P300 correlates with learning & memory abilities and fluid intelligence

The influence of psilocybin on subconscious and conscious emotional learning

Serotonergic psychedelics hold promise as a treatment modality for various psychiatric disorders and are currently applied in psychedelic-assisted psychotherapy. We investigated the learning effects of the serotonin receptor agonist psilocybin in a probabilistic cue-reward task with emotional cues in the form of neutral or fearful faces, presented either consciously or subconsciously. This study represents the first investigation into reinforcement learning with psilocybin. Across different dosages, psilocybin preserved learning effects and was statistically noninferior compared to placebo, while suggesting a higher exploratory behavior. Notably, the 20 mg group exhibited significantly better learning rates against the placebo group. Psilocybin induced inferior results with subconscious cues compared to placebo, and better results with conscious neutral cues in some conditions. These findings suggest that modulating serotonin signaling in the brain with psilocybin sufficiently preservers reinforcement learning.

Amplitude Modulation Perception and Cortical Evoked Potentials in Children With Listening Difficulties and Their Typically Developing Peers

PURPOSE

Amplitude modulations (AMs) are important for speech intelligibility, and deficits in speech intelligibility are a leading source of impairment in childhood listening difficulties (LiD). The present study aimed to explore the relationships between AM perception and speech-in-noise (SiN) comprehension in children and to determine whether deficits in AM processing contribute to childhood LiD. Evoked responses were used to parse the neural origins of AM processing.

METHOD

Forty-one children with LiD and 44 typically developing children, ages 8-16 years, participated in the study. Behavioral AM depth thresholds were measured at 4 and 40 Hz. SiN tasks included the Listening in Spatialized Noise-Sentences Test (LiSN-S) and a coordinate response measure (CRM)-based task. Evoked responses were obtained during an AM change detection task using alternations between 4 and 40 Hz, including the N1 of the acoustic change complex, auditory steady-state response (ASSR), P300, and a late positive response (late potential [LP]). Maturational effects were explored via age correlations.

RESULTS

Age correlated with 4-Hz AM thresholds, CRM separated talker scores, and N1 amplitude. Age-normed LiSN-S scores obtained without spatial or talker cues correlated with age-corrected 4-Hz AM thresholds and area under the LP curve. CRM separated talker scores correlated with AM thresholds and area under the LP curve. Most behavioral measures of AM perception correlated with the signal-to-noise ratio and phase coherence of the 40-Hz ASSR. AM change response time also correlated with area under the LP curve. Children with LiD exhibited deficits with respect to 4-Hz thresholds, AM change accuracy, and area under the LP curve.

CONCLUSIONS

The observed relationships between AM perception and SiN performance extend the evidence that modulation perception is important for understanding SiN in childhood. In line with this finding, children with LiD demonstrated poorer performance on some measures of AM perception, but their evoked responses implicated a primarily cognitive deficit.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25009103.

Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG

Evoked responses and oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how most frequently studied EEG signals: the P300-evoked response and alpha oscillations (8-12 Hz) can be linked with the baseline-shift mechanism. This mechanism states that oscillations generate evoked responses if oscillations have a non-zero mean and their amplitude is modulated by the stimulus. Therefore, the following predictions should hold: (1) the temporal evolution of P300 and alpha amplitude is similar, (2) spatial localisations of the P300 and alpha amplitude modulation overlap, (3) oscillations are non-zero mean, (4) P300 and alpha amplitude correlate with cognitive scores in a similar fashion. To validate these predictions, we analysed the data set of elderly participants (N=2230, 60-82 years old), using (a) resting-state EEG recordings to quantify the mean of oscillations, (b) the event-related data, to extract parameters of P300 and alpha rhythm amplitude envelope. We showed that P300 is indeed linked to alpha rhythm, according to all four predictions. Our results provide an unifying view on the interdependency of evoked responses and neuronal oscillations and suggest that P300, at least partly, is generated by the modulation of alpha oscillations.

Amplitude modulation perception and cortical evoked potentials in children with listening difficulties and their typically-developing peers

Purpose

Amplitude modulations (AM) are important for speech intelligibility, and deficits in speech intelligibility are a leading source of impairment in childhood listening difficulties (LiD). The present study aimed to explore the relationships between AM perception and speech-in-noise (SiN) comprehension in children and to determine whether deficits in AM processing contribute to childhood LiD. Evoked responses were used to parse the neural origin of AM processing.

