Single-trial Responses Research Articles

Distinct brain mechanisms for conscious versus subliminal error detection

Metacognition, the ability to monitor one's own cognitive processes, is frequently assumed to be univocally associated with conscious processing. However, some monitoring processes, such as those associated with the evaluation of one's own performance, may conceivably be sufficiently automatized to be deployed non-consciously. Here, we used simultaneous electro- and magneto-encephalography (EEG/MEG) to investigate how error detection is modulated by perceptual awareness of a masked target digit. The Error-Related Negativity (ERN), an EEG component occurring ~100 ms after an erroneous response, was exclusively observed on conscious trials: regardless of masking strength, the amplitude of the ERN showed a step-like increase when the stimulus became visible. Nevertheless, even in the absence of an ERN, participants still managed to detect their errors at above-chance levels under subliminal conditions. Error detection on conscious trials originated from the posterior cingulate cortex, while a small response to non-conscious errors was seen in dorsal anterior cingulate. We propose the existence of two distinct brain mechanisms for metacognitive judgements: a conscious all-or-none process of single-trial response evaluation, and a non-conscious statistical assessment of confidence.

Limb motion dictates how motor learning arises from arbitrary environmental dynamics

A key idea in motor learning is that internal models of environmental dynamics are internally represented as functions of spatial variables including position, velocity, and acceleration of body motion. We refer to such a representation as motion dependent. The evidence for a motion-dependent representation is, however, primarily based on examination of the adaptation to motion-dependent dynamic environments. To more rigorously test this idea, we examined the adaptive response to perturbations that cannot be well approximated by motion-state: force-impulses--brief, high-amplitude pulses of force. The induced adaptation characterizes the impulse response of the system--a widely used technique for probing system dynamics in engineering systems identification. Here we examined the adaptive responses to two different force-impulse perturbations during human voluntary reaching movements. We found that although neither could be well approximated by motion-state (R(2) < 0.18 in both cases), both perturbations induced single-trial adaptive responses that were (R(2) > 0.87). Moreover, these responses were similar in shape to those induced by low-fidelity motion-based approximations of the force-impulses (r > 0.88). Remarkably, we found that the motion dependence of the adaptive responses to force-impulses persisted, even after prolonged exposure (R(2) > 0.95). During a 300-trial training period, trial-to-trial fluctuations in the position, velocity, and acceleration of motion accurately predicted trial-to-trial fluctuations in the adaptive response, and the adaptation gradually became more specific to the perturbation, but only via reorganization of the structure of the motion-dependent representation. These results indicate that internal models of environmental dynamics represent these dynamics in a motion-dependent manner, regardless of the nature of the dynamics encountered.

Traveling waves and trial averaging: The nature of single-trial and averaged brain responses in large-scale cortical signals

Analyzing single trial brain activity remains a challenging problem in the neurosciences. We gain purchase on this problem by focusing on globally synchronous fields in within-trial evoked brain activity, rather than on localized peaks in the trial-averaged evoked response (ER). We analyzed data from three measurement modalities, each with different spatial resolutions: magnetoencephalogram (MEG), electroencephalogram (EEG) and electrocorticogram (ECoG). We first characterized the ER in terms of summation of phase and amplitude components over trials. Both contributed to the ER, as expected, but the ER topography was dominated by the phase component. This means the observed topography of cross-trial phase will not necessarily reflect the phase topography within trials. To assess the organization of within-trial phase, traveling wave (TW) components were quantified by computing the phase gradient. TWs were intermittent but ubiquitous in the within-trial evoked brain activity. At most task-relevant times and frequencies, the within-trial phase topography was described better by a TW than by the trial-average of phase. The trial-average of the TW components also reproduced the topography of the ER; we suggest that the ER topography arises, in large part, as an average over TW behaviors. These findings were consistent across the three measurement modalities. We conclude that, while phase is critical to understanding the topography of event-related activity, the preliminary step of collating cortical signals across trials can obscure the TW components in brain activity and lead to an underestimation of the coherent motion of cortical fields.

