Scaling brain size, keeping timing: evolutionary preservation of brain rhythms

Entrainment by transcranial alternating current stimulation: Insights from models of cortical oscillations and dynamical systems theory

Signature of neuronal oscillations can be found in nearly every brain function. However, abnormal oscillatory activity is linked with several brain disorders. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that can potentially modulate neuronal oscillations and influence behavior both in health and disease. Yet, a complete understanding of how interacting networks of neurons are affected by tACS remains elusive. Entrainment effects by which tACS synchronizes neuronal oscillations is one of the main hypothesized mechanisms, as evidenced in animals and humans. Computational models of cortical oscillations may shed light on the entrainment effects of tACS, but current modeling studies lack specific guidelines to inform experimental investigations. This study addresses the existing gap in understanding the mechanisms of tACS effects on rhythmogenesis within the brain by providing a comprehensive overview of both theoretical and experimental perspectives. We explore the intricate interactions between oscillators and periodic stimulation through the lens of dynamical systems theory. Subsequently, we present a synthesis of experimental findings that demonstrate the effects of tACS on both individual neurons and collective oscillatory patterns in animal models and humans. Our review extends to computational investigations that elucidate the interplay between tACS and neuronal dynamics across diverse cortical network models. To illustrate these concepts, we conclude with a simple oscillatory neuron model, showcasing how fundamental theories of oscillatory behavior derived from dynamical systems, such as phase response of neurons to external perturbation, can account for the entrainment effects observed with tACS. Studies reviewed here render the necessity of integrated experimental and computational approaches for effective neuromodulation by tACS in health and disease.

Electroencephalography-driven brain-network models for personalized interpretation and prediction of neural oscillations

OBJECTIVE

Develop an encephalography (EEG)-driven method that integrates interpretability, predictiveness, and personalization to assess the dynamics of the brain network, with a focus on pathological conditions such as pharmacoresistant epilepsy.

METHODS

We propose a method to identify dominant coherent oscillations from EEG recordings. It relies on the Koopman operator theory to achieve individualized EEG prediction and electrophysiological interpretability. We extend it with concepts from adiabatic theory to address the nonstationary and noisy EEG signals.

RESULTS

By simultaneously capturing the local spectral and connectivity aspects of patient-specific oscillatory dynamics, we are able to clarify the underlying dynamical mechanism. We use it to construct the corresponding generative models of the brain network. We demonstrate the proposed approach on recordings of patients in status epilepticus.

CONCLUSIONS

The proposed EEG-driven method opens new perspectives on integrating interpretability, predictiveness, and personalization within a unified framework. It provides a quantitative approach for assessing EEG recordings, crucial for understanding and modulating pathological brain activity.

SIGNIFICANCE

This work bridges theoretical neuroscience and clinical practice, offering a novel framework for understanding and predicting brain network dynamics. The resulting approach paves the way for data-driven insights into brain network mechanisms and the design of personalized neuromodulation therapies.

Neuronal oscillations in cognition: Down syndrome as a model of mouse to human translation

Down syndrome (DS), a prevalent cognitive disorder resulting from trisomy of human chromosome 21 (Hsa21), poses a significant global health concern. Affecting approximately 1 in 800 live births worldwide, DS is the leading genetic cause of intellectual disability and a major predisposing factor for early-onset Alzheimer's dementia. The estimated global population of individuals with DS is 6 million, with increasing prevalence due to advances in DS health care. Global efforts are dedicated to unraveling the mechanisms behind the varied clinical outcomes in DS. Recent studies on DS mouse models reveal disrupted neuronal circuits, providing insights into DS pathologies. Yet, translating these findings to humans faces challenges due to limited systematic electrophysiological analyses directly comparing human and mouse. Additionally, disparities in experimental procedures between the two species pose hurdles to successful translation. This review provides a concise overview of neuronal oscillations in human and rodent cognition. Focusing on recent DS mouse model studies, we highlight disruptions in associated brain function. We discuss various electrophysiological paradigms and suggest avenues for exploring molecular dysfunctions contributing to DS-related cognitive impairments. Deciphering neuronal oscillation intricacies holds promise for targeted therapies to alleviate cognitive disabilities in DS individuals.

