Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.     

Mechanisms of intrinsic beating variability in cardiac cell cultures and model pacemaker networks

Heart rate variability (HRV) exhibits fluctuations characterized by a power law behavior of its power spectrum. The interpretation of this nonlinear HRV behavior, resulting from interactions between extracardiac regulatory mechanisms, could be clinically useful. However, the involvement of intrinsic variations of pacemaker rate in HRV has scarcely been investigated. We examined beating variability in spontaneously active incubating cultures of neonatal rat ventricular myocytes using microelectrode arrays. In networks of mathematical model pacemaker cells, we evaluated the variability induced by the stochastic gating of transmembrane currents and of calcium release channels and by the dynamic turnover of ion channels. In the cultures, spontaneous activity originated from a mobile focus. Both the beat-to-beat movement of the focus and beat rate variability exhibited a power law behavior. In the model networks, stochastic fluctuations in transmembrane currents and stochastic gating of calcium release channels did not reproduce the spatiotemporal patterns observed in vitro. In contrast, long-term correlations produced by the turnover of ion channels induced variability patterns with a power law behavior similar to those observed experimentally. Therefore, phenomena leading to long-term correlated variations in pacemaker cellular function may, in conjunction with extracardiac regulatory mechanisms, contribute to the nonlinear characteristics of HRV.     

Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging

The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.     

Fluctuations in central and peripheral temperatures associated with feeding behavior in rats

We examined the pattern of temperature fluctuations in the nucleus accumbens (NAcc), temporal muscle, and skin, along with locomotion in food-deprived and nondeprived rats following the presentation of an open or closed food container and during subsequent eating or food-seeking behavior without eating. Although rats in food-deprived, quiet resting conditions had more than twofold lower spontaneous locomotion and lower temperature values than in nondeprived conditions, after presentation of a container, they consistently displayed food-seeking behavior, showing much larger and longer temperature changes. When the container was open, rats rapidly retrieved food and consumed it. Food consumption was preceded and accompanied by gradual increases in brain and muscle temperatures ( approximately 1.5 degrees C) and a weaker, delayed increase in skin temperature ( approximately 0.8 degrees C). All temperatures began to rapidly fall immediately after eating was completed, but NAcc and muscle temperatures returned to baseline after approximately 35 min. When the container was closed and rats were unable to obtain food, they continued food-seeking activity during the entire period of presentation. Similar to eating, this activity was preceded and accompanied by gradual temperature increases in the brain and muscle, which were somewhat smaller than those during eating ( approximately 1.2 degrees C), with no changes in skin temperature. In contrast to trials with eating, NAcc and muscle temperatures continued to increase for approximately 10 min after the container was removed from the cage and the rat continued food-seeking behavior, with a return to baselines after approximately 50 min. These temperature fluctuations are discussed with respect to alterations in metabolic brain activity associated with feeding behavior, depending upon deprivation state and food availability.     

Rhythms,rhythmicity and aggression

The relationships between biological rhythms and human aggressive behavior are addressed and discussed in this article: First, circadian rhythms and aggression are considered. Studies of sleep/waking cycle disturbances in aggression are reported. Severe aggression is associated with profound changes in sleep architecture. Causal link is difficult to establish given that sleep disturbance and aggressive behavior could be the symptoms of the same disorder. Specific aggressive behavior developed during sleep is also described. In addition, hormonal circadian rhythm studies are reported. Thus, low cortisol levels, in particular low cortisol variability, are associated with aggressive behavior, suggesting an inhibitory role of cortisol. Testosterone has daily and seasonal fluctuations, but no link with aggression has been established. Neurophysiological underlying mechanisms are discussed in the last part of this article, with a focus on the relationship between brain rhythm and aggression. Increase of slow-wave EEG activities is observed in individuals with aggressive behavior. Epilepsy, as a disease of brain rhythm could be associated with aggressive behavior, in pre, post and inter ictal periodes. Incidence of aggression is not likely more prevalent in epileptic individuals compared to those with other neurological conditions. Ictal changes take the form of profound behavioral changes, including aggressive behavior which has been interpreted as the emergence of “archeical” or innate motor patterns. In this multidisciplinary approach, the main difficulty is the categorization of the differents types of aggression. Finally, taken together, these studies suggest that biological rhythms, especially circadian rhythms, could provide therapeutic benefits to human aggressive behavior. Biological rhythymicity seems to be a necessary permanent training offering interesting perspectives for the adaptation to changes in the field of aggression.     