Method

Forty-one children with LiD and forty-four typically-developing children, ages 8-16 y.o., participated in the study. Behavioral AM depth thresholds were measured at 4 and 40 Hz. SiN tasks included the LiSN-S and a Coordinate Response Measure (CRM)-based task. Evoked responses were obtained during an AM Change detection task using alternations between 4 and 40 Hz, including the N1 of the acoustic change complex, auditory steady-state response (ASSR), P300, and a late positive response (LP). Maturational effects were explored via age correlations.

Results

Age correlated with 4 Hz AM thresholds, CRM Separated Talker scores, and N1 amplitude. Age-normed LiSN-S scores obtained without spatial or talker cues correlated with age-corrected 4 Hz AM thresholds and area under the LP curve. CRM Separated Talker scores correlated with AM thresholds and area under the LP curve. Most behavioral measures of AM perception correlated with the SNR and phase coherence of the 40 Hz ASSR. AM Change RT also correlated with area under the LP curve. Children with LiD exhibited deficits with respect to 4 Hz thresholds, AM Change accuracy, and area under the LP curve.

Conclusions

The observed relationships between AM perception and SiN performance extend the evidence that modulation perception is important for understanding SiN in childhood. In line with this finding, children with LiD demonstrated poorer performance on some measures of AM perception, but their evoked responses implicated a primarily cognitive deficit.

The Neurotoxic Effect of Environmental Temperature Variation in Adult Zebrafish (Danio rerio)

Neurotoxicity consists of the altered functionality of the nervous system caused by exposure to chemical agents or altered chemical-physical parameters. The neurotoxic effect can be evaluated from the molecular to the behavioural level. The zebrafish Danio rerio is a model organism used in many research fields, including ecotoxicology and neurotoxicology. Recent studies by our research group have demonstrated that the exposure of adult zebrafish to low (18 °C) or high (34 °C) temperatures alters their brain proteome and fish behaviour compared to control (26 °C). These results showed that thermal variation alters the functionality of the nervous system, suggesting a temperature-induced neurotoxic effect. To demonstrate that temperature variation can be counted among the factors that generate neurotoxicity, eight different protein datasets, previously published by our research group, were subjected to new analyses using an integrated proteomic approach by means of the Ingenuity Pathway Analysis (IPA) software (Release December 2022). The datasets consist of brain proteome analyses of wild type adult zebrafish kept at three different temperatures (18 °C, 26 °C, and 34 °C) for 4 days (acute) or 21 days (chronic treatment), and of BDNF+/- and BDNF-/- zebrafish kept at 26 °C or 34 °C for 21 days. The results (a) demonstrate that thermal alterations generate an effect that can be defined as neurotoxic (p value ≤ 0.05, activation Z score ≤ -2 or ≥2), (b) identify 16 proteins that can be used as hallmarks of the neurotoxic processes common to all the treatments applied and (c) provide three protein panels (p value ≤ 0.05) related to 18 °C, 34 °C, and BDNF depletion that can be linked to anxiety-like or boldness behaviour upon these treatments.

Neurophysiological markers of successful learning in healthy aging

The capacity to learn and memorize is a key determinant for the quality of life but is known to decline to varying degrees with age. However, neural correlates of memory formation and the critical features that determine the extent to which aging affects learning are still not well understood. By employing a visual sequence learning task, we were able to track the behavioral and neurophysiological markers of gradual learning over several repetitions, which is not possible in traditional approaches that utilize a remember vs. forgotten comparison. On a neurophysiological level, we focused on two learning-related centro-parietal event-related potential (ERP) components: the expectancy-driven P300 and memory-related broader positivity (BP). Our results revealed that although both age groups showed significant learning progress, young individuals learned faster and remembered more stimuli than older participants. Successful learning was directly linked to a decrease of P300 and BP amplitudes. However, young participants showed larger P300 amplitudes with a sharper decrease during the learning, even after correcting for an observed age-related longer P300 latency and increased P300 peak variability. Additionally, the P300 amplitude predicted learning success in both age groups and showed good test-retest reliability. On the other hand, the memory formation processes, reflected by the BP amplitude, revealed a similar level of engagement in both age groups. However, this engagement did not translate into the same learning progress in the older participants. We suggest that the slower and more variable timing of the stimulus identification process reflected in the P300 means that despite the older participants engaging the memory formation process, there is less time for it to translate the categorical stimulus location information into a solidified memory trace. The results highlight the important role of the P300 and BP as a neurophysiological marker of learning and may enable the development of preventive measures for cognitive decline.