D2 receptor activation modulates NMDA receptor antagonist-enhanced high-frequency oscillations in the olfactory bulb of freely moving rats

RATIONALE

NMDA receptor antagonists, used to model psychotic-like states and treat depression, enhance the power of high-frequency oscillations (HFO) in many mammalian brain regions. In rodents, the olfactory bulb (OB) is a particularly important site for generating this rhythm. OB projection neurons express D1 and D2 receptors (D1R and D2R) which interact with NMDA receptors.

OBJECTIVES

The aim of this study was to explore the effect of dopamine (DA) signalling in the OB on MK801-enhanced HFO.

METHODS

Local field potentials from the OB and locomotor activity were recorded in adult male freely moving rats. MK801 was injected systemically or infused locally to the OB. The effects of D1R and D2R agonists (SKF38393, quinpirole) and antagonists (SCH23390, eticlopride), administered systemically or locally to the OB, were examined on MK801-enhanced HFO. Effects of the antipsychotics risperidone and aripiprazole were also examined.

RESULTS

Local infusion of MK801 enhanced HFO power in the OB to levels similar to those observed after systemic injection. Neither systemic nor local blockade of D1R or D2R affected the MK801-enhanced HFO, despite reductions in hyperlocomotion. However, direct (systemic and local) D2R, but not D1R, stimulation caused a short-lasting reduction of MK801-enhanced HFO power and longer lasting reduction in frequency. Risperidone, but not aripiprazole, reduced MK801-enhanced HFO frequency.

CONCLUSIONS

These results suggest that NMDA receptor antagonist-enhanced HFO in the OB is generated predominantly independently of DA influence, however exogenous stimulation of D2R can modulate this rhythm. A second, but not third generation antipsychotic reduced HFO frequency.

Changes in Early Aperiodic EEG Activity Are Linked to Autism Diagnosis and Language Development in Infants With Family History of Autism

Delays in language often co-occur among toddlers diagnosed with autism. Despite the high prevalence of language delays, the neurobiology underlying such language challenges remains unclear. Prior research has shown reduced EEG power across multiple frequency bands in 3-to-6-month-old infants with an autistic sibling, followed by accelerated increases in power with age. In this study, we decompose the power spectra into aperiodic (broad band neural firing) and periodic (oscillations) activity to explore possible links between aperiodic changes in the first year of life and later language outcomes. Combining EEG data across two longitudinal studies of infants with and without autistic siblings, we assessed whether infants with an elevated familial likelihood (EFL) exhibit altered changes in both periodic and aperiodic EEG activity at 3 and 12 months of age, compared to those with a low likelihood (LL), and whether developmental change in activity is associated with language development. At 3 months of age (n = LL 59, EFL 57), we observed that EFL infants have significantly lower aperiodic activity from 6.7 to 55 Hz (p < 0.05). However, change in aperiodic activity from 3 to 12 months was significantly increased in infants with a later diagnosis of autism, compared to EFL infants without an autism diagnosis (n = LL-NoASD 41, EFL-noASD 16, EFL-ASD 16). In addition, greater increases in aperiodic offset and slope from 3 to 12 months were associated with worse language development measured at 18 months (n = 24). Findings suggest that early age-dependent changes in EEG aperiodic power may serve as potential indicators of autism and language development in infants with a family history of autism.

