Delayed monitoring of accuracy errors compared to commission errors in ACC

Error detection is essential for monitoring performance and preparing subsequent behavioral adjustments, and is associated with specific neural responses in the anterior cingulate cortex (ACC). To investigate whether different brain mechanisms subserve the processing of commission vs. accuracy errors, we recorded EEG in adult participants while they performed a novel speeded GO-NOGO aiming task ("the Shoot-NoShoot paradigm"). Our ERP results show that commission errors (responding during NOGO trials) elicited a classical error-related negativity (ERN) component, followed by an error-related positivity (Pe), as well as a negativity peaking before response onset (pre-ERN). By contrast, spatial accuracy errors elicited a feedback-related negativity (FRN), which correlated with the spatial discrepancy between response and target position across subjects. Fast hits also elicited a pre-ERN but no ERN, suggesting that this pre-response monitoring component might be related to the detection of error likelihood. Although source analysis revealed similar generators in ACC for these different error-related negativities, the respective timing differed, suggesting that commission errors are detected rapidly based on internal motor representations, whereas the detection of accuracy errors in ACC relies on the additional and swift processing of external visual information.     

Frontal theta links prediction errors to behavioral adaptation in reinforcement learning

Investigations into action monitoring have consistently detailed a frontocentral voltage deflection in the event-related potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the feedback-related negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single-trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single-trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Mediofrontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single-trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations, with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice.     

Reward expectation modulates feedback-related negativity and EEG spectra

The ability to evaluate outcomes of previous decisions is critical to adaptive decision-making. The feedback-related negativity (FRN) is an event-related potential (ERP) modulation that distinguishes losses from wins, but little is known about the effects of outcome probability on these ERP responses. Further, little is known about the frequency characteristics of feedback processing, for example, event-related oscillations and phase synchronizations. Here, we report an EEG experiment designed to address these issues. Subjects engaged in a probabilistic reinforcement learning task in which we manipulated, across blocks, the probability of winning and losing to each of two possible decision options. Behaviorally, all subjects quickly adapted their decision-making to maximize rewards. ERP analyses revealed that the probability of reward modulated neural responses to wins, but not to losses. This was seen both across blocks as well as within blocks, as learning progressed. Frequency decomposition via complex wavelets revealed that EEG responses to losses, compared to wins, were associated with enhanced power and phase coherence in the theta frequency band. As in the ERP analyses, power and phase coherence values following wins but not losses were modulated by reward probability. Some findings between ERP and frequency analyses diverged, suggesting that these analytic approaches provide complementary insights into neural processing. These findings suggest that the neural mechanisms of feedback processing may differ between wins and losses.     

Joint ICA of ERP and fMRI during error-monitoring

The anterior cingulate cortex (ACC) is commonly separated into two functional divisions: the cognitive division, which lies in the caudal region and the affective division, which lies in the rostral region of the ACC. Both regions of the ACC are engaged during error-monitoring tasks; however, little is known about the temporal sequencing associated with cognition and affective processes during error-monitoring. Here we use joint Independent Component Analysis (jICA) to couple event-related potential (ERP) time courses and functional magnetic resonance imaging (fMRI) spatial maps to examine the spatio-temporal stages of engagement in the two divisions of the ACC during error-monitoring. Consistent with hypotheses, two of the five significant spatio-temporal components identified by jICA revealed that the error-related negativity (ERN) ERP was associated with distinct spatial fMRI patterns in the ACC. The ERN(1) was associated with activity in the caudal ACC and lateral prefrontal cortex (lPFC) while the ERN(2) was associated with activity in the rostral ACC. These results suggest that during error-monitoring the caudal ACC and lPFC engage prior to the rostral ACC. These results suggest that cognition precedes affect during error-monitoring.     

Error-related brain activity and adjustments of selective attention following errors

The present study investigated adjustments of selective attention following errors and their relation to the error-related negativity (Ne/ERN), a correlate of errors in event-related potentials. We hypothesized that, if post-error adjustments reflect an adaptive mechanism that should prevent the occurrence of further errors, then adjustments of attentional selectivity should be observed only following errors due to insufficient selective attention. To test this, a four-choice flanker task was used in which errors due to insufficient selective attention (flanker errors) and other errors (nonflanker errors) could be distinguished. We found strong adjustments of selective attention following flanker errors but not following nonflanker errors. Moreover, the Ne/ERN amplitude was correlated with adjustments of selective attention on a trial-by-trial basis. The results provide support for the notion that the Ne/ERN is a correlate of adaptive adjustments following errors.     

Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more “interesting” sources than with time-domain ICA, and to more meaningfully sort the obtained components. The method is especially useful for finding sources of rhythmic activity. Furthermore, we propose to use a complex mixing matrix to model sources which are spatially extended and have different phases in different EEG/MEG channels. Simulations with artificial data and experiments on resting-state MEG demonstrate the utility of the method.     

Combined ICA-LORETA analysis of mismatch negativity

A major challenge for neuroscience is to map accurately the spatiotemporal patterns of activity of the large neuronal populations that are believed to underlie computing in the human brain. To study a specific example, we selected the mismatch negativity (MMN) brain wave (an event-related potential, ERP) because it gives an electrophysiological index of a "primitive intelligence" capable of detecting changes, even abstract ones, in a regular auditory pattern. ERPs have a temporal resolution of milliseconds but appear to result from mixed neuronal contributions whose spatial location is not fully understood. Thus, it is important to separate these sources in space and time. To tackle this problem, a two-step approach was designed combining the independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. Here we implement this approach to analyze the subsecond spatiotemporal dynamics of MMN cerebral sources using trial-by-trial experimental data. We show evidence that a cerebral computation mechanism underlies MMN. This mechanism is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, which activate at distinct time intervals and are grouped in six main statistically independent components.     

事件相关电位对精神分裂症患者大脑认知功能的研究

Event related potentials(ERP) is an objective method for assessing brain function.It's components include P 50,N 200,P 300, mismatch negativity,processing negativity, late positive potentials. This paper reviews the new development of ERP research in patients with schizophrenia .     

Recipes for the linear analysis of EEG

In this paper, we describe a simple set of "recipes" for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources.     

Independent component analysis for biomedical signals

Independent component analysis (ICA) is increasing in popularity in the field of biomedical signal processing. It is generally used when it is required to separate measured multi-channel biomedical signals into their constituent underlying components. The use of ICA has been facilitated in part by the free availability of toolboxes that implement popular flavours of the techniques. Fundamentally ICA in biomedicine involves the extraction and separation of statistically independent sources underlying multiple measurements of biomedical signals. Technical advances in algorithmic developments implementing ICA are reviewed along with new directions in the field. These advances are specifically summarized with applications to biomedical signals in mind. The basic assumptions that are made when applying ICA are discussed, along with their implications when applied particularly to biomedical signals. ICA as a specific embodiment of blind source separation (BSS) is also discussed, and as a consequence the criterion used for establishing independence between sources is reviewed and this leads to the introduction of ICA/BSS techniques based on time, frequency and joint time-frequency decomposition of the data. Finally, advanced implementations of ICA are illustrated as applied to neurophysiologic signals in the form of electro-magnetic brain signals data.     

Co-modulatory spectral changes in independent brain processes are correlated with task performance

This study investigates the independent modulators that mediate the power spectra of electrophysiological processes, measured by electroencephalogram (EEG), in a sustained-attention experiment. EEG and behavioral data were collected during 1-2 hour virtual-reality based driving experiments in which subjects were instructed to maintain their cruising position and compensate for randomly induced drift using the steering wheel. Independent component analysis (ICA) applied to 30-channel EEG data separated the recorded EEG signals into a sum of maximally temporally independent components (ICs) for each of 30 subjects. Logarithmic spectra of resultant IC activities were then decomposed by principal component analysis, followed by ICA, to find spectrally fixed and temporally independent modulators (IM). Across subjects, the spectral ICA consistently found four performance-related independent modulators: delta, delta-theta, alpha, and beta modulators that multiplicatively affected the spectra of spatially distinct IC processes when the participants experienced waves of alternating alertness and drowsiness during long-hour simulated driving. The activation of the delta-theta modulator increased monotonically as subjects' task performances decreased. Furthermore, the time courses of the theta-beta modulator were highly correlated with concurrent changes in driving errors across subjects (r=0.77±0.13).     

