Brain and grammar: revealing electrophysiological basic structures with competing statistical models.

Acoustic, lexical, and syntactic information are simultaneously processed in the brain requiring complex strategies to distinguish their electrophysiological activity. Capitalizing on previous works that factor out acoustic information, we could concentrate on the lexical and syntactic contribution to language processing by testing competing statistical models. We exploited electroencephalographic recordings and compared different surprisal models selectively involving lexical information, part of speech, or syntactic structures in various combinations. Electroencephalographic responses were recorded in 32 participants during listening to affirmative active declarative sentences. We compared the activation corresponding to basic syntactic structures, such as noun phrases vs. verb phrases. Lexical and syntactic processing activates different frequency bands, partially different time windows, and different networks. Moreover, surprisal models based on part of speech inventory only do not explain well the electrophysiological data, while those including syntactic information do. By disentangling acoustic, lexical, and syntactic information, we demonstrated differential brain sensitivity to syntactic information. These results confirm and extend previous measures obtained with intracranial recordings, supporting our hypothesis that syntactic structures are crucial in neural language processing. This study provides a detailed understanding of how the brain processes syntactic information, highlighting the importance of syntactic surprisal in shaping neural responses during language comprehension.

The hemodynamic response to co-occurring interictal epileptiform discharges and high-frequency oscillations localizes the seizure-onset zone.

OBJECTIVE: To use intracranial electroencephalography (EEG) to characterize functional magnetic resonance imaging (fMRI) activation maps associated with high-frequency oscillations (HFOs) (80-250 Hz) and examine their proximity to HFO- and seizure-generating tissue. METHODS: Forty-five patients implanted with intracranial depth electrodes underwent a simultaneous EEG-fMRI study at 3 T. HFOs were detected algorithmically from cleaned EEG and visually confirmed by an experienced electroencephalographer. HFOs that co-occurred with interictal epileptiform discharges (IEDs) were subsequently identified. fMRI activation maps associated with HFOs were generated that occurred either independently of IEDs or within ±200 ms of an IED. For all significant analyses, the Maximum, Second Maximum, and Closest activation clusters were identified, and distances were measured to both the electrodes where the HFOs were observed and the electrodes involved in seizure onset. RESULTS: We identified 108 distinct groups of HFOs from 45 patients. We found that HFOs with IEDs produced fMRI clusters that were closer to the local field potentials of the corresponding HFOs observed within the EEG than HFOs without IEDs. In addition to the fMRI clusters being closer to the location of the EEG correlate, HFOs with IEDs generated Maximum clusters with greater z-scores and larger volumes than HFOs without IEDs. We also observed that HFOs with IEDs resulted in more discrete activation maps. SIGNIFICANCE: Intracranial EEG-fMRI can be used to probe the hemodynamic response to HFOs. The hemodynamic response associated with HFOs that co-occur with IEDs better identifies known epileptic tissue than HFOs that occur independently.

Unravelling the origin of the reward positivity: a human intracranial event-related brain potential study.

