Evoked Brain Activity Research Articles

Gender Differences in Spontaneous and Evoked Activities of the Human Brain

The purpose of this study was to analyze sex-related differences in spontaneous and evoked brain activity. The study included 11 women and 10 men aged between 18 and 27 years; the subjects were adapted to the conditions of electrophysiological experiment. Differences in spontaneous activity were estimated by EEG spectral characteristics in frequency ranges corresponding to θ, α, β1, β2, γ1, and γ2 rhythms at rest with the eyes open and closed. The differences in evoked activity were estimated by the parameters of evoked potentials (EP) recorded upon the presentation of different types of stimuli (images and words) and categories (“fruits” and “vessels”). Behavioral differences were estimated by the efficiency of stimuli recognition. No gender differences were observed in the efficiency of stimuli recognition (number of errors, time of reaction). In the EEG of male subjects at rest, fast frequencies (β and γ) were less marked than in the female subjects, which may indicate a higher level of activation of CNS structures in women. Regardless of stimuli type and category, the amplitude of EP components in men is lower than that in women, which may be caused by lower level of CNS activation. However, the difference in the latency of EP components depended on the type of stimuli used. During the recognition of visual images, the latency of earlier EP components (N1, P2) in men was longer mostly on back and/or right electrodes. The latencies of the later EP components (P3, N3) in male subjects were longer on the back electrodes and shorter on the front electrodes than those in female ones. During the recognition of words, the latency of P1, N1, P2 and N2 components in men were shorter on temporal and temporoparietal electrodes of the left hemisphere. This may indicate that visual image in men are perceived slower but comprehended faster than in women. On the other hand, verbal stimuli in men were perceived faster but comprehended slower than in women.

Network connectivity correlates of variability in fluid intelligence performance

Abstract reasoning requires a pattern of spatial and temporal coordination among regions across the entire brain. Recent evidence suggests a very high similarity between spontaneous and evoked brain activity in humans, implying that a fine characterization of brain dynamics recorded during resting-state might be informative for the understanding of evoked behavior. In a recent work, we listed and detailed the sets of regions showing robust co-activation during the solution of fluid intelligence (gf) tasks, decomposing such meta-analytic maps in stimulus- and reasoning stage-specific sub-maps. However, while anatomical overlap with well-known resting-state fMRI networks (RSNs) has been documented, we here propose a quantitative validation of such findings via functional connectivity analysis in a sample of healthy participants. Results highlight a striking degree of similarity between the connectivity profile of the gf network and that of the dorsal attention network, with additional overlap with the left and right fronto-parietal control networks. Interestingly, a strong negative correlation with structures of the default mode network (DMN) was also identified. Results of regression models built on two independent fMRI datasets confirmed the negative correlation between gf regions and medial prefrontal structures of the DMN as a significant predictor of individual gf scores. These might suggest a framework to interpret previously reported aging-related decline in both gf and the correlation between “task-positive” networks and DMN, possibly pointing to a common neurophysiological substrate.

Identifying Disruptions in Intrinsic Brain Dynamics due to Severe Brain Injury.

Recent studies suggest that disruptions in resting state functional connectivity - a measure of stationary statistical association between brain regions - can be used as an objective marker of brain injury. However, fewer characterizations have examined the disruption of intrinsic brain dynamics after brain injury. Here, we examine this issue using electroencephalographic (EEG) data from brain-injured patients, together with a control analysis wherein we quantify the effect of the injury on the ability of intrinsic event responses to traverse their respective state spaces. More specifically, the lability of intrinsically evoked brain activity was assessed by collapsing three sigma event responses in all channels of the obtained EEG signals into a low-dimensional space. The directional derivative of these responses was then used to assay the extent to which brain activity reaches low-variance subspaces. Our findings suggest that intrinsic dynamics extracted from resting state EEG signals can differentiate various levels of consciousness in severe cases of coma. More specifically the cost of moving from one state to another in the state-space trajectories of the underlying dynamics becomes lower as the level of consciousness of patients deteriorates.

