Early Caregiver Predictability Shapes Neural Indices of Statistical Learning Later in Infancy.

Caregivers play an outsized role in shaping early life experiences and development, but we often lack mechanistic insight into how exactly caregiver behavior scaffolds the neurodevelopment of specific learning processes. Here, we capitalized on the fact that caregivers differ in how predictable their behavior is to ask if infants' early environmental input shapes their brains' later ability to learn about predictable information. As part of an ongoing longitudinal study in South Africa, we recorded naturalistic, dyadic interactions between 103 (46 females and 57 males) infants and their primary caregivers at 3-6 months of age, from which we calculated the predictability of caregivers' behavior, following caregiver vocalization and overall. When the same infants were 6-12-months-old they participated in an auditory statistical learning task during EEG. We found evidence of learning-related change in infants' neural responses to predictable information during the statistical learning task. The magnitude of statistical learning-related change in infants' EEG responses was associated with the predictability of their caregiver's vocalizations several months earlier, such that infants with more predictable caregiver vocalization patterns showed more evidence of statistical learning later in the first year of life. These results suggest that early experiences with caregiver predictability influence learning, providing support for the hypothesis that the neurodevelopment of core learning and memory systems is closely tied to infants' experiences during key developmental windows.

Classification of cyclic alternating patterns of sleep using EEG signals.

Cyclic alternating patterns (CAP) occur in electroencephalogram (EEG) signals during non-rapid eye movement sleep. The analysis of CAP can offer insights into various sleep disorders. The first step is the identification of phases A and B for the CAP cycles. In this work, we develop an easy-to-implement accurate system to differentiate between CAP A and CAP B. Small segments of the EEG signal are processed using Gaussian filters to obtain sub-band components. Features are extracted using some statistical characteristics of these signal components. Minimum redundancy maximum relevance test is employed to identify the more significant features. Three different machine learning classifiers are considered and their performance is compared. The results are analyzed for both the balanced and unbalanced datasets. The k-nearest neighbour (kNN) classifier achieves 79.14 % accuracy and F-1 score of 79.24 % for the balanced dataset. The proposed method outperforms the existing methods for CAP classification. It is easy-to-implement and can be considered as a candidate for real-time deployment.

Is frontal EEG gamma power a neural correlate of language in toddlerhood? An examination of late talking and expressive language ability.

Few studies have examined neural correlates of late talking in toddlers, which could aid in understanding etiology and improving diagnosis of developmental language disorder (DLD). Greater frontal gamma activity has been linked to better language skills, but findings vary by risk for developmental disorders, and this has not been investigated in late talkers. This study examined whether frontal gamma power (30-50 Hz), from baseline-state electroencephalography (EEG), was related to DLD risk (categorical late talking status) and a continuous measure of expressive language in n = 124 toddlers. Frontal gamma power was significantly associated with late talker status when controlling for demographic factors and concurrent receptive language (ß = 1.96, McFadden's Pseudo R2 = 0.21). Demographic factors and receptive language did not significantly moderate the association between frontal gamma power and late talker status. A continuous measure of expressive language ability was not significantly associated with gamma (r = -0.07). Findings suggest that frontal gamma power may be useful in discriminating between groups of children that differ in DLD risk, but not for expressive language along a continuous spectrum of ability.

Microstate Analyses to Study face Processing in Healthy Individuals and Psychiatric Disorders: A Review of ERP Findings.

Microstates represent brief periods of quasi-stable electroencephalography (EEG) scalp topography, offering insights into dynamic fluctuations in event-related potential (ERP) topographies. Despite this, there is a lack of a comprehensive systematic overview of microstate findings concerning cognitive face processing. This review aims to summarize ERP findings on face processing using microstate analyses and assess their effectiveness in characterizing face-related neural representations. A literature search was conducted for microstate ERP studies involving healthy individuals and psychiatric populations, utilizing PubMed, Google Scholar, Web of Science, PsychInfo, and Scopus databases. Twenty-two studies were identified, primarily focusing on healthy individuals (n = 16), with a smaller subset examining psychiatric populations (n = 6). The evidence reviewed in this study suggests that various microstates are consistently associated with distinct ERP stages involved in face processing, encompassing the processing of basic visual facial features to more complex functions such as analytical processing, facial recognition, and semantic representations. Furthermore, these studies shed light on atypical attentional neural mechanisms in Autism Spectrum Disorder (ASD), facial recognition deficits among emotional dysregulation disorders, and encoding and semantic dysfunctions in Post-Traumatic Stress Disorder (PTSD). In conclusion, this review underscores the practical utility of ERP microstate analyses in investigating face processing. Methodologies have evolved towards greater automation and data-driven approaches over time. Future research should aim to forecast clinical outcomes and conduct validation studies to directly demonstrate the efficacy of such analyses in inverse space.