A system based on machine learning for improving sleep

Closed-loop auditory stimulation is one of the well-known and emerging sensory stimulation techniques, which achieves the purpose of sleep regulation by driving the EEG slow oscillation (SO, <1 Hz) through auditory stimulation. The main challenge is to accurately identify the stimulation timing and provide feedback in real-time, which has high requirements on the response time and recognition accuracy of the closed-loop auditory stimulation system. To reduce the impact of systematic errors on the regulation results, most traditional closed-loop auditory stimulation systems try to identify a single feature to determine the timing of stimulus delivery and reduce the system feedback delay by simplifying the calculation. Unlike existing closed-loop regulation systems that identify specific brain features, this paper proposes a closed-loop auditory stimulation sleep regulation system deploying machine learning. The process is: through online sleep real-time automatic staging, tracking the sleep stage to provide feedback quickly, and continuously offering external auditory stimulation at a specific SO phase. This paper uses this system to conduct sleep auditory stimulation regulation experiments on ten subjects. The experimental results show that the sleep closed-loop regulation system proposed in this paper can achieve consistency (effective for almost all subjects in the experiment) and immediate (taking effect immediately after stimulation) modulation effects on SOs. More importantly, the proposed method is superior to existing advanced methods. Therefore, the system designed in this paper has great potential to be more reliable and flexible in sleep regulation.

Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

BACKGROUND

Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.

METHODS

EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.

RESULTS

The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.

CONCLUSION

The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

Global Functional Connectivity at Rest Is Associated with Attention: An Arterial Spin Labeling Study

Neural markers of attention, including those frequently linked to the event-related potential P3 (P300) or P3b component, vary widely within and across participants. Understanding the neural mechanisms of attention that contribute to the P3 is crucial for better understanding attention-related brain disorders. All ten participants were scanned twice with a resting-state PCASL perfusion MRI and an ERP with a visual oddball task to measure brain resting-state functional connectivity (rsFC) and P3 parameters (P3 amplitudes and P3 latencies). Global rsFC (average rsFC across the entire brain) was associated with both P3 amplitudes (r = 0.57, p = 0.011) and P3 onset latencies (r = -0.56, p = 0.012). The observed P3 parameters were correlated with predicted P3 amplitude from the global rsFC (amplitude: r = +0.48, p = 0.037; latency: r = +0.40, p = 0.088) but not correlated with the rsFC over the most significant individual edge. P3 onset latency was primarily related to long-range connections between the prefrontal and parietal/limbic regions, while P3 amplitudes were related to connections between prefrontal and parietal/occipital, between sensorimotor and subcortical, and between limbic/subcortical and parietal/occipital regions. These results demonstrated the power of resting-state PCASL and P3 correlation with brain global functional connectivity.

Types of Intelligence and Academic Performance: A Systematic Review and Meta-Analysis

The concept of intelligence has been extensively studied, undergoing an evolution from a unitary concept to a more elaborate and complex multidimensional one. In addition, several research studies have focused their efforts for decades on the study of intelligence as a predictor of academic performance of students at different educational stages, being a stable and highly relevant predictor along with other variables such as executive functions, social context, culture or parental guardianship. Thus, the present study, based on a systematic review and meta-analysis, includes 27 studies with a total sample of 42,061 individuals. The main objective was to analyse the relationship between intelligence and academic performance using different predictive models that include moderating variables such as country of origin, type of intelligence, gender and age. The findings of this research highlight the significant, positive and moderate relationship between intelligence and academic performance (r = 0.367; p < 0.001), highlighting the predictive capacity on school performance when the type of intelligence (general and implicit; 35%) or the country of origin (45%) is taken as a moderating variable, with the explanatory models on age or sex not being significant. Therefore, it can be concluded that intelligence, in addition to being a good predictor of academic performance, is influenced depending on the type of intelligence or theoretical model taken as a reference, and also depending on the country or culture of origin.

Educational neurotechnology: Where do we go from here?

Recent educational trends point to an interest in educational neurotechnology. While these tools have the potential to change education, little is known about whether their use improves educational outcomes. Additionally, their adoption may be negatively impacted by teachers' lack of knowledge about the brain. In this paper we outline the potential of educational neurotechnology including what we know, what we do not yet know, and additional considerations for the ethical, successful adoption of these tools in classrooms around the world. Special consideration is given to the training needs of pre- and in-service educators whose support will be essential to the successful adoption of educational neurotechnology.