Mapping single-trial EEG records on the cortical surface through a spatiotemporal modality

Event-related potentials (ERPs) induced by visual perception and cognitive tasks have been extensively studied in neuropsychological experiments. ERP activities time-locked to stimulus presentation and task performance are often observed separately at individual scalp channels based on averaged time series across epochs and experimental subjects. An analysis using averaged EEG dynamics could discount information regarding interdependency between ongoing EEG and salient ERP features. Advanced tools such as independent component analysis (ICA) have been developed for decomposing collections of single-trial EEG records into separate features. Those features (or independent components) can then be mapped onto the cortical surface using source localization algorithms to visualize brain activation maps and to study between-subject consistency. In this study, we propose a statistical framework for estimating the time course of spatiotemporally independent EEG components simultaneously with their cortical distributions. Within this framework, we implemented Bayesian spatiotemporal analysis for imaging the sources of EEG features on the cortical surface. The framework allows researchers to include prior knowledge regarding spatial locations as well as spatiotemporal independence of different EEG sources. The use of the Electromagnetic Spatiotemporal ICA (EMSICA) method is illustrated by mapping event-related EEG dynamics induced by events in a visual two-back continuous performance task. The proposed method successfully identified several interesting components with plausible corresponding cortical activation topographies, including processes contributing to the late positive complex (LPC) located in central parietal, frontal midline, and anterior cingulate cortex, to atypical mu rhythms associated with the precentral gyrus, and to the central posterior alpha activity in the precuneus.     

Dissociating action inhibition, conflict monitoring and sensory mismatch into independent components of event related potentials in GO/NOGO task

The anterior N2 and P3 waves of event related potentials (ERPs) in the GO/NOGO paradigm in trials related to preparatory set violations in previous studies were inconsistently associated either with action inhibition or conflict monitoring operations. In the present study a paired stimulus GO/NOGO design was used in order to experimentally control the preparatory sets. Three variants of the same stimulus task manipulated sensory mismatch, action inhibition and conflict monitoring operations by varying stimulus-response associations. The anterior N2 and P3 waves were decomposed into components by means of independent component analysis (ICA). The ICA was performed on collection of 114 individual ERPs in the three experimental conditions. Three of the independent components were selectively affected by the task manipulations indicating association of these components with sensory mismatch, action inhibition and conflict monitoring operations. According to sLORETA the sensory mismatch component was generated in the left and right temporal areas, the action suppression component was generated in the supplementary motor cortex, and the conflict monitoring component was generated in the anterior cingulate cortex.     

Motivation and semantic context affect brain error-monitoring activity: an event-related brain potentials study

During speech production, we continuously monitor what we say. In situations in which speech errors potentially have more severe consequences, e.g. during a public presentation, our verbal self-monitoring system may pay special attention to prevent errors than in situations in which speech errors are more acceptable, such as a casual conversation. In an event-related potential study, we investigated whether or not motivation affected participants' performance using a picture naming task in a semantic blocking paradigm. Semantic context of to-be-named pictures was manipulated; blocks were semantically related (e.g., cat, dog, horse, etc.) or semantically unrelated (e.g., cat, table, flute, etc.). Motivation was manipulated independently by monetary reward. The motivation manipulation did not affect error rate during picture naming. However, the high-motivation condition yielded increased amplitude and latency values of the error-related negativity (ERN) compared to the low-motivation condition, presumably indicating higher monitoring activity. Furthermore, participants showed semantic interference effects in reaction times and error rates. The ERN amplitude was also larger during semantically related than unrelated blocks, presumably indicating that semantic relatedness induces more conflict between possible verbal responses.     

Attributional analysis of chronic illness outcomes

The Weiner et al. attribution model has generated a great deal of research on attributions for success and failure in academic achievement situations. Studies of success and failure attributions in real-life situations of high personal concern are limited. If the attribution model is to lead to a general theory of motivation, such tests in real-life situations are critical. In this study, causal attributions for success and failure outcomes of chronically ill patients were examined. Results indicated at least partial support for the model. Patients tended to attribute success internally and failure externally, but stability and expectations were not linked in this sample. Moreover, a tendency to respond with no cause to an open-ended measure and to hold little commitment to any causes on a closed-ended measure was characteristic of failure subjects.     

EEG oscillatory patterns are associated with error prediction during music performance and are altered in musician's dystonia

Skilled performance requires the ability to monitor ongoing behavior, detect errors in advance and modify the performance accordingly. The acquisition of fast predictive mechanisms might be possible due to the extensive training characterizing expertise performance. Recent EEG studies on piano performance reported a negative event-related potential (ERP) triggered in the ACC 70 ms before performance errors (pitch errors due to incorrect keypress). This ERP component, termed pre-error related negativity (pre-ERN), was assumed to reflect processes of error detection in advance. However, some questions remained to be addressed: (i) Does the electrophysiological marker prior to errors reflect an error signal itself or is it related instead to the implementation of control mechanisms? (ii) Does the posterior frontomedial cortex (pFMC, including ACC) interact with other brain regions to implement control adjustments following motor prediction of an upcoming error? (iii) Can we gain insight into the electrophysiological correlates of error prediction and control by assessing the local neuronal synchronization and phase interaction among neuronal populations? (iv) Finally, are error detection and control mechanisms defective in pianists with musician's dystonia (MD), a focal task-specific dystonia resulting from dysfunction of the basal ganglia-thalamic-frontal circuits? Consequently, we investigated the EEG oscillatory and phase synchronization correlates of error detection and control during piano performances in healthy pianists and in a group of pianists with MD. In healthy pianists, the main outcomes were increased pre-error theta and beta band oscillations over the pFMC and 13-15 Hz phase synchronization, between the pFMC and the right lateral prefrontal cortex, which predicted corrective mechanisms. In MD patients, the pattern of phase synchronization appeared in a different frequency band (6-8 Hz) and correlated with the severity of the disorder. The present findings shed new light on the neural mechanisms, which might implement motor prediction by means of forward control processes, as they function in healthy pianists and in their altered form in patients with MD.     

