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The question of how the brain represents reward prediction errors is central to reinforcement learning and adaptive, goal‐directed behavior. Previous studies have revealed prediction error representations in multiple electrophysiological signatures, but it remains elusive whether these electrophysiological correlates underlying prediction errors are sensitive to valence (in a signed form) or to salience (in an unsigned form). One possible reason concerns the loose correspondence between objective probability and subjective prediction resulting from the optimistic bias, that is, the tendency to overestimate the likelihood of encountering positive future events. In the present electroencephalography (EEG) study, we approached this question by directly measuring participants' idiosyncratic, trial‐to‐trial prediction errors elicited by subjective and objective probabilities across two experiments. We adopted monetary gain and loss feedback in Experiment 1 and positive and negative feedback as communicated by the same zero‐value feedback in Experiment 2. We provided electrophysiological evidence in time and time‐frequency domains supporting both reward and salience prediction error signals. Moreover, we showed that these electrophysiological signatures were highly flexible and sensitive to an optimistic bias and various forms of salience. Our findings shed new light on multiple presentations of prediction error in the human brain, which differ in format and functional role. We measured subjective and objective prediction error signals across two EEG tasks. We found prediction error representations in multiple neural signatures. The variety of neural prediction errors is further modulated by optimistic bias.

•Stimulus driven modulations of neural variability are richly structured in time.•Event Related Variability (ERV) patterns are already structured in early infancy.•ERV patterns depend on task and age (in 2-6 month old infants and adults).•ERV patterns are not fully explained by induced phase reset or microstate switching.•Within-trial variability may reflect sampling of a structured dynamical landscape. In carefully designed experimental paradigms, cognitive scientists interpret the mean event-related potentials (ERP) in terms of cognitive operations. However, the huge signal variability from one trial to the next, questions the representability of such mean events. We explored here whether this variability is an unwanted noise, or an informative part of the neural response. We took advantage of the rapid changes in the visual system during human infancy and analyzed the variability of visual responses to central and lateralized faces in 2-to 6-month-old infants compared to adults using high-density electroencephalography (EEG). We observed that neural trajectories of individual trials always remain very far from ERP components, only moderately bending their direction with a substantial temporal jitter across trials. However, single trial trajectories displayed characteristic patterns of acceleration and deceleration when approaching ERP components, as if they were under the active influence of steering forces causing transient attraction and stabilization. These dynamic events could only partly be accounted for by induced microstate transitions or phase reset phenomena. Importantly, these structured modulations of response variability, both between and within trials, had a rich sequential organization, which in infants, was modulated by the task difficulty and age. Our approaches to characterize Event Related Variability (ERV) expand on classic ERP analyses and provide the first evidence for the functional role of ongoing neural variability in human infants.

Background The study aimed to determine how the resting-state EEG (rsEEG) complexity changes both over time and space (channels). The complexity of rsEEG and its sex/gender differences were examined using the multivariate Multiscale Entropy ( mMSE ) in 95 healthy adults. Following the probability maps (Giacometti et al. in J Neurosci Methods 229:84–96, 2014), channel sets have been identified that correspond to the functional networks. For each channel set the area under curve (AUC), which represents the total complexity, MaxSlope —the maximum complexity change of the EEG signal at thefine scales (1:4 timescales), and AvgEnt —to the average entropy level at coarse-grained scales (9:12 timescales), respectively, were extracted. To check dynamic changes between the entropy level at the fine and coarse-grained scales, the difference in mMSE between the #9 and #4 timescale ( DiffEnt ) was also calculated. Results We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlop e and DiffEnt but lower AvgEnt in all channel sets. Conclusions Novel results of the present study are: (1) an identification of the mMSE features that capture entropy at the fine and coarse timescales in the channel sets corresponding to the main resting-state networks; (2) the sex/gender differences in these features.

Objectives: This study aimed to evaluate the influence of lower limb loss (LL) on mental workload by assessing neurocognitive measures in individuals with unilateral transtibial (TT) versus those with transfemoral (TF) LL while dual-task walking under varying cognitive demand. Methods: Electroencephalography (EEG) was recorded as participants performed a task of varying cognitive demand while being seated or walking (i.e., varying physical demand). Results: The findings revealed both groups of participants (TT LL vs. TF LL) exhibited a similar EEG theta synchrony response as either the cognitive or the physical demand increased. Also, while individuals with TT LL maintained similar performance on the cognitive task during seated and walking conditions, those with TF LL exhibited performance decrements (slower response times) on the cognitive task during the walking in comparison to the seated conditions. Furthermore, those with TF LL neither exhibited regional differences in EEG low-alpha power while walking, nor EEG high-alpha desynchrony as a function of cognitive task difficulty while walking. This lack of alpha modulation coincided with no elevation of theta/alpha ratio power as a function of cognitive task difficulty in the TF LL group. Conclusions: This work suggests that both groups share some common but also different neurocognitive features during dual-task walking. Although all participants were able to recruit neural mechanisms critical for the maintenance of cognitive-motor performance under elevated cognitive or physical demands, the observed differences indicate that walking with a prosthesis, while concurrently performing a cognitive task, imposes additional cognitive demand in individuals with more proximal levels of amputation.