Behavioral Analysis of EEG Signals in Loss-Gain Decision-Making Experiments

Extraction and analysis of the EEG (electroencephalograph) information features generated during behavioral decision-making can provide a better understanding of the state of mind. Previous studies have focused more on the brainwave features after behavioral decision-making. In fact, the EEG before decision-making is more worthy of our attention. In this study, we introduce a new index based on the reaction time of subjects before decision-making, called the Prestimulus Time (PT), which have important reference value for the study of cognitive function, neurological diseases, and other fields. In our experiments, we use a wearable EEG feature signal acquisition device and a systematic reward and punishment experiment to obtain the EEG features before and after behavioral decision-making. The experimental results show that the EEG generated after behavioral decision due to loss is more intense than that generated by gain in the medial frontal cortex (MFC). In addition, different characteristics of EEG signals are generated prior to behavioral decisions because people have different expectations of the outcome. It will produce more significant negative-polarity event-related potential (ERP) in the forebrain area when the humans are optimistic about the outcomes.

A resting-state network for novelty: Similar involvement of a global network under rest and task conditions

Neuroimaging research provides converging evidence in support of functional networks active under rest conditions. While these networks are typically locally-distributed, a globally-distributed resting-state network (gRSN) was recently identified. The gRSN component is characterized by a scalp topography similar to that of the widely-studied P3 component of the event related potential, thought to represent the brain's response to novelty. In this study, we investigate similarities between the neural generators underlying these two networks to test the hypothesis that the gRSN is a resting-state network for novelty. By using the second-order blind identification (SOBI) algorithm, which works with temporal information, we show that (1) a resting-state component resembling the topography of the P3 can be recovered in all participants; (2) this gRSN component can be modeled with a set of ECDs with high goodness of fit; (3) ECD locations of the gRSN correspond to a network of globally-distributed brain structures overlapping heavily with the networking underlying the P3; and, (4) structures underlying these two networks are similarly involved during task and resting-state conditions. We interpret this as evidence in support of a resting-state network for detection and response to novelty.

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.

Functional Significance of Individual Differences in P3 Network Spatial Configuration

Abstract. The amplitude and latency of the P3 component in the electroencephalogram (EEG) event-related potentials (ERPs) are among the most extensively used markers for individual differences in normal and abnormal brain functions. In contrast, individual variations in spatial topography of the temporally-defined P3 component are relatively under-explored. Development in EEG-based source imaging opened up the possibility that individual-specific spatial configuration of the neural network underlying the temporally-defined P3 component bear a novel source of information for marking an individual difference in behavioral and cognitive function. In testing this hypothesis, a hybrid method consisting of blind source separation (BSS), equivalent current dipole (ECD) modeling, and hits-vector-based analysis was applied to continuous un-epoched EEG data collected from 13 healthy human participants performing a visual color oddball task. By analyzing the spatial configuration of the network underlying the temporally-defined P3 component, hereafter referred to as the P3N, we discovered that the contribution of each constituent structure within the P3N is not uniform. Instead, frontal lobe structures have significantly more involvement than other constituent structures, as quantitatively characterized by cross-individual reliability and a within-individual contribution to the P3N. A factor analysis of the hits vector data revealed that although P3 latency and amplitude did not show significant correlations with measures of the behavioral outcomes, scores of two factors derived from the hits vectors selectively predict behavioral reaction time and response correctness. These results support the hypothesis that variations in P3 spatial configuration reflect not merely noise but individual-specific features with functional significance.

Psychological, Physical, and Heat Stress Indicators Prior to and after a 15-Minute Structural Firefighting Task

Firefighters work in strenuous conditions for prolonged periods wearing up to 20 kg of personal protective equipment. This often contributes to significant heat and cardiovascular strain. This study examined the relationships between psychological and physical measures taken prior to undertaking a 15 min firefighting task, and the occurrence of heat stress and high levels of fatigue following the task. Nine qualified firefighters completed a 15 min “live burn” scenario designed to mimic a fire started by a two-seater couch in a lounge room and completed simulated tasks throughout the duration. Logical reasoning, speed and accuracy, general motivation and fatigue, and physical and mental effort were recorded pre-scenario, and at 0- and 20-min post-scenario. General motivation and fatigue scores at 0- and 20-min post-scenario were highly correlated with each other (rs = 0.90; p = 0.001). The general motivation and fatigue scores, at 0- and 20-min post-scenario, were also strongly related to pre-task logic/reasoning test scores (Post 0 rs = −0.77, p = 0.016; Post 20 rs = −0.87, p = 0.002). Firefighters with lower logical reasoning and speed and accuracy scores were more susceptible to fatigue and impaired cognition when exposed to rises in core temperature and heat stress.