The reward positivity (RewP) is an event-related brain potential (ERP) component that emerges approximately 250 to 350 milliseconds (ms) after receiving reward-related feedback stimuli and is believed to be important for reinforcement learning and reward processing. Although numerous localization studies have indicated that the anterior cingulate cortex (ACC) is the neural generator of this component, other studies have identified sources outside of the ACC, fuelling a debate about its origin. Because the results of EEG and MEG source localization studies are severely limited by the inverse problem, we addressed this question by leveraging the high spatial and temporal resolution of intracranial EEG. We predicted that we would identify a neural generator of the RewP in the caudal ACC. We recorded intracranial EEG in 19 refractory epilepsy patients who underwent invasive video-EEG monitoring at Ghent University Hospital, Belgium. Participants engaged in the virtual T-maze task (vTMT), a trial-and-error task known to elicit a canonical RewP, while scalp and intracranial EEG were simultaneously recorded. The RewP was identified using a difference wave approach for both scalp and intracranial EEG. The data were aggregated across participants to create a virtual "meta-participant" that contained all the recorded intracranial ERPs (iERPs) with respect to their intracranial contact locations. We used both a hypothesis-driven (focused on ACC) and exploratory (whole-brain analysis) approach to segment the brain into regions of interest (ROI). For each ROI, we evaluated the degree to which the time course of the absolute current density (ACD) activity mirrored the time course of the RewP, and confirmed the statistical significance of the results using permutation analysis. The grand average waveform of the scalp data revealed a RewP at 309 ms after reward feedback with a frontocentral scalp distribution, consistent with the identification of this component as the RewP. The meta-participant contained iERPs recorded from 582 intracranial contacts in total. The ACD activity of the aggregated iERPs were most similar to the RewP in left caudal ACC, left dorsolateral prefrontal cortex, left frontomedial cortex, and left white matter, with the highest score attributed to caudal ACC, as predicted. To our knowledge, this is the first study that uses intracranial EEG aggregated across multiple human epilepsy patients and current source density analysis to identify the neural generator(s) of the RewP. These results provide direct evidence that the ACC is a neural generator of the RewP.

Catecholaminergic Modulation of Metacontrol is Reflected by Changes in Aperiodic EEG Activity.

BACKGROUND: "Metacontrol" describes the ability to maintain an optimal balance between cognitive control styles that are either more persistent or more flexible. Recent studies have shown a link between metacontrol and aperiodic EEG patterns. The present study aimed to gain more insight into the neurobiological underpinnings of metacontrol by using Methylphenidate (MPH), a compound known to increase postsynaptic catecholamine levels and to modulate cortical noise. METHODS: In a double-blind, randomized, placebo-controlled study design, we investigated the effect of methylphenidate (0.5 mg/kg) on aperiodic EEG activity during a flanker task in a sample of n = 25 neurotypical adults. To quantify cortical noise, we employed the FOOOF (fitting oscillations & one over f) algorithm. RESULTS: Compared to placebo, MPH increased the aperiodic exponent, suggesting that it reduces cortical noise in two ways: First, it did so in a state-like fashion, as the main effect of the drug was visible and significant in both pre-trial and within-trial periods. Second, the electrode-specific analyses showed that the drug also affects specific processes by dampening the downregulation of noise in conditions requiring more control. CONCLUSIONS: Our findings suggest that the aperiodic exponent provides a neural marker of metacontrol states and changes therein. Further, we propose that the effectiveness of medications targeting catecholaminergic signaling can be evaluated by studying changes of cortical noise; fostering the idea of using the quantification of cortical noise as an indicator in pharmacological treatment.

Loneliness and brain rhythmic activity in resting state: An exploratory report.

Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.

DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection.

Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural networks designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0% and 79.6% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times.

Network excitability of stimulation-induced spectral responses helps localize the seizure onset zone.

OBJECTIVE: While evoked potentials elicited by single pulse electrical stimulation (SPES) may assist seizure onset zone (SOZ) localization during intracranial EEG (iEEG) monitoring, induced high frequency activity has also shown promising utility. We aimed to predict SOZ sites using induced cortico-cortical spectral responses (CCSRs) as an index of excitability within epileptogenic networks. METHODS: SPES was conducted in 27 epilepsy patients undergoing iEEG monitoring and CCSRs were quantified by significant early (10-200 ms) increases in power from 10 to 250 Hz. Using response power as CCSR network connection strengths, graph centrality measures (metrics quantifying each site's influence within the network) were used to predict whether sites were within the SOZ. RESULTS: Across patients with successful surgical outcomes, greater CCSR centrality predicted SOZ sites and SOZ sites targeted for surgical treatment with median AUCs of 0.85 and 0.91, respectively. We found that the alignment between predicted and targeted SOZ sites predicted surgical outcome with an AUC of 0.79. CONCLUSIONS: These findings indicate that network analysis of CCSRs can be used to identify increased excitability of SOZ sites and discriminate important surgical targets within the SOZ. SIGNIFICANCE: CCSRs may supplement traditional passive iEEG monitoring in seizure localization, potentially reducing the need for recording numerous seizures.

