Sharp-wave ripples (SWR) are central for cognition and hallmark sleep in the rodent hippocampus. Recently, ripple-like activity was also observed in the human hippocampus and neocortex during wakefulness and sleep. However, ripple detection across brain states and cortical regions remains challenging. We demonstrate that putative ripples largely index noise originating from region-, state-, and demand-dependent modulation of cortical background activity. We establish the noise sensitivity for five common detection algorithms across three intracranial EEG studies during sleep and cognitive engagement. On average, 77% of awake ripples in the medial temporal lobe, including the hippocampus, reflect false positives within the 1/f χ noise floor. We also report task-related 1/f χ modulations that lead to spurious ripple activity, and demonstrate scenarios where ripple detections are less impacted by noise. Our results offer a simulation-based approach to estimate the false positive rate and demonstrate the importance of 1/f χ activity for state- and context-dependent cortical processing.

Elevated pre-pregnancy body mass index (BMI) and perinatal depressive symptoms have been linked to neonatal alterations in brain structure and function. This study examined associations between neonatal functional brain dynamics, maternal BMI, and perinatal depressive symptoms measured by the Edinburgh Postnatal Depression Scale (EPDS) in a community-based, largely low-risk cohort. Funcitonal MRI and Leading Eigenvector Analysis (LEiDA) were applied in a neonatal cohort (N = 437; 236 males; mean gestational age 39.6 weeks) from the developing Human Connectome Project. We assessed whether neonatal brain-state probabilities related to maternal BMI and EPDS scores (M = 5.6, SD = 4.3), testing main effects and, separately, their interaction. The sample included 291 healthy-weight (BMI < 25), 98 overweight (25 BMI < 30), and 48 obese (BMI 30) mothers. EPDS scores were low in this cohort and did not demonstrate associations with brain states or a significant BMI × EPDS interaction. Higher maternal pre-pregnancy BMI was negatively associated with the stability of a functional network encompassing superior frontal, superior parietal, and temporal regions (ß = -0.129, p = 0.006). As this network is normally recruited more with age, reduced stability suggests slowed maturation of fronto-parieto-temporal systems and may signal early risk for later behavioral challenges. Higher maternal pre-pregnancy BMI is associated with reduced stability in a neonatal frontoparietal brain state, characterized by coordinated activity in frontal, parietal, and temporal regions. This state is one of six distinct dynamic connectivity patterns identified, reflecting core neonatal resting-state networks. The association was robust across multiple analytic models and clustering solutions. No significant effects were found for maternal depressive symptoms. These findings underscore the selective impact of maternal metabolic health on early brain organization, suggesting prenatal influences on the functional architecture of the newborn brain that may shape long-term neurodevelopmental trajectories.

Visual perception appears largely stable in time. However, psychophysical studies have revealed that low frequency (0.5 - 7 Hz) oscillatory dynamics can modulate perception and have been linked to various cognitive states and functions. Neither the contribution of waves around 5 Hz (theta or alpha-like) to cortical activity nor their impact during aberrant brain states have been resolved at high spatiotemporal scales. Here, using cortex-wide population voltage imaging in awake mice, we found that bouts of 5-Hz oscillations in the visual cortex are accompanied by similar oscillations in the retrosplenial cortex, occurring both spontaneously and evoked by visual stimulation. Injection of psychotropic 5-HT2AR agonist induced a significant increase in spontaneous 5-Hz oscillations, and also increased the power, occurrence probability and temporal persistence of visually evoked 5-Hz oscillations. This modulation of 5-Hz oscillations in both cortical areas indicates a strengthening of top-down control of perception, supporting an underlying mechanism of perceptual filling and visual hallucinations.

