Brain Waves Research Articles

Intracranial EEG-Based Directed Functional Connectivity in Alpha to Gamma Frequency Range Reflects Local Circuits of the Human Mesiotemporal Network.

To date, it is largely unknown how frequency range of neural oscillations measured with EEG is related to functional connectivity. To address this question, we investigated frequency-dependent directed functional connectivity among the structures of mesial and anterior temporal network including amygdala, hippocampus, temporal pole and parahippocampal gyrus in the living human brain. Intracranial EEG recording was obtained from 19 consecutive epilepsy patients with normal anterior mesial temporal MR imaging undergoing intracranial presurgical epilepsy diagnostics with multiple depth electrodes. We assessed intratemporal bidirectional functional connectivity using several causality measures such as Granger causality (GC), directed transfer function (DTF) and partial directed coherence (PDC) in a frequency-specific way. In order to verify the obtained results, we compared the spontaneous functional networks with intratemporal effective connectivity evaluated by means of SPES (single pulse electrical stimulation) method. The overlap with the evoked network was found for the functional connectivity assessed by the GC method, most prominent in the higher frequency bands (alpha, beta and low gamma), yet vanishing in the lower frequencies. Functional connectivity assessed by means of DTF and PCD obtained a similar directionality pattern with the exception of connectivity between hippocampus and parahippocampal gyrus which showed opposite directionality of predominant information flow. Whereas previous connectivity studies reported significant divergence between spontaneous and evoked networks, our data show the role of frequency bands for the consistency of functional and evoked intratemporal directed connectivity. This has implications for the suitability of functional connectivity methods in characterizing local brain circuits.

Parametric resonance brain model

The paper introduces a parametric resonance model for characterizing some features of the brain’s electrical activity. This activity is assumed to be a fundamental aspect of brain functionality underpinning functions from basic sensory processing to complex cognitive operations such as memory, reasoning, and emotion. A pivotal element of the proposed parametric model is neuron synchronization which is crucial for generating detectable brain waves. The analysis of the frequency content of brain waves, categorized as delta (0÷4 Hz), theta (4÷7 Hz), alpha (8÷12 Hz), beta (13÷30 Hz), and gamma (30÷100 Hz) reveals, notably, that the mean frequency of each of these brain wave classes is, in sequence, approximately the double of that of the previous one. Based on this observation, the proposed parametric resonance model suggests a cascade of amplification effects. Following the proposed model, in the transition from wakefulness to sleep, the brain wave bands are energized at double frequency by higher frequency neighboring bands; on the contrary, in the sleep to awake transition, brain waves are energized at a half frequency by their lower frequency neighbor waves. Finally, the trend of increasing amplitude values from higher to lower frequencies lends empirical support to the parametric resonant brain model validity.

Near-infrared light stimulation regulates neural oscillation and memory behavior of mice with Alzheimer's disease.

Photobiomodulation (PBM) is a non-invasive neuromodulation technique for the brain. Low-intensity near-infrared light (1-500 mw) has demonstrated the ability to improve memory in Alzheimer's disease (AD) model mice, suggesting its potential for AD treatment. However, the impact of PBM on neural oscillations in the hippocampal region affected by AD remains unknown. In this study, AD model mice were subjected to PBM for 60 days and then tested using novel object recognition behavior (NOR) experiments. During behavioral experiments, local field potential signals (LFP) of the mice was recorded using a single electrode in the CA1 region to analyze memory ability and neural oscillation characteristics. The results revealed that mice stimulated with PBM exhibited significantly higher new object differentiation indices compared to the Sham group (p < 0.01). Furthermore, PBM stimulation led to a significant increase in relative power and sample entropy of theta and gamma bands (p < 0.01). The coupling intensities of θ-low-γ and θ-high-γ were also significantly higher in the PBM group compared to the Sham group (p < 0.01). In conclusion, these findings suggest that PBM may improve memory ability in AD mice through regulation of neural oscillation characteristics, providing a theoretical basis for utilizing PBM as a treatment modality for Alzheimer's disease.