Low-Cost Classroom and Laboratory Exercises for Investigating Both Wave and Event-Related Electroencephalogram Potentials.

Electroencephalography (EEG) has given rise to a myriad of new discoveries over the last 90 years. EEG is a noninvasive technique that has revealed insights into the spatial and temporal processing of brain activity over many neuroscience disciplines, including sensory, motor, sleep, and memory formation. Most undergraduate students, however, lack laboratory access to EEG recording equipment or the skills to perform an experiment independently. Here, we provide easy-to-follow instructions to measure both wave and event-related EEG potentials using a portable, low-cost amplifier (Backyard Brains, Ann Arbor, MI) that connects to smartphones and PCs, independent of their operating system. Using open-source software (SpikeRecorder) and analysis tools (Python, Google Colaboratory), we demonstrate tractable and robust laboratory exercises for students to gain insights into the scientific method and discover multidisciplinary neuroscience research. We developed 2 laboratory exercises and ran them on participants within our research lab (N = 17, development group). In our first protocol, we analyzed power differences in the alpha band (8-13 Hz) when participants alternated between eyes open and eyes closed states (n = 137 transitions). We could robustly see an increase of over 50% in 59 (43%) of our sessions, suggesting this would make a reliable introductory experiment. Next, we describe an exercise that uses a SpikerBox to evoke an event-related potential (ERP) during an auditory oddball task. This experiment measures the average EEG potential elicited during an auditory presentation of either a highly predictable ("standard") or low-probability ("oddball") tone. Across all sessions in the development group (n=81), we found that 64% (n=52) showed a significant peak in the standard response window for P300 with an average peak latency of 442ms. Finally, we tested the auditory oddball task in a university classroom setting. In 66% of the sessions (n=30), a clear P300 was shown, and these signals were significantly above chance when compared to a Monte Carlo simulation. These laboratory exercises cover the two methods of analysis (frequency power and ERP), which are routinely used in neurology diagnostics, brain-machine interfaces, and neurofeedback therapy. Arming students with these methods and analysis techniques will enable them to investigate this laboratory exercise's variants or test their own hypotheses.

Delayed modulation of alpha band activity increases response inhibition deficits in adolescents with AD(H)D.

Deficiencies in inhibitory control are one of the hallmarks of attention-deficit-(hyperactivity) disorder (AD(H)D). Response inhibition demands can become increased through additional conflicts, namely when already integrated representations of perception-action associations have to be updated. Yet, the neural mechanisms of how such conflicts worsen response inhibition in AD(H)D are unknown, but, if identified, could help to better understand the complex nature of AD(H)D-associated impulsivity. We investigated both behavioral performance and EEG activity in the theta and alpha band of adolescents (10-18 years of age) with AD(H)D (n = 28) compared to neurotypical (NT) controls (n = 33) in a conflict-modulated Go/Nogo paradigm. We used multivariate pattern analysis (MVPA) and EEG-beamforming to examine how changes in representational content are coded by oscillatory activity and to delineate the cortical structures involved in it. The presented behavioral and neurophysiological data show that adolescents with AD(H)D are more strongly affected by increased response inhibition demands through additional conflicts than NT controls. Precisely, AD(H)D participants showed higher false alarm rates than NT controls in both, non-overlapping and overlapping Nogo trials, but performed even worse in the latter. This is likely due to an inefficient updating of representations related to delayed modulations of alpha band activity in the ventral stream and orbitofrontal regions. Theta band activity is also modulated by conflict but was not differentially affected in the two groups. By this, the present study provides novel insights into underlying neurophysiological mechanisms of the complex nature of response inhibition deficits in adolescents with AD(H)D, stressing the importance to examine the interplay of theta and alpha band activity more closely to better understand inhibitory control deficits in AD(H)D.

Optogenetic targeting of cortical astrocytes selectively improves NREM sleep in an Alzheimer's disease mouse model.

