Increased brain signal variability accompanies lower behavioral variability in development

As the brain matures, its responses become optimized. Behavioral measures show this through improved accuracy and decreased trial-to-trial variability. The question remains whether the supporting brain dynamics show a similar decrease in variability. We examined the relation between variability in single trial evoked electrical activity of the brain (measured with EEG) and performance of a face memory task in children (8–15 y) and young adults (20–33 y). Behaviorally, children showed slower, more variable response times (RT), and less accurate recognition than adults. However, brain signal variability increased with age, and showed strong negative correlations with intrasubject RT variability and positive correlations with accuracy. Thus, maturation appears to lead to a brain with greater functional variability, which is indicative of enhanced neural complexity. This variability may reflect a broader repertoire of metastable brain states and more fluid transitions among them that enable optimum responses. Our results suggest that the moment-to-moment variability in brain activity may be a critical index of the cognitive capacity of the brain.     

Revealing the distinction between perception and cognition through intra-individual variability of visual evoked responses

Searching for a method to objectively detect the cognitive activity of the brain, the variability of visual evoked responses (ER) was analysed in 75 human subjects and 10 animals. The individual ERs of a normal subject were found typically very scattered in the first approx. 120 ms after stimulation, converging at 160–220 ms and then diverging again progressively. This variability pattern (VP) is event-related and is not attributable to background noise. On the other hand, statistically significant correlation showed that in most patients, with anatomically intact visual structures but with pronounced mental troubles, the VP is absent and the ERs are randomly scattered. Based on these results we consider that the event-related variability reflects the cognition function of the subjects and that it is instrumental in evidencing the distinction between cognitive and perceptive processes. The results are further consistent with the idea that cognition implies the chaotic activity of certain neural populations and that the VP reflects this chaotic, non-repetitive, non-linear and impredictable but effective neural activity.     

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.     

Cognitive neuropsychiatry: towards a scientific psychopathology

Cognitive neuropsychiatry represents a systematic and theoretically driven approach to explain clinical psychopathologies in terms of deficits to normal cognitive mechanisms. A concern with the neural substrates of impaired cognitive mechanisms links cognitive neuropsychiatry to the basic neurosciences. The emergence of cognitive neuropsychiatry in the 1990s illustrates the growing rapprochement between cognitive neuropsychology, clinical medicine and the neurosciences in addressing common questions about disorders of the mind/brain. In reviewing recent applications, we highlight how this hybrid discipline will make a distinctive contribution to the science of psychopathology.     

The role of stimulus salience and attentional capture across the neural hierarchy in a stop-signal task

Inhibitory motor control is a core function of cognitive control. Evidence from diverse experimental approaches has linked this function to a mostly right-lateralized network of cortical and subcortical areas, wherein a signal from the frontal cortex to the basal ganglia is believed to trigger motor-response cancellation. Recently, however, it has been recognized that in the context of typical motor-control paradigms those processes related to actual response inhibition and those related to the attentional processing of the relevant stimuli are highly interrelated and thus difficult to distinguish. Here, we used fMRI and a modified Stop-signal task to specifically examine the role of perceptual and attentional processes triggered by the different stimuli in such tasks, thus seeking to further distinguish other cognitive processes that may precede or otherwise accompany the implementation of response inhibition. In order to establish which brain areas respond to sensory stimulation differences by rare Stop-stimuli, as well as to the associated attentional capture that these may trigger irrespective of their task-relevance, we compared brain activity evoked by Stop-trials to that evoked by Go-trials in task blocks where Stop-stimuli were to be ignored. In addition, region-of-interest analyses comparing the responses to these task-irrelevant Stop-trials, with those to typical relevant Stop-trials, identified separable activity profiles as a function of the task-relevance of the Stop-signal. While occipital areas were mostly blind to the task-relevance of Stop-stimuli, activity in temporo-parietal areas dissociated between task-irrelevant and task-relevant ones. Activity profiles in frontal areas, in turn, were activated mainly by task-relevant Stop-trials, presumably reflecting a combination of triggered top-down attentional influences and inhibitory motor-control processes.     

Encoding brain network response to free viewing of videos

A challenging goal for cognitive neuroscience researchers is to determine how mental representations are mapped onto the patterns of neural activity. To address this problem, functional magnetic resonance imaging (fMRI) researchers have developed a large number of encoding and decoding methods. However, previous studies typically used rather limited stimuli representation, like semantic labels and Wavelet Gabor filters, and largely focused on voxel-based brain patterns. Here, we present a new fMRI encoding model to predict the human brain’s responses to free viewing of video clips which aims to deal with this limitation. In this model, we represent the stimuli using a variety of representative visual features in the computer vision community, which can describe the global color distribution, local shape and spatial information and motion information contained in videos, and apply the functional connectivity to model the brain’s activity pattern evoked by these video clips. Our experimental results demonstrate that brain network responses during free viewing of videos can be robustly and accurately predicted across subjects by using visual features. Our study suggests the feasibility of exploring cognitive neuroscience studies by computational image/video analysis and provides a novel concept of using the brain encoding as a test-bed for evaluating visual feature extraction.     