Electrophysiological Proxy of Cognitive Reserve Index

Cognitive reserve (CR) postulates that individual differences in task performance can be attributed to differences in the brain’s ability to recruit additional networks or adopt alternative cognitive strategies. Variables that are descriptive of lifetime experience such as socioeconomic status, educational attainment, and leisure activity are common proxies of CR. CR is mostly studied using neuroimaging techniques such as functional MRI (fMRI) in which case individuals with a higher CR were observed to activate a smaller brain network compared to individuals with a lower CR, when performing a task equally effectively (higher efficiency), and electroencephalography (EEG) where a particular EEG component (P300) that reflects the attention and working memory load, has been targeted. Despite the contribution of multiple factors such as age, education (formal and informal), working, leisure, and household activities in CR formation, most neuroimaging studies, and those using EEG in particular, focus on formal education level only. The aim of the current EEG study is to investigate how the P300 component, evoked in response to an oddball paradigm, is associated with other components of CR besides education, such as working and leisure activity in older adults. We have used hereto a recently introduced CR index questionnaire (CRIq) that quantifies both professional and leisure activities in terms of their cognitive demand and number of years practiced, as well as a data-driven approach for EEG analysis. We observed complex relationships between CRIq subcomponents and P300 characteristics. These results are especially important given that, unlike previous studies, our measurements (P300 and CRIq) do not require active use of the same executive function and, thus, render our results free of a collinearity bias.

P300 in mesial temporal lobe epilepsy and its correlation with cognition - A MEG based prospective case-control study

PURPOSE

To assess the role of P300 in patients with temporal lobe epilepsy (TLE) with unilateral hippocampal sclerosis (HS) using magnetoencephalography (MEG) based auditory and visual oddball tasks, and to assess its correlation with neuropsychological tests.

METHODS

Thirty-patients (M:F-17:13, onset-11.77 ± 8.75 years, duration-16.10 ± 9.61 years) with TLE-HS (Left:15, Right:15) and fifteen-healthy age, gender and years of education matched controls (M:F-10:5, age-28.13 ± 4.76 years) underwent auditory and visual oddball tasks in MEG and cognition assessment using Indian Council of Medical Research (ICMR)-cognitive test battery. Independent component analysis (ICA) was applied to the magnetic evoked field responses for the detection of the P300 component. Source localization of P300 was performed with Classical LORETA Analysis Recursively Applied (CLARA). The latency and amplitude of P300 were estimated and subsequently correlated with cognitive scores.

RESULTS

The visual P300 amplitude in the TLE group was lower when compared to the control group. In subgroup comparison (controls vs. right HS vs. left HS), visual P300 amplitudes were lower in the right HS group compared to both left HS and control groups (p-value = 0.014). On the other hand, no significant difference for auditory P300 latency or amplitude was noted between patients and controls as well as between subgroups. A negative correlation found between the MEG visual P300 amplitude and Indian Trial Making Test (TMT)-B duration in the patient group.

CONCLUSION

Patients with TLE-HS have decreased visual-P300 amplitude. A significant correlation found between visual P300 amplitude and cognitive tests of visuospatial attention and working memory. Overall, MEG based visual P300 amplitude can be further explored with large sample size studies to establish as a complementary objective test for cognitive assessment in TLE.

