Electroencephalographic Features Research Articles

Neurological and psychiatric phenotype of a multicenter cohort of patients with SETD5-related neurodevelopmental disorder

Pathogenic variants in the SETD5 gene cause a neurodevelopmental disorder characterized by intellectual disability, autism, and facial dysmorphisms, with incomplete penetrance. To date, no distinctive neurological, psychiatric, electroencephalographic, and neuroimaging features have been identified in this condition. We expand the clinical phenotype of SETD5-related disorder by describing 28 previously unreported patients, 26 carrying single nucleotide variants, and 2 with copy number variations involving SETD5 gene, focusing on neurological, psychiatric, EEG, and brain MRI data. In our cohort neurological symptoms include hypotonia (39.2 %), hyperkinetic movement disorders including stereotypies and chorea (21.4 %) and gait abnormalities ranging from tip-toe or unsteady walking and alterations of fine motor skills (35.7 %). Epilepsy was present in about 14 % of patients, including different types of seizures as epileptic spasms, focal motor, and non-motor seizures. Concerning the cognitive phenotype, intellectual disability or global developmental delay depending on age, ranging from mild to severe, was present in 75 % of cohort, 21.4 % exhibit borderline intellectual functioning while an individual has a normal intelligence quotient.Other psychiatric comorbidities include autism, ADHD, psychotic disorder and other internalizing and externalizing symptoms.Finally, we conduct a comprehensive review of the available literature, suggesting a possible genotype-phenotype correlation.

A Modified Transformer Network for Seizure Detection Using EEG Signals.

Seizures have a serious impact on the physical function and daily life of epileptic patients. The automated detection of seizures can assist clinicians in taking preventive measures for patients during the diagnosis process. The combination of deep learning (DL) model with convolutional neural network (CNN) and transformer network can effectively extract both local and global features, resulting in improved seizure detection performance. In this study, an enhanced transformer network named Inresformer is proposed for seizure detection, which is combined with Inception and Residual network extracting different scale features of electroencephalography (EEG) signals to enrich the feature representation. In addition, the improved transformer network replaces the existing Feedforward layers with two half-step Feedforward layers to enhance the nonlinear representation of the model. The proposed architecture utilizes discrete wavelet transform (DWT) to decompose the original EEG signals, and the three sub-bands are selected for signal reconstruction. Then, the Co-MixUp method is adopted to solve the problem of data imbalance, and the processed signals are sent to the Inresformer network for seizure information capture and recognition. Finally, discriminant fusion is performed on the results of three-scale EEG sub-signals to achieve final seizure recognition. The proposed network achieves the best accuracy of 100% on Bonn dataset and the average accuracy of 98.03%, sensitivity of 95.65%, and specificity of 98.57% on the long-term CHB-MIT dataset. Compared to the existing DL networks, the proposed method holds significant potential for clinical research and diagnosis applications with competitive performance.

Spatiotemporal feature extraction method combining common spatial pattern and hybrid spatial‐temporal attention‐Net in motor imagery–brain‐computer interface system

AbstractIn recent years, motor imagery (MI) based on brain‐computer interface (BCI) has gained the attention of researchers and been widely used in many fields, such as medical rehabilitation and entertainment. The temporal and spatial features of electroencephalography (EEG) are very important for MI‐BCI classification. This study proposed a better spatiotemporal feature extraction method by combining the common spatial pattern (CSP) with a hybrid spatiotemporal attention convolutional neural network (HSTA‐Net) model. The method first intercepts the MI‐EEG into multiple time windows and projects them into the feature space and feeds into the HSTA‐Net in parallel. Subsequently, the scaled dot product attention module and multi‐head attention module are used to extract spatial feature attention. Finally, the features in multiple time windows obtained through HSTA‐Net are integrated and used for classification. The proposed method achieves a classification accuracy of 77.3% on the BCI Competition IV2a dataset. The experimental results show that the proposed model has better classification performance. The CSP‐HSTA‐Net model can quickly adapt to the distribution of MI‐EEG data during the training process, which increases the interclass distance of the extracted features and decreases its intraclass distance; it can better accomplish the MI‐BCI classification task.

Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation

BackgroundThere is rapid growth of neuroelectrophysiological study aspiring to uncover the “black box” of mindfulness and meditation. Reliance on traditional data analysis methods hinders understanding of the complex, nonlinear, multi-dimensional and systemic nature of the functional neuroelectrophysiology of meditation states. MethodsThus, to reveal complex systemic neuroelectrophysiology of meditation, we applied Machine Learning Extreme Gradient Boosting classification algorithm, and four complementary feature importance methods, to extract systemic Electroencephalography (EEG) features characterizing mindful states from EEG recorded during a Focused Attention meditation and a control mind-wandering state among 26 experienced meditators. ResultsAlgorithm classified meditation vs. mind-wandering states with 83% accuracy, AUC-ROC of 79% and F1 score of 74%. Feature importance techniques identified 10 EEG features associated with increased power and coherence of high frequency oscillations during Focused Attention meditation relative to an instructed mind-wandering state. ConclusionsFindings help delineate the complex systemic oscillatory activity characterizing meditation.

The value of electroencephalography features in the prognostic evaluation of large hemispheric infarction patients at different time intervals.

Large Hemispheric Infarction (LHI) is a devastating disease with high mortality. This study aimed to use electroencephalography (EEG) to evaluate the death risk of LHI patients and identify suitable evaluation time. This study retrospectively collected clinical and EEG data from 73 LHI patients, dividing them into death and survival group at discharge. EEG data was classified as 1-5 days and 6-14 days after onset according to the time intervals of cerebral edema. Regression and receiver operator characteristic curve (ROC) analysis were applied to explore the impact of temporal changes in various EEG and clinical features on death. The areas under ROC curve (AUC) of death prediction for non-α frequency on non-infarct side at 6-14 days after onset was significantly higher than that at 1-5 days (p = 0.004). And there was no significant difference between the AUC of seizure activity for death prediction at 1-5 days and 6-14 days (p = 0.418). Multivariate regression analysis revealed that non-α frequency on non-infarct side and seizure activity at 6-14 days after onset were the independent risk factors for the death of LHI patients. Additionally, above two EEG features significantly improved the death predictive efficacy of clinical features in LHI patients with the integrated discrimination improvement index (IDI) of 0.174 (p = 0.015) and the net reclassification improvement (NRI) of 1.314 (p<0.001). Non-α frequency on non-infarct side and seizure activity were reliable indicators for death prediction. 6-14 days after onset was the better time window for death evaluation of LHI patients through EEG.

Temporal progression of sleep electroencephalography features in isolated rapid eye movement sleep behaviour disorder.

Previous studies indicated that patients with isolated rapid eye movement (REM) sleep behaviour disorder (iRBD) exhibit alterations in spectral electroencephalographic (EEG), spindle, and slow-wave features. As it is currently unknown how these EEG features evolve over time, this study aimed to evaluate their temporal progression in patients with iRBD in comparison to controls. We included 23 patients with iRBD and 23 controls. Two polysomnographies (baseline and follow-up) were recorded with a mean (standard deviation) interval of 4.0 (2.5) years and were automatically analysed for sleep stages, spectral bandpower, spindles, and slow waves. We used linear models to evaluate differences at each time point, and linear mixed-effects models to analyse differences in temporal progression between the groups. At baseline, patients with iRBD presented EEG slowing both in REM (expressed as significantly reduced α-bandpower and increased δ-bandpower in frontal channels) and in non-REM (NREM) sleep (significantly increased slow-to-fast ratio in central channels). These differences vanished at follow-up. In both REM and NREM sleep, γ-bandpower was increased at follow-up in patients with iRBD, resulting in significantly different temporal progression between groups (in occipital channels during REM sleep and frontal channels during NREM sleep). Relative power of sleep spindles was significantly higher at baseline in patients with iRBD in frontal channels, but we observed a significant reduction over time in central channels. Finally, slow waves were significantly shorter in patients with iRBD at both time-points. Our results underscore the need of considering longitudinal data when analysing sleep EEG features in patients with iRBD. The observed temporal changes as markers of progression of neurodegeneration require further investigations.