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by memory impairments and distinct histopathological features such as amyloid-beta (Aß) accumulations. Alzheimer's patients experience sleep disturbances at early stages of the disease. APPswe/PS1dE9 (APP) mice exhibit sleep disruptions, including reductions in non-rapid eye movement (NREM) sleep, that contribute to their disease progression. In addition, astrocytic calcium transients associated with a sleep-dependent brain rhythm, slow oscillations prevalent during NREM sleep, are disrupted in APP mice. However, at present it is unclear whether restoration of circuit function by targeting astrocytic activity could improve sleep in APP mice. To that end, APP mice expressing channelrhodopsin-2 (ChR2) targeted to astrocytes underwent optogenetic stimulation at the slow oscillation frequency. Optogenetic stimulation of astrocytes significantly increased NREM sleep duration but not duration of rapid eye movement (REM) sleep. Optogenetic treatment increased delta power and reduced sleep fragmentation in APP mice. Thus, optogenetic activation of astrocytes increased sleep quantity and improved sleep quality in an AD mouse model. Astrocytic activity provides a novel therapeutic avenue to pursue for enhancing sleep and slowing AD progression.

Adaptation patterns and their associations with mismatch negativity: An electroencephalogram (EEG) study with controlled expectations.

Adaptation refers to the decreased neural response that occurs after repeated exposure to a stimulus. While many electroencephalogram (EEG) studies have investigated adaptation by using either single or multiple repetitions, the adaptation patterns under controlled expectations manifested in the two main auditory components, N1 and P2, are still largely unknown. Additionally, although multiple repetitions are commonly used in mismatch negativity (MMN) experiments, it is unclear how adaptation at different time windows contributes to this phenomenon. In this study, we conducted an EEG experiment with 37 healthy adults using a random stimulus arrangement and extended tone sequences to control expectations. We tracked the amplitudes of the N1 and P2 components across the first 10 tones to examine adaptation patterns. Our findings revealed an L-shaped adaptation pattern characterised by a significant decrease in N1 amplitude after the first repetition (N1 initial adaptation), followed by a continuous, linear increase in P2 amplitude after the first repetition (P2 subsequent adaptation), possibly indicating model adjustment. Regression analysis demonstrated that the peak amplitudes of both the N1 initial adaptation and the P2 subsequent adaptation significantly accounted for variance in MMN amplitude. These results suggest distinct adaptation patterns for multiple repetitions across different components and indicate that the MMN reflects a combination of two processes: the initial adaptation in the N1 and a continuous model adjustment effect in the P2. Understanding these processes separately could have implications for models of cognitive processing and clinical disorders.

Electroencephalography can Ubiquitously Delineate the Brain Dysfunction of Neurodegenerative Dementia by Both Visual and Automatic Analysis Methods: A Preliminary Study.

Introduction: The aim was to examine the differences in electroencephalography (EEG) findings by visual and automated quantitative analyses between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Methods: EEG data of 20 patients with AD and 24 with DLB/PDD (12 DLB and 12 PDD) were retrospectively analyzed. Based on the awake EEG, the posterior dominant rhythm frequency and proportion of patients who showed intermittent focal and diffuse slow waves (IDS) were visually and automatically compared between the AD and DLB/PDD groups. Results: On visual analysis, patients with DLB/PDD showed a lower PDR frequency than patients with AD. In patients with PDR <8 Hz and occipital slow waves or patients with PDR <8 Hz and IDS, DLB/PDD was highly suspected (PPV 100%) and AD was unlikely (PPV 0%). On automatic analysis, the findings of the PDR were similar to those on visual analysis. Comparisons between visual and automatic analysis showed an overlap in the focal slow wave commonly detected by both methods in 10 of 44 patients, and concordant presence or absence of IDS in 29 of 43 patients. With respect to PDR <8 Hz and the combination of PDR <8 Hz and IDS, PPV and NPV in DLB/PDD and AD were not different between visual and automatic analysis. Conclusions: As the noninvasive, widely available clinical tool of low expense, visual analysis of EEG findings provided highly sufficient information to delineate different brain dysfunction in AD and DLB/PDD, and automatic EEG analysis could support visual analysis especially about PD.

Generalized spike-waves in idiopathic generalized epilepsies: Does their frequency matter?

