Brain event-related potentials predict individual differences in inhibitory control

Machine learning reveals differential effects of depression and anxiety on reward and punishment processing

Recent studies suggest that depression and anxiety are associated with unique aspects of EEG responses to reward and punishment, respectively; also, abnormal responses to punishment in depressed individuals are related to anxiety, the symptoms of which are comorbid with depression. In a non-clinical sample, we aimed to investigate the relationships between reward processing and anxiety, between punishment processing and anxiety, between reward processing and depression, and between punishment processing and depression. Towards this aim, we separated feedback-related brain activity into delta and theta bands to isolate activity that indexes functionally distinct processes. Based on the delta/theta frequency and feedback valence, we then used machine learning (ML) to classify individuals with high severity of depressive symptoms and individuals with high severity of anxiety symptoms versus controls. The significant difference between the depression and control groups was driven mainly by delta activity; there were no differences between reward- and punishment-theta activities. The high severity of anxiety symptoms was marginally more strongly associated with the punishment- than the reward-theta feedback processing. The findings provide new insights into the differences in the impacts of anxiety and depression on reward and punishment processing; our study shows the utility of ML in testing brain-behavior hypotheses and emphasizes the joint effect of theta-RewP/FRN and delta frequency on feedback-related brain activity.

Neurodevelopmental vulnerability to psychosis: developmentally-based methods enable detection of early life inhibitory control deficits that predict psychotic-like experiences at the transition to adolescence

BACKGROUND

Inhibitory control develops in early childhood, and atypical development may be a measurable marker of risk for the later development of psychosis. Additionally, inhibitory control may be a target for intervention.

METHODS

Behavioral performance on a developmentally appropriate Go/No-Go task including a frustration manipulation completed by children ages 3-5 years (early childhood; n = 107) was examined in relation to psychotic-like experiences (PLEs; 'tween'; ages 9-12), internalizing symptoms, and externalizing symptoms self-reported at long-term follow-up (pre-adolescence; ages 8-11). ERP N200 amplitude for a subset of these children (n = 34) with electrophysiological data during the task was examined as an index of inhibitory control.

RESULTS

Children with lower accuracy on No-Go trials compared to Go trials in early childhood (F(1,101) = 3.976, p = 0.049), evidenced higher PLEs at the transition to adolescence 4-9 years later, reflecting a specific deficit in inhibitory control. No association was observed with internalizing or externalizing symptoms. Decreased accuracy during the frustration manipulation predicted higher internalizing, F(2,202) = 5.618, p = 0.004, and externalizing symptoms, F(2,202) = 4.663, p = 0.010. Smaller N200 amplitudes were observed on No-Go trials for those with higher PLEs, F(1,101) = 6.075, p = 0.020; no relationship was observed for internalizing or externalizing symptoms.

CONCLUSIONS

Long-term follow-up demonstrates for the first time a specific deficit in inhibitory control behaviorally and electrophysiology, for individuals who later report more PLEs. Decreases in task performance under frustration induction indicated risk for internalizing and externalizing symptoms. These findings suggest that pathophysiological mechanisms for psychosis are relevant and discriminable in early childhood, and further, suggest an identifiable and potentially modifiable target for early intervention.

Characterizing social and cognitive EEG-ERP through multiple kernel learning

EEG-ERP social-cognitive studies with healthy populations commonly fail to provide significant evidence due to low-quality data and the inherent similarity between groups. We propose a multiple kernel learning-based approach to enhance classification accuracy while keeping the traceability of the features (frequency bands or regions of interest) as a linear combination of kernels. These weights determine the relevance of each source of information, which is crucial for specialists. As a case study, we classify healthy ex-combatants of the Colombian armed conflict and civilians through a cognitive valence recognition task. Although previous works have shown accuracies below 80% with these groups, our proposal achieved an F1 score of 98%, revealing the most relevant bands and brain regions, which are the base for socio-cognitive trainings. With this methodology, we aim to contribute to standardizing EEG analyses and enhancing their statistics.