Mechanisms of face specificity - Differentiating speed and accuracy in face cognition by event-related potentials of central processing

Given the crucial role of face recognition in social life, it is hardly surprising that cognitive processes specific for faces have been identified. In previous individual differences studies, the speed (measured in easy tasks) and accuracy (difficult tasks) of face cognition (FC, involving perception and recognition of faces) have been shown to form distinct abilities, going along with divergent factorial structures. This result has been replicated, but remained unexplained. To fill this gap, we first parameterized the sub-processes underlying speed vs. accuracy in easy and difficult memory tasks for faces and houses in a large sample. Then, we analyzed event-related potentials (ERPs) extracted from the EEG by using residue iteration decomposition (RIDE), yielding a central (C) component that is comparable to a purified P300. Structural equation modeling (SEM) was applied to estimate face specificity of C component latencies and amplitudes. If performance in easy tasks relies on purely general processes that are insensitive to stimulus content, there should be no specificity of individual differences in the latency recorded in easy tasks. However, in difficult tasks specificity was expected. Results indicated that, contrary to our predictions, specificity occurred in the C component latency of both speed-based and accuracy-based measures, but was stronger in accuracy. Further analyses suggested specific relationships between the face-related C latency and FC ability. Finally, we detected specificity in RTs of easy tasks when single tasks were modeled, but not when multiple tasks were jointly modeled. This suggests that the mechanisms leading to face specificity in performance speed are distinct across tasks.

Neurophysiological markers of cognitive deficits and recovery in concussed adolescents

OBJECTIVE

The present study sought to determine: 1) whether concussed adolescents exhibited deficits in neurocognitive functioning as reflected by neurophysiological alterations; 2) if neurophysiological alterations could be linked to supplementary data such as the number of previous concussions and days since injury; and 3) if deficits in psychological health and behavioural tests increased during diagnosis duration.

METHODS

Twenty-six concussed adolescents were compared to twenty-eight healthy controls with no prior concussions. Self-report inventories evaluated depressive and concussive symptomatology, while behavioral tests evaluated cognitive ability qualitatively. To assess neurophysiological markers of cognitive function, two separate auditory oddball tasks were employed: 1) an active oddball task measuring executive control and attention as reflected by the N2b and P300, respectively; and 2) a passive oddball task assessing the early, automatic pre-conscious awareness processes as reflected by the MMN.

RESULTS

Concussed adolescents displayed delayed N2b and attenuated P300 responses relative to controls; showed elevated levels of depressive and concussive symptomatology; scored average-to- low-average in behavioral tests; and exhibited N2b response latencies that correlated with number of days since injury.

CONCLUSION

These findings demonstrate that concussed adolescents exhibit clear deficiencies in neurocognitive function, and that N2b response latency may be a marker of concussion recovery.

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

BACKGROUND

Non-invasive brain-computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants.

FINDINGS

We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method.

CONCLUSIONS

The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.

Symptoms of depersonalisation/derealisation disorder as measured by brain electrical activity: A systematic review

Depersonalisation/derealisation disorder (DPD) refers to frequent and persistent detachment from bodily self and disengagement from the outside world. As a dissociative disorder, DPD affects 1-2 % of the population, but takes 7-12 years on average to be accurately diagnosed. In this systematic review, we comprehensively describe research targeting the neural correlates of core DPD symptoms, covering publications between 1992 and 2020 that have used electrophysiological techniques. The aim was to investigate the diagnostic potential of these relatively inexpensive and convenient neuroimaging tools. We review the EEG power spectrum, components of the event-related potential (ERP), as well as vestibular and heartbeat evoked potentials as likely electrophysiological biomarkers to study DPD symptoms. We argue that acute anxiety- or trauma-related impairments in the integration of interoceptive and exteroceptive signals play a key role in the formation of DPD symptoms, and that future research needs analysis methods that can take this integration into account. We suggest tools for prospective studies of electrophysiological DPD biomarkers, which are urgently needed to fully develop their diagnostic potential.

Probing the relationship between late endogenous ERP components with fluid intelligence in healthy older adults

The world population is rapidly aging, bringing together the necessity to better understand the advancing age. This characterization may be used to aid early diagnosis and to guide individually-tailored interventions. While some event-related potential (ERP) components, such as the P300 and late positive complex (LPC), have been associated with fluid intelligence (Gf) in young population; little is known whether these associations hold for older people. Therefore, the main goal of this study was to assess whether these ERP components are associated with Gf in the elderly. Fifty-seven older adults performed a continuous performance task (CPT) and a visual oddball paradigm while EEG was recorded. Participants were divided into two groups, according to their performance in the Raven’s Advanced Progressive Matrices test: high-performance (HP) and low-performance (LP). Results showed that the HP group, compared to the LP group, had higher LPC amplitudes in the CPT and shorter P300 latencies in the oddball task, highlighting the role of ERP components as a potential electrophysiological proxy of Gf abilities in the elderly.

Can Working Memory Task-Related EEG Biomarkers Measure Fluid Intelligence and Predict Academic Achievement in Healthy Children?