Toward noninvasive monitoring of ongoing electrical activity of human uterus and fetal heart and brain

ObjectiveTo evaluate whether a full-coverage fetal-maternal scanner can noninvasively monitor ongoing electrophysiological activity of maternal and fetal organs. MethodsA simulation study was carried out for a scanner with an array of magnetic field sensors placed all around the torso from the chest to the hip within a horizontal magnetic shielding enclosure. The magnetic fields from internal organs and an external noise source were computed for a pregnant woman with a 35-week old fetus. Signal processing methods were used to reject the external and internal interferences, to visualize uterine activity, and to detect activity of fetal heart and brain. ResultsExternal interference was reduced by a factor of 1000, sufficient for detecting signals from internal organs when combined with passive and active shielding. The scanner rejects internal interferences better than partial-coverage arrays. It can be used to estimate currents around the uterus. It clearly detects spontaneous activity from the fetal heart and brain without averaging and weaker evoked brain activity at all fetal head positions after averaging. ConclusionThe simulated device will be able to monitor the ongoing activity of the fetal and maternal organs. SignificanceThis type of scanner may become a novel tool in fetal medicine.

Spatially regularized multifractal analysis for fMRI data.

Scale-free dynamics is nowadays a massively used paradigm to model infraslow macroscopic brain activity. Multifractal analysis is becoming the standard tool to characterize scale-free dynamics. It is commonly used on various modalities of neuroimaging data to evaluate whether arrhythmic fluctuations in ongoing or evoked brain activity are related to pathologies (Alzheimer, epilepsy) or task performance. The success of multifractal analysis in neurosciences remains however so far contrasted: While it lead to relevant findings on M/EEG data, less clear impact was shown when applied to fMRI data. This is mostly due to their poor time resolution and very short duration as well as to the fact that analysis remains performed voxelwise. To take advantage of the large amount of voxels recorded jointly in fMRI, the present contribution proposes the use of a recently introduced Bayesian formalism for multifractal analysis, that regularizes the estimation of the multifractality parameter of a given voxel using information from neighbor voxels. The benefits of this regularized multifractal analysis are illustrated by comparison against classical multifractal analysis on fMRI data collected on one subject, at rest and during a working memory task: Though not yet statistically significant, increased multifractality is observed in task-negative and task-positive networks, respectively.

Functional MRI BOLD response in sickle mice with hyperalgesia

Patients with sickle cell anemia (SCA) have abnormal hemoglobin (sickle hemoglobin S) leading to the crystallization of hemoglobin chains in red blood cells (RBCs), which assume sickle shape and display reduced flexibility. Sickle RBCs (sRBCs) adhere to vessel walls and block blood flow, thus preventing oxygen delivery to the tissues leading to vaso-occlusive crises (VOC), acute pain and organ damage. SCA patients often have chronic pain that can be attributed to inflammation, vasculopathy, neuropathy, ischemia-reperfusion injury and organ damage. Blood oxygenation level-dependent (BOLD) based functional magnetic resonance imaging (fMRI) technique that is commonly used for noninvasively mapping spontaneous or evoked brain activation in human or animal models has been applied in this study to assess abnormal oxygenation change in the brains of mice with SCA in response to hypoxia. We found that hyperalgesic HbSS-BERK sickle mice with chronic pain display reduced BOLD response to a hypoxia challenge compared to their control HbAA-BERK mice. Hypoxia/reoxygenation (H/R) treated sickle mice under acute pain episode exhibit even smaller BOLD signal changes than sickle mice without H/R, suggestive of correlations between cerebral BOLD signal changes and nociception.

A new approach to estimating the evoked hemodynamic response applied to dual channel functional near infrared spectroscopy

BackgroundBrain activity can be measured non-invasively by means of functional near-infrared spectroscopy (fNIRS) which records hemodynamics of the brain tissue. Sensitivity of fNIRS to the brain activity is however being affected by natural physiological brain hemodynamics (systemic interferences). Functional hemodynamic signal extraction from physiological interferences still remains a challenging task. New methodThis paper presents a novel effective algorithm for real-time physiological interference reduction and recovery of the evoked brain activity using a dual channel fNIRS system. ResultsPerformance of the proposed algorithm was evaluated using both synthetic and semi-real fNIRS data using three different metrics of: 1) correlation coefficient (CC), relative mean square error (rMSE), percentage estimation error of peak amplitude (EPA). Comparison with existing methodsThe results were compared to those of ensemble empirical mode decomposition based recursive least squares (EEMD-RLS) method which has proved to have a better performance than other widely used algorithms such as block averaging, band-pass filtering and principal and/or independent component analysis. This study showed that the proposed method outperforms the EEMD-RLS method producing a smaller average rMSE and EPA and a larger average CC even in the cases of shorter signal lengths and smaller signal to noise ratio conditions. ConclusionsThe proposed method has no assumption on the amplitude, shape and duration of the hemodynamic response such as those needed in other previously reported methods. Moreover, it is computationally low cost and simple, and needs no parameter updating.