Cerebral Cortical Encephalitis and Other Meningocortical Manifestations of Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease in Children: Case Series and Review of the Literature.

Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease is a neuroinflammatory disorder (MOGAD) with heterogeneous phenotype including paroxysms of optic neuritis, transverse myelitis, acute disseminated encephalomyelitis, brainstem demyelination, and encephalitis. Fluid-attenuated inversion recovery hyperintense cortical lesions in MOG-associated encephalitis with seizures, or FLAMES, is a manifestation of cerebral cortical encephalitis seen less frequently than other typical MOG antibody-associated disease presentations. Cases of FLAMES are rarer in children, and frequently initially misdiagnosed with infectious meningoencephalitis. Other meningocortical manifestations of MOG antibody-associated disease have been described and likely exist along a continuum. In this retrospective single-center case series, we describe the demographic, clinical, radiographic, laboratory, and electroencephalographic features of 5 children with clinicoradiographic features consistent with the spectrum of MOG-IgG-positive meningocortical syndromes.

The 70 years since the discovery of rapid eye movement sleep: history, electroencephalographic features and unsolved issues.

This article aims at providing a comprehensive review of the historical discovery and following research on rapid eye movement (REM) sleep, highlighting its manifold nature as a behavioural, electrophysiological and dreaming state. Pioneering works conducted by Aserinsky, Kleitman, Dement and Jouvet established the foundational understanding of REM sleep recurrence, brainstem mechanisms, and the paradoxical coexistence of electroencephalographic activation and muscle atonia. We focus on REM sleep homeostasis, emphasising its role in emotional recovery and the consequences of REM deprivation, such as the REM rebound effect. We also analyse the periodicity of REM sleep, its ultradian rhythm, and the physiological mechanisms underlying its regulation. Additionally, the article discusses the entangled relationship between arousals, sleep, and consciousness, pointing out the distinction between non-REM and REM sleep-related arousals, and the similarities between REM sleep and wakefulness.

Electroencephalographic features of alcohol use disorders with different decision-making efficiency in risk conditions

In order to identify the neurophysiological mechanisms underlying the violation of decision-making in risk conditions, we conducted a comparative analysis of spectral EEG indicators of patients with alcohol use disorders with different effectiveness of their decision-making in a number of cognitive tasks. As a result of the cluster analysis, two subgroups of patients were identified: with “moderate” and with “pronounced” decision-making deficit, which did not differ in socio–demographic and clinical indicators (p 0.05). The subgroup of patients with a “pronounced” decision-making deficit differed statistically significantly lower values of the spectral power of θ- and α-rhythm in the central (p = 0.018 for θ-rhythm and p = 0.017 for α-rhythm), parietal (p = 0.031 for θ-rhythm and p = 0.014 for α-rhythm), occipital (p = 0.029 for θ-rhythm and p = 0.016 for α-rhythm) and temporal (p = 0.022 on the left and p = 0.043 on the right for α-rhythm) leads compared with patients with “moderate” decision-making deficit. Thus, in a subgroup of patients with a “pronounced” deficit of decision-making, a certain deficit of the brain’s inhibitory systems was noted.

Preoperative biomarkers associated with delayed neurocognitive recovery.

To identify baseline biomarkers of delayed neurocognitive recovery (dNCR) using monitors commonly used in anesthesia. In this sub-study of observational prospective cohorts, we evaluated adult patients submitted to general anesthesia in a tertiary academic center in the United States. Electroencephalographic (EEG) features and cerebral oximetry were assessed in the perioperative period. The primary outcome was dNCR, defined as a decrease of 2 scores in the global Montreal Cognitive Assessment (MoCA) between the baseline and postoperative period. Forty-six adults (median [IQR] age, 65 [15]; 57% females; 65% American Society of Anesthesiologists (ASA) 3 were analyzed. Thirty-one patients developed dNCR (67%). Baseline higher EEG power in the lower alpha band (AUC = 0.73 (95% CI 0.48-0.93)) and lower alpha peak frequency (AUC = 0.83 (95% CI 0.48-1)), as well as lower cerebral oximetry (68 [5] vs 72 [3], p = 0.011) were associated with dNCR. Higher EEG power in the lower alpha band, lower alpha peak frequency, and lower cerebral oximetry values can be surrogates of baseline brain vulnerability.