Background

Educational psychology research has linked fluid intelligence (Gf) with working memory (WM), but it is still dubious whether electroencephalography (EEG) markers robustly indicate Gf. This study addresses this issue and notes the relationship between WM task-related EEG markers with Gf and academic performance.

Method

A sample of 62 healthy children between the ages of 9 and 12 years was selected to perform three tasks: (1) Raven’s Standard Progressive Matrices (RSPM) test to assess Gf; (2) 2-back task to assess central executive system (CES); and (3) delayed match-to-sample task to assess short-term storage. These subjects were divided into high ability (HA) and low ability (LA) groups based on their RSPM scores. Support vector machine and logistic regression were used to train the EEG candidate indicators. A multiple regression was used to predict children’s academic performance using P3 amplitude, P2 latency, and θ-ERS.

Results

Behavioral results demonstrated that the correct rate of the HA group is higher than that of the LA group. The event-related potential results of the 2-back task showed that the P3 amplitude of the HA group was relatively larger and that the P2 latency was shorter than that observed in the LA group. For the delayed matching to sample task, the θ-ERS of the LA group was higher than that of the HA group. However, the area under the curve of these three indicators for Gf was < 0.75 for each and < 0.85 for the combined indicators. In predicting academic performance, only P3 amplitude showed a significant effect.

Conclusion

These results challenge previous findings, which reported that P3, P2, or theta power might be used in standard psychometric tests to assess an individual’s intelligence.

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8-10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbor (k-NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k-NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k-NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.

Electrophysiological markers of poor versus superior math abilities in healthy individuals

Interindividual differences in the numerical ability of healthy adults have been previously demonstrated, mainly with tasks involving mental number line or size representation. However, electrophysiological correlates of superior versus poor arithmetic ability (in the healthy population) have been scarcely investigated. We correlated electric potentials with math performance in 13 skilled and 13 poor calculators selected from a sample of 41 graduate students on the basis of their poor or superior math abilities assessed through a timed test. EEG was recorded from 128 channels while participants solved 352 arithmetical operations (additions, subtractions, multiplications, divisions) and decided whether the provided solution was correct or incorrect. Overall skilled individuals correctly solved a higher number of operations than poor calculators and had faster response times. Consistently, the latency of fronto-central P300 component of event-related potentials (ERPs) peaked earlier in the skilled than poor group. The P300 was larger in amplitude to correct than incorrect solutions, but just in the skilled group, with a tendency found in poor calculators. Spearman's ρ correlation coefficient analyses showed that the larger P300 response was to correct arithmetic solutions, the better the performance; conversely, the larger the P300 amplitude was to incorrect solutions, the worse the performance. The results suggest that poor calculators had a less clear representation of arithmetic solutions and difficulty in quickly accessing it. This study provides a standard method for directly investigating math abilities throughout ERP recordings that could be useful for assessing acalculia/dyscalculia in the clinical population (children, elderly, brain-damaged patients).

[Omega-6/omega-3 ratio and cognition in children with epilepsy]

INTRODUCTION

Cognitive impairment is a common consequence of epilepsy in children. This study aimed to assess the ratio of omega-6 to omega-3 fatty acid levels and its impact on cognitive function in children with idiopathic epilepsy.

PATIENTS AND METHODS

We performed a case-control study in 30 children with idiopathic epilepsy and 20 healthy children. We measured levels of alpha-linolenic acid (omega-3) and linoleic acid (omega-6) by means of gas-liquid chromatography. We assessed cognitive function with the Arabic version of the fourth edition of the Stanford-Binet test and the P300 component of event-related potentials. All children had an intelligent quotient greater than 70.

RESULTS

Children with epilepsy had lower levels of omega-3 and higher levels of omega-6 fatty acids and an abnormal omega-6/omega-3 ratio compared to non-epileptic children. We found a significant positive correlation of serum omega-3 levels and a significant negative correlation of serum omega-6 levels with cognitive function scores and P300 latency in children with epilepsy.

CONCLUSION

Children with epilepsy have abnormal ratios of omega-6 to omega-3 fatty acid serum levels, which is associated with impaired cognitive function in these children.