Functional MRI-based graph theory has provided profound insights into the brain′s functional organization, yet the neuroenergetic meaning of widely used graph-theoretical metrics remains poorly understood. Although resting-state research suggests a positive coupling between network topology and glucose metabolism, it remains unclear whether this relationship reflects a general principle of brain organization or a state-specific phenomenon. Here, we test the neuroenergetic interpretability of nodal graph-theoretical metrics by linking complex network topology to cerebral glucose consumption across diverse brain states. Leveraging simultaneous functional PET-MRI, we directly compare state-dependent fluctuations in glucose consumption and network topology during sensory, cognitive, and arousal conditions. We further assess metabolic-topological couplings in disease through a meta-analysis of resting-state FDG-PET and fMRI studies involving Alzheimer′s disease, Parkinson′s disease, major depressive disorder, and schizophrenia. Our results show that nodal graph-theoretical metrics exhibit state- and network-dependent metabolic associations, with coupling patterns diverging across experimental and disease contexts. Notably, frontoparietal and attentional networks show more conserved metabolic–topological coupling than other large-scale networks across states. These findings underscore a dynamic, complex interplay between metabolic demand and complex network organization, highlighting the need for a nuanced interpretation of the energetic underpinnings of nodal graph-theoretical metrics in health and disease.

Brain states analysis of EEG predicts multiple sclerosis and mirrors disease duration and burden.

Hierarchical Brain Dynamics Associated with Remission from Major Depression Across Diverse Therapeutic Modalities.

Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states are found to correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analyses, we exploit the generative nature of the HMM, using neural sequences to generate synthetic swimming trajectories, whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.

Psychedelics lead to profound changes in subjective experience and behaviour, which are typically conceptualised in psychological terms rather than corresponding to an altered brain state or a distinct state of vigilance. Here, we performed chronic electrophysiological recordings from the neocortex concomitant with pupillometry in freely moving adult male mice following an injection of a short-acting psychedelic 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT). We report an acute induction of a dissociated state, characterised by prominent slow oscillations in the cortex and marked pupil dilation in behaviourally awake, moving animals. REM sleep was initially markedly suppressed, but was overcompensated in the subsequent 48 hours, while administration of 5-MeO-DMT immediately after sleep deprivation attenuated the subsequent rebound of sleep slow-wave activity. We argue that the occurrence of a dissociated state combining features of waking and sleep may fundamentally underpin the known and hypothesised effects of psychedelics - from dream-like hallucinations to reopening of the critical period for plasticity.

Can music depict the development of a neurological process leading to impairment? This article studies the case of the progressive loss of control due to alcohol intoxication, represented musically in a scene of Verdi???s opera Otello. After a first qualitative assessment, we propose a quantitative comparison between musically derived dynamics, capturing pitch and onset distributions, and a toy model of a brain transformational process. This yields a combination of parameters that makes the two dynamics comparable.

Conceptualizing the substrates and sequelae of decreased sound tolerance as a developmental cascade: A pilot study.

The brain state of sleep contributes in a specific way to the formation of long-term memory. Over the past 10 years, research on the psychological and neuronal mechanisms underlying this process has rapidly increased, including studies in humans and rodents across early and late life. Intended to comprehensively cover this research, our review reveals that the majority of findings are consistent with the concept of long-term memory formation during sleep as an active systems consolidation process that concurs with widespread synaptic downselection. In this concept, the repeated neuronal replay of encoded representations, particularly in the hippocampus, in conjunction with brain oscillations hallmarking non-rapid eye movement (non-REM) sleep, provide mechanisms for regulating information flow across brain networks. This interplay drives the consolidation of newly encoded memory into neocortical long-term stores, whereby this neocorticalization of representations goes along with a transformation of memories into more abstract representations. The findings, however, remain controversial as to the nature of memory transformation: What kind of information is eventually consolidated into neocortical networks and how is storage of this information achieved at the synaptic level? Furthermore, the roles of REM sleep in consolidation of, in particular, emotional memory and in shaping representations at the synaptic level are unclear. Future research also needs to elaborate on how consolidation during sleep differs from that during wakefulness, as well as on the changes in sleep-dependent consolidation across the life span. A promising new area arising from this research pertains to brain stimulation techniques developed to enhance memory consolidation during human sleep.

Dynamic functional connectivity brain state dynamics and topological organization in major depressive disorder, anxiety disorder and childhood trauma.