Effect of sleep-disordered breathing on stable electroencephalographic characteristics during night sleep in patients with arterial hypertension

Introduction. Obstructive sleep apnea (OSA) is the most common sleep-disordered breathing condition. A significant number of OSA patients often present with cardiovascular comorbidities, particularly arterial hypertension. OSA is associated with changes in bioelectrical activity of the brain, such as slowing of electroencephalographic activity in the cortex and reduced interhemispheric synchronization. These changes can become pathophysiological markers of sleep-disordered breathing.Aim. To investigate the effect of sleep-disordered breathing on a range of quantitative electroencephalogram (EEG) characteristics during nighttime sleep in patients with arterial hypertension and clinically significant OSA.Materials and methods. The material for this retrospective study consisted of 84 polysomnographic records of patients predominantly diagnosed with arterial hypertension. Patients were divided into three groups based on the apneahypopnea index (AHI). Polysomnographic records were used to assess the synchronization measure of brain electrical activity between occipital EEG leads. The synchronization measure was evaluated using a method based on wavelet bicoherence calculation.Results. Statistically significant differences were observed in the low-frequency ranges Δf1-Δf4: 0.2-1.0 Hz, 0.8-1.6 Hz, 1.0-2.0 Hz, 1.0-4.0 Hz. In these frequencies, the interhemispheric synchronization measure significantly decreased with increasing severity of apnea.Conclusion. To determine the severity of obstructive sleep apnea, a parameter based on the synchronization measure evaluated from symmetrical occipital EEG signals in the frequency ranges 0.2-1.0 Hz, 0.8-1.6 Hz, 1.0-2.0 Hz, and 1.0-4.0 Hz can be considered. This may serve as the basis for developing and implementing new diagnostic tools for assessing the severity of sleep-disordered breathing in practice.

Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection.

Analysis of electroencephalography characteristics during walking in stroke patients under different conditions: a cross-sectional study.

Aims/Background Backward walking is gaining traction in rehabilitation therapy, showing promise as an intervention for stroke patients with walking difficulties. However, the brain activity patterns (neurophysiological mechanisms) underlying backward walking in these patients remain unclear. This study investigated the neurophysiological mechanism in stroke patients within 1 year of their stroke. Methods Twenty-four subjects walked forward and backward for 5 min on an 8-m track while their electroencephalographic signals were collected. The power values of each frequency band were compared during forward and backward walking, and the delta to alpha power ratio (DAR) was calculated. Results The results showed a significant increase in α-band activity within the frontal cortex during backward walking (p < 0.05). This increase correlated positively with scores on the Fugl-Meyer lower extremity motor function assessment scale. Similarly, α-band activity showed significant enhancement within the right parietal cortex during backward walking (p < 0.05). There were no significant differences between forward and backward walking states in δ, θ, and β wavebands across the entire brain region (p > 0.05). Additionally, the DAR was significantly lower during backward walking than during forward walking (p < 0.05). Conclusion This study suggests that backward walking may more effectively activate neural activity in the prefrontal and right posterior parietal cortices. This finding supports the potential of backward walking to enhance motor execution and walking function in stroke patients, thereby supporting its application as a rehabilitation method.

Combined generalized and focal epilepsy with reflex features in Adaptor protein complex 4-associated hereditary spastic paraplegias: A cohort observational study