Neuroelectrophysiological alteration associated with cognitive flexibility after 24 h sleep deprivation in adolescents.

The cognitive neural mechanisms by which sleep deprivation affects cognitive flexibility are poorly understood. Therefore, the study investigated the neuroelectrophysiological basis of the effect of 24 h sleep deprivation on cognitive flexibility in adolescents. 72 participants (36 females, mean age ± SD=20.46 ± 2.385 years old) participated in the study and were randomly assigned to the sleep deprivation group and control group. They were instructed to complete a task switch paradigm, during which participants' behavioral and electroencephalographic data were recorded. Behaviorally, there were significant between-group differences in accuracy. The results of event-related potential showed that the P2, N2 and P3 components had significant group effects or interaction effects. At the time-frequency level, there were statistically significant differences between the delta and theta bands. These results suggested that 24 h sleep deprivation affected problem-solving effectiveness rather than efficiency, mainly because it systematically impaired cognitive processing associated with cognitive flexibility.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements.

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Analysis of individual alpha frequency in a large cohort from a tertiary memory center.

BACKGROUND AND PURPOSE: Precise and timely diagnosis is crucial for the optimal use of emerging disease-modifying treatments for Alzheimer disease (AD). Electroencephalography (EEG), which is noninvasive and cost-effective, can capture neural abnormalities linked to various dementias. This study explores the use of individual alpha frequency (IAF) derived from EEG as a diagnostic and prognostic tool in cognitively impaired patients. METHODS: This retrospective study included 375 patients from the tertiary Memory Clinic of IRCCS San Raffaele Hospital, Milan, Italy. Participants underwent clinical and neuropsychological assessments, brain imaging, cerebrospinal fluid biomarker analysis, and resting-state EEG. Patients were categorized by amyloid status, the AT(N) classification system, clinical diagnosis, and mild cognitive impairment (MCI) progression to AD dementia. IAF was calculated and compared among study groups. Receiver operating characteristic (ROC) analysis was used to calculate its discriminative performance. RESULTS: IAF was higher in amyloid-negative subjects and varied significantly across AT(N) groups. ROC analysis confirmed IAF's ability to distinguish A-T-N- from the A+T+N+ and A+T-N+ groups. IAF was lower in AD and Lewy body dementia patients compared to MCI and other dementia types, with moderate discriminatory capability. Among A+ MCI patients, IAF was significantly lower in those who converted to AD within 2 years compared to stable MCI patients and predicted time to conversion (p < 0.001, R = 0.38). CONCLUSIONS: IAF is a valuable tool for dementia diagnosis and prognosis, correlating with amyloid status and neurodegeneration. It effectively predicts MCI progression to AD, supporting its use in early, targeted interventions in the context of disease-modifying treatments.

Neural correlates of listening to nonnative-accented speech in multi-talker background noise.

We examined the neural correlates underlying the semantic processing of native- and nonnative-accented sentences, presented in quiet or embedded in multi-talker noise. Implementing a semantic violation paradigm, 36 English monolingual young adults listened to American-accented (native) and Chinese-accented (nonnative) English sentences with or without semantic anomalies, presented in quiet or embedded in multi-talker noise, while EEG was recorded. After hearing each sentence, participants verbally repeated the sentence, which was coded and scored as an offline comprehension accuracy measure. In line with earlier behavioral studies, the negative impact of background noise on sentence repetition accuracy was higher for nonnative-accented than for native-accented sentences. At the neural level, the N400 effect for semantic anomaly was larger for native-accented than for nonnative-accented sentences, and was also larger for sentences presented in quiet than in noise, indicating impaired lexical-semantic access when listening to nonnative-accented speech or sentences embedded in noise. No semantic N400 effect was observed for nonnative-accented sentences presented in noise. Furthermore, the frequency of neural oscillations in the alpha frequency band (an index of online cognitive listening effort) was higher when listening to sentences in noise versus in quiet, but no difference was observed across the accent conditions. Semantic anomalies presented in background noise also elicited higher theta activity, whereas processing nonnative-accented anomalies was associated with decreased theta activity. Taken together, we found that listening to nonnative accents or background noise is associated with processing challenges during online semantic access, leading to decreased comprehension accuracy. However, the underlying cognitive mechanism (e.g., associated listening efforts) might manifest differently across accented speech processing and speech in noise processing.

