EEG Changes in Migraine-Can EEG Help to Monitor Attack Susceptibility?

Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack of reliable predictors for upcoming attacks, which may help in understanding pathophysiological mechanisms to identify new treatment targets that may be positioned between the acute and preventive possibilities that are currently available. So far, a large range of studies using conventional hospital-based EEG recordings have provided contradictory results, with indications of both cortical hyper- as well as hypo-excitability. These heterogeneous findings may largely be because most studies were cross-sectional in design, providing only a snapshot in time of a patient's brain state without capturing day-to-day fluctuations. The scope of this narrative review is to (i) reflect on current knowledge on EEG changes in the context of migraine, the attack cycle, and underlying pathophysiology; (ii) consider the effects of migraine treatment on EEG features; (iii) outline challenges and opportunities in using EEG for monitoring attack susceptibility; and (iv) discuss future applications of EEG in home-based settings.

Annual Research Review: Early intervention viewed through the lens of developmental neuroscience

The overarching goal of this paper is to examine the efficacy of early intervention when viewed through the lens of developmental neuroscience. We begin by briefly summarizing neural development from conception through the first few postnatal years. We emphasize the role of experience during the postnatal period, and consistent with decades of research on critical periods, we argue that experience can represent both a period of opportunity and a period of vulnerability. Because plasticity is at the heart of early intervention, we next turn our attention to the efficacy of early intervention drawing from two distinct literatures: early intervention services for children growing up in disadvantaged environments, and children at elevated likelihood of developing a neurodevelopmental delay or disorder. In the case of the former, we single out interventions that target caregiving and in the case of the latter, we highlight recent work on autism. A consistent theme throughout our review is a discussion of how early intervention is embedded in the developing brain. We conclude our article by discussing the implications our review has for policy, and we then offer recommendations for future research.

Etiologic Evaluation of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is clinically and etiologically heterogeneous. A causal genetic variant can be identified in approximately 20% to 25% of affected individuals with current clinical genetic testing, and all patients with an ASD diagnosis should be offered genetic etiologic evaluation. We suggest that exome sequencing with copy number variant coverage should be the first-line etiologic evaluation for ASD. Neuroimaging, neurophysiologic, metabolic, and other biochemical evaluations can provide insight into the pathophysiology of ASD but should be recommended in the appropriate clinical circumstances.

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain versus non-pain images of human facial expressions or scenes depicting pain being inflicted has not been explored. The present study aimed to address this by training Random Forest (RF) models on cortical event-related potentials (ERPs) recorded while participants passively viewed faces displaying either pain or neutral expressions, as well as action scenes depicting pain or matched non-pain (neutral) scenarios. Ninety-one participants were recruited across three samples, which included a model development group (n = 40) and a cross-subject validation group (n = 51). Additionally, 25 participants from the model development group completed a second experimental session, providing a within-subject temporal validation sample. The analysis of ERPs revealed an enhanced N170 component in response to faces compared to action scenes. Moreover, an increased late positive potential (LPP) was observed during the viewing of pain scenes compared to neutral scenes. Additionally, an enhanced P3 response was found when participants viewed faces displaying pain expressions compared to neutral expressions. Subsequently, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The RF model achieved classification accuracies of 75%, 64%, and 69% for cross-validation, cross-subject, and within-subject classifications, respectively, along with reasonably calibrated predictions for the classification of face versus scene images. However, the RF model was unable to classify pain versus neutral stimuli above chance levels when presented with subsequent tasks involving images from either category. These results expand upon previous findings by externally validating the use of ML in classifying ERPs related to different categories of visual images, namely faces and scenes. The results also indicate the limitations of ML in distinguishing pain and non-pain connotations using ERP responses to the passive viewing of visually similar images.

Plasticity Changes in Central Auditory Systems of School-Age Children Following a Brief Training With a Remote Microphone System

OBJECTIVES

The objective of this study was to investigate whether a brief speech-in-noise training with a remote microphone (RM) system (favorable listening condition) would contribute to enhanced post-training plasticity changes in the auditory system of school-age children.