Corrigendum to “Brain state model: A novel method to represent the rhythmicity of object-specific selective attention from magnetoencephalography data” [Neurocomputing 634 (2025) 129920

Background/Objectives: This systematic review examines neuroplasticity-informed approaches to learning under cognitive load, synthesizing evidence from functional imaging, brain stimulation, and educational technology research. As digital learning environments increasingly challenge learners with complex cognitive demands, understanding how neuroplasticity principles can inform adaptive educational design becomes critical. This review examines how neural mechanisms underlying learning under cognitive load can inform the development of evidence-based educational technologies that optimize neuroplastic potential while mitigating cognitive overload. Methods: Following PRISMA guidelines, we synthesized 94 empirical studies published between 2005 and 2025 across PubMed, Scopus, Web of Science, and PsycINFO. Studies were selected based on rigorous inclusion criteria that emphasized functional neuroimaging (fMRI, EEG), non-invasive brain stimulation (tDCS, TMS), and educational technology applications, which examined learning outcomes under varying cognitive load conditions. Priority was given to research with translational implications for adaptive learning systems and personalized educational interventions. Results: Functional imaging studies reveal an inverted-U relationship between cognitive load and neuroplasticity, with a moderate challenge in optimizing prefrontal-parietal network activation and learning-related neural adaptations. Brain stimulation research demonstrates that tDCS and TMS can enhance neuroplastic responses under cognitive load, particularly benefiting learners with lower baseline abilities. Educational technology applications demonstrate that neuroplasticity-informed adaptive systems, which incorporate real-time cognitive load monitoring and dynamic difficulty adjustment, significantly enhance learning outcomes compared to traditional approaches. Individual differences in cognitive capacity, neurodiversity, and baseline brain states substantially moderate these effects, necessitating the development of personalized intervention strategies. Conclusions: Neuroplasticity-informed learning approaches offer a robust framework for educational technology design that respects cognitive load limitations while maximizing adaptive neural changes. Integration of functional imaging insights, brain stimulation protocols, and adaptive algorithms enables the development of inclusive educational technologies that support diverse learners under cognitive stress. Future research should focus on scalable implementations of real-time neuroplasticity monitoring in authentic educational settings, as well as on developing ethical frameworks for deploying neurotechnology-enhanced learning systems across diverse populations.

ABSTRACT Background: Network control theory can quantify controllability to evaluate how altered transitions between brain states contribute to cognitive, emotional, and behavioural challenges. Childhood abuse, influenced by genetics, is associated with disrupted network function, though the exact control processes are not yet understood. Objective: This study aims to investigate the association between brain network controllability and childhood abuse experiences, and to elucidate its potential mediating role in the relationship among polygenic risk scores (PRS) for childhood abuse, childhood abuse experiences, and adult health outcomes. Methods: This study measured the controllability of functional brain networks, including both average and modal controllability, in a cohort of 214 young adults with varied histories of childhood abuse. Participants also completed psychological assessments, whole-exome sequencing, and the calculation of PRS for childhood abuse. This study investigate the association between brain network controllability and childhood abuse. Furthermore, a mediation model was performed to explore the potential mediating role of brain network controllability in the relationship between genetic risk, childhood abuse experiences, and adult health outcomes. Results: The controllability of the dorsal attention and sensorimotor networks, as well as the controllability of key ROIs within the sensorimotor, default mode, dorsal attention, visual, and control networks, demonstrated significant correlations with abuse scores. Despite no direct correlation between PRS and self-reported childhood abuse, indirect effects through the controllability of visual and control network regions were identified. The controllability of the left postcentral gyrus in the dorsal attention network mediated the relationship between childhood abuse and adult anxiety. Conclusions: This study reveals that brain network controllability is a pivotal factor, not only bridging PRS and childhood abuse but also serving as a potential mediator between childhood trauma and adult anxiety, offering a new perspective on the neurobiology of childhood abuse-related psychopathology.