DETERMINING THE ROLE OF SPATIAL ABILITY IN PERFORMING LUNAR PHASE CHANGE TASK USING BRAIN ACTIVITY ANALYSIS

Many students have difficulty understanding the concept of lunar phase changes (LPCs) due to spatial ability problems such as perspective-taking (PT) and mental rotation (MR). Therefore, this study aimed to compare brain activity during PT and MR tasks while performing the LPC task to determine the involvement of PT and MR. This study measured brain waves using EEG in 20 participants while solving the 3 tasks. First, the power values in the theta band of all cortical areas showed a significant difference between MR and LPC tasks. Second, in the strategy execution section with a statistically significant difference, the occipital lobe and limbic system were mainly active during the PT task, whereas the frontal lobe was mainly active during the LPC task. Third, for strategy execution, during MR and LPC tasks, the frontal lobe, temporal lobe, and limbic system were all activated to significantly different degrees. Therefore, both PT and MR, particularly PT, are required to solve the LPC task. Moreover, for students who have difficulty learning LPC, it is necessary to recognize the need for spatial ability, such as PT ability, and establish an appropriate teaching strategy. Keywords: brain activation, electroencephalography, lunar phase change, mental rotation, perspective-taking

Long-term administration of paroxetine increases cortical EEG beta and gamma band activities in healthy awake rats

Understanding the electrophysiological properties of antidepressant medications is important to resolve the response heterogeneity of these drugs in clinical practice. Administration of paroxetine, a selective serotonin reuptake inhibitor, has been shown to increase serotonin levels that affect cortical activities in healthy subjects. However, the extent to which cortical oscillations can be altered by ongoing administration of paroxetine is not known. Here, we develop EEG biomarkers showing long-term effects of paroxetine. EEG changes were analyzed using Neuroscan in healthy wakeful rats administered paroxetine (4 mg/kg/day) for six weeks. Subsequent EEG recordings taken at 3 and 6 weeks after treatment showed differences in cortical oscillations obtained from both hemispheres and frontal-central-parietal regions. Chronic paroxetine administration resulted in an increase in gamma band activity. Comparison of EEG frequency bands of paroxetine and saline groups showed an enhancement in higher frequency activities at third weeks after the treatment. Higher activity of alpha oscillations in the temporal cortex was persistent at sixth week of the administration. Overall, our results suggest that chronic paroxetine administration affects cortical oscillations across an expansive network.

Oxygen control in bioreactor drives high yield production of functional hiPSC-like hepatocytes for advanced liver disease modelling

Hepatocytes-like cells (HLC) derived from human induced pluripotent stem cells show great promise for cell-based liver therapies and disease modelling. However, their application is currently hindered by the low production yields of existing protocols. We aim to develop a bioprocess able to generate high numbers of HLC. We used stirred-tank bioreactors with a rational control of dissolved oxygen concentration (DO) for the optimization of HLC production as 3D aggregates. We evaluated the impact of controlling DO at physiological levels (4%O2) during hepatic progenitors’ stage on cell proliferation and differentiation efficiency. Whole transcriptome analysis and biochemical assays were performed to provide a detailed characterization of HLC quality attributes. When DO was controlled at 4%O2 during the hepatic progenitors’ stage, cells presented an upregulation of genes associated with hypoxia-inducible factor pathway and a downregulation of oxidative stress genes. This condition promoted higher HLC production (maximum cell concentration: 2 × 106 cell/mL) and improved differentiation efficiencies (80% Albumin-positive cells) when compared to the bioreactor operated under atmospheric oxygen levels (21%O2, 0.6 × 106 cell/mL, 43% Albumin positive cells). These HLC exhibited functional characteristics of hepatocytes: capacity to metabolize drugs, ability to synthesize hepatic metabolites, and inducible cytochrome P450 activity. Bioprocess robustness was confirmed with HLC derived from different donors, including a primary hyperoxaluria type 1 (PH1) patient. The generated PH1.HLC showed metabolic features of PH1 disease with higher secretion of oxalate compared with HLC generated from healthy individuals. This work reports a reproducible bioprocess, that shows the importance of controlling DO at physiological levels to increase HLC production, and the HLC capability to display PH1 disease features.

Local sleep in songbirds: different simultaneous sleep states across the avian pallium.

Wakefulness and sleep have often been treated as distinct and global brain states. However, an emerging body of evidence on the local regulation of sleep stages challenges this conventional view. Apart from unihemispheric sleep, the current data that support local variations of neural oscillations during sleep are focused on the homeostatic regulation of local sleep, i.e., the role preceding awake activity. Here, to examine local differences in brain activity during natural sleep, we recorded the electroencephalogram and the local field potential across multiple sites within the avian pallium of zebra finches without perturbing the previous awake state. We scored the sleep stages independently in each pallial site and found that the sleep stages are not pallium-wide phenomena but rather deviate widely across electrode sites. Importantly, deeper electrode sites had a dominant role in defining the temporal aspects of sleep state congruence. Altogether, these findings show that local regulation of sleep oscillations also occurs in the avian brain without prior awake recruitment of specific pallial circuits and in the absence of mammalian cortical neural architecture.

An IoT-enabled EEG headphones with customized music for chronic tinnitus assessment and symptom management

Chronic tinnitus often affects elderly or hearing-impaired individuals, which can disturb their daily lives by disrupting concentration and limiting communication. Clinically, sound masking using external sounds like white noise (WN) aims to mask tinnitus and relieve secondary symptoms. Even when symptoms are relieved, tinnitus often requires long-term management, and for patients to visit healthcare professionals regularly. Generally, it could make maintaining symptom management challenging due to the time and effort required for consistent follow-ups. EEG is considered as one of the objective marker for assessing tinnitus symptoms. In this study, we designed IoT-enabled EEG sensing (IEES) headphones, an innovative IoT device that provided customized music (CM) and EEG measurement. The headphones employed a pitch-matching (PM) method to create CM tailored to each patient at specific frequencies for tinnitus patients. To collect EEG measurements, the device incorporated OpenBCI electrodes and a sensing chip to monitor brain waves and evaluate the outcomes.. After 30 days of experiment, participants showed significant reductions in both tinnitus handicap inventory (THI) scores and visual analog scale for annoyance (VAS-A) scores. In comparison, tinnitus frequency showed a slight reduction. EEG measurements demonstrated an increase in alpha band activity. In questionnaires, patients reported high satisfaction with their experiences. These findings highlight the potential of the proposed method for chronic tinnitus assessment and symptom management.

Association of neuroimaging measures with facial emotional processing in healthy adults: a task fMRI study.

Investigating the neural processing of emotion-related neural circuits underlying emotional facial processing may help in understanding mental disorders. We used two subscales of the Toronto Alexithymia Scale to assess the emotional cognitive of 25 healthy participants. A higher score indicates greater difficulty in emotional perception. In addition, participants completed a n-back task during functional magnetic resonance imaging. Psychophysiological interaction analysis was used to explore the functional connectivity (FC) of neural circuits. Next, we used elastic-net regression analysis for feature selection and conducted correlation analysis between the neuroimaging measures and questionnaire scores. Following a 3-fold cross-validation, 5 neuroimaging measures emerged as significant features. Results of correlation analysis demonstrated that participants with higher TAS scores exhibited increased FC between the amygdala and occipital face area (OFA) during facial stimulus processing, but decreased connectivity during emotional processing. These findings suggested that individuals with poor emotional recognition exhibited increased connectivity among face-related brain regions during facial processing. However, during emotional processing, decreasing neural synchronization among neural circuits involved in emotional processing affects facial expression processing. These findings suggest potential neural marker related to subjective emotional perception, which may contribute to the diagnosis and treatment of emotional dysregulation in individuals with psychiatric conditions.