BackgroundPatients with genetic deficiency of the adaptor protein complex 4 (AP-4) exhibit earlyonset developmental delay, spastic diplegia, intellectual disability, speech impairment. The phenotype overlaps with other hereditary spastic paraplegias and cerebral palsies. Febrile seizures are common at onset. Epilepsy has been described in more than half of cases, arising in early infancy often with status epilepticus, but no typical seizure semiology or electroencephalographic features have been identified thus far. PurposeWe aimed to specifically investigate the epileptological characteristics of the syndrome to unveil possible biomarkers of seizure development and prognosis in AP-4 deficiency. MethodsObservational cohort study on patients with bi-allelic pathogenic variants in AP-4 subunits and epilepsy. We focused on the seizure semiology, electroencephalographic characteristics and response to antiseizure medications. ResultsPatients harboured pathogenic variants in AP4S1 (n = 5) or AP4M1 (n = 1). The phenotype included spastic paraparesis, intellectual disability, speech/language impairment, microcephaly, and MRI evidence of hypoplasia of the corpus callosum. In 66 % of the patients, febrile seizures preceded the onset of epilepsy, which spanned from infancy to adolescence (range=14 months-13 years). Absences (66 %) and focal motor seizures (50 %) were common. No patient met the criteria for drug-resistance. Peculiar electroencephalographic features arose after the epilepsy onset and persisted at long-term follow-up: bilateral and asynchronous focal discharges combined with independent diffuse spike-wave-discharges (100 %) and reflex abnormalities (66 %). ConclusionIn AP-4 complex disease, epilepsy could arise beyond early infancy, until adolescence, with variable combination of generalized and focal seizures. The prognosis was favourable. We observed a common electroencephalographic signature - combined focal/generalized and reflex abnormalities - which may constitute a biomarker of AP-4 deficiency with epilepsy, applicable to inform genetic testing and disentangle the differential diagnosis.

Electro-Clinical Features and Functional Connectivity Analysis in SYN1-Related Epilepsy.

There is currently scarce data on the electroclinical characteristics of epilepsy associated with synapsin 1 (SYN1) pathogenic variations. We examined clinical and electro-encephalographic (EEG) features in patients with epilepsy and SYN1 variants, with the aim of identifying a distinctive electroclinical pattern. In this retrospective multicenter study, we collected and reviewed demographic, genetic, and epilepsy data of 19 male patients with SYN1 variants. Specifically, we analyzed interictal EEG data for all patients, and electro-clinical data from 10 epileptic seizures in 5 patients, using prolonged video-EEG monitoring recordings. Inter-ictal EEG functional connectivity parameters and frequency spectrum of the 10 patients over 12 years of age, were computed and compared with those of 56 age- and sex-matched controls. The main electroclinical features of epilepsy in patients with SYN1 were (1) EEG background and organization mainly normal; (2) interictal abnormalities are often rare or not visible on EEG; (3) more than 60% of patients had reflex seizures (cutaneous contact with water and defecation being the main triggers) isolated or associated with spontaneous seizures; (4) electro-clinical semiology of seizures was mainly temporal or temporo-insulo/perisylvian with a notable autonomic component; and (5) ictal EEG showed a characteristic rhythmic theta/delta activity predominating in temporo-perisylvian regions at the beginning of most seizures. Comparing patients with SYN1 to healthy subjects, we observed a shift to lower frequency bands in power spectrum of interictal EEG and an increased connectivity in both temporal regions. A distinct epilepsy syndrome emerges in patients with SYN1, with a rather characteristic clinical and EEG pattern suggesting predominant temporo-insular involvement. ANN NEUROL 2024.

FBSA-Net: A novel model based on attention mechanisms for emotion recognition in VR and 2D scenes

Recent studies have found that electroencephalographic (EEG) features from different frequency bands and different brain regions contribute differently to emotion recognition (ER) in virtual reality (VR) and two-dimensional (2D) scenes. Despite achievements in the task of ER in both VR and 2D environments, there has not been much prior effort to develop a unified ER model that can be applied to both VR and 2D environments. We propose a novel Frequency-Band-Spatial-based Attention Network, named FBSA-Net, for ER in VR and 2D scenes. Specifically, FBSA-Net adaptively captures the frequency-band-spatial relationship of EEG signals through cascade fusion of frequency-band attention (FBA) module and spatial attention (SA) module. The FBA module can automatically assign FBA weights to each band feature. The SA module then applies the ProbeParse attention mechanism to adaptively explore channel relationships both within and across brain regions, thus facilitating a comprehensive understanding of the global features of the EEG signals. The model's efficacy was validated through the VRSDEED dataset, created from emotion induction experiments using VR and 2D induction programs. Multiple comparative experiments have shown that FBSA-Net achieved state-of-the-art recognition results on VRSDEED datasets, achieving average recognition accuracies of 96.63% and 96.49% in VR and 2D scenes, respectively. The research results indicate that the significant contribution of FBSA-Net is not only reflected in the ability to provide ER using the same model in VR and 2D environments, but also in its excellent ability to distinguish between the emotional states in these two scenarios, providing a valuable reference for using the same model for ER.