SUMMARYNeurotransmitters and neuromodulators regulate brain states through diverse mechanisms, yet how their activities are coordinated during sleep remains unresolved. Usingin vivofiber photometry in adult mice expressing genetically encoded fluorescent biosensors, combined with EEG/EMG recordings, we investigated the temporal organization of multiple neuromodulators during sleep in barrel cortex, with norepinephrine (NE) as a reference signal. All five neuromodulators examined, acetylcholine, serotonin, dopamine, histamine, and NE, exhibited synchronized infraslow cortical oscillations during NREM sleep. Optogenetic suppression of locus coeruleus (LC) neurons abolished NE oscillations and selectively reduced acetylcholine fluctuations in barrel cortex, whereas targeted inhibition of basal forebrain cholinergic neurons attenuated REM-associated acetylcholine elevations without disrupting NREM-related oscillations or NE dynamics. The synchronized infraslow cortical oscillations spanning multiple neuromodulators reveal a previously unrecognized mechanism for organizing sleep architecture.

Rumination—repetitive, negatively valenced, self-focused thought—is a maladaptive cognitive style linked to emotional dysregulation and psychiatric risk. To investigate its neural underpinnings in a naturalistic context, we developed an fMRI paradigm in which participants observed and reflected on videos of their own past group-based problem-solving sessions, a naturalistic self-relevant context rarely examined in fMRI studies. Thirty-two adults (mean age = 30.4 ± 5.4 years; 13 F) were recorded during collaborative design-thinking tasks in triads. In a subsequent scanning session, each participant viewed two self-relevant team videos and one control team video, followed by a structured reflection period. We assessed trait rumination using the Rumination–Reflection Questionnaire (RRQ) and applied Topological Data Analysis (TDA) via the Mapper algorithm to model individual-level whole-brain dynamics during the task. Mapper shape graphs captured temporal transitions between brain states, allowing us to quantify the similarity of timepoints across the session. Individuals with higher trait rumination showed significantly higher temporal similarity, indicating reduced brain-state variability, during self-relevant conditions (r = 0.46, p = 0.018). This effect was not observed during the control condition. These findings suggest that rumination is associated with rigid brain dynamics during self-observation and evaluative processing. Traditional GLM and inter-subject correlation (ISC) analyses confirmed task engagement of key self-referential and social-evaluative regions, while Mapper revealed dynamic features not captured by static or group-averaged methods. Together, these findings demonstrate that trait rumination is associated with rigid large-scale brain dynamics during self-relevant cognition and highlight the value of combining naturalistic paradigms with topological approaches to capture behaviorally meaningful signatures.

BackgroundLewy body dementia (LBD) is the second most common degenerative dementia after Alzheimer's disease, yet its underlying neural mechanisms, particularly those contributing to cognitive fluctuations, remain poorly understood.MethodWe analyzed resting‐state MEG data from 28 participants in three cohorts: (a) LBD (n = 7, age = 70.1 ± 5.7 years, 5 males), (b) Parkinson's disease (PD) without cognitive impairment (n = 8, age = 71.1 ± 7.6 years, 3 males), and (c) normal cognitive (NC) (n = 13; age = 72.3 ± 6.5 years, 5 males). A six‐state hidden Markov model (HMM) was applied to investigate brain state dynamics and cognitive fluctuations. Brain states were characterized by activation patterns across 52 regions of interest (ROIs). Four dynamic connectivity metrics were assessed: fractional occupancy (FO), mean lifetime (MLT), mean interval, and switching rate (SWR). Their correlations with the Clinician Assessment of Fluctuation (CAF) scale were also evaluated. Additionally, power spectral density (PSD) analysis was conducted to uncover neural signatures underlying cognitive variability in LBD.ResultPower maps for each brain state revealed distinct activation patterns (Figure 1). The most pronounced group differences were observed in States 1 and 5 (Figure 2). State 1, primarily involving the medial prefrontal and lateral temporal cortices, showed significantly prolonged occupancy (higher FO and MLT) in LBD patients compared to NC and PD groups (p < 0.001). Conversely, State 5, which engaged the right temporo‐parieto‐occipital regions, was less active in LBD. PSD analysis indicated slowing in LBD, with activity shifting from the alpha band in NC and PD groups to the theta band in LBD (Figure 3). Moreover, dynamic connectivity measures were significantly correlated with CAF across all states. Notably, FO in State 1 exhibited a strong positive correlation with CAF (r = 0.72, p < 10‐24), while FO in State 5 showed a strong negative correlation (r = ‐0.65, p < 10‐18).ConclusionOur findings reveal distinct LBD‐specific spectral slowing and dynamic connectivity patterns, characterized by prolonged engagement in temporal‐prefrontal networks and reduced involvement in temporo‐occipito‐parietal regions. These alterations strongly correlate with cognitive fluctuations, suggesting potential biomarkers for LBD. Future research should explore targeting these abnormal state transitions to mitigate cognitive fluctuations.