Supplementary analysis at the conclusion of a sequential clinical trial

Suppose that a clinical trial has been carried out using a sequential design. Any analysis that neglects the monitoring of the trial is potentially biased, and this holds for analyses of responses not directly used to determine whether the study should be stopped. Although valid methods exist to deal with the responses actually used in the sequential design, little has been written about secondary analyses of further responses. In this paper, two approaches to secondary analyses are explored. One is a conditional approach that avoids the problems of bias, the other an unconditional approach that allows for the sequential nature of the trial. The two methods are illustrated by application to three examples, each concerning a different type of response variable.     

Adaptive Filtering Methods for Identifying Cross-Frequency Couplings in Human EEG

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.     

Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories

The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.     

Evaluation of cognitive performance in the heat by functional brain imaging and psychometric testing

Military operations in tropical environments have imposed a significant challenge to the Australian Defence Forces (ADF). The hot and humid conditions are known to cause debilitating effects on soldiers deployed to northern regions of Australia, with the consequence that the effectiveness and efficiency of operations are severely compromised. While the adverse effects of thermal stress on soldiers' physiological capability are well established, this has not been confirmed for cognitive performance. A select range of psychometric tests were administered and functional brain electrical activity imaging was performed to investigate the impact of thermal stress on cognitive performance. The brain electrical activity of subjects was measured while undertaking a range of cognitive tasks. Steady State Probe Topography (SSPT), a novel brain imaging technology, was employed to monitor the changes in regional brain activity and neural processing speed of subjects under thermal stress. The psychometric test batteries included the following tasks; Rey Auditory Verbal Learning Test; Inspection Time; Digit Span test; a spatial working memory task; and the AX-continuous performance task. These tasks measure a range of cognitive processes including attention, memory, verbal learning, information processing and concentration. The functional brain imaging provided topographical information, which showed changes in electrical activity in response to thermal stress during cognitive performance. These changes in brain electrical activity and neural speed induced by thermal stress may help to identify the type of cognitive functions that are likely to be impaired under operational conditions. Results indicated that subjects experienced increasing cardiovascular strain through thermally neutral to thermally straining conditions. The results from the psychometric test battery showed some promising effects given the small sample size including deficits in working memory, in information retention and in information processing. There was also marked differences in the electrical responses of the brain when subjects were thermally strained. The Steady-State Visual Evoked Potential recordings showed an increase in amplitude and a decrease in latency, suggesting an increase in the utilisation of neural resources or effort by subjects to maintain the same level of performance as under thermally neutral conditions. The data are suggestive of the high sensitivity of brain imaging techniques with high temporal resolution to identify important decrements in cognitive performance in hostile environments.     

Personality influences the neural responses to viewing facial expressions of emotion

Cognitive research has long been aware of the relationship between individual differences in personality and performance on behavioural tasks. However, within the field of cognitive neuroscience, the way in which such differences manifest at a neural level has received relatively little attention. We review recent research addressing the relationship between personality traits and the neural response to viewing facial signals of emotion. In one section, we discuss work demonstrating the relationship between anxiety and the amygdala response to facial signals of threat. A second section considers research showing that individual differences in reward drive (behavioural activation system), a trait linked to aggression, influence the neural responsivity and connectivity between brain regions implicated in aggression when viewing facial signals of anger. Finally, we address recent criticisms of the correlational approach to fMRI analyses and conclude that when used appropriately, analyses examining the relationship between personality and brain activity provide a useful tool for understanding the neural basis of facial expression processing and emotion processing in general.     

综述: 无创穿颅电刺激对人脑神经活动和认知功能的调控

穿颅电刺激被认为可以无创调节大脑的神经活动,为研究特定脑区与某一认知功能间的因果关系提供了可能.近些年,对穿颅电刺激作用机制和其对认知、运动功能调控的研究方面取得了很多重要进展.在这篇综述中,我们总结了以往关于穿颅直流电刺激、穿颅交流电刺激和穿颅随机噪声电刺激三种刺激方式的发展历史及其作用机制,同时总结了其对感知觉(主要是视觉知觉)、注意和记忆等认知功能的调控,并对未来的研究方向进行了展望.     

Reduced variability of ongoing and evoked cortical activity leads to improved behavioral performance

Sensory responses of the brain are known to be highly variable, but the origin and functional relevance of this variability have long remained enigmatic. Using the variable foreperiod of a visual discrimination task to assess variability in the primate cerebral cortex, we report that visual evoked response variability is not only tied to variability in ongoing cortical activity, but also predicts mean response time. We used cortical local field potentials, simultaneously recorded from widespread cortical areas, to gauge both ongoing and visually evoked activity. Trial-to-trial variability of sensory evoked responses was strongly modulated by foreperiod duration and correlated both with the cortical variability before stimulus onset as well as with response times. In a separate set of experiments we probed the relation between small saccadic eye movements, foreperiod duration and manual response times. The rate of eye movements was modulated by foreperiod duration and eye position variability was positively correlated with response times. Our results indicate that when the time of a sensory stimulus is predictable, reduction in cortical variability before the stimulus can improve normal behavioral function that depends on the stimulus.     