Relating Clinical and Electrophysiological Parameters in Death Determination in a Laboratory Model of Progressive Hypoxemia.

Death after withdrawal of mechanical ventilation frequently follows the sequence of progressive hypoxemia and hypotension leading to cardiac arrest. Accurate timing of the determination of death is fundamental to trust in controlled donation after circulatory death (cDCD) programs and is generally based on cessation of circulation (pulselessness), brain function (apnea), and the passage of time. If death is understood to be the unresuscitatable loss of brain function, the clinical determination that death following apnea and pulselessness has occurred is largely inferential. We sought to elucidate the relationship between the available clinical variables and the loss of brain function and its inability to be resuscitated. We developed a rat model of progressive hypoxia resulting in apnea and circulatory failure. We monitored clinical physiological variables including heart rate, respiration, and arterial pulse pressure. In addition, we simultaneously monitored spontaneous and evoked brain activity within the hippocampus through microelectrode field potential recordings. We also examined neurological function following restoration of pulmonary and circulatory function. Our data provide evidence that in a model of progressive hypoxemia, loss of spontaneous and evoked brain activity preceded the loss of circulation. Importantly, the data suggest that the loss of brain function, in the presence of restored cardiopulmonary indices, occurred at a time point after apnea but before the loss of detectable arterial pulse pressure. These are important data that act as a conceptual reference point when clinicians undertake the inferential activity of identifying the time prior to which a patient has died following progressive hypoxemia and while observing apnea and pulselessness.

Amplified induced neural oscillatory activity predicts musicians’ benefits in categorical speech perception

Event-related brain potentials (ERPs) reveal musical experience refines neural encoding and confers stronger categorical perception (CP) and neural organization for speech sounds. In addition to evoked brain activity, the human EEG can be decomposed into induced (non-phase-locked) responses whose various frequency bands reflect different mechanisms of perceptual-cognitive processing. Here, we aimed to clarify which spectral properties of these neural oscillations are most prone to music-related neuroplasticity and which are linked to behavioral benefits in the categorization of speech. We recorded electrical brain activity while musicians and nonmusicians rapidly identified speech tokens from a sound continuum. Time-frequency analysis parsed evoked and induced EEG into alpha- (∼10Hz), beta- (∼20Hz), and gamma- (>30Hz) frequency bands. We found that musicians’ enhanced behavioral CP was accompanied by improved evoked speech responses across the frequency spectrum, complementing previously observed enhancements in evoked potential studies (i.e., ERPs). Brain-behavior correlations implied differences in the underlying neural mechanisms supporting speech CP in each group: modulations in induced gamma power predicted the slope of musicians’ speech identification functions whereas early evoked alpha activity predicted behavior in nonmusicians. Collectively, findings indicate that musical training tunes speech processing via two complementary mechanisms: (i) strengthening the formation of auditory object representations for speech signals (gamma-band) and (ii) improving network control and/or the matching of sounds to internalized memory templates (alpha/beta-band). Both neurobiological enhancements may be deployed behaviorally and account for musicians’ benefits in the perceptual categorization of speech.

Temporal orienting precedes intersensory attention and has opposing effects on early evoked brain activity

Intersensory attention (IA) describes the process of directing attention to a specific modality. Temporal orienting (TO) characterizes directing attention to a specific moment in time. Previously, studies indicated that these two processes could have opposite effects on early evoked brain activity. The exact time-course and processing stages of both processes are still unknown. In this human electroencephalography study, we investigated the effects of IA and TO on visuo-tactile stimulus processing within one paradigm. IA was manipulated by presenting auditory cues to indicate whether participants should detect visual or tactile targets in visuo-tactile stimuli. TO was manipulated by presenting stimuli block-wise at fixed or variable inter-stimulus intervals. We observed that TO affects evoked activity to visuo-tactile stimuli prior to IA. Moreover, we found that TO reduces the amplitude of early evoked brain activity, whereas IA enhances it. Using beamformer source-localization, we observed that IA increases neural responses in sensory areas of the attended modality whereas TO reduces brain activity in widespread cortical areas. Based on these findings we derive an updated working model for the effects of temporal and intersensory attention on early evoked brain activity.

Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity.

The effectiveness of today's human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responses were evoked by violating the operators' expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators' expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from the medial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our understanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operator's mindset. Neuroadaptive technology significantly widens the communication bottleneck and has the potential to fundamentally change the way we interact with technology.

Decoding the Semantic Content of Natural Movies from Human Brain Activity.