DESIGN

Before training, event-related potentials (ERPs) were recorded from 49 typically developing children, who actively identified two syllables in quiet and in noise (+5 dB signal-to-noise ratio [SNR]). During training, children completed the same syllable identification task as in the pre-training noise condition, but received feedback on their performance. Following random assignment, half of the sample used an RM system during training (experimental group), while the other half did not (control group). That is, during training' children in the experimental group listened to a more favorable speech signal (+15 dB SNR) than children from the control group (+5 dB SNR). ERPs were collected after training at +5 dB SNR to evaluate the effects of training with and without the RM system. Electrical neuroimaging analyses quantified the effects of training in each group on ERP global field power (GFP) and topography, indexing response strength and network changes, respectively. Behavioral speech-perception-in-noise skills of children were also evaluated and compared before and after training. We hypothesized that training with the RM system (experimental group) would lead to greater enhancement of GFP and greater topographical changes post-training than training without the RM system (control group). We also expected greater behavioral improvement on the speech-perception-in-noise task when training with than without the RM system.

RESULTS

GFP was enhanced after training only in the experimental group. These effects were observed on early time-windows corresponding to traditional P1-N1 (100 to 200 msec) and P2-N2 (200 to 400 msec) ERP components. No training effects were observed on response topography. Finally, both groups increased their speech-perception-in-noise skills post-training.

CONCLUSIONS

Enhanced GFP after training with the RM system indicates plasticity changes in the neural representation of sound resulting from listening to an enriched auditory signal. Further investigation of longer training or auditory experiences with favorable listening conditions is needed to determine if that results in long-term speech-perception-in-noise benefits.

External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals

Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time-frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML's clinical potential for pain classification.

Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging

We review recent advances in using mathematical models of the relationship between the brain structure and function that capture features of brain dynamics. We argue the need for models that can jointly capture temporal, spatial, and spectral features of brain functional activity. We present recent work on spectral graph theory based models that can accurately capture spectral as well as spatial patterns across multiple frequencies in MEG reconstructions.

Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem

Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects ([Formula: see text]) or trials ([Formula: see text]). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects' EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.

Affect and Cognition in Managerial Decision Making: A Systematic Literature Review of Neuroscience Evidence

How do affect and cognition interact in managerial decision making? Over the last decades, scholars have investigated how managers make decisions. However, what remains largely unknown is the interplay of affective states and cognition during the decision-making process. We offer a systematization of the contributions produced on the role of affect and cognition in managerial decision making by considering the recent cross-fertilization of management studies with the neuroscience domain. We implement a Systematic Literature Review of 23 selected contributions dealing with the role of affect and cognition in managerial decisions that adopted neuroscience techniques/points of view. Collected papers have been analyzed by considering the so-called reflexive (X-) and reflective (C-) systems in social cognitive neuroscience and the type of decisions investigated in the literature. Results obtained help to support an emerging "unified" mind processing theory for which the two systems of our mind are not in conflict and for which affective states have a driving role toward cognition. A research agenda for future studies is provided to scholars who are interested in advancing the investigation of affect and cognition in managerial decision making, also through neuroscience techniques - with the consideration that these works should be at the service of the behavioral strategy field.

Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing

Electroencephalography (EEG) is among the most widely diffused, inexpensive, and adopted neuroimaging techniques. Nonetheless, EEG requires measurements against a reference site(s), which is typically chosen by the experimenter, and specific pre-processing steps precede analyses. It is therefore valuable to obtain quantities that are minimally affected by reference and pre-processing choices. Here, we show that the topological structure of embedding spaces, constructed either from multi-channel EEG timeseries or from their temporal structure, are subject-specific and robust to re-referencing and pre-processing pipelines. By contrast, the shape of correlation spaces, that is, discrete spaces where each point represents an electrode and the distance between them that is in turn related to the correlation between the respective timeseries, was neither significantly subject-specific nor robust to changes of reference. Our results suggest that the shape of spaces describing the observed configurations of EEG signals holds information about the individual specificity of the underlying individual's brain dynamics, and that temporal correlations constrain to a large degree the set of possible dynamics. In turn, these encode the differences between subjects' space of resting state EEG signals. Finally, our results and proposed methodology provide tools to explore the individual topographical landscapes and how they are explored dynamically. We propose therefore to augment conventional topographic analyses with an additional-topological-level of analysis, and to consider them jointly. More generally, these results provide a roadmap for the incorporation of topological analyses within EEG pipelines.