Uncovering the Mechanisms of Conscious Face Perception: A Single-Trial Study of the N170 Responses

When a face is flashed to an observer, a large negative component is elicited in the occipitotemporal cortex at ∼170 ms from the onset of presentation (N170). Previous studies have shown that the average N170 is correlated with conscious face perception; however, the single-trial mechanisms underlying such modulation remain largely unexplored. Here, we studied in human subjects the average and the single-trial N170 responses to briefly flashed faces, coupled with backward masking and varying degrees of Gaussian noise. In the average evoked responses we observed that, at fixed levels of noise, supraliminal faces exhibited significantly larger N170 amplitudes than subliminal faces. Moreover, the average N170 amplitude decreased with noise level both for the perceived and the nonperceived faces. At the single-trial level, the N170 amplitude was modulated by conscious recognition, which allowed predicting the subjects' perceptual responses above chance. In contrast, the single-trial N170 amplitudes were not modulated by the amount of noise and the effect found in the average responses was due to different latency jitters, as confirmed with latency-corrected averages. Altogether, these results suggest that conscious face perception is correlated with a boost in the activity of face-selective neural assemblies, whereas the stimulus uncertainty introduced by the added noise decreases the timing consistency (but not the amplitude) of this activation.

Decoding of single-trial auditory mismatch responses for online perceptual monitoring and neurofeedback

Multivariate pattern classification methods are increasingly applied to neuroimaging data in the context of both fundamental research and in brain-computer interfacing approaches. Such methods provide a framework for interpreting measurements made at the single-trial level with respect to a set of two or more distinct mental states. Here, we define an approach in which the output of a binary classifier trained on data from an auditory mismatch paradigm can be used for online tracking of perception and as a neurofeedback signal. The auditory mismatch paradigm is known to induce distinct perceptual states related to the presentation of high- and low-probability stimuli, which are reflected in event-related potential (ERP) components such as the mismatch negativity (MMN). The first part of this paper illustrates how pattern classification methods can be applied to data collected in an MMN paradigm, including discussion of the optimization of preprocessing steps, the interpretation of features and how the performance of these methods generalizes across individual participants and measurement sessions. We then go on to show that the output of these decoding methods can be used in online settings as a continuous index of single-trial brain activation underlying perceptual discrimination. We conclude by discussing several potential domains of application, including neurofeedback, cognitive monitoring and passive brain-computer interfaces.

Single-trial linear correlation analysis: application to characterization of stimulus modality effects

A key objective in systems and cognitive neuroscience is to establish associations between behavioral measures and concurrent neuronal activity. Single-trial analysis has been proposed as a novel method for characterizing such correlates by first extracting neural components that maximally discriminate trials on a categorical variable, (e.g., hard vs. easy, correct vs. incorrect etc.), and then correlate those components to a continues dependent variable of interest, e.g., reaction time, difficulty Index, etc. However, often times in experiment design it is difficult to either define meaningful categorical variables, or to record enough trials for the method to extract the discriminant components. Experiments designed for the study of the effects of stimulus presentation modality in working memory provide such a scenario, as will be exemplified. In this paper, we proposed a new approach to single-trial analysis in which we directly extract neural activity that maximally correlates to single-trial manual response times; eliminating the need to define an arbitrary categorical variable. We demonstrate our method on real electroencephalography (EEG) data recordings from the study of stimulus presentation modality effect (SPME).

Epoch versus impulse models in the analysis of parametric fMRI studies

ObjectiveIn parametric fMRI studies the relationship between the amplitude of the hemodynamic response and electrophysiological or behavioral parameters is commonly analyzed using the general linear model (GLM). We examined ways of using single-trial response time (RT) in the analysis of a decision-making task to better isolate task-specific activation. MethodsfMRI and RT data were recorded in twenty-one subjects performing a visual-oddball-task. Four explanatory variables (EVs) were generated for the GLM-analysis: A conventional (constant impulse) EV, a constant epoch EV informed using subjects’ average RT, a variable impulse EV and a variable epoch EV both informed using single-trial RT. EVs were tested individually and as orthogonalized pairs. ResultsThe individual EVs all detected similar extensive patterns of activation, while orthogonalized EVs were mainly correlated with BOLD signal variance in sensorimotor and parietal areas. Orthogonalizing the variable epoch EV to the constant epoch EV isolated cortical regions resembling the “dorsal frontoparietal attention network” from activation detected by the conventional (i.e., constant impulse) analysis. ConclusionFor short event durations, the activation detected by individual EVs is very similar, but orthogonalized, parametrically informed EVs can improve isolation of task-specific BOLD signal change. SignificanceDifferent approaches for integrating parametric timing measures in fMRI analyses can significantly influence outcomes, refining or confounding findings.