EEGER – A Model for Recognition of Human Emotion Using Brain Signal

Emotions significantly impact human thinking, judgment, health, and communication. EEG-based emotion detection has advanced with the use of Brain-Computer Interface (BCI) technology, proving more effective than other physiological data. Despite progress in affective computing, emotion recognition remains a challenge. However, it's increasingly common in brain–machine interfaces, and research shows EEG brain waves are valuable in identifying emotional states. This research introduces a novel automated system for emotion recognition using deep learning techniques on EEG data collected from the GAMEEMO dataset, where participants played emotional assessment games. The system is designed to identify four emotions experienced during gameplay. The proposed model, called EEGER, was trained exclusively on EEG signals and demonstrated a 99.99% accuracy with minimal computational time. Key to its efficiency is the use of LSTM (Long Short-Term Memory) classifiers, which simplify the process by automatically extracting relevant features. The system was tested across different learning rates and epoch values, showing that 10 epochs with a learning rate of 0.0001 were sufficient to achieve the best accuracy. EEGER was also compared with other methods like K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Adaptive Boosting (AdaBoost), outperforming them in both accuracy and efficiency. These findings suggest that EEGER offers a promising new approach to EEG-based emotion recognition, optimizing performance with lower complexity and computation time.

A third kind of episodic memory: Context familiarity is distinct from item familiarity and recollection.

Memory for past events is widely believed to operate through two different processes: one called 'recollection' when retrieving confident, specific details of a memory, and another called 'familiarity' when only having an unsure but conscious awareness that an item was experienced before. When people successfully retrieve details such as the source or context of a prior event, it has been assumed to reflect recollection. We demonstrate that familiarity of context is functionally distinct from familiarity of items and recollection and offer a new, tri-component model of memory. The three memory responses were differentiated across multiple behavioral and brain wave measures. What has traditionally been thought to be two kinds of memory processes are actually three, becoming evident when using sensitive enough multi-measures. Results are independently replicated across studies from different labs. These data reveal that context familiarity is a third process of human episodic memory.

A novel animal model of spontaneous epilepsy: Cdk5 knockout in pericyte-specific mice.

Changes in neurovascular unit components and their interactions play a crucial role in epileptogenesis and the pathological process of epilepsy. Currently, there is a lack of animal models that can accurately reflect the etiological impact of cerebrovascular lesions on epilepsy. In this study, we constructed cyclin-dependent kinase 5 conditional knockout mice in Cspg4 (pericyte marker)-positive cells using the Cre-LoxP system. The results revealed that this strain of mice exhibited significant seizure behaviors and epileptiform brain waves, loss of hippocampal and amygdala neurons, astrogliosis, decreased pericyte coverage, and reduced AQP4 polar distribution. Herein, we have developed a novel mouse model of spontaneous epilepsy, providing a critical animal model for studying the involvement of neurovascular unit factors in the development and progression of epilepsy.

Modulation of Cerebellar Oscillations with Subthalamic Stimulation in Patients with Parkinson's Disease.

Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) has emerged as a potent treatment for alleviating motor symptoms in Parkinson's disease (PD). Despite its effectiveness, the impact of high frequency STN-DBS on cerebellar oscillations remains unclear, posing an intriguing challenge for neural modulation. Given the direct and indirect connections between the STN and cerebellum, we investigated whether STN-DBS affects cerebellar oscillations. To observe the effects of STN-DBS on cerebellar oscillations in patients with PD. We recruited 15 PD patients receiving STN-DBS. Electroencephalographic (EEG) signals were recorded from cerebellar regions during resting-state conditions in both the OFF-DBS and STN-DBS conditions. Our analyses centered on spectral features, particularly theta and beta oscillations, guided by prior research and correlation tests to investigate the relationship between oscillatory changes and motor symptom severity. In the mid-cerebellar (Cbz) region, we observed a significant increase in the relative power in all frequency bands, including theta and beta oscillations during STN-DBS, showing the global effect of DBS. Importantly, the correlation results indicated significant associations between mid-cerebellar (Cbz) beta power during the OFF condition and motor severity, which were not evident during STN-DBS. Interestingly, correlations between beta power and motor severity were not observed at the mid-occipital (Oz) and mid-frontal (Cz) regions. Notably, signal similarity analyses demonstrated no evidence of volume conduction effects between the mid-cerebellar (Cbz) and nearby mid-occipital (Oz) regions. While these findings provide valuable insights into the complex interplay between STN-DBS and neural oscillations, further research is essential to decipher their precise functional significance and clinical implications. Understanding these intricacies may contribute to the optimization of DBS therapies for PD.