Nonhuman primate event-related potentials associated with pro- and anti-saccades

Non-invasive event-related potential (ERP) recordings have become a popular technique to study neural activity associated with saccades in humans. To date, it is not known whether nonhuman primates exhibit similar saccade-related ERPs. Here, we recorded ERPs associated with the performance of randomly interleaved pro- and anti-saccades in macaque monkeys. Stimulus-aligned ERPs showed short-latency visual component with more negative P2 and N2 peak amplitudes on anti- than on pro-saccade trials. Saccade-aligned ERPs showed a larger presaccadic negativity on anti- than pro-saccade trials, and a presaccadic positivity on pro-saccade trials, which was attenuated or absent on anti-saccade trials. This was followed by sharp negative spike potential immediately prior to the movement. Overall, these findings demonstrate that macaque monkeys, like humans, exhibit task-related differences of visual ERPs associated with pro- and anti-saccades and furthermore share presaccadic positivity as well as a spike potential prior to these tasks. We suggest that the presaccadic positivity on pro-saccade trials is generated by a source in the contralateral frontal eye fields and that the more negative voltage on anti-saccade trials is the result of additional sources of opposite polarity in neighboring frontal areas.     

The categorization of natural scenes: brain attention networks revealed by dense sensor ERPs

The present study examined cortical indicators of selective attention underlying categorization based on target features in natural scenes. The primary focus was to determine the neural sources associated with the processing of target stimuli containing animals compared to non-target control stimuli. Neural source estimation techniques [current source density (CSD) and L2-minimum norm estimate (L2-MNE)] were used to determine the sources of the potential fields measured from 58 sensor sites. Assuring an excellent signal-to-noise ratio, the categorization task consisted of 2400 trials. Replicating previous findings, target and non-target ERP activity diverged sharply around 150 ms after stimulus onset and the early differential ERP activity appeared as positive deflection over fronto-central sensor sites and as negative deflection over temporo-occipital regions. Both source estimation techniques (CSD and L2-MNE) suggested primary sources of the early differential ERP activity in posterior, visual-associative brain regions and, although less pronounced, revealed the contribution of additional anterior sources. These findings suggest that selective attention to category-relevant features reflects the interactions between prefrontal and inferior temporal cortex during visual processing of natural scenes.     

An MEG signature corresponding to an axiomatic model of reward prediction error

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data.     

Object knowledge during entry-level categorization is activated and modified by implicit memory after 200 ms

The timing of activating memory about visual objects is important for theories of human cognition but largely unknown, especially for tasks like entry level categorization that activate semantic memory. We tested an implicit memory-categorization “equivalence” hypothesis of multiple memory systems theory that a cortical system that stores structural knowledge to support entry level categorization also stores long-term, perceptual implicit memory, resulting in priming of this knowledge. Event-related brain potentials (ERPs) were recorded to impoverished pictures of new and repeated objects that were similar in perceptual properties but differed in categorization success. The cortical dynamics of object knowledge were defined using categorization ratings and naming. As predicted, rating, naming, and repetition effects on a frontocentral N350 show that implicit memory modifies the object knowledge network supporting categorization. This ERP is a complex of components between 200 and 500 ms indexing temporally overlapping substates from more perceptual to more conceptual knowledge. A frontopolar N350 subcomponent defines the first substate of a process of object model selection from occipitotemporal cortex based on shape similarity, and indicates that implicit memory in this system is greater with better categorization success. Afterwards, parietal positivity and a slow wave index secondary, post-model selection processes, like evaluating the success of a decision or memory match, and working memory for overt report, respectively. Altogether, ERP findings support the equivalence hypothesis and a two-state interactive account of visual object knowledge, and delineate the timing of multiple memory systems.