Increased sensorimotor activity during categorisation of emotionally ambiguous faces.

Actions are rarely devoid of emotional content. Thus, a more complete picture of the neural mechanisms underlying the mental simulation of observed actions requires more research using emotion information. The present study used high-density electroencephalography to investigate mental simulation associated with facial emotion categorisation. Alpha-mu rhythm modulation was measured at each frequency, from 8 Hz to 13 Hz, to infer the degree of sensorimotor simulation. Results suggest the sensitivity of the sensorimotor activity to emotional information, because (1) categorising static images of neutral faces as happy or sad was associated with stronger suppression in the central region than categorising clearly happy faces, (2) there was preliminary evidence indicating that the strongest suppression in the central region was in response to neutral faces, followed by sad and then happy faces and (3) in the control task, which required categorising images with the head oriented right, left, or forward as right or left, differences between conditions showed a pattern more indicative of task difficulty rather than sensorimotor engagement. Dissociable processing of emotional information in facial expressions and directionality information in head orientations was further captured in beta band activity (14-20 Hz). Stronger mu suppression to neutral faces indicates that sensorimotor simulation extends beyond crude motor mimicry. We propose that mu rhythm responses to facial expressions may serve as a biomarker for empathy circuit activation. Future research should investigate whether atypical or inconsistent mu rhythm responses to facial expressions indicate difficulties in understanding or sharing emotions.

Rasmussen's encephalitis: structural, functional, and clinical correlates of contralesional epileptiform activity.

Progressive inflammation of one hemisphere characterises Rasmussen's encephalitis (RE), but contralesional epileptiform activity has been repeatedly reported. We aimed to quantify contralesional epileptiform activity in RE and uncover its functional and structural underpinnings. We retrospectively ascertained people with RE treated between 2000 and 2018 at a tertiary centre (Centre 1) and reviewed all available EEG datasets. The temporal occurrence of preoperative contralesional epileptiform activity (interictal/ictal) was evaluated using mixed-effects logistic regression. Cases with/without contralesional epileptiform activity were compared for cognition, inflammation (ipsilesional brain biopsies), and MRI (cortical and fixel-based morphometry). EEG findings were validated in a second cohort treated at another tertiary centre (Centre 2) between 1995 and 2020. We included 127 people with RE and 687 EEG samples. Preoperatively, contralesional epileptiform activity was seen in 30/68 (44%, Centre 1) and 8/59 (14%, Centre 2). In both cohorts, this activity was associated with younger onset age (OR = 0.9; 95% CI 0.83-0.97; P = 0.006). At centre 1, contralesional epileptiform activity was associated with contralesional MRI alterations, lower intelligence (OR = 5.19; 95% CI 1.28-21.08; P = 0.021), and impaired verbal memory (OR = 10.29; 95% CI 1.97-53.85; P = 0.006). After hemispherotomy, 11/17 (65%, Centre 1) and 28/37 (76%, Centre 2) were seizure-free. Contralesional epileptiform activity was persistent postoperatively in 6/12 (50%, Centre 1) and 2/34 (6%, Centre 2). Preoperative contralesional epileptiform activity reduced the chance of postoperative seizure freedom in both cohorts (OR = 0.69; 95% CI 0.50-0.95; P = 0.029). Our findings question the concept of strict unilaterality of RE and provide the evidence of contralesional epileptiform activity as a possible EEG predictor for persisting postoperative seizures.