OBJECTIVES: We hypothesized that the frequency (in Hertz) of generalized spike-waves (GSWs) in patients with idiopathic generalized epilepsy (IGE) has associations with the syndromic diagnosis as well as with the prognosis of patients (their response to medical treatment). METHODS: This was a retrospective study of a prospectively developed database. All patients with a diagnosis of IGE were studied at the epilepsy center at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2022. Patients were classified into four IGE syndromes: childhood absence epilepsy; juvenile absence epilepsy; juvenile myoclonic epilepsy; and generalized tonic-clonic seizures alone. RESULTS: Five hundred and eighty-three patients were studied. GSWs were commonly observed in all four syndromes of IGE. Frequency of GSW (in Hertz) did not have a significant association with the syndromic diagnosis of the patients (p = .179). The presence of GSW did not have a significant association with the seizure outcome (becoming seizure free or not) of the patients (p = .416). Frequency of GSW did not have a significant association with the seizure outcome of the patients either (p = .574). CONCLUSION: GSWs are the hallmark electroencephalographic footprints of idiopathic generalized epilepsies; however, neither their presence nor their frequency has practical associations with the syndromic diagnosis of IGEs or their outcome (response to treatment).

Corrigendum: Neurophysiological hallmarks of Huntington's disease progression: an EEG and fMRI connectivity study.

[This corrects the article DOI: 10.3389/fnagi.2023.1270226.].

Seizure recurrence following the first unprovoked seizure: Risk factors among children in UAE.

In a retrospective study of paediatric and adolescent patients in Abu Dhabi, UAE, who experienced their first unprovoked seizure between March 2016 and March 2020, with a minimum one-year follow-up, we identified significant risk factors associated with seizure recurrence. Among 317 patients, 96.2% experienced seizure recurrence, with the majority (68.8%) occurring within the first 6-month follow-up period. Notable risk factors for recurrence included focal seizures, symptomatic seizure causes, abnormal initial electroencephalogram (EEG) findings, abnormal brain magnetic resonance imaging results, and the presence of neurological disorders. Interestingly, the type of epileptiform activity in the initial EEG did not predict recurrence risk. Over a 3-year period, the overall recurrence risk was 98.4%, particularly higher in cases with symptomatic seizures compared to idiopathic (genetic) ones. These findings underscore the importance of vigilant monitoring, particularly in the early post-seizure follow-up period, and advocate for initial EEG assessments, especially in cases of remote symptomatic first unprovoked seizures.

Improving working memory by electrical stimulation and cross-frequency coupling.

Working memory (WM) is essential for the temporary storage and processing of information required for complex cognitive tasks and relies on neuronal theta and gamma oscillations. Given the limited capacity of WM, researchers have investigated various methods to improve it, including transcranial alternating current stimulation (tACS), which modulates brain activity at specific frequencies. One particularly promising approach is theta-gamma peak-coupled-tACS (TGCp-tACS), which simulates the natural interaction between theta and gamma oscillations that occurs during cognitive control in the brain. The aim of this study was to improve WM in healthy young adults with TGCp-tACS, focusing on both behavioral and neurophysiological outcomes. Thirty-one participants completed five WM tasks under both sham and verum stimulation conditions. Electroencephalography (EEG) recordings before and after stimulation showed that TGCp-tACS increased power spectral density (PSD) in the high-gamma region at the stimulation site, while PSD decreased in the theta and delta regions throughout the cortex. From a behavioral perspective, although no significant changes were observed in most tasks, there was a significant improvement in accuracy in the 14-item Sternberg task, indicating an improvement in phonological WM. In conclusion, TGCp-tACS has the potential to promote and improve the phonological component of WM. To fully realize the cognitive benefits, further research is needed to refine the stimulation parameters and account for individual differences, such as baseline cognitive status and hormonal factors.

A historical perspective on the neurobiology of speech and language: from the 19th century to the present.

In this essay, we review 19th century conceptions on the neurobiology of speech and language, including the pioneer work of Franz Gall, Jean-Baptiste Bouillaud, Simon Alexandre Ernest Aubertin, Marc Dax, Paul Broca, and Carl Wernicke. We examine how these early investigations, anchored in the study of neurological disorders, have broadened their scope via neuropsychological and psycholinguistic theories and models. Then, we discuss how major technological advances have led to an important paradigm shift, through which the study of the brain slowly detached from the study of disease to become the study of individuals of all ages, with or without brain pathology or language disorders. The profusion of neuroimaging studies that were conducted in the past four decades, inquiring into various aspects of language have complemented-and often challenged-classical views on language production. Our understanding of the "motor speech center," for instance, has been entirely transformed. The notion of cerebral dominance has also been revisited. We end this paper by discussing the challenges and controversies of 21st century neurobiology of speech and language as well as modern views of the neural architecture supporting speech and language functions.

Structural inequality and temporal brain dynamics across diverse samples.