Noise during rest enables the exploration of the brain's dynamic repertoire

Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1–100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space–time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.     

A Correspondence between Individual Differences in the Brain's Intrinsic Functional Architecture and the Content and Form of Self-Generated Thoughts

Although neural activity often reflects the processing of external inputs, intrinsic fluctuations in activity have been observed throughout the brain. These may relate to patterns of self-generated thought that can occur while not performing goal-driven tasks. To understand the relationship between self-generated mental activity and intrinsic neural fluctuations, we developed the New York Cognition Questionnaire (NYC-Q) to assess the content and form of an individual''s experiences during the acquisition of resting-state fMRI data. The data were collected as a part of the Nathan Kline Rockland Enhanced sample. We decomposed NYC-Q scores using exploratory factor analysis and found that self-reported thoughts clustered into distinct dimensions of content (future related, past related, positive, negative, and social) and form (words, images, and specificity). We used these components to perform an individual difference analysis exploring how differences in the types of self-generated thoughts relate to whole brain measures of intrinsic brain activity (fractional amplitude of low frequency fluctuations, regional homogeneity, and degree centrality). We found patterns of self-generated thoughts related to changes that were distributed across a wide range of cortical areas. For example, individuals who reported greater imagery exhibited greater low frequency fluctuations in a region of perigenual cingulate cortex, a region that is known to participate in the so-called default-mode network. We also found certain forms of thought were associated with other areas, such as primary visual cortex, the insula, and the cerebellum. For example, individuals who reported greater future thought exhibited less homogeneous neural fluctuations in a region of lateral occipital cortex, a result that is consistent with the claim that particular types of self-generated thought depend on processes that are decoupled from sensory processes. These data provide evidence that self-generated thought is a heterogeneous category of experience and that studying its content can be helpful in understanding brain dynamics.     

Fractal properties of human muscle sympathetic nerve activity

Muscle sympathetic nerve activity (MSNA) in resting humans is characterized by cardiac-related bursts of variable amplitude that occur sporadically or in clusters. The present study was designed to characterize the fluctuations in the number of MSNA bursts, interburst interval, and burst amplitude recorded from the peroneal nerve of 15 awake, healthy human subjects. For this purpose, we used the Allan and Fano factor analysis and dispersional analysis to test whether the fluctuations were time-scale invariant (i.e., fractal) or random in occurrence. Specifically, we measured the slopes of the power laws in the Allan factor, Fano factor, and dispersional analysis curves. In addition, the Hurst exponent was calculated from the slope of the power law in the Allan factor curve. Whether the original time series contained fractal fluctuations was decided on the basis of a comparison of the values of these parameters with those for surrogate data blocks. The results can be summarized as follows. Fluctuations in the number of MSNA bursts and interburst interval were fractal in each of the subjects, and fluctuations in burst amplitude were fractal in four of the subjects. We also found that fluctuations in the number of heartbeats and heart period (R-R interval) were fractal in each of the subjects. These results demonstrate for the first time that apparently random fluctuations in human MSNA are, in fact, dictated by a time-scale-invariant process that imparts "long-term memory" to the sequence of cardiac-related bursts. Whether sympathetic outflow to the heart also is fractal and contributes to the fractal component of heart rate variability remains an open question.     

Modeling fluctuations in default-mode brain network using a spiking neural network

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.     