Gender differences in behavioral inhibitory control under evoked acute stress: An event-related potential study

Purpose

This study investigated gender differences in behavioral inhibitory control among college students under acute stress state by using event-related potential technique.

Methods

Acute stress was evoked in 41 college students (22 males and 19 females) using the Trier Social Stress paradigm, and the neutral state was matched using out-of-speech reading, with subjects completing a two-choice Oddball task in each of the two states. In combination with the ERP technique, the area under the stress curve, reaction time, number of errors, and the difference waves between the two stimulus conditions in the frontal-central region N2 wave amplitude and the parietal-central region P3 wave amplitude were compared between the two groups of subjects in the stressful and neutral state.

Results

The results revealed that the area under the stress curve was larger under the stress condition compared to the neutral condition, and the area under the stress curve was larger in females than in males. Behavioral results showed no statistically significant differences in reaction time and number of errors between the two genders in the acute stress condition. The ERP results showed that the wave amplitudes of N2 and P3 decreased significantly in both genders in the acute stress state. The decrease in N2 amplitude was greater in females during the transition from neutral to stressful condition, while the difference in P3 amplitude was not statistically significant in both genders.

Conclusion

The findings suggest that evoked acute stress can promote behavioral inhibitory control in both genders and that females are more sensitive to acute stress state.

Spatio-Temporal Brain Dynamic Differences in Fluid Intelligence

Human fluid intelligence is closely linked to the sequential solving of complex problems. It has been associated with a distributed cognitive control or multiple-demand (MD) network, comprising regions of lateral frontal, insular, dorsomedial frontal, and parietal cortex. Previous neuroimaging research suggests that the MD network may orchestrate the allocation of attentional resources to individual parts of a complex task: in a complex target detection task with multiple independent rules, applied one at a time, reduced response to rule-critical events across the MD network in lower fluid intelligence was observed. This was in particular the case with increasing task complexity (i.e., larger sets of rules), and was accompanied by impairment in performance. Here, we examined the early spatiotemporal neural dynamics of this process in electroencephalography (EEG) source analyses using a similar task paradigm. Levels of fluid intelligence specifically predicted early neural responses in a left inferiorparietal MD region around 200-300 ms post stimulus onset. Evoked source amplitudes in left parietal cortex within this early time window also correlated with behavioural performance measures. Like in previous research, we observed impaired performance in lower fluid intelligence with increasing number of task rules. This links fluid intelligence to a process of attentional focus on those parts of a task that are most critical for the current behaviour. Within the MD system, our time re-resolved measures suggest that the left parietal cortex specifically impacts on early processes of attentional focus on task critical features. This is novel evidence on the neurocognitive correlates of fluid intelligence suggesting that individual differences are critically linked to an early process of attentional focus on task-relevant information, which is supported by left parietal MD regions.

Individual differences in motor skill learning: Past, present and future

Humans vary considerably in their ability to perform and learn new motor skills. In addition, they respond to different performance and practice conditions in varying ways. Historically, experimental psychologists have characterized these differences as 'experimental noise', yet for those who embrace differential psychology, the study of individual differences promises to deepen insights into the processes that mediate motor control and learning. In this paper, we highlight what we know about predicting motor learning based on individual difference characteristics and renew a call made by Lee Cronbach several decades ago to combine the methodologies used by experimental and differential psychologists to further our understanding of how to promote motor learning. The paper provides a brief historical overview of research on individual differences and motor learning followed by a systematic review of the last 20 years of research on this issue. The paper ends by highlighting some of the methodological challenges associated with conducting research on individual differences, as well as providing suggestions for future research. The study of individual differences has important implications for furthering our understanding of motor learning and when tailoring interventions for diverse learners at different stages of practice.