Theta responses are abnormal in mild cognitive impairment: evidence from analysis of theta event-related synchronization during a temporal expectancy task

We examined the hypothesis that the attention/executive deficits in mild cognitive impairment (MCI) due to Alzheimer's disease is associated to an abnormal cortical activation, revealed by the method of event-related synchronization/desynchronization (ERS/ERD) in the theta band during a paradigm of temporal orienting of attention. MCI patients (n=25) and healthy elderly (HE) matched controls (n=15) performed a task in which periodically omitted tones had to be predicted and their virtual onset time had to be marked by pressing a button. Single-trial theta responses were measured, respectively, before and after the motor response. Then, theta responses were compared to theta power during eyes closed resting state (ERD/ERS method).The temporal course of the task was characterized by two different behavioural conditions: (1) a pre-event epoch, in which the subject awaited the virtual onset of the omitted tone, (2) a post-event (after button pressing) epoch, in which the subject was in a post-motor response condition. The most important findings are summarized as follows: (1) in both groups, the pre-event epoch was characterized by theta ERS on temporal electrodes, but HE had a greater theta ERS compared to that of MCI group; (2) in both groups, during the post-motor condition, there was a theta ERS on prefrontal regions, and, also in this case, HE showed a greater theta enhancement compared to that of MCI patients; (3) HE showed evidence of lateralization: during the waiting epoch, theta ERS was dominant on the right posterior temporal lead (T6), whilst, during the post-motor epoch, theta ERS was greater on the left, as well as the midline prefrontal leads. Compared to the traditional neuropsychological measures for the episodic memory, these theta ERS indicators were less accurate in differentiating MCI patients from healthy elderly. The clinical relevance of these findings is that the weaker theta reactivity in MCI would indicate an early impairment in the temporal orienting of attention in the early stage of the clinical course of this neurodegenerative disease.

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses.We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis.A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure.The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

Automatic denoising of single-trial evoked potentials

We present an automatic denoising method based on the wavelet transform to obtain single trial evoked potentials. The method is based on the inter- and intra-scale variability of the wavelet coefficients and their deviations from baseline values. The performance of the method is tested with simulated event related potentials (ERPs) and with real visual and auditory ERPs. For the simulated data the presented method gives a significant improvement in the observation of single trial ERPs as well as in the estimation of their amplitudes and latencies, in comparison with a standard denoising technique (Donoho's thresholding) and in comparison with the noisy single trials. For the real data, the proposed method largely filters the spontaneous EEG activity, thus helping the identification of single trial visual and auditory ERPs. The proposed method provides a simple, automatic and fast tool that allows the study of single trial responses and their correlations with behavior.

Phasic bursting pattern of postural responses may reflect internal dynamics: Simulation of trunk reflexes with a neural oscillator model

Postural responses are usually investigated as reflexes. Several trials are averaged, and trial-to-trial variations are interpreted as noise. Several studies providing single-trial data plots revealed oscillations that may be cancelled out in averaged time series. Variations between single trials may also be interpreted as a consequence of changed dynamic properties of the neural circuitries. Therefore, we propose a Matsuoka oscillator model to describe single-trial postural responses to external perturbations. The applicability of the model was demonstrated by a comparison between simulations and experimental electromyographic (EMG) data. Vertical force perturbations of durations 0.4s and 0.2s were applied via a handle to 10 subjects. Handle force was used as model input, and EMG data from the external oblique muscles was compared with simulation output. Model coefficients were optimized by a least-squares algorithm. The optimization produced a good similarity between simulation and experimental data with determination coefficients of r2=0.7 and greater. Furthermore, as a model validation, the model coefficients were used to predict other perturbation trials with similarities between predictions and respective EMG data of about r2=0.45, which was in the range of trial-to-trial EMG variability. The observed oscillations are assumed to originate from the central nervous system with changes in the neural circuitries between trials. Hence, the oscillations in single trial responses which are usually regarded as noise might be generated by the dynamics of a neural oscillator.

Feature-Independent Neural Coding of Target Detection during Search of Natural Scenes

Visual search requires humans to detect a great variety of target objects in scenes cluttered by other objects or the natural environment. It is unknown whether there is a general purpose neural detection mechanism in the brain that codes the presence of a wide variety of categories of objects embedded in natural scenes. We provide evidence for a feature-independent coding mechanism for detecting behaviorally relevant targets in natural scenes in the dorsal frontoparietal network. Pattern classifiers using single-trial fMRI responses in the dorsal frontoparietal network reliably predicted the presence of 368 different target objects and also the observer's choices. Other vision-related areas such as the primary visual cortex, lateral occipital complex, the parahippocampal, and the fusiform gyri did not predict target presence, while high-level association areas related to general purpose decision making, including the dorsolateral prefrontal cortex and anterior cingulate, did. Activity in the intraparietal sulcus, a main area in the dorsal frontoparietal network, correlated with observers' decision confidence and with the task difficulty of individual images. These results cannot be explained by physical differences across images or eye movements. Thus, the dorsal frontoparietal network detects behaviorally relevant targets in natural scenes independent of their defining visual features and may be the human analog of the priority map in monkey lateral intraparietal cortex.