Transcranial magnetic stimulation in cognitive neuroscience--virtual lesion, chronometry, and functional connectivity

Fifteen years after its introduction by Anthony Barker, transcranial magnetic stimulation (TMS) appears to be 'coming of age' in cognitive neuroscience and promises to reshape the way we investigate brain-behavior relations. Among the many methods now available for imaging the activity of the human brain, magnetic stimulation is the only technique that allows us to interfere actively with brain function. As illustrated by several experiments over the past couple of years, this property of TMS allows us to investigate the relationship between focal cortical activity and behavior, to trace the timing at which activity in a particular cortical region contributes to a given task, and to map the functional connectivity between brain regions.     

Transient coordinated activity within the developing brain��s default network

The concept of a brain default network postulates that specific brain regions are more active when a person is engaged in introspective mental activity. Transient functional coordination between groups of neurons is thought to be necessary for information processing. Since children develop introspection as they mature, regions of the default network may establish increasing functional coordination with age, resulting in fewer fluctuations in synchronization patterns. We investigated the transient coordinated activity in regions of the default network in seventeen children aged 11 months to 17 years of age using EEG recordings while subjects were resting quietly with eyes closed. The temporal and spatial fluctuations in the phase synchrony patterns were estimated across sites associated with the default network pattern and compared to other regions. Lower variability of the spatio-temporal patterns of phase synchronization associated with the default network was observed in the older group as compared to the younger group. This indicates that functional coordination increases among regions of the default network as children develop.     

Simultaneous Brain–Cervical Cord fMRI Reveals Intrinsic Spinal Cord Plasticity during Motor Sequence Learning

The spinal cord participates in the execution of skilled movements by translating high-level cerebral motor representations into musculotopic commands. Yet, the extent to which motor skill acquisition relies on intrinsic spinal cord processes remains unknown. To date, attempts to address this question were limited by difficulties in separating spinal local effects from supraspinal influences through traditional electrophysiological and neuroimaging methods. Here, for the first time, we provide evidence for local learning-induced plasticity in intact human spinal cord through simultaneous functional magnetic resonance imaging of the brain and spinal cord during motor sequence learning. Specifically, we show learning-related modulation of activity in the C6–C8 spinal region, which is independent from that of related supraspinal sensorimotor structures. Moreover, a brain–spinal cord functional connectivity analysis demonstrates that the initial linear relationship between the spinal cord and sensorimotor cortex gradually fades away over the course of motor sequence learning, while the connectivity between spinal activity and cerebellum gains strength. These data suggest that the spinal cord not only constitutes an active functional component of the human motor learning network but also contributes distinctively from the brain to the learning process. The present findings open new avenues for rehabilitation of patients with spinal cord injuries, as they demonstrate that this part of the central nervous system is much more plastic than assumed before. Yet, the neurophysiological mechanisms underlying this intrinsic functional plasticity in the spinal cord warrant further investigations.     

Intrinsic fluctuations, robustness, and tunability in signaling cycles

Covalent modification cycles (e.g., phosphorylation-dephosphorylation) underlie most cellular signaling and control processes. Low molecular copy number, arising from compartmental segregation and slow diffusion between compartments, potentially renders these cycles vulnerable to intrinsic chemical fluctuations. How can a cell operate reliably in the presence of this inherent stochasticity? How do changes in extrinsic parameters lead to variability of response? Can cells exploit these parameters to tune cycles to different ranges of stimuli? We study the dynamics of an isolated phosphorylation cycle. Our model shows that the cycle transmits information reliably if it is tuned to an optimal parameter range, despite intrinsic fluctuations and even for small input signal amplitudes. At the same time, the cycle is sensitive to changes in the concentration and activity of kinases and phosphatases. This sensitivity can lead to significant cell-to-cell response variability. It also provides a mechanism to tune the cycle to transmit signals in various amplitude ranges. Our results show that signaling cycles possess a surprising combination of robustness and tunability. This combination makes them ubiquitous in eukaryotic signaling, optimizing signaling in the presence of fluctuations using their inherent flexibility. On the other hand, cycles tuned to suppress intrinsic fluctuations can be vulnerable to changes in the number and activity of kinases and phosphatases. Such trade-offs in robustness to intrinsic and extrinsic fluctuations can influence the evolution of signaling cascades, making them the weakest links in cellular circuits.     

Formal Models of the Network Co-occurrence Underlying Mental Operations

Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-unconstrained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition.     