Long-term outcomes and adaptive behavior in adult patients with Lennox-Gastaut syndrome.

Lennox-Gastaut syndrome (LGS) is a severe form of epilepsy characterized by difficult-to-control seizures and cognitive dysfunction. Previous studies mainly focused on pediatric populations, and little is known about the long-term cognitive outcome in adult patients with LGS. The objective of this study was to investigate the long-term functional and adaptive behavior in adult patients with LGS. This cross-sectional study enrolled adult patients diagnosed with LGS according to the recently published International League Against Epilepsy (ILAE) diagnostic criteria. The adaptive behavior of participants was assessed using the Vineland Adaptive Behavior Scales, Survey Interview, Second Edition (VABS-II). Demographic, clinical, electroencephalography (EEG), and antiseizure medication (ASM) data were also collected at different timepoints, to investigate their association with VABS-II scores. The study included 38 adult patients with LGS. A low score on the Adaptive Behavior Composite Scale was found in all patients. When considering single VABS-II domains, particularly low scores were found in daily living skills and socialization, whereas slightly higher performances were observed in communication. An earlier age at LGS diagnosis was identified as the most significant predictor of worse adaptive outcomes in adult life. At the time of study evaluation, high seizure frequency, higher EEG background slowing, and multifocal EEG epileptiform abnormalities were significantly associated with lower VABS-II raw scores. Furthermore, in an exploratory correlation analysis with ASM regimen at the study visit, treatment with cannabidiol was associated with higher adaptive behavior scores, whereas benzodiazepine intake correlated with lower scores. This study provides relevant insights into the long-term challenges faced by adults with Lennox-Gastaut syndrome (LGS), highlighting significant impairments in adaptive behavior as well as the associated clinical and electroencephalography features. Additionally, this study provides a more specific neuropsychological profile in adults with LGS and underscores the importance of comprehensive care approaches that go beyond seizure control in this population. This study examined adults with Lennox-Gastaut syndrome (LGS), a severe type of epilepsy, to understand their long-term abilities to perform daily tasks and adapt socially. We found that these adults have significant difficulties with daily living and social skills, although not all areas were equally affected. They performed somewhat better in communication, particularly in understanding others (receptive communication). Importantly, the younger the age at which LGS was diagnosed, the worse their outcomes were as adults. This study highlights the need for research and treatment approaches that focus not only on controlling seizures but also on improving daily life skills.

Detection of neurophysiological markers of cognitive reserve: an EEG study.

Cognitive reserve (CR) is a property of the brain that allows for better-than-expected cognitive performance relative to the degree of brain change over the course of life. However, neurophysiological markers of CR remain under-investigated. Electroencephalography (EEG) features may function as suitable neurophysiological markers of CR. To assess this, we investigated whether the dorsal attention network (DAN) and ventral attention network (VAN) activities, as measured during resting-state EEG, moderate the relationship between hippocampal volume and episodic memory. Participants were recruited as part of the National Center for Geriatrics and Gerontology-Study of Geriatric Syndromes. Hippocampal volume was determined using magnetic MRI, and episodic memory was measured using word lists. After testing the effect of hippocampal volume on memory performance using multiple regression analysis, we evaluated the interactions between hippocampal volume and DAN and VAN network activities. We further used the Johnson-Neyman technique to quantify the moderating effects of DAN and VAN network activities on the relationship between hippocampal volume and word list memory, as well as to identify specific ranges of DAN and VAN network activity with significant hippocampal-memory association. A total of 449 participants were included in this study. Our analysis revealed significant moderation of DAN with a slope of β = -0.00012 (95% CI: -0.00024; -0.00001, p = 0.040), and VAN with a slope of β = 0.00014 (95% CI: 0.00001; 0.00026, p = 0.031). Further, we found that a larger hippocampal volume was associated with improved memory performance, and that this association became stronger as the DAN activity decreased until a limit of DAN activity of 944.9, after which the hippocampal volume was no longer significantly related to word-list memory performance. For the VAN, we found that a higher hippocampal volume was more strongly associated with better memory performance when VAN activity was higher. However, when VAN activity extended beyond -914.6, the hippocampal volume was no longer significantly associated with word-list memory. Our results suggest that attentional networks help to maintain memory performance in the face of age-related structural decline, meeting the criteria for the neural implementation of cognitive reserve.