The excessive tonic inhibition of the peri-infarct cortex depresses low gamma rhythm power during post-stroke recovery.

The cortex immediately surrounding a brain ischemic lesion, the peri-infarct cortex, harbors a large part of the potential to recover lost functions. However, our understanding of the neurophysiological conditions in which synaptic plasticity operates, remains limited. Here we hypothesized that the chronic imbalance between excitation and inhibition of the peri-infarct cortex prevents the normalization of the gamma rhythm, a waveband of neural oscillations thought to orchestrate action potential trafficking. Probing the local field potential activity of the forelimb primary sensory cortex (S1FL) located in the peri-infarct cortex of male adult mice, we found a constant, deep reduction of low-gamma oscillation power (L-gamma; 30-50 Hz) precisely during the critical time window for recovery (1 to 3 weeks after stroke). The collapse of L-gamma power negatively corelated with behavioral progress in affected forelimb use. Mapping astrocyte reactivity and GABA-like immunoreactivity in the peri-infarct cortex revealed a parallel high signal, which gradually increased when approaching the lesion. Increasing tonic inhibition with local infusion of GABA or by blocking its recapture reduced L-gamma oscillation power in a magnitude similar to stroke. Conversely, the negative allosteric modulation of tonic GABA conductance using L655,708 or the gliopeptide ODN rescued the L-gamma power of the peri-infarct cortex. Altogether the present data point-out that the chronic excess of ambient GABA in the peri-infarct cortex limits the generation of L-gamma oscillations in the repairing cortex and suggests that rehabilitative interventions aimed at normalizing low-gamma power within the critical period of stroke recovery could optimize the restitution of lost functions.Significance Statement After a stroke, the recovery of lost motor function depends on the reorganization of surviving neural networks. However, the excitation/inhibition balance in the repairing area is suboptimal as it leans excessively towards inhibition. In this work, using an in vivo approach, we demonstrate here that this imbalance, occurring during the critical window for recovery, leads to the collapse of gamma oscillations, a crucial cerebral rhythm for organizing neural communication. This study reinforces the concept of timely therapeutic interventions aimed at correcting the pathological oscillatory regimen of stroke recovery to enhance plasticity.

Developmental Syngap1 haploinsufficiency in medial ganglionic eminence-derived interneurons impairs auditory cortex activity, social behavior and extinction of fear memory.