One crucial test for any quantitative model of the brain is to show that the model can be used to accurately decode information from evoked brain activity. Several recent neuroimaging studies have decoded the structure or semantic content of static visual images from human brain activity. Here we present a decoding algorithm that makes it possible to decode detailed information about the object and action categories present in natural movies from human brain activity signals measured by functional MRI. Decoding is accomplished using a hierarchical logistic regression (HLR) model that is based on labels that were manually assigned from the WordNet semantic taxonomy. This model makes it possible to simultaneously decode information about both specific and general categories, while respecting the relationships between them. Our results show that we can decode the presence of many object and action categories from averaged blood-oxygen level-dependent (BOLD) responses with a high degree of accuracy (area under the ROC curve > 0.9). Furthermore, we used this framework to test whether semantic relationships defined in the WordNet taxonomy are represented the same way in the human brain. This analysis showed that hierarchical relationships between general categories and atypical examples, such as organism and plant, did not seem to be reflected in representations measured by BOLD fMRI.

Developing a one-channel BCI system using a dry claw-like electrode.

An eight-class SSVEP-based BCI system was designed and demonstrated in this study. To minimize the complexity of the traditional equipment and operation, only one work electrode was used. The work electrode was fabricated in our laboratory and designed as a claw-like structure with a diameter of 15 mm, featuring 8 small fingers of 4mm length and 2 mm diameter, and the weight was only 0.1g. The structure and elasticity can help the fingers pass through the hair and contact the scalp when placed on head. The electrode was capable to collect evoked brain activities such as steady-state visual evoked potentials (SSVEPs). This study showed that although the amplitude and SNR of SSVEPs obtained from a dry claw electrode was relatively lower than that from a wet electrode, the difference was not significant. This study further implemented an eight-class SSVEP-based BCI system using a dry claw-like electrode. Three subjects participated in the experiment. Using infinite impulse response (IIR) filtering and a simplified threshold method based on fast Fourier transform (FFT), the average accuracy of the three participants was 89.3% using 4 sec-long SSVEPs, leading to an average information transfer rate (ITR) of 26.5 bits/min. The results suggested the ability of using a dry claw-like electrode to perform practical BCI applications.

New Perspectives on Spontaneous Brain Activity: Dynamic Networks and Energy Matter.

Spontaneous brain activity has received increasing attention as demonstrated by the exponential rise in the number of published article on this topic over the last 30 years. Such “intrinsic” brain activity, generated in the absence of an explicit task, is frequently associated with resting-state or default-mode networks (DMN)s. The focus on characterizing spontaneous brain activity promises to shed new light on questions concerning the structural and functional architecture of the brain and how they are related to “mind”. However, many critical questions have yet to be addressed. In this review, we focus on a scarcely explored area, specifically the energetic requirements and constraints of spontaneous activity, taking into account both thermodynamical and informational perspectives. We argue that the “classical” definitions of spontaneous activity do not take into account an important feature, that is, the critical thermodynamic energetic differences between spontaneous and evoked brain activity. Spontaneous brain activity is associated with slower oscillations compared with evoked, task-related activity, hence it exhibits lower levels of enthalpy and “free-energy” (i.e., the energy that can be converted to do work), thus supporting noteworthy thermodynamic energetic differences between spontaneous and evoked brain activity. Increased spike frequency during evoked activity has a significant metabolic cost, consequently, brain functions traditionally associated with spontaneous activity, such as mind wandering, require less energy that other nervous activities. We also review recent empirical observations in neuroscience, in order to capture how spontaneous brain dynamics and mental function can be embedded in a non-linear dynamical framework, which considers nervous activity in terms of phase spaces, particle trajectories, random walks, attractors and/or paths at the edge of the chaos. This takes us from the thermodynamic free-energy, to the realm of “variational free-energy”, a theoretical construct pertaining to probability and information theory which allows explanation of unexplored features of spontaneous brain activity.

Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities.

Pain is a highly subjective experience. Self-report is the gold standard for pain assessment in clinical practice, but it may not be available or reliable in some populations. Neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have the potential to be used to provide physiology-based and quantitative nociceptive pain assessment tools that complements self-report. However, existing neuroimaging-based nociceptive pain assessments only rely on the information in pain-evoked brain activities, but neglect the fact that the perceived intensity of pain is also encoded by ongoing brain activities prior to painful stimulation. Here, we proposed to use machine learning algorithms to decode pain intensity from both pre-stimulus ongoing and post-stimulus evoked brain activities. Neural features that were correlated with intensity of laser-evoked nociceptive pain were extracted from high-dimensional pre- and post-stimulus EEG and fMRI activities using partial least-squares regression (PLSR). Further, we used support vector machine (SVM) to predict the intensity of pain from pain-related time-frequency EEG patterns and BOLD-fMRI patterns. Results showed that combining predictive information in pre- and post-stimulus brain activities can achieve significantly better performance in classifying high-pain and low-pain and in predicting the rating of perceived pain than only using post-stimulus brain activities. Therefore, the proposed pain prediction method holds great potential in basic research and clinical applications.