BackgroundThe oscillatory nature of slow waves during non‐rapid eye movement (NREM) sleep has recently been proposed as crucial for the glymphatic system, facilitating the clearance of metabolic waste from the brain. While aging‐related reductions in slow wave quantity and amplitude are well‐documented and linked to this cleansing function, we propose that the rhythmic dynamics in which slow waves occur may also play a critical role.MethodThus, we introduce a novel classification of slow waves based on their temporal dynamics, categorizing them into isolated waves and oscillation trains. Using overnight EEG recordings from young and elderly adults, we compared the proportions of these wave types. Additionally, we analyzed train composition, including the proportion of slow waves that initiate a train (lead waves) and the lengths of the oscillation trains (number of consecutive slow waves initiated by one lead wave).ResultOur results revealed that elderly adults exhibited a higher prevalence of isolated waves and a lower proportion of oscillation trains. Moreover, while elderly adults showed a higher proportion of lead waves, their oscillation trains were significantly shorter compared to those of young adults.ConclusionWe propose that natural aging may result in a less oscillatory brain state, characterized by a diminished ability to produce sustained, periodic oscillations. This diminished rhythmicity could impair cerebrospinal fluid pulsation, potentially reducing the brain's ability to efficiently clear pathogenic substances during sleep. Given the established link between impaired glymphatic clearance and neurodegenerative diseases such as Alzheimer's, this diminished capacity to sustain slow wave trains may contribute to age‐related decline in neurological functioning.

BackgroundNeurometabolism plays a critical role in modulating dynamic brain activity through its impact on energy supply. While glucose is the primary energy source for neurons, ketone bodies are emerging as a key alternative, using an astrocytic pathway specially active under under high energy demand states. The ketogenic diet, known for its ability to enhance ketone production, has been suggested to influence cognitive performance and protect the aging brain. However, the neural mechanisms are still unknown.MethodThis study uses an open dataset (van Nieuwenhuizen et al., 2024) to examine the impact of the ketogenic diet on modulating neuronal activity through the modulation of neuronal energy supply, using non‐invasive electroencephalography (EEG). A sample of 36 healthy adults was assessed under two conditions: (1) a ketogenic condition, where participants consumed a ketone ester supplement (395 mg/kg) and (2) a glucogenic condition, where they consumed an equivalent caloric bolus of glucose. Resting‐state EEG data were recorded using a 65‐channel system under both conditions, and preprocessing techniques, including artifact removal and frequency band separation, were applied. Neuronal dynamics were assessed via the spectral analysis of the 1/f aperiodic activity.ResultsFindings indicate significant modulation of EEG aperiodic 1/f activity under the ketogenic condition and not glucogenic condition. Specifically, a flatter 1/f aperiodic slope suggest an enhanced information processing state with increased neuronal neuronal dynamic brain state, similar to a healthy attentive state.ConclusionThe ketogenic diet enhances neuronal aperiodic 1/f activity by providing a fast and alternative energy source in the form of ketone bodies. These findings suggest potential therapeutic applications for aging and neurodegenerative disorders for ketogenic interventions and suggest a key modulation of the central nervous system, likely involving astrocyte‐neuron pathway.Authors:Felipe González*, Daniel Franco‐O'Byrne*, Ana María Castro‐Laguardia, Tomás Bosch, Tomás Ossandón, Vicente Medel*=equally contributing authors °=corresponding authorFunding: FONDECYT Exploración 13240170 and FONDECYT de Iniciación 11251578.