Mutations in SYNGAP1, a protein enriched at glutamatergic synapses, cause intellectual disability associated with epilepsy, autism spectrum disorder and sensory dysfunctions. Several studies showed that Syngap1 regulates the time course of forebrain glutamatergic synapse maturation; however, the developmental role of Syngap1 in inhibitory GABAergic neurons is less clear. GABAergic neurons can be classified into different subtypes based on their morphology, connectivity and physiological properties. Whether Syngap1 expression specifically in Parvalbumin (PV) and Somatostatin (SST)-expressing interneurons, which are derived from the medial ganglionic eminence, plays a role in the emergence of distinct brain functions remains largely unknown. We used genetic strategies to generate Syngap1 haploinsufficiency in a) prenatal interneurons derived from the medial ganglionic eminence, b) in postnatal PV cells and c) in prenatal SST interneurons. We further performed in vivo recordings and behavioral assays to test whether and how these different genetic manipulations alter brain function and behavior in mice of either sex.Mice with prenatal-onset Syngap1 haploinsufficiency restricted to Nkx2.1-expressing neurons show abnormal cortical oscillations and increased entrainment induced by 40Hz auditory stimulation, but lack of stimulus-specific adaptation. This latter phenotype was reproduced in mice with Syngap1 haploinsufficiency restricted to PV, but not SST, interneurons. Prenatal-onset Syngap1 haploinsufficiency in Nkx2.1-expressing neurons led to impaired social behavior and inability to extinguish fear memories; however, neither postnatal PV- nor prenatal SST-specific mutant mice show these phenotypes. We speculate that Syngap1 haploinsufficiency in prenatal/perinatal PV interneurons may contribute to cortical activity and cognitive alterations associated with Syngap1 mutations.Significance statement Mutations in the human gene cause a form of developmental epileptic encephalopathy associated with intellectual disability, autism and sensory dysfunctions. Several studies have shown that in addition to playing a major role in the synaptic maturation and plasticity of forebrain excitatory neurons, Syngap1 affects GABAergic circuit function as well. Forebrain GABAergic neurons can be divided into different subtypes. Whether Syngap1 expression specifically in distinct interneuron populations and during specific developmental time windows plays a role in the emergence of distinct brain functions remains largely unknown. Here, we report that early, pre or perinatal Syngap1 expression in developing GABAergic neurons derived from the medial ganglionic eminence promotes the development of auditory cortex function, social behavior and ability to extinguish fear memories.

Comparative exploration on EEG signal filtering using window control methods

Electroencephalography (EEG) is used to monitor brain activity. The brain signals consist of different frequency band signals delta, theta, alpha, beta, and gamma waves. The signals are affected by external noise which reduces the quality of the EEG signal due to which it becomes difficult to do further processing of EEG signals like feature extraction or extraction of meaningful features from EEG signal. Therefore, it becomes important to filter the noise from the EEG signal before feature extraction or classification of the EEG signal. The research article presents an overview of different types of windowing filter techniques like Rectangular, Bartlett, Hamming, Hanning, and Kaiser windows applied for finite impulse response (FIR) behavior which is used for EEG signal processing for different brain waves processed in different frequency bands. The comparative analysis is carried out in terms of the response time of brain frequency bands for different windowing filter techniques using the MATLAB 2023 signal processing simulation tool. The novelty of the work lies in estimating minimum latency and appropriate filter selection for various typical EEG waves, since the EEG signals are pre-supposed in the hardware chip design, noise elimination is the first step in high-performance computing applications. The Bartlett window band stop has an optimal response time of 12.666 s for delta waves, a highpass filter with a response time of 16.187 s for theta waves, a bandpass with a response time of 13.122 s for alpha waves, a highpass filter with a response time of 17.866 s for beta waves, and a highpass filter with a response time of 13.797 s for gamma waves. The Barlett window FIR filter is well-suited for EEG applications.

The structure and statistics of language jointly shape cross-frequency neural dynamics during spoken language comprehension

Humans excel at extracting structurally-determined meaning from speech despite inherent physical variability. This study explores the brain’s ability to predict and understand spoken language robustly. It investigates the relationship between structural and statistical language knowledge in brain dynamics, focusing on phase and amplitude modulation. Using syntactic features from constituent hierarchies and surface statistics from a transformer model as predictors of forward encoding models, we reconstructed cross-frequency neural dynamics from MEG data during audiobook listening. Our findings challenge a strict separation of linguistic structure and statistics in the brain, with both aiding neural signal reconstruction. Syntactic features have a more temporally spread impact, and both word entropy and the number of closing syntactic constituents are linked to the phase-amplitude coupling of neural dynamics, implying a role in temporal prediction and cortical oscillation alignment during speech processing. Our results indicate that structured and statistical information jointly shape neural dynamics during spoken language comprehension and suggest an integration process via a cross-frequency coupling mechanism.

Impaired speaking-induced suppression in schizophrenia predicts auditory verbal hallucinations.