Rude mechanicals in brain haemodynamics: non-neural actors that influence blood flow

Fluctuations in blood oxygenation and flow are widely used to infer brain activity during resting-state functional magnetic resonance imaging (fMRI). However, there are strong systemic and vascular contributions to resting-state signals that are unrelated to ongoing neural activity. Importantly, these non-neural contributions to haemodynamic signals (or ‘rude mechanicals’) can be as large as or larger than the neurally evoked components. Here, we review the two broad classes of drivers of these signals. One is systemic and is tied to fluctuations in external drivers such as heart rate and breathing, and the robust autoregulatory mechanisms that try to maintain a constant milieu in the brain. The other class comprises local, active fluctuations that appear to be intrinsic to vascular tissue and are likely similar to active local fluctuations seen in vasculature all over the body. In this review, we describe these non-neural fluctuations and some of the tools developed to correct for them when interpreting fMRI recordings. However, we also emphasize the links between these vascular fluctuations and brain physiology and point to ways in which fMRI measurements can be used to exploit such links to gain valuable information about neurovascular health and about internal brain states.This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.     

From single-cell genetic architecture to cell population dynamics: quantitatively decomposing the effects of different population heterogeneity sources for a genetic network with positive feedback architecture

Phenotypic cell-to-cell variability or cell population heterogeneity originates from two fundamentally different sources: unequal partitioning of cellular material at cell division and stochastic fluctuations associated with intracellular reactions. We developed a mathematical and computational framework that can quantitatively isolate both heterogeneity sources and applied it to a genetic network with positive feedback architecture. The framework consists of three vastly different mathematical formulations: a), a continuum model, which completely neglects population heterogeneity; b), a deterministic cell population balance model, which accounts for population heterogeneity originating only from unequal partitioning at cell division; and c), a fully stochastic model accommodating both sources of population heterogeneity. The framework enables the quantitative decomposition of the effects of the different population heterogeneity sources on system behavior. Our results indicate the importance of cell population heterogeneity in accurately predicting even average population properties. Moreover, we find that unequal partitioning at cell division and sharp division rates shrink the region of the parameter space where the population exhibits bistable behavior, a characteristic feature of networks with positive feedback architecture. In addition, intrinsic noise at the single-cell level due to slow operator fluctuations and small numbers of molecules further contributes toward the shrinkage of the bistability regime at the cell population level. Finally, the effect of intrinsic noise at the cell population level was found to be markedly different than at the single-cell level, emphasizing the importance of simulating entire cell populations and not just individual cells to understand the complex interplay between single-cell genetic architecture and behavior at the cell population level.     

Circadian rhythm in behavioral activities and diurnal abundance of stray street dogs in the city of Sambalpur,Odisha, India

ABSTRACT

This is the first research article that documents circadian variability in behavioral variables, namely resting (Rt) and standing (St) in stray street dogs of Sambalpur city, India. We also estimated the abundance as a function of time of the day and gender in a population of stray dog inhabiting streets of the city. In addition, we determined the association between the behavioral variables and the environmental variables, such as light intensity, sound intensity, temperature and humidity. We determined the abundance of street dogs at 10 hotspots using the photographic capture-recapture technique and Lincoln index equation. In another study, we determined dogs’ density along the three randomly selected routes that connect the beginning (Dhanupali) and end (P.C. Bridge) of the city precincts. We recorded the resting and standing activities of the stray street dogs using still and video cameras at four times of the day continuously over a longitudinal timescale of 72 hours. This study was conducted at four randomly selected dog hotspots. Subjecting the log-transformed time series data to the Cosinor rhythmometry we obtained three different rhythm parameters, such as mesor (M), amplitude (A) and acrophase (Ø) of the rhythm in resting and standing behavior of stray street dogs. We found out both spatial and temporal variability in the behavior of street dogs. The sightings of dogs were always more during the evening and nighttime irrespective of the investigated routes and hotspots. Further, we also observed that the abundance of male dogs was always significantly more as compared with the bitches. A lack of association between two attributes the time of the day and gender apropos the number of sightings of the street dogs was validated by the Fisher’s exact test. Using Pearson’s correlation analysis technique we found a negative relationship between light intensity and resting activity. In addition, we also found a negative association between standing activity and ambient environmental temperature. These findings were complimentary to the observed circadian variability in the resting and standing behavior of the stray street dogs. In conclusion, despite a few limitations, this study documents a statistically significant circadian rhythm in activities of stray street dogs. It also highlights spatial variability in the abundance of dogs on the streets and hotspot localities of the urban Sambalpur. We do have a hunch. It is likely that similar phenomenamight be of common occurrence in many urban areas of the world. These data might also help in addressing street dog menace – one of the major problems the people and administrative authorities of most of the Indian cities and elsewhere worldwide are experiencing since quite long.     

