EEG Source Imaging Indices of Cognitive Control Show Associations with Dopamine System Genes.

Cognitive or executive control is a critical mental ability, an important marker of mental illness, and among the most heritable of neurocognitive traits. Two candidate genes, catechol-O-methyltransferase (COMT) and DRD4, which both have a roles in the regulation of cortical dopamine, have been consistently associated with cognitive control. Here, we predicted that individuals with the COMT Met/Met allele would show improved response execution and inhibition as indexed by event-related potentials in a Go/NoGo task, while individuals with the DRD4 7-repeat allele would show impaired brain activity. We used independent component analysis (ICA) to separate brain source processes contributing to high-density EEG scalp signals recorded during the task. As expected, individuals with the DRD4 7-repeat polymorphism had reduced parietal P3 source and scalp responses to response (Go) compared to those without the 7-repeat. Contrary to our expectation, the COMT homozygous Met allele was associated with a smaller frontal P3 source and scalp response to response-inhibition (NoGo) stimuli, suggesting that while more dopamine in frontal cortical areas has advantages in some tasks, it may also compromise response inhibition function. An interaction effect emerged for P3 source responses to Go stimuli. These were reduced in those with both the 7-repeat DRD4 allele and either the COMT Val/Val or the Met/Met homozygous polymorphisms but not in those with the heterozygous Val/Met polymorphism. This epistatic interaction between DRD4 and COMT replicates findings that too little or too much dopamine impairs cognitive control. The anatomic and functional separated maximally independent cortical EEG sources proved more informative than scalp channel measures for genetic studies of brain function and thus better elucidate the complex mechanisms in psychiatric illness

EEG Source Imaging Indices of Cognitive Control Show Associations with Dopamine System Genes

Cognitive or executive control is a critical mental ability, an important marker of mental illness, and among the most heritable of neurocognitive traits. Two candidate genes, catechol-O-methyltransferase (COMT) and DRD4, which both have a roles in the regulation of cortical dopamine, have been consistently associated with cognitive control. Here, we predicted that individuals with the COMT Met/Met allele would show improved response execution and inhibition as indexed by event-related potentials in a Go/NoGo task, while individuals with the DRD4 7-repeat allele would show impaired brain activity. We used independent component analysis (ICA) to separate brain source processes contributing to high-density EEG scalp signals recorded during the task. As expected, individuals with the DRD4 7-repeat polymorphism had reduced parietal P3 source and scalp responses to response (Go) compared to those without the 7-repeat. Contrary to our expectation, the COMT homozygous Met allele was associated with a smaller frontal P3 source and scalp response to response-inhibition (NoGo) stimuli, suggesting that while more dopamine in frontal cortical areas has advantages in some tasks, it may also compromise response inhibition function. An interaction effect emerged for P3 source responses to Go stimuli. These were reduced in those with both the 7-repeat DRD4 allele and either the COMT Val/Val or the Met/Met homozygous polymorphisms but not in those with the heterozygous Val/Met polymorphism. This epistatic interaction between DRD4 and COMT replicates findings that too little or too much dopamine impairs cognitive control. The anatomic and functional separated maximally independent cortical EEG sources proved more informative than scalp channel measures for genetic studies of brain function and thus better elucidate the complex mechanisms in psychiatric illness

The quest for the genuine visual mismatch negativity (vMMN): Event-related potential indications of deviance detection for low-level visual features

Research shows that the visual system monitors the environment for changes. For example, a left‐tilted bar, a deviant, that appears after several presentations of a right‐tilted bar, standards, elicits a classic visual mismatch negativity (vMMN): greater negativity for deviants than standards in event‐related potentials (ERPs) between 100 and 300 ms after onset of the deviant. The classic vMMN is contributed to by adaptation; it can be distinguished from the genuine vMMN that, through use of control conditions, compares standards and deviants that are equally adapted and physically identical. To determine whether the vMMN follows similar principles to the auditory mismatch negativity (MMN), in two experiments we searched for a genuine vMMN from simple, physiologically plausible stimuli that change in fundamental dimensions: orientation, contrast, phase, and spatial frequency. We carefully controlled for attention and eye movements. We found no evidence for the genuine vMMN, despite adequate statistical power. We conclude that either the genuine vMMN is a rather unstable phenomenon that depends on still‐to‐be‐identified experimental parameters, or it is confined to visual stimuli for which monitoring across time is more natural than monitoring over space, such as for high‐level features. We also observed an early deviant‐related positivity that we propose might reflect earlier predictive processing

Improving object segmentation by using EEG signals and rapid serial visual presentation

This paper extends our previous work on the potential of EEG-based brain computer interfaces to segment salient objects in images. The proposed system analyzes the Event Related Potentials (ERP) generated by the rapid serial visual presentation of windows on the image. The detection of the P300 signal allows estimating a saliency map of the image, which is used to seed a semi-supervised object segmentation algorithm. Thanks to the new contributions presented in this work, the average Jaccard index was improved from $0.47$ to $0.66$ when processed in our publicly available dataset of images, object masks and captured EEG signals. This work also studies alternative architectures to the original one, the impact of object occupation in each image window, and a more robust evaluation based on statistical analysis and a weighted F-score

Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces

The N2pc is a lateralised Event-Related Potential (ERP) that signals a shift of attention towards the location of a potential object of interest. We propose a single-trial target-localisation collaborative Brain-Computer Interface (cBCI) that exploits this ERP to automatically approximate the horizontal position of targets in aerial images. Images were presented by means of the rapid serial visual presentation technique at rates of 5, 6 and 10 Hz. We created three different cBCIs and tested a participant selection method in which groups are formed according to the similarity of participants’ performance. The N2pc that is elicited in our experiments contains information about the position of the target along the horizontal axis. Moreover, combining information from multiple participants provides absolute median improvements in the area under the receiver operating characteristic curve of up to 21% (for groups of size 3) with respect to single-user BCIs. These improvements are bigger when groups are formed by participants with similar individual performance, and much of this effect can be explained using simple theoretical models. Our results suggest that BCIs for automated triaging can be improved by integrating two classification systems: one devoted to target detection and another to detect the attentional shifts associated with lateral targets

Cross‐modal predictive processing depends on context rather than local contingencies

Visual symbols or events may provide predictive information on to-be-expected sound events. When the perceived sound does not confirm the visual prediction, the incongruency response (IR), a prediction error signal of the event-related brain potentials, is elicited. It is unclear whether predictions are derived from lower-level local contingencies (e.g., recent events or repetitions) or from higher-level global rules applied top-down. In a recent study, sound pitch was predicted by a preceding note symbol. IR elicitation was confined to the condition where one of two sounds was presented more frequently and was not present with equal probability of both sounds. These findings suggest that local repetitions support predictive cross-modal processing. On the other hand, IR has also been observed with equal stimulus probabilities, where visual patterns predicted the upcoming sound sequence. This suggests the application of global rules. Here, we investigated the influence of stimulus repetition on the elicitation of the IR by presenting identical trial trains of a particular visual note symbol cueing a particular sound resulting either in a congruent or an incongruent pair. Trains of four different lengths: 1, 2, 4, or 7 were presented. The IR was observed already after a single presentation of a congruent visual-cue-sound combination and did not change in amplitude as trial train length increased. We conclude that higher-level associations applied in a top-down manner are involved in elicitation of the prediction error signal reflected by the IR, independent from local contingencies

Selective Transfer Learning for EEG-Based Drowsiness Detection

© 2015 IEEE. On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject's pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual's pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject