Discriminant Brain Connectivity Patterns of Performance Monitoring at Average and Single-Trial Levels

Electrophysiological and neuroimaging evidence suggest the existence of common mechanisms for monitoring erroneous events, independent of the source of errors. Previous works have described modulations of theta activity in the medial frontal cortex elicited by either self-generated errors or erroneous feedback. In turn, similar patterns have recently been reported to appear after the observation of external errors. We report cross-regional interactions after observation of errors at both average and single-trial levels. We recorded scalp electroencephalography (EEG) signals from 15 subjects while monitoring the movement of a cursor on a computer screen. Connectivity patterns, estimated using multivariate auto-regressive models, show increased error-related modulations of the information transfer in the theta and alpha bands between frontocentral and frontolateral areas. Conversely, a decrease of connectivity in the beta band is also observed. These network patterns are similar to those elicited by self-generated errors. However, since no motor response is required, they appear to be related to intrinsic mechanisms of error processing, instead of being linked to co-activation of motor areas. Noticeably, we demonstrate that cross-regional interaction patterns can be estimated on a trial-by-trial basis. These trial-specific patterns, consistent with the multi-trial analysis, convey discriminant information on whether a trial was elicited by observation of an erroneous action. Overall, our study supports the role of frequency-specific modulations in the medial frontal cortex in coordinating cross-regional activity during cognitive monitoring at a single-trial basis

A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation

Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs. © 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni

Detecting Pilot's Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario

Monitoring pilot's mental states is a relevant approach to mitigate human error and enhance human machine interaction. A promising brain imaging technique to perform such a continuous measure of human mental state under ecological settings is Functional Near-InfraRed Spectroscopy (fNIRS). However, to our knowledge no study has yet assessed the potential of fNIRS connectivity metrics as long as passive Brain Computer Interfaces (BCI) are concerned. Therefore, we designed an experimental scenario in a realistic simulator in which 12 pilots had to perform landings under two contrasted levels of engagement (manual vs. automated). The collected data were used to benchmark the performance of classical oxygenation features (i.e., Average, Peak, Variance, Skewness, Kurtosis, Area Under the Curve, and Slope) and connectivity features (i.e., Covariance, Pearson's, and Spearman's Correlation, Spectral Coherence, and Wavelet Coherence) to discriminate these two landing conditions. Classification performance was obtained by using a shrinkage Linear Discriminant Analysis (sLDA) and a stratified cross validation using each feature alone or by combining them. Our findings disclosed that the connectivity features performed significantly better than the classical concentration metrics with a higher accuracy for the wavelet coherence (average: 65.3/59.9 %, min: 45.3/45.0, max: 80.5/74.7 computed for HbO/HbR signals respectively). A maximum classification performance was obtained by combining the area under the curve with the wavelet coherence (average: 66.9/61.6 %, min: 57.3/44.8, max: 80.0/81.3 computed for HbO/HbR signals respectively). In a general manner all connectivity measures allowed an efficient classification when computed over HbO signals. Those promising results provide methodological cues for further implementation of fNIRS-based passive BCIs

Dorsal Anterior Cingulate Cortices Differentially Lateralize Prediction Errors and Outcome Valence in a Decision-Making Task.

The dorsal anterior cingulate cortex (dACC) is proposed to facilitate learning by signaling mismatches between the expected outcome of decisions and the actual outcomes in the form of prediction errors. The dACC is also proposed to discriminate outcome valence—whether a result has positive (either expected or desirable) or negative (either unexpected or undesirable) value. However, direct electrophysiological recordings from human dACC to validate these separate, but integrated, dimensions have not been previously performed. We hypothesized that local field potentials (LFPs) would reveal changes in the dACC related to prediction error and valence and used the unique opportunity offered by deep brain stimulation (DBS) surgery in the dACC of three human subjects to test this hypothesis. We used a cognitive task that involved the presentation of object pairs, a motor response, and audiovisual feedback to guide future object selection choices. The dACC displayed distinctly lateralized theta frequency (3–8 Hz) event-related potential responses—the left hemisphere dACC signaled outcome valence and prediction errors while the right hemisphere dACC was involved in prediction formation. Multivariate analyses provided evidence that the human dACC response to decision outcomes reflects two spatiotemporally distinct early and late systems that are consistent with both our lateralized electrophysiological results and the involvement of the theta frequency oscillatory activity in dACC cognitive processing. Further findings suggested that dACC does not respond to other phases of action-outcome-feedback tasks such as the motor response which supports the notion that dACC primarily signals information that is crucial for behavioral monitoring and not for motor control

Cognitive-Motor Integration In Normal Aging And Preclinical Alzheimer's Disease: Neural Correlates And Early Detection

The objectives of the studies included in this dissertation were to characterize how the ability to integrate cognition into action is disrupted by both normal and pathological aging, to evaluate the effectiveness of kinematic measures in discriminating between individuals who are and are not at increased Alzheimer’s disease (AD) risk, and to examine the structural and functional neural correlates of cognitive-motor impairment in individuals at increased AD risk. The underlying hypothesis, based on previous research, is that measuring visuomotor integration under conditions that place demands on visual-spatial and cognitive-motor processing may provide an effective behavioural means for the early detection of brain alterations associated with AD risk. To this end, the first study involved testing participants both with and without AD risk factors on visuomotor tasks using a dual-touchscreen tablet. Comparisons between high AD risk participants and both young and old healthy control groups revealed significant performance disruptions in at-risk participants in the most cognitively demanding task. Furthermore, a stepwise discriminant analysis was able to distinguish between high and low AD risk participants with a classification accuracy of 86.4%. Based on the prediction that the impairments observed in high AD risk participants reflect disruption to the intricate reciprocal communication between hippocampal, parietal, and frontal brain regions required to successfully prepare and update complex reaching movements, the second and third studies were designed to examine the underlying structural and functional connectivity associated with cognitive-motor performance. Young adult and both low AD risk and high AD risk older adult participants underwent anatomical, diffusion-weighted, and resting-state functional connectivity scans. These data revealed significant age-related declines in white matter integrity that were more pronounced in the high AD risk group. Decreased functional connectivity in the default mode network (DMN) was also found in high AD risk participants. Furthermore, measures of white matter integrity and resting-state functional connectivity with DMN seed-regions were significantly correlated with task performance. These data support our hypothesis that disease-related disruptions in visuomotor control are associated with identifiable brain alterations, and thus behavioural assessments incorporating both cognition and action together may be useful in identifying individuals at increased AD risk