Towards understanding how we pay attention in naturalistic visual search settings

Research on attentional control has largely focused on single senses and the importance of behavioural goals in controlling attention. However, everyday situations are multisensory and contain regularities, both likely influencing attention. We investigated how visual attentional capture is simultaneously impacted by top-down goals, the multisensory nature of stimuli, and the contextual factors of stimuli's semantic relationship and temporal predictability. Participants performed a multisensory version of the Folk et al. (1992) spatial cueing paradigm, searching for a target of a predefined colour (e.g. a red bar) within an array preceded by a distractor. We manipulated: 1) stimuli's goal-relevance via distractor's colour (matching vs. mismatching the target), 2) stimuli's multisensory nature (colour distractors appearing alone vs. with tones), 3) the relationship between the distractor sound and colour (arbitrary vs. semantically congruent) and 4) the temporal predictability of distractor onset. Reaction-time spatial cueing served as a behavioural measure of attentional selection. We also recorded 129-channel event-related potentials (ERPs), analysing the distractor-elicited N2pc component both canonically and using a multivariate electrical neuroimaging framework. Behaviourally, arbitrary target-matching distractors captured attention more strongly than semantically congruent ones, with no evidence for context modulating multisensory enhancements of capture. Notably, electrical neuroimaging of surface-level EEG analyses revealed context-based influences on attention to both visual and multisensory distractors, in how strongly they activated the brain and type of activated brain networks. For both processes, the context-driven brain response modulations occurred long before the N2pc time-window, with topographic (network-based) modulations at ∼30 ms, followed by strength-based modulations at ∼100 ms post-distractor onset. Our results reveal that both stimulus meaning and predictability modulate attentional selection, and they interact while doing so. Meaning, in addition to temporal predictability, is thus a second source of contextual information facilitating goal-directed behaviour. More broadly, in everyday situations, attention is controlled by an interplay between one's goals, stimuli's perceptual salience, meaning and predictability. Our study calls for a revision of attentional control theories to account for the role of contextual and multisensory control.

Eliciting Psychological Ownership of Object by Marking Organizational Name: The Role of Belongingness

Psychological ownership critically entails the need for home (a place in which to dwell or a place of belongingness). However, the question of how an individual’s need for belongingness within an organization affects their psychological ownership of organization-linked objects remains unexplored. We first conducted a behavioral study to determine whether psychological ownership of object can be elicited by marking the object with the name of the subjects’ organization. The participants in this behavioral study reported a higher level of psychological ownership when objects were marked with their own organization’s name (i.e., in-organization objects) compared with objects marked with another organization’s name (i.e., out-organization objects). Importantly, this effect was more pronounced among subjects who experienced a stronger sense of organizational belongingness. We subsequently conducted a second study to explore its underlying neural mechanism. Our findings indicated that participants with a higher level of perceived organizational belongingness exhibited a significantly larger amplitude of the P300 component of event-related potential in response to in-organization objects compared with their response to out-organization objects. However, no significant difference in the P300 component was found for participants who lacked a sense of organizational belongingness.

Improved Pre-attentive Processing With Occipital rTMS Treatment in Major Depressive Disorder Patients Revealed by MMN

OBJECTIVES

To investigate the improvement effect of occipital repetitive transcranial magnetic stimulation (rTMS) combined with escitalopram oxalate tablets on pre-attentive processing in patients with first-episode, medication-naive depression.