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesized that the right inferior frontal cortex (rIFC) plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalography (EEG)-derived β-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal β-power often observed before successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of β-activity. There is evidence that the rate of β-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of β-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) EEG Stop Signal task (SST) dataset (n = 218) to examine averaged normalized β-power, β-burst rate, and β-burst "volume" (which we defined as burst duration × frequency span × amplitude). We first sought to optimize the β-burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful versus failed stopping and to (2) predict individual stop signal reaction time (SSRT). β-burst volume was significantly more predictive of successful and fast stopping than β-burst rate and normalized β-power. The classification model generalized to an external dataset (n = 201). We suggest β-burst volume is a sensitive and reliable measure for investigation of human response inhibition.SIGNIFICANCE STATEMENT The electroencephalography (EEG)-derived β-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of β-activity that contribute to successful and fast inhibition. In order to search for the most relevant features of β-activity, across the whole scalp and with high temporal precision, we employed machine learning on two large datasets. Spatial and temporal features of β-burst "volume" (duration × frequency span × amplitude) predicted response inhibition outcomes in our data significantly better than β-burst rate and normalized β-power. These findings suggest that multidimensional measures of β-bursts, such as burst volume, can add to our understanding of human response inhibition.

A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment

Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs ​= ​1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs ​= ​0.67-0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.

Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms

OBJECTIVE

Altered brain functional connectivity has been shown in youth with attention-deficit/hyperactivity disorder (ADHD). However, relatively little is known about functional connectivity in adult ADHD, and how it is linked with the heritability of ADHD.

METHODS

We measured eyes-open and eyes-closed resting electroencephalography (EEG) from 38 adults with ADHD, 45 1st degree relatives of people with ADHD and 51 healthy controls. Functional connectivity among all scalp channels was calculated using a weighted phase lag index for delta, theta, alpha, beta and gamma frequency bands. A machine learning analysis using penalized linear regression was used to identify if connectivity features (10,080 connectivity pairs) could predict ADHD symptoms. Furthermore, we examined if EEG connectivity could accurately classify participants into ADHD, 1st degree relatives and/or control groups.

RESULTS

Hyperactive symptoms were best predicted by eyes-open EEG connectivity in delta, beta and gamma bands. Inattentive symptoms were predicted by eyes-open EEG connectivity in delta, alpha and gamma bands, and eyes-closed EEG connectivity in delta and gamma bands. EEG connectivity features did not reliably classify participants into groups.

CONCLUSIONS

EEG connectivity may represent a neuromarker for ADHD symptoms.

SIGNIFICANCE

EEG connectivity may help elucidate the neural basis of adult ADHD symptoms.

Can Brain Waves Really Tell If a Product Will Be Purchased? Inferring Consumer Preferences From Single-Item Brain Potentials

Recent research has shown that event-related brain potentials (ERPs) recorded while participants view lists of different consumer goods can be modulated by their preferences toward these products. However, it remains largely unknown whether ERP activity specific to a single consumer item can be informative about whether or not this item will be preferred in a shopping context. In this study, we examined whether single-item ERPs could reliably predict consumer preferences toward specific consumer goods. We recorded scalp EEG from 40 participants while they were viewing pictures of consumer goods and we subsequently asked them to indicate their preferences for each of these items. Replicating previous results, we found that ERP activity averaged over the six most preferred products was significantly differentiated from ERP activity averaged across the six least preferred products for three ERP components: The N200, the late positive potential (LPP) and positive slow waves (PSW). We also found that using single-item ERPs to infer behavioral preferences about specific consumer goods led to an overall predictive accuracy of 71%, although this figure varied according to which ERPs were targeted. Later positivities such as the LPP and PSW yielded relatively higher predictive accuracy rates than the frontal N200. Our results suggest that ERPs related to single consumer items can be relatively accurate predictors of behavioral preferences depending on which type of ERP effects are chosen by the researcher, and ultimately on the level of prediction errors that users choose to tolerate.