A successful efference copy self-prediction suppresses auditory signals in the primary auditory cortex (A1) is necessary for speakers to successfully compare auditory feedback during speech production with auditory feedback during passive listening, this is called speaker-induced suppression (SIS). The top-rank positive symptom in schizophrenic (SZ) patients, auditory verbal hallucination, for instance, is hypothesized to relate to failure to distinguish the internal voice and external sounds, and this deficit is thought to be associated with impaired self-prediction in comparing external and self-generated contents. In this magnetoencephalographic imaging (MEGI) study, we compared SIS M100 in the primary auditory cortex (A1) between the healthy controls (HC; N = 30) and SZ patients (N = 22). The SZ patients displayed reduced SIS and M100 in the A1, and this impairment is negatively correlated with auditory hallucinations. These outcomes suggest that the SZ patients' hallucinatory symptoms are caused by misattribution between the external and self-generated stimuli. We proposed that the weakened self-agency and neural oscillations may lead to this misattribution.

Modulation of neural oscillations in escitalopram treatment: a Canadian biomarker integration network in depression study

Current pharmacological agents for depression have limited efficacy in achieving remission. Developing and validating new medications is challenging due to limited biological targets. This study aimed to link electrophysiological data and symptom improvement to better understand mechanisms underlying treatment response. Longitudinal changes in neural oscillations were assessed using resting-state electroencephalography (EEG) data from two Canadian Biomarker Integration Network in Depression studies, involving pharmacological and cognitive behavioral therapy (CBT) trials. Patients in the pharmacological trial received eight weeks of escitalopram, with treatment response defined as ≥ 50% decrease in Montgomery–Åsberg Depression Rating Scale (MADRS). Early (baseline to week 2) and late (baseline to week 8) changes in neural oscillation were investigated using relative power spectral measures. An association was found between an initial increase in theta and symptom improvement after 2 weeks. Additionally, late increases in delta and theta, along with a decrease in alpha, were linked to a reduction in MADRS after 8 weeks. These late changes were specifically observed in responders. To assess specificity, we extended our analysis to the independent CBT cohort. Responders exhibited an increase in delta and a decrease in alpha after 2 weeks. Furthermore, a late (baseline to week 16) decrease in alpha was associated with symptom improvement following CBT. Results suggest a common late decrease in alpha across both treatments, while modulatory effects in theta may be specific to escitalopram treatment. This study offers insights into electrophysiological markers indicating a favorable response to antidepressants, enhancing our comprehension of treatment response mechanisms in depression.

Food intake enhances hippocampal sharp wave-ripples.

Effective regulation of energy metabolism is critical for survival. Metabolic control involves various nuclei within the hypothalamus, which receive information about the body's energy state and coordinate appropriate responses to maintain homeostasis, such as thermogenesis, pancreatic insulin secretion, and food-seeking behaviors. It has recently been found that the hippocampus, a brain region traditionally associated with memory and spatial navigation, is also involved in metabolic regulation. Specifically, hippocampal sharp wave ripples (SWRs), which are high-frequency neural oscillations supporting memory consolidation and foraging decisions, have been shown to influence peripheral glucose metabolism. However, whether SWRs are enhanced by recent feeding-when the need for glucose metabolism increases, and if so, whether feeding-dependent modulation of SWRs is communicated to other brain regions involved in metabolic regulation, remains unknown. To address these gaps, we recorded SWRs from the dorsal CA1 region of the hippocampus of mice during sleep sessions before and after consumption of meals of varying caloric values. We found that SWRs occurring during sleep are significantly enhanced following food intake, with the magnitude of enhancement being dependent on the caloric content of the meal. This pattern occurred under both food-deprived and ad libitum feeding conditions. Moreover, we demonstrate that neuronal populations in the lateral hypothalamus, which regulates food intake, exhibit robust SWR-triggered increase in activity. These findings identify the satiety state as a factor modulating SWRs and suggest that hippocampal-lateral hypothalamic communication is a potential mechanism by which SWRs could modulate peripheral metabolism and food intake.