METHODS

Patients who were hospitalized between January and December 2019 were selected. They were randomly allocated to real occipital rTMS stimulation group with 27 cases receiving intermittent theta-burst (iTBS) and sham stimulation group with 24 cases over 20 days. The rTMS treatment target is located at the Oz point of the occipital region. Both groups took escitalopram oxalate tablets, and the average daily drug dose was 15.294 ± 5.041 mg. Hamilton Depression Rating Scale (HAMD) was used to assess the symptoms of depression before and after treatment, and mismatch negativity (MMN) was used to assess the improvement of pre-attentive processing before and after treatment.

RESULTS

After 20 days of treatment, the total score of HAMD (13.495 ± 3.700) in both groups was significantly lower than that before treatment [21.910 ± 3.841, F(1, 49) = 46, 3.690, p < 0.001]. After treatment, the latency of MMN in the real stimulation group (182.204 ± 31.878 ms) was significantly lower than that in the sham stimulation group (219.896 ± 42.634 ms, p < 0.001), and the amplitude of MMN in the real stimulation group (-7.107 ± 3.374 ms) was significantly higher than that in the sham stimulation group (-2.773 ± 3.7 32 ms, p < 0.001).

CONCLUSION

Occipital rTMS treatment can enhance the early therapeutic effect and effectively improve the pre-attentive processing of patients with depression and provide a scientific basis for the new target of rTMS therapy in clinical patients with depression.

The Neurophysiological Processing of Music in Children: A Systematic Review With Narrative Synthesis and Considerations for Clinical Practice in Music Therapy

Introduction: Evidence supporting the use of music interventions to maximize arousal and awareness in adults presenting with a disorder of consciousness continues to grow. However, the brain of a child is not simply a small adult brain, and therefore adult theories are not directly translatable to the pediatric population. The present study aims to synthesize brain imaging data about the neural processing of music in children aged 0-18 years, to form a theoretical basis for music interventions with children presenting with a disorder of consciousness following acquired brain injury. Methods: We conducted a systematic review with narrative synthesis utilizing an adaptation of the methodology developed by Popay and colleagues. Following the development of the narrative that answered the central question "what does brain imaging data reveal about the receptive processing of music in children?", discussion was centered around the clinical implications of music therapy with children following acquired brain injury. Results: The narrative synthesis included 46 studies that utilized EEG, MEG, fMRI, and fNIRS scanning techniques in children aged 0-18 years. From birth, musical stimuli elicit distinct but immature electrical responses, with components of the auditory evoked response having longer latencies and variable amplitudes compared to their adult counterparts. Hemodynamic responses are observed throughout cortical and subcortical structures however cortical immaturity impacts musical processing and the localization of function in infants and young children. The processing of complex musical stimuli continues to mature into late adolescence. Conclusion: While the ability to process fundamental musical elements is present from birth, infants and children process music more slowly and utilize different cortical areas compared to adults. Brain injury in childhood occurs in a period of rapid development and the ability to process music following brain injury will likely depend on pre-morbid musical processing. Further, a significant brain injury may disrupt the developmental trajectory of complex music processing. However, complex music processing may emerge earlier than comparative language processing, and occur throughout a more global circuitry.

The development of attentional control mechanisms in multisensory environments

Outside the laboratory, people need to pay attention to relevant objects that are typically multisensory, but it remains poorly understood how the underlying neurocognitive mechanisms develop. We investigated when adult-like mechanisms controlling one's attentional selection of visual and multisensory objects emerge across childhood. Five-, 7-, and 9-year-olds were compared with adults in their performance on a computer game-like multisensory spatial cueing task, while 129-channel EEG was simultaneously recorded. Markers of attentional control were behavioural spatial cueing effects and the N2pc ERP component (analysed traditionally and using a multivariate electrical neuroimaging framework). In behaviour, adult-like visual attentional control was present from age 7 onwards, whereas multisensory control was absent in all children groups. In EEG, multivariate analyses of the activity over the N2pc time-window revealed stable brain activity patterns in children. Adult-like visual-attentional control EEG patterns were present age 7 onwards, while multisensory control activity patterns were found in 9-year-olds (albeit behavioural measures showed no effects). By combining rigorous yet naturalistic paradigms with multivariate signal analyses, we demonstrated that visual attentional control seems to reach an adult-like state at ∼7 years, before adult-like multisensory control, emerging at ∼9 years. These results enrich our understanding of how attention in naturalistic settings develops.