Electrophysiological correlates of why humans deviate from rational decision-making: A registered replication study.

In contrast to rational choice theory predicting humans to optimize expected utilities of choices, humans deviate from rational behavior in decision-making paradigms. Hewig et al. (2011) explored affective correlates of decision-making in the ultimatum game (UG) and the dictator game (DG). They found that feedback-related negativity (FRN), subjective valence ratings, and autonomic nervous system activity predicted rejection of monetary offers. This registered replication aimed to validate and extend these findings. Although behavioral patterns and results of subjective ratings closely matched the original study, not all psychophysiological effects were successfully replicated. Firstly, we could not replicate the reported effects of autonomic nervous system activity. Secondly, a quadratic instead of the originally proposed linear relation between the offer and the FRN emerged, possibly driven by the offer evaluation in economic games and the rewarding anticipation of successful punishment for low offers. Thirdly, P3 amplitudes mirrored the quadratic offer response pattern, generally peaking for the lowest offer. In contrast to the original study, P3 responses were larger in the UG compared with the DG. Finally, our findings indicate that participant-related higher midfrontal theta activation predicted lower acceptance behavior in the UG, with a systematic dampening effect for fairer offers. This highlights cognitive control as a crucial mechanism in economic decision-making to overcome behavioral defaults. Overall, our results conceptually support the original conclusion that decision-making in economic games is non-rational and dependent on the objective situation as well as emotional and neural markers, though not precisely as suggested by Hewig et al. (2011).

Assessing the Yield of Reduced Duration-EEG Recordings in Children: A Cross-Sectional Study.

Introduction An EEG is an important tool in the diagnosis of neurological diseases. Performing an EEG on children can be challenging due to their tendency to not cooperate for the recommended duration. We aim to optimize the duration of EEG recording in children by finding the optimal duration of recording. Materials and methods A single-center prospective observational study was done after appropriate ethical clearance. Children aged 0-14 were recruited and examined, and the recommended EEG was done. Data were collected and analyzed. Results Of the 112 EEGs analyzed, 29 EEGs were normal, i.e., no diagnostic anomaly was noticed. In the remaining 83 EEGs, if the duration of the EEG was reduced to 20 minutes, it resulted in missing the diagnostic anomaly in 20 cases (24.1%; 95% CI: 11.2%-26.2%). Reducing the duration of the EEG recording to 10 minutes resulted in missing 63 of the diagnostic anomalies (75.9%; 95% CI: 46.6%-65.6%). Of the 86 drug-induced EEGs, 22 were normal (25.6%; 95% CI: 16.8%-36.1%). Of the 24 routine EEGs, seven were normal (29.2%; 95% CI: 12.6%-51.1%). Of the two sleep-deprived EEGs, neither was normal (0.0%; 95% CI: 0.0%-84.1%). Conclusion In our study, we observed that optimization of the duration of EEG recording can be done to 20 minutes in all populations. We also observed that if we find a diagnostic abnormality early during EEG recording, then continuation of the EEG may not be necessary to make a valid report. Having said so, having a negative EEG may not necessarily rule out a diagnosis. We did not find the superiority of any of the EEG protocols over others, as their yield was comparable.

When the Crown has Thorns - Epileptic Spike-Wave Discharges over the Vertex.

Epileptiform abnormalities that arise over the midline can sometimes be confused with normal sleep transients, such as vertex sharp waves, because of their location and their activation during sleep. However, epileptiform transients can be distinguished from sleep architecture by their waveform and their occurrence during wakefulness. Here, we report a 24-year-old man with drug-resistant epilepsy whose seizures began with tonic posturing of the left leg before progressing to bilateral tonic-clonic activity. During presurgical scalp video-EEG monitoring, his interictal background showed focal spike-wave discharges maximal over the vertex (phase reversal at Cz), with a more-well-defined field over the right parasagittal region (C4/F4), that were present during both sleep and awake states. The discharges met the IFCN criteria for focal interictal epileptiform discharges (spiky morphology, duration shorter than background activity, asymmetric waveform, after-going slow wave, and physiologic distribution) and appeared to be distinct from the patient's vertex sharp waves. Prior to electroclinical seizures, these discharges would increase in prevalence and appear as repetitive spike-wave discharges. When distinguishing epileptiform from nonepileptiform transients, it is critical to consider both their morphology, especially the degree of background disruption and presence of an after-going slow wave, and their variability with state changes.