The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction.

Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l1-norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in the forward model. In this work, we demonstrate that an inverse solver based on a block-separable penalty with a Frobenius norm per block and a l0.5-quasinorm over blocks addresses both of these issues. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), an optimization scheme based on iterative reweighted convex surrogate optimization problems, which are solved efficiently using a block coordinate descent scheme and an active set strategy. We compare the proposed sparse imaging method to the dSPM and the RAP-MUSIC approach based on two MEG data sets. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method improves on the standard Mixed Norm Estimate (MxNE) in terms of amplitude bias, support recovery, and stability.

Every brain is different

Brain Connectivity We all differ in how we perceive, think, and act. What drives individual differences in evoked brain activity? Tavor et al. applied computational models to functional magnetic resonance imaging (fMRI) data from the Human Connectome Project. Brain activity in the “resting” state when subjects were not performing any explicit task predicted differences in fMRI activation across a range of cognitive paradigms. This suggests that individual differences in many cognitive tasks are a stable trait marker. Resting-state functional connectivity thus already contains the repertoire that is then expressed during task-based fMRI. Science , this issue p. [216][1] [1]: /lookup/doi/10.1126/science.aad8127

Compensatory Neural Activity in Response to Cognitive Fatigue.

Fatigue refers to changes in objective performance and subjective effort induced by continuous task performance. We examined the neural underpinnings of cognitive fatigue in humans using a prolonged continuous performance task and high-density electroencephalography with the goal of determining whether compensatory processes exist to maintain performance in the face of fatigue. We identified brain activity demonstrating an inverted U-shaped time-on-task profile. This brain activity showed features consistent with a compensatory role including: peaking between 60 and 100 min into the task, a positive association with behavioral performance only during this interval, and accelerated performance decline following its peak. These findings may be relevant to understanding individual differences in cognitive fatigue and developing interventions for clinical conditions afflicted by fatigue.

Changes in choice evoked brain activations after a weight loss intervention in adolescents

This study was aimed to investigate if treatment-related success in weight loss (i.e., reductions of BMI and fat percentage) is linked to significant changes in choice evoked brain activity in adolescents with excess weight. Sixteen adolescents with excess weight (age range: 12–18; BMI range: 22–36) performed the Risky-Gains Task during functional Magnetic Resonance Imaging (fMRI) both before and after a 12-week weight loss intervention. Success in weight loss was selectively associated with increased activation in the anterior insula. We concluded that adolescents with the greatest increases in activation of the insula-related interoceptive neural circuitry also show greater reductions in BMI and fat mass.

MSG-Evoked c-Fos Activity in the Nucleus of the Solitary Tract Is Dependent upon Fluid Delivery and Stimulation Parameters.

The marker of neuronal activation, c-Fos, can be used to visualize spatial patterns of neural activity in response to taste stimulation. Because animals will not voluntarily consume aversive tastes, these stimuli are infused directly into the oral cavity via intraoral cannulae, whereas appetitive stimuli are given in drinking bottles. Differences in these 2 methods make comparison of taste-evoked brain activity between results that utilize these methods problematic. Surprisingly, the intraoral cannulae experimental conditions that produce a similar pattern of c-Fos activity in response to taste stimulation remain unexplored. Stimulation pattern (e.g., constant/intermittent) and hydration state (e.g., water-restricted/hydrated) are the 2 primary differences between delivering tastes via bottles versus intraoral cannulae. Thus, we quantified monosodium glutamate (MSG)-evoked brain activity, as measured by c-Fos, in the nucleus of the solitary tract (nTS; primary taste nucleus) across several conditions. The number and pattern of c-Fos neurons in the nTS of animals that were water-restricted and received a constant infusion of MSG via intraoral cannula most closely mimicked animals that consumed MSG from a bottle. Therefore, in order to compare c-Fos activity between cannulae-stimulated and bottle-stimulated animals, cannulated animals should be water restricted prior to stimulation, and receive taste stimuli at a constant flow.