Computational Models in Electroencephalography

Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

Neonatal Multisensory Processing in Preterm and Term Infants Predicts Sensory Reactivity and Internalizing Tendencies in Early Childhood

Multisensory processes include the capacity to combine information from the different senses, often improving stimulus representations and behavior. The extent to which multisensory processes are an innate capacity or instead require experience with environmental stimuli remains debated. We addressed this knowledge gap by studying multisensory processes in prematurely born and full-term infants. We recorded 128-channel event-related potentials (ERPs) from a cohort of 55 full-term and 61 preterm neonates (at an equivalent gestational age) in response to auditory, somatosensory, and combined auditory-somatosensory multisensory stimuli. Data were analyzed within an electrical neuroimaging framework, involving unsupervised topographic clustering of the ERP data. Multisensory processing in full-term infants was characterized by a simple linear summation of responses to auditory and somatosensory stimuli alone, which furthermore shared common ERP topographic features. We refer to the ERP topography observed in full-term infants as "typical infantile processing" (TIP). In stark contrast, preterm infants exhibited non-linear responses and topographies less-often characterized by TIP; there were distinct patterns of ERP topographies to multisensory and summed unisensory conditions. We further observed that the better TIP characterized an infant's ERPs, independently of prematurity, the more typical was the score on the Infant/Toddler Sensory Profile (ITSP) at 12 months of age and the less likely was the child to the show internalizing tendencies at 24 months of age. Collectively, these results highlight striking differences in the brain's responses to multisensory stimuli in children born prematurely; differences that relate to later sensory and internalizing functions.

Dynamic Changes in EEG Power Spectral Densities During NIH-Toolbox Flanker, Dimensional Change Card Sort Test and Episodic Memory Tests in Young Adults

Much is known about electroencephalograph (EEG) patterns during sleep, but until recently, it was difficult to study EEG patterns during conscious, awake behavior. Technological advances such as powerful wireless EEG systems have led to a renewed interest in EEG as a clinical and research tool for studying real-time changes in the brain. We report here the first normative study of EEG activity while healthy young adults completed a series of cognitive tests recently published by the National Institutes of Health Toolbox Cognitive Battery (NIH-TCB), a commonly-used standardized measure of cognition primarily used in clinical populations. In this preliminary study using a wireless EEG system, we examined power spectral density (PSD) in four EEG frequency bands. During baseline and cognitive testing, PSD activity for the lower frequency bands (theta and alpha) was greater, relative to the higher frequency bands (beta and gamma), suggesting participants were relaxed and mentally alert. Alpha, beta and gamma activity was increased during a memory test compared to two other, less demanding executive function tests. Gamma activity was also inversely correlated with performance on the memory test, consistent with the neural efficiency hypothesis which proposes that better cognitive performance may link with lower cortical energy consumption. In summary, our study suggests that cognitive performance is related to the dynamics of EEG activity in a normative young adult population.

Electroencephalography

Electroencephalography (EEG) is the non-invasive measurement of the brain's electric fields. Electrodes placed on the scalp record voltage potentials resulting from current flow in and around neurons. EEG is nearly a century old: this long history has afforded EEG a rich and diverse spectrum of applications. On the one hand, foundations of EEG in clinical diagnostics have dovetailed more recently into brain-triggered neurorehabilitation treatments. On the other hand, EEG has not only been a workhorse for providing brain correlates of constructs in the field of experimental psychology, but has also been used as a true neuroimaging method with more recent extensions in translational as well as computational neuroscience. The versatility and accessibility of the technique, in combination with advances in signal processing, allow for this 'old dog' to still deliver new tricks and innovations.