Use of sedation-awakening electroencephalography in dogs with epilepsy.

BACKGROUND: Electroencephalography (EEG) recording protocols have been standardized for humans. Although the utilization of techniques in veterinary medicine is increasing, a standard protocol has not yet been established. HYPOTHESIS: Assessment of a sedation-awakening EEG protocol in dogs. ANIMALS: Electroencephalography examination was performed in a research colony of 6 nonepileptic dogs (control [C]) and 12 dogs with epilepsy admitted to the clinic because of the epileptic seizures. METHODS: It was a prospective study with retrospective control. Dogs with epilepsy were divided into 2 equal groups, wherein EEG acquisition was performed using a "sedation" protocol (IE-S, n = 6) and a "sedation-awakening" protocol (IE-SA, n = 6). All animals were sedated using medetomidine. In IE-SA group, sedation was reversed 5 minutes after commencing the EEG recording by injecting atipamezole IM. Type of background activity (BGA) and presence of EEG-defined epileptiform discharges (EDs) were evaluated blindly. Statistical significance was set at P > 0.05. RESULTS: Epileptiform discharges were found in 1 of 6 of the dogs in group C, 4 of 6 of the dogs in IE-S group, and 5 of 6 of the dogs in IE-SA group. A significantly greater number of EDs (spikes, P = .0109; polyspikes, P = .0109; sharp waves, P = .01) were detected in Phase 2 in animals subjected to the "sedation-awakening" protocol, whereas there was no statistically significant greater number of discharges in sedated animals. CONCLUSIONS AND CLINICAL IMPORTANCE: A "sedation-awakening" EEG protocol could be of value for ambulatory use if repeated EEG recordings and monitoring of epilepsy in dogs is needed.

Deciphering the impairment of perimenopausal insomnia on visual search from a neurocognitive processing perspective.

STUDY OBJECTIVES: Perimenopausal insomnia (PMI) is associated with observable performance impairments in visual search tasks. This study examines how various cognitive processing stages contribute to search performance delays in PMI compared to healthy controls (HCs). METHODS: We recruited 76 participants diagnosed with PMI and 63 HCs. Event-related potentials (ERPs) were recorded as participants engaged in a visual search task, reporting the orientation of a color popout target within an array of ellipses. We analyzed group differences in behavioral performance and ERP components across cognitive processing stages. RESULTS: Compared to HCs, PMI patients exhibited behavioral response delays, although accuracy was not different between groups. Electrophysiological analyses revealed group differences across several ERP components. Firstly, the N1 component's amplitude increased bilaterally, suggesting enhanced visual sensory processing. Secondly, a slower and smaller N2pc indicated reduced attentional orienting. Thirdly, a decreased SPCN amplitude pointed to deficits in target discrimination. Fourthly, an increased amplitude of the stimulus-locked LRP, with unchanged latency, suggested heightened neural inputs for maintaining motor initiation speed. Fifthly, prolonged response-locked LRP latency indicated slower motor execution. Finally, these changes in ERP components, along with significant correlations between LRP components and insomnia symptoms, suggest potential neural biomarkers for PMI. CONCLUSIONS: Our findings provide high-temporal-resolution insights into the neurocognitive disruptions associated with PMI, highlighting how sleep disturbances affect cognitive processing in visual tasks. These insights enhance our understanding of PMI and contribute to discussions on neural mechanisms driving behavioral performance in various conditions.

Generalizability of electroencephalographic interpretation using artificial intelligence: An external validation study.