What electrical microstimulation has revealed about the neural basis of cognition

Neurophysiologists have shown repeatedly that neural activity in different brain structures can be correlated with specific perceptual and cognitive functions, but the causal efficacy of the observed activity has generally been a matter of conjecture. By contrast, electrical microstimulation, which allows the experimenter to manipulate the activity of small groups of neurons with spatial and temporal precision, can now be used to demonstrate causal links between neural activity and specific cognitive functions. Here, we review this growing literature, including applications to the study of attention, visual and somatosensory perception, 'read-out' mechanisms for interpreting sensory maps, and contextual effects on perception. We also discuss potential applications of microstimulation to studies of higher cognitive functions such as decision-making and subjective experience.     

Manipulating neural activity in physiologically classified neurons: triumphs and challenges

Understanding brain function requires knowing both how neural activity encodes information and how this activity generates appropriate responses. Electrophysiological, imaging and immediate early gene immunostaining studies have been instrumental in identifying and characterizing neurons that respond to different sensory stimuli, events and motor actions. Here we highlight approaches that have manipulated the activity of physiologically classified neurons to determine their role in the generation of behavioural responses. Previous experiments have often exploited the functional architecture observed in many cortical areas, where clusters of neurons share response properties. However, many brain structures do not exhibit such functional architecture. Instead, neurons with different response properties are anatomically intermingled. Emerging genetic approaches have enabled the identification and manipulation of neurons that respond to specific stimuli despite the lack of discernable anatomical organization. These approaches have advanced understanding of the circuits mediating sensory perception, learning and memory, and the generation of behavioural responses by providing causal evidence linking neural response properties to appropriate behavioural output. However, significant challenges remain for understanding cognitive processes that are probably mediated by neurons with more complex physiological response properties. Currently available strategies may prove inadequate for determining how activity in these neurons is causally related to cognitive behaviour.     

Sustained neural rhythms reveal endogenous oscillations supporting speech perception

Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or “entrained”) to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research—including neural entrainment and tACS—and reveal endogenous neural oscillations as a key underlying principle for speech perception.

Just as a child on a swing continues to move after the pushing stops, this study reveals similar entrained rhythmic echoes in brain activity after hearing speech and electrical brain stimulation; perturbation with tACS shows that these brain oscillations help listeners to understand speech.     

Exploiting the potential of three dimensional spatial wavelet analysis to explore nesting of temporal oscillations and spatial variance in simultaneous EEG-fMRI data

Synchronization of the activity in neural networks is a fundamental mechanism of brain function, putatively serving the integration of computations on multiple spatial and temporal scales. Time scales are thought to be nested within distinct spatial scales, so that whereas fast oscillations may integrate local networks, slow oscillations might integrate computations across distributed brain areas. We here describe a newly developed approach that provides potential for the further substantiation of this hypothesis in future studies. We demonstrate the feasibility and important caveats of a novel wavelet-based means of relating time series of three-dimensional spatial variance (energy) of fMRI data to time series of temporal variance of EEG. The spatial variance of fMRI data was determined by employing the three-dimensional dual-tree complex wavelet transform. The temporal variance of EEG data was estimated by using traditional continuous complex wavelets. We tested our algorithm on artificial signals with known signal-to-noise ratios and on empirical resting state EEG-fMRI data obtained from four healthy human subjects. By employing the human posterior alpha rhythm as an exemplar, we demonstrated face validity of the approach. We believe that the proposed method can serve as a suitable tool for future research on the spatiotemporal properties of brain dynamics, hence moving beyond analyses based exclusively in one domain or the other.     

Functional Network Overlap as Revealed by fMRI Using sICA and Its Potential Relationships with Functional Heterogeneity,Balanced Excitation and Inhibition,and Sparseness of Neuron Activity

Functional magnetic resonance imaging (fMRI) studies traditionally use general linear model-based analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-related modulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively across most or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICA might relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation.     

The graph theoretical analysis of the SSVEP harmonic response networks

Steady-state visually evoked potentials (SSVEP) have been widely used in the neural engineering and cognitive neuroscience researches. Previous studies have indicated that the SSVEP fundamental frequency responses are correlated with the topological properties of the functional networks entrained by the periodic stimuli. Given the different spatial and functional roles of the fundamental frequency and harmonic responses, in this study we further investigated the relation between the harmonic responses and the corresponding functional networks, using the graph theoretical analysis. We found that the second harmonic responses were positively correlated to the mean functional connectivity, clustering coefficient, and global and local efficiencies, while negatively correlated with the characteristic path lengths of the corresponding networks. In addition, similar pattern occurred with the lowest stimulus frequency (6.25 Hz) at the third harmonic responses. These findings demonstrate that more efficient brain networks are related to larger SSVEP responses. Furthermore, we showed that the main connection pattern of the SSVEP harmonic response networks originates from the interactions between the frontal and parietal–occipital regions. Overall, this study may bring new insights into the understanding of the brain mechanisms underlying SSVEP.