A Deep Learning–Derived Transdiagnostic Signature Indexing Hypoarousal and Impulse Control: Implications for Treatment Prediction in Psychiatric Disorders

BackgroundPsychiatric disorders are traditionally classified within diagnostic categories, but this approach has limitations. The Research Domain Criteria (RDoC) constitute a research classification system for psychiatric disorders based on dimensions within domains that cut across these psychiatric diagnoses. The overall aim of RDoC is to better understand mental illness in terms of dysfunction in fundamental neurobiological and behavioral systems, leading to better diagnosis, prevention, and treatment. MethodsA unique electroencephalographic feature, referred to as spindling excessive beta, has been studied in relation to impulse control and sleep as part of the arousal/regulatory system RDoC domain. Here, we studied electroencephalographic frontal beta activity as a potential transdiagnostic biomarker capable of diagnosing and predicting impulse control and sleep problems. ResultsWe showed in the first dataset (n = 3279) that the probability of having spindling excessive beta, classified by a deep learning algorithm, was associated with poor sleep maintenance and low daytime impulse control. Furthermore, in 2 additional, independent datasets (iSPOT-A [International Study to Predict Optimized Treatment in ADHD], n = 336; iSPOT-D [International Study to Predict Optimized Treatment in Depression], n = 1008), we revealed that conventional frontocentral beta power and/or spindling excessive beta probability, referred to as Brainmarker-III, is associated with a diagnosis of attention-deficit/hyperactivity disorder, with remission to methylphenidate in children with attention-deficit/hyperactivity disorder in a sex-specific manner, and with remission to antidepressant medication in adults with major depressive disorder in a drug-specific manner. ConclusionOur results demonstrate the value of the RDoC approach in psychiatry research for the discovery of biomarkers with diagnostic and treatment prediction capacities.

An automated sleep staging tool based on simple statistical features of mice electroencephalography (EEG) and electromyography (EMG) data.

Electroencephalogram (EEG) and electromyogram (EMG) are fundamental tools in sleep research. However, investigations into the statistical properties of rodent EEG/EMG signals in the sleep-wake cycle have been limited. The lack of standard criteria in defining sleep stages forces researchers to rely on human expertise to inspect EEG/EMG. The recent increasing demand for analysing large-scale and long-term data has been overwhelming the capabilities of human experts. In this study, we explored the statistical features of EEG signals in the sleep-wake cycle. We found that the normalized EEG power density profile changes its lower and higher frequency powers to a comparable degree in the opposite direction, pivoting around 20-30 Hz between the NREM sleep and the active brain state. We also found that REM sleep has a normalized EEG power density profile that overlaps with wakefulness and a characteristic reduction in the EMG signal. Based on these observations, we proposed three simple statistical features that could span a 3D space. Each sleep-wake stage formed a separate cluster close to a normal distribution in the 3D space. Notably, the suggested features are a natural extension of the conventional definition, making it useful for experts to intuitively interpret the EEG/EMG signal alterations caused by genetic mutations or experimental treatments. In addition, we developed an unsupervised automatic staging algorithm based on these features. The developed algorithm is a valuable tool for expediting the quantitative evaluation of EEG/EMG signals so that researchers can utilize the recent high-throughput genetic or pharmacological methods for sleep research.