection and localization of frontal and medial temporal sources. We verified our results by using simulations of sources placed in frontal and medial temporal regions with various types of background noise (eye, heart, and visual). We report that the detection and localization accuracy of frontal and medial temporal sources with beamformer techniques is highly dependent on the magnitude and location of background sources and that removing artifacts can substantially improve the beamformer's performance., (Copyright © 2013 Elsevier Inc. All rights reserved.)