OBJECTIVE: The automated interpretation of clinical electroencephalograms (EEGs) using artificial intelligence (AI) holds the potential to bridge the treatment gap in resource-limited settings and reduce the workload at specialized centers. However, to facilitate broad clinical implementation, it is essential to establish generalizability across diverse patient populations and equipment. We assessed whether SCORE-AI demonstrates diagnostic accuracy comparable to that of experts when applied to a geographically different patient population, recorded with distinct EEG equipment and technical settings. METHODS: We assessed the diagnostic accuracy of a "fixed-and-frozen" AI model, using an independent dataset and external gold standard, and benchmarked it against three experts blinded to all other data. The dataset comprised 50% normal and 50% abnormal routine EEGs, equally distributed among the four major classes of EEG abnormalities (focal epileptiform, generalized epileptiform, focal nonepileptiform, and diffuse nonepileptiform). To assess diagnostic accuracy, we computed sensitivity, specificity, and accuracy of the AI model and the experts against the external gold standard. RESULTS: We analyzed EEGs from 104 patients (64 females, median age = 38.6 [range = 16-91] years). SCORE-AI performed equally well compared to the experts, with an overall accuracy of 92% (95% confidence interval [CI] = 90%-94%) versus 94% (95% CI = 92%-96%). There was no significant difference between SCORE-AI and the experts for any metric or category. SCORE-AI performed well independently of the vigilance state (false classification during awake: 5/41 [12.2%], false classification during sleep: 2/11 [18.2%]; p = .63) and normal variants (false classification in presence of normal variants: 4/14 [28.6%], false classification in absence of normal variants: 3/38 [7.9%]; p = .07). SIGNIFICANCE: SCORE-AI achieved diagnostic performance equal to human experts in an EEG dataset independent of the development dataset, in a geographically distinct patient population, recorded with different equipment and technical settings than the development dataset.

Decreased Delta/Beta ratio index as the sleep state-independent electrophysiological signature of sleep state misperception in Insomnia disorder: A focus on the sleep onset and the whole night.

PURPOSE: Sleep State Misperception (SSM) is described as the tendency of Insomnia Disorder (ID) patients to overestimate Sleep Latency (SL) and underestimate Total Sleep Time (TST). Literature exploring topographical components in ID with SSM is scarce and does not allow us to fully understand the potential mechanisms underlying this phenomenon. This study aims to evaluate the existence of sleep EEG topography alterations in ID patients associated with SSM compared to Healthy Controls (HC), focusing on two distinct periods: the Sleep Onset (SO) and the whole night. METHODS: Twenty ID patients (mean age: 43.5 ± 12.7; 7 M/13F) and 18 HCs (mean age: 41.6 ± 11.9; 8 M/10F) underwent a night of Polysomnography (PSG) and completed sleep diaries the following morning upon awakening. Two SSM indices, referring to the misperception of SL (SLm) and TST (TSTm), were calculated by comparing objective and subjective sleep indices extracted by PSG and sleep diary. According to these indices, the entire sample was split into 4 sub-groups: ID +SLm vs HC -SLm; ID +TSTm vs HC -TSTm. RESULTS: Considering the SO, the two-way mixed-design ANOVA showed a significant main effect of Groups pointing to a decreased delta/beta ratio in the whole scalp topography. Moreover, we found a significant interaction effect for the sigma and beta bands. Post Hoc tests showed higher sigma and beta power in anterior and temporo-parietal sites during the SO period in IDs +SLm compared to HC -SLm. Considering the whole night, the unpaired t-test revealed in IDs +TSTm significantly lower delta power during NREM, and lower delta/beta ratio index during NREM and REM sleep compared to HCs -TSTm. Finally, we found diffuse significant negative correlations between SSM indices and the delta/beta ratio during SO, NREM, and REM sleep. CONCLUSION: The main finding of the present study suggests that higher SL overestimation and TST underestimation are both phenomena related to diffuse cortical hyperarousal interpreted as a sleep state-independent electrophysiological correlate of the SSM, both during the SO and the whole night.