Localizing the component processes of lexical access using modern neuroimaging techniques

Neuroimaging plays an increasingly important role in the investigation of all aspects of human cognition, including language. Historically, experimental psychology and neuroimaging relied on very different techniques, as neuroimaging studies required comparisons between different tasks rather than manipulation of conditions within a single task, as is standard in behavioural experiments. However, methodology has advanced in the past decade such that many classic behavioural paradigms can now be employed in studies that measure brain activity. We review the technical foundations of conducting studies on single-trial brain responses, using event-related fMRI and electrophysiological recordings. We focus in particular on studies of picture naming, illustrating how the same techniques that were originally used to define temporal processing stages in reaction time studies can now be applied to brain imaging studies to reveal the neural localization of those stages.

Statistical evaluation of recurrence quantification analysis applied on single trial evoked potential studies

ObjectiveWe evaluated the potential of recurrence quantification analysis (RQA) to improve the analysis of trial-by-trial-variability in event-related potentials (ERPs) experiments. MethodsWe use an acoustic oddball paradigm to compare the efficiency of RQA with a linear amplitude based analysis of single trial ERPs with regard to the power to distinguish responses to different tone types. We further probed the robustness of both analyses towards structured noise induced by parallel magnetic resonance imaging (MRI). ResultsRQA provided robust discrimination of responses to different tone types, even when EEG data were contaminated by structured noise. Yet, its power to discriminate responses to different tone types was not significantly superior to a linear amplitude analysis. RQA measures were only moderately correlated with EEG amplitudes, suggesting that RQA may extract additional information from single trial responses not detected by amplitude evaluation. ConclusionsRQA allows quantifying signal characteristics of single trial ERPs measured with and without noise induced by parallel MRI. RQA power to discriminate responses to different tone types was similar to linear amplitude based analysis. SignificanceRQA has the potential to detect differences of signal features in response to a standard oddball paradigm and provide additional trial-by-trial information compared to classical amplitude based analysis.

Paradigm free mapping with sparse regression automatically detects single-trial functional magnetic resonance imaging blood oxygenation level dependent responses.

The ability to detect single trial responses in functional magnetic resonance imaging (fMRI) studies is essential, particularly if investigating learning or adaptation processes or unpredictable events. We recently introduced paradigm free mapping (PFM), an analysis method that detects single trial blood oxygenation level dependent (BOLD) responses without specifying prior information on the timing of the events. PFM is based on the deconvolution of the fMRI signal using a linear hemodynamic convolution model. Our previous PFM method (Caballero-Gaudes et al., 2011: Hum Brain Mapp) used the ridge regression estimator for signal deconvolution and required a baseline signal period for statistical inference. In this work, we investigate the application of sparse regression techniques in PFM. In particular, a novel PFM approach is developed using the Dantzig selector estimator, solved via an efficient homotopy procedure, along with statistical model selection criteria. Simulation results demonstrated that, using the Bayesian information criterion to select the regularization parameter, this method obtains high detection rates of the BOLD responses, comparable with a model-based analysis, but requiring no information on the timing of the events and being robust against hemodynamic response function variability. The practical operation of this sparse PFM method was assessed with single-trial fMRI data acquired at 7T, where it automatically detected all task-related events, and was an improvement on our previous PFM method, as it does not require the definition of a baseline state and amplitude thresholding and does not compromise on specificity and sensitivity.

Single-Unit Responses Selective for Whole Faces in the Human Amygdala

The human amygdala is critical for social cognition from faces, as borne out by impairments in recognizing facial emotion following amygdala lesions [1] and differential activation of the amygdala by faces [2-5]. Single-unit recordings in the primate amygdala have documented responses selective for faces, their identity, or emotional expression [6, 7], yet how the amygdala represents face information remains unknown. Does it encode specific features of faces that are particularly critical for recognizing emotions (such as the eyes), or does it encode the whole face, a level of representation that might be the proximal substrate for subsequent social cognition? We investigated this question by recording from over 200 single neurons in the amygdalae of seven neurosurgical patients with implanted depth electrodes [8]. We found that approximately half of all neurons responded to faces or parts of faces. Approximately 20% of all neurons responded selectively only to the whole face. Although responding most to whole faces, these neurons paradoxically responded more when only a small part of the face was shown compared to when almost the entire face was shown. We suggest that the human amygdala plays a predominant role in representing global information about faces, possibly achieved through inhibition between individual facial features.