Audio-Visual Spatiotemporal Perceptual Training Enhances the P300 Component in Healthy Older Adults

In older adults, cognitive abilities, such as those associated with vision and hearing, generally decrease with age. According to several studies, audio-visual perceptual training can improve perceived competence regarding visual and auditory stimuli, suggesting that perceptual training is effective and beneficial. However, whether audio-visual perceptual training can induce far-transfer effects in other forms of untrained cognitive processing that are not directly trained in older adults remains unclear. In this study, the classic P300 component, a neurophysiological indicator of cognitive processing of a stimulus, was selected as an evaluation index of the training effect. We trained both young and older adults on the ability to judge the temporal and spatial consistency of visual and auditory stimuli. P300 amplitudes were significantly greater in the posttraining session than in the pretraining session in older adults (P = 0.001). However, perceptual training had no significant effect (P = 0.949) on the P300 component in young adults. Our results illustrate that audio-visual perceptual training can lead to far-transfer effects in healthy older adults. These findings highlight the robust malleability of the aging brain, and further provide evidence to motivate exploration to improve cognitive abilities in older adults.

Individual differences in working memory and general intelligence indexed by P200 and P300: A latent variable model

A robust relationship between working memory (WM) and general intelligence (g) has been well established. Nevertheless, explanations for this relationship in terms of underlying neurocognitive processes are still inadequate. This study addresses this issue using an individual differences approach in which Central Executive System (CES) and Short-Term Storage (STS) components of WM are measured comprehensively and examined for their relationship with g via event-related potentials components (P200 and P300) as mediators. Participants (n = 115) completed tests of the WM, CES and STS, as well as g. P200 and P300 components were recorded during 3-back WM task performance. Structural equation modelling showed significant negative associations between the P200 latency for target stimuli and CES shifting processes, and between the P300 amplitude for target stimuli and CES inhibition and updating processes. The relationship between CES processes and g was mediated in a localized fashion by the P300 amplitude. These findings further support the notion that the CES has a multidimensional structure and, importantly, reveal that the inhibition and updating functions of the CES are crucial in explaining the relationship between WM and g. Negative relations between ERP indices (P200 latency and P300 amplitude for target stimuli) and g support a neural efficiency hypothesis related to high intelligence.

Intelligence and uncertainty: Implications of hierarchical predictive processing for the neuroscience of cognitive ability

Hierarchical predictive processing (PP) has recently emerged as a candidate theoretical paradigm for neurobehavioral research. To date, PP has found support through its success in offering compelling explanations for a number of perceptual, cognitive, and psychiatric phenomena, as well as from accumulating neurophysiological evidence. However, its implications for understanding intelligence and its neural basis have received relatively little attention. The present review outlines the key tenets and evidence for PP, and assesses its implications for intelligence research. It is argued that PP suggests indeterminacy as a unifying principle from which to investigate the cognitive hierarchy and brain-ability correlations. The resulting framework not only accommodates prominent psychometric models of intelligence, but also incorporates key findings from neuroanatomical and functional activation research, and motivates new predictions via the mechanisms of prediction-error minimization. Because PP also suggests unique neural signatures of experience-dependent activity, it may also help clarify environmental contributions to intellectual development. It is concluded that PP represents a plausible, integrative framework that could enhance progress in the neuroscience of intelligence.

Neural Correlates of Feedback Processing in Visuo-Tactile Crossmodal Paired-Associate Learning

Previous studies have examined the neural correlates for crossmodal paired-associate (PA) memory and the temporal dynamics of its formation. However, the neural dynamics for feedback processing of crossmodal PA learning remain unclear. To examine this process, we recorded event-related scalp electrical potentials for PA learning of unimodal visual-visual pairs and crossmodal visual-tactile pairs when participants performed unimodal and crossmodal tasks. We examined event-related potentials (ERPs) after the onset of feedback in the tasks for three effects: feedback type (positive feedback vs. negative feedback), learning (as the learning progressed) and the task modality (crossmodal vs. unimodal). The results were as follows: (1) feedback type: the amplitude of P300 decreased with incorrect trials and the P400/N400 complex was only present in incorrect trials; (2) learning: progressive positive voltage shifts in frontal recording sites and negative voltage shifts in central and posterior recording sites were identified as learning proceeded; and (3) task modality: compared with the unimodal PA learning task, positive voltage shifts in frontal sites and negative voltage shifts in posterior sites were found in the crossmodal PA learning task. To sum up, these results shed light on cortical excitability related to feedback processing of crossmodal PA learning.

Classification of EEG Signals Based on Pattern Recognition Approach

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0–1.875 Hz (delta low) and 1.875–3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.