BACKGROUND: Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. METHODS: Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed. FINDINGS: Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions. CONCLUSION: These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

A spectrum of altered non-rapid eye movement sleep in schizophrenia.

Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations, are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, ageing, cognitive profiles and medication regimens. Using newly collected high-density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported group-level differences between patients and controls (original N=130) during N2 stage. Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability. We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (AUC=0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics. Although multiple clinical and cognitive factors were associated with NREM metrics, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater contributor to variability. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on the sleep EEG; further, among patients, certain medications exacerbated these effects, in particular olanzapine. Collectively, our results point to a spectrum of N2 sleep deficits among SCZ patients that can be measured objectively and at scale, with relevance to both the etiological heterogeneity of SCZ as well as potential iatrogenic effects of antipsychotic medication.

REM sleep remains paradoxical: sub-states determined by thalamo-cortical and cortico-cortical functional connectivity.

During paradoxical sleep (PS, aka REM sleep) the cerebral cortex displays rapid electroencephalographic activity similar to that of wakefulness, whereas in the posterior associative thalamus, rapid activity is interrupted by frequent periods of slow-wave (delta) oscillations at 2-3 Hz, thereby dissociating the intrinsic frequency in thalamus and cortex. Here we studied the functional consequences of such a dissociation using intrathalamic and intracortical recordings in 21 epileptic patients, applying coherence analysis to examine changes in functional connectivity between the posterior thalamus (mainly medial pulvinar) and six cortical functional networks, and also between each cortical network with respect to the others. Periods of slow-wave thalamic activity ('delta PS') were more prevalent than phases of 'rapid PS,' and the delta/rapid thalamic alternance did not overlap with the classical tonic/phasic dichotomy based on rapid eye movements. Thalamo-cortical and cortico-cortical functional connectivity significantly decreased during delta PS, relative to both rapid PS periods and to wakefulness. The fact that delta thalamic activity and low thalamo-cortical binding coincided with a suppression of cortico-cortical connectivity supports a crucial role for the posterior associative thalamus, and particularly the medial pulvinar, in ensuring trans-thalamic communication between distant cortical areas. Disruption of such a trans-thalamic communication during delta PS compromises the functional binding between cortical areas, and consequently might contribute to the alteration of perceptual experiences commonly reported during dreams. KEY POINTS: During paradoxical, or REM, sleep (PS), rapid thalamic activity is interrupted by frequent periods of slow delta waves at 2-3 Hz. During these periods of thalamic delta activity there was a drastic drop of functional connectivity between associative thalamus and cortex, and also among different cortical networks. The delta/rapid alternance did not overlap with the classically defined 'tonic/phasic' periods and therefore suggests a distinct dichotomy of functional states in PS. Recurrent decrease in thalamo-cortical and cortico-cortical functional connectivity during PS may compromise the spatio-temporal binding between cortical areas, which in turn could hinder the formation of coherent mental content during dreams.

Is lack of goal-conflict-specific rhythmicity a biomarker for treatment resistance in generalised anxiety but not social anxiety or major depression?

BACKGROUND: Anxiety and depression cause major detriment to the patient, family, and society - particularly in treatment-resistant (TR) cases, which are highly prevalent. TR prevalence may be due to current diagnoses being based not on biological measures but on symptom lists that suffer from clinical subjectivity, variation in symptom presentation, and comorbidity. AIMS: Goal-conflict-specific rhythmicity (GCSR) measured using the Stop-Signal Task (SST) may provide the first neural biomarker for an anxiety process and disorder. This GCSR has been validated with selective drugs for anxiety. So, we proposed that GCSR could differ between TR and non-TR individuals and do so differently between those diagnoses normally sensitive to selective anxiolytics and those not. METHODS: We recorded electroencephalograms (EEG) from 20 TR participants (4 GAD, 5 SAD and 11 MDD) and 24 non-TR participants (4 GAD, 5 SAD and 15 Comorbid GAD/MDD (GMD)) while they performed the SST. RESULTS: There was significant positive GCSR in all groups except the GAD-TR group. GAD-TR lacked GCSR in the low-frequency range. However, TR had little effect in SAD or MDD/GMD populations with apparent increases not decreases. CONCLUSIONS: Overall, these results suggest that GAD may occur in two forms: one resulting from excessive GCSR and so being drug sensitive, and the other resulting from some other mechanism and so being TR. In SAD and MDD groups, heightened GCSR could be a consequence rather than the cause, driven by mechanisms that are normally more sensitive to non-selective panicolytic antidepressants.