How Ongoing Neuronal Oscillations Account for Evoked fMRI Variability

Variability of evoked single-trial responses despite constant input or task is a feature of large-scale brain signals recorded by fMRI. Initial evidence signified relevance of fMRI signal variability for perception and behavior. Yet the underlying intrinsic neuronal sources have not been previously substantiated. Here, we address this issue using simultaneous EEG-fMRI and real-time classification of ongoing alpha-rhythm states triggering visual stimulation in human subjects. We investigated whether spontaneous neuronal oscillations-as reflected in the posterior alpha rhythm-account for variability of evoked fMRI responses. Based on previous work, we specifically hypothesized linear superposition of fMRI activity related to fluctuations of ongoing alpha rhythm and a visually evoked fMRI response. We observed that spontaneous alpha-rhythm power fluctuations largely explain evoked fMRI response variance in extrastriate, thalamic, and cerebellar areas. For extrastriate areas, we confirmed the linear superposition hypothesis. We hence linked evoked fMRI response variability to an intrinsic rhythm's power fluctuations. These findings contribute to our conceptual understanding of how brain rhythms can account for trial-by-trial variability in stimulus processing.

Decoding an Individual's Sensitivity to Pain from the Multivariate Analysis of EEG Data

The perception of pain is characterized by its tremendous intra- and interindividual variability. Different individuals perceive the very same painful event largely differently. Here, we aimed to predict the individual pain sensitivity from brain activity. We repeatedly applied identical painful stimuli to healthy human subjects and recorded brain activity by using electroencephalography (EEG). We applied a multivariate pattern analysis to the time-frequency transformed single-trial EEG responses. Our results show that a classifier trained on a group of healthy individuals can predict another individual's pain sensitivity with an accuracy of 83%. Classification accuracy depended on pain-evoked responses at about 8 Hz and pain-induced gamma oscillations at about 80 Hz. These results reveal that the temporal-spectral pattern of pain-related neuronal responses provides valuable information about the perception of pain. Beyond, our approach may help to establish an objective neuronal marker of pain sensitivity which can potentially be recorded from a single EEG electrode.

Investigating static nonlinearities in neurovascular coupling

Many statistical models of coupling between time changes of the band-limited power of neural signals and functional magnetic resonance imaging Blood Oxygenation Level Dependent (BOLD) signal time changes rely on linear convolution. The effect of nonlinear behaviors in single-trial relationships between neural signals and BOLD responses is rarely tested and included in models. Here we investigate whether using a static nonlinearity improves the prediction of single-trial BOLD responses from neural signals. A static nonlinearity is a nonlinear transformation of the convolution of neural responses which is implemented by the same nonlinear function for all time points. We evaluated this approach by applying it to simultaneous recordings of functional magnetic resonance imaging BOLD and band-limited neural signals (Local Field Potentials and Multi Unit Activity) from primary visual cortex of anaesthetized macaques. We found that using a simple polynomial static nonlinearity was sufficient to obtain highly significant improvements of the accuracy of single-trial BOLD prediction over the accuracy obtained with linear convolution. This suggests that static nonlinearities may be a useful tool for a compact and accurate statistical description of neurovascular coupling.

Single-trial reconstruction of auditory evoked magnetic fields by means of Template Matching Pursuit

We present a new paradigm for the adaptive estimation of evoked brain responses in single trials, based upon the combination of the matching pursuit (MP) algorithm and template matching, and referred to as Template Matching Pursuit (TMP). In contrast to the classical template matching with invariant single-trial morphology and to previous approaches using MP with strong similarity constraint on functions in sequential trials, this adaptive approach allows for a wide variety of waveforms, and its universality is retained by parametrizing all relevant waveforms in terms of Gabor functions. A survey of single-trial estimates obtained for 10 subjects (∼4000 individual trials in total) confirms the validity of the assumption of a good approximation of single-trial waveforms. Owing to the fully parametric approach, we can easily perform also any quantitative analysis of such a huge dataset. As an example we take the trial-to-trial variability of the peak amplitude and latency of the auditory M100 component. This methodology provides estimates of diversified morphologies, which makes it free from the limitations inherent to any restrictive model. This seems advantageous in the context of the ongoing debate as to the neural mechanisms of average evoked brain responses.