Asymmetry in frontal electrocortical alpha-band (8-13 Hz) activity recorded during resting situations (i.e., in absence of a specific task) has been investigated in relation to emotion and depression for over 30 years. This asymmetry reflects an aspect of endogenous cortical dynamics that is stable over repeated measurements and that may serve as an endophenotype for mood or other psychiatric disorders. In nearly all of this research, EEG activity is averaged across several minutes, obscuring transient dynamics that unfold on the scale of milliseconds to seconds. Such dynamic states may ultimately have greater value in linking brain activity to surface EEG asymmetry, thus improving its status as an endophenotype for depression. Here we introduce novel metrics for characterizing frontal alpha asymmetry that provide a more in-depth neurodynamical understanding of recurrent endogenous cortical processes during the resting-state. The metrics are based on transient \"bursts\" of asymmetry that occur frequently during the resting-state. In a sample of 306 young adults, 143 with a lifetime diagnosis of major depressive disorder (62 currently symptomatic), three questions were addressed: (1) How do novel peri-burst metrics of dynamic asymmetry compare to conventional fast-Fourier transform-based metrics? (2) Do peri-burst metrics adequately differentiate depressed from non-depressed participants? and, (3) what EEG dynamics surround the asymmetry bursts? Peri-burst metrics correlated with traditional measures of asymmetry, and were sensitive to both current and past episodes of major depression. Moreover, asymmetry bursts were characterized by a transient lateralized alpha suppression that is highly consistent in phase across bursts, and a concurrent contralateral transient alpha enhancement that is less tightly phase-locked across bursts. This approach opens new possibilities for investigating rapid cortical dynamics during resting-state EEG.

It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations' patterns in Theta, Alpha and Beta sub-bands also play important role in brain's emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances for each subject were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208\\% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when math k=1 math) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for math K >1 math in K-NN, SVM has better average classification rate.

When the brain is active, the neural activities of different regions are integrated on various spatial and temporal scales; this is termed the synchronization phenomenon in neurobiological theory. This synchronicity is also the main underlying mechanism for information integration and processing in the brain. Clinical medicine has found that some of the neurological diseases that are difficult to cure have deficiencies or abnormalities in the whole or local integration processes of the brain. By studying the synchronization capabilities of the brain-network, we can intensively describe and characterize both the state of the interactions between brain regions and their differences between people with a mental illness and a set of controls by measuring the rapid changes in brain activity in patients with psychiatric disorders and the strength and integrity of their entire brain network. This is significant for the study of mental illness. Because static brain network connection methods are unable to assess the dynamic interactions within the brain, we introduced the concepts of dynamics and variability in a constructed EEG brain functional network based on dynamic connections, and used it to analyze the variability in the time characteristics of the EEG functional network. We used the spectral features of the brain network to extract its synchronization features and used the synchronization features to describe the process of change and the differences in the brain network's synchronization ability between a group of patients and healthy controls during a working memory task. We propose a method based on the fusion of traditional features and spectral features to achieve an adjustment of the patient's brain network synchronization ability, so that its synchronization ability becomes consistent with that of healthy controls, theoretically achieving the purpose of the treatment of the diseases. Studying the stability of brain network synchronization can provide new insights into the pathogenic mechanism and cure of mental diseases and has a wide range of potential applications.

Mental rotation is generally analyzed based on event-related potential (ERP) in a time domain with several characteristic electrodes, but neglects the whole spatial-temporal brain pattern in the cognitive process which may reflect the underlying cognitive mechanism. In this paper, we mainly proposed an approach based on microstates to examine the encoding of mental rotation from the spatial-temporal changes of EEG signals. In particular, we collected EEG data from 11 healthy subjects in a mental rotation cognitive task using 12 different stimulus pictures representing left and right hands at various rotational angles. We applied the microstate method to investigate the microstates conveyed by the event-related potential extracted from EEG data during mental rotation, and obtained four microstate modes (referred to as modes A, B, C, D, respectively). Subsequently, we defined several measures, including microstate sequences, topographical map, hemispheric lateralization, and duration of microstate, to characterize the dynamics of microstates during mental rotation. We observed that (1) the microstates sequence had a specified progressing mode, i.e., A → B → A ; (2) the activation of the right parietal occipital region was stronger than that of the left parietal occipital region according to the hemispheric lateralization of the microstates mode A; and (3) the duration of the second microstates mode A showed the shorter duration in the vertical stimuli, named “angle effect”.

The aim of this study was to analyse the high density EEG during movement execution guided by visual attention to reveal the detailed topographic distributions of delta and theta oscillations. Twenty right-handed young subjects performed a finger tapping task, paced by a continuously transited repeating visual stimuli. Baseline corrected power of scalp current density transformed EEG was statistically assessed with cluster-based permutation testing. Delta and theta activities revealed differences in their spatial properties at the time of finger tapping execution. Theta synchronization showed a contralateral double activation in the parietal and fronto-central regions, while delta activity appeared in the central contralateral channels. Differences in the spatiotemporal topography between delta and theta activity in the course of movement execution were identified on high density EEG.

Little is known about the neurophysiological mechanisms underlying the human sleep–wake cycle. Using intracranial electrodes in humans, we investigated changes in topographic distribution of gamma power and local- and long-range gamma EEG coherence in neocortex and hippocampus during different cerebral states. We report significantly greater variability in gamma power across cortical regions during wakefulness than during either slow wave or rapid eye movement (REM) sleep. In addition, local (within cortical regions) and long-range (between cortical regions) gamma coherence was significantly higher during wakefulness than during sleep, and functional gamma-range coupling between the neocortex and hippocampus was seen during wakefulness, but not during sleep. These findings demonstrate a functional link between different stages of conscious awareness and the level of coupling of gamma-band oscillations in the human brain.