Conformational dynamics of free and catalytically active thermolysin are indistinguishable by hydrogen/deuterium exchange mass spectrometry

Conformational dynamics are thought to be a prerequisite for the catalytic activity of enzymes. However, the exact relationship between structural fluctuations and function is not well understood. In this work hydrogen/deuterium exchange (HDX) and electrospray ionization mass spectrometry (ESI-MS) are used for exploring the conformational dynamics of thermolysin. Amide HDX reflects the internal mobility of proteins; regions that undergo frequent unfolding-refolding show faster exchange than segments that are highly stable. Thermolysin is a zinc protease with an active site that is located between two lobes. Substrate turnover is associated with hinge bending that leads to a closed conformation. Product release regenerates the open form, such that steady-state catalysis involves a continuous closing/opening cycle. HDX/ESI-MS with proteolytic peptide mapping in the absence of substrate shows that elements in the periphery of the two lobes are most mobile. A comparison with previous X-ray data suggests that these peripheral regions undergo quite pronounced structural changes during the catalytic cycle. In contrast, active site residues exhibit only a moderate degree of backbone flexibility, and the central zinc appears to be in a fairly rigid environment. The presence of both rigid and moderately flexible elements in the active site may reflect a carefully tuned balance that is required for function. Interestingly, the HDX behavior of catalytically active thermolysin is indistinguishable from that of the free enzyme. This result is consistent with the view that catalytically relevant motions preexist in the resting state and that enzyme function can only be performed within the limitations given by the intrinsic dynamics of the protein. The data presented in this work indicate the prevalence of stochastic elements in the function of thermolysin, rather than supporting a deterministic mechanism.     

Increased Low-Frequency Oscillation Amplitude of Sensorimotor Cortex Associated with the Severity of Structural Impairment in Cervical Myelopathy

Decreases in metabolites and increased motor-related, but decreased sensory-related activation of the sensorimotor cortex (SMC) have been observed in patients with cervical myelopathy (CM) using advanced MRI techniques. However, the nature of intrinsic neuronal activity in the SMC, and the relationship between cerebral function and structural damage of the spinal cord in patients with CM are not fully understood. The purpose of this study was to assess intrinsic neuronal activity by calculating the regional amplitude of low frequency fluctuations (ALFF) using resting-state functional MRI (rs-fMRI), and correlations with clinical and imaging indices. Nineteen patients and 19 age- and sex-matched healthy subjects underwent rs-fMRI scans. ALFF measurements were performed in the SMC, a key brain network likely to impaired or reorganized patients with CM. Compared with healthy subjects, increased amplitude of cortical low-frequency oscillations (LFO) was observed in the right precentral gyrus, right postcentral gyrus, and left supplementary motor area. Furthermore, increased z-ALFF values in the right precentral gyrus and right postcentral gyrus correlated with decreased fractional anisotropy values at the C2 level, which indicated increased intrinsic neuronal activity in the SMC corresponding to the structural impairment in the spinal cord of patients with CM. These findings suggest a complex and diverging relationship of cortical functional reorganization and distal spinal anatomical compression in patients with CM and, thus, add important information in understanding how spinal cord integrity may be a factor in the intrinsic covariance of spontaneous low-frequency fluctuations of BOLD signals involved in cortical plasticity.