Modulating excitation of the mediodorsal thalamus rescues dysfunction after administration of MK-801 in rats

The excitation/inhibition (E/I) balance in the prefrontal cortex (PFC) is a dynamic equilibrium maintained by the concerted efforts of excitatory glutamatergic neurons and inhibitory γ-aminobutyric acid neurons (INs). The medial dorsal nucleus (MD) of the thalamus provides abundant pyramidal glutamatergic neural (PNs) projections to the PFC and regulates the E/I balance within the PFC. In schizophrenia, an imbalance in the E/I ratio in the PFC, along with reduced thalamocortical connectivity, has been observed. Nevertheless, the precise mechanisms underlying the modulation of the MD to PFC activity remain elusive. We posited a hypothesis that the MD may serve as a potential therapeutic target for schizophrenia. To investigate the role of PFC in the pathogenesis of schizophrenia, we induced schizophrenia-related neuronal activation and motor behavioral abnormalities in adult rats through intraperitoneal injection of MK-801. We measured alterations in neuronal firing activity and neural oscillations by monitoring deep brain neuronal signals under resting state and auditory response task conditions, while simultaneously assessing their motor activities. In our study, the results indicated that systemic administration of MK-801 preferentially leads to an increase in the firing frequency of PFC-PNs and disrupts the E/I balance in the PFC. Concurrently, this is accompanied by mid-to-high (14-80 and 130-180 Hz) frequency oscillations and abnormalities in the auditory steady-state responses and autonomous activities. Subsequently, we employed optogenetics to stimulate the activity of MD neurons selectively, aiming to elucidate the role of the MD-to-PFC neural circuit in modulating the PFC E/I ratio. The results confirmed thatincreased activity of MD neurons in schizophrenia leads to heightened excitability of PFC-INs and decreased firing rates of PFC-PNs, thereby restoring the E/I balance in the PFC and improving gamma oscillations, auditory steady-state responses, and behavioral abnormalities. Overall, these findings reveal the pivotal role of MD-to-PFC connectivity in modulating PFC E/I balance and provide valuable insights for potential therapeutic strategies targeting this circuitry in the context of E/I dysregulation seen in schizophrenia.

Advances in Ga2O3-based memristor devices, modeling, properties, and applications for low power neuromorphic computing

About a decade ago, gallium oxide (Ga2O3) was found to be a very attractive ultrawide-bandgap (4.6-4.9 eV) semiconductor for next-generation low-power devices. Ga2O3 materials have attracted a lot of scientific and technical interest because of their outstanding properties and numerous application opportunities in the field of semiconductor based memristor technology. This review is focused on Ga2O3 thin-film memristors for smart technologies. The capacitance behavior of memristors is very important for adapting nonlinear memristor responses. Also, this comprehensive review explores in depth the ideas, device construction, and manufacturing procedures for Ga2O3-based memristor devices. To improve the device's behavior and performance improvement, a detailed analysis of many modeling and simulation techniques is given. Also, advanced characterization techniques, such as electrical, structural, and thermal evaluations, for studying artificial optoelectronic synaptic characteristics, which are important for use in computational neuroscience, are discussed in detail. The synaptic activities revealed that learning and memory processes were aided by potentiation and depression similar to those found in biological synapses. The most notable accomplishment is the realization of quaternary memory storage in a single device. This idea is supported by empirical evidence and simulations, which demonstrate the possibility of storing and maintaining multiple memory states. This study establishes oxide semiconductor memristors as a doorway to quaternary memory storage and improved synaptic functioning, paving the way for optoelectronic synaptic devices with greater memory capacity.

The predictive nature of spontaneous brain activity across scales and species

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

Cognitive control and consciousness in open biological systems

Thermodynamically open biological systems not only sustain a life-supporting mutual relationship with their environment by exchanging matter and energy but also constantly seek information to navigate probabilistic changes in their surroundings. This work argues that cognition and conscious thought should not be viewed in isolation but rather as parts of an integral control of biological systems to identify and act upon meaningful, semantic information to sustain viability. Under this framework, the development of key cognitive control capacities in centralized nervous systems and the resulting behavior are categorized into distinct Markov decision processes: decision-making with partially observable sensory exteroceptive and interoceptive information, learning and memory, and symbolic communication. It is proposed that the state of conscious thought arises from a control mechanism for speech production resembling actuator control in engineered systems. Also known as the phonological loop, this feedback from the motor to the sensory cortex provides a third type of information flowing into the sensory cortex. The continuous, dissipative loop updates the fleeting working memory and provides humans with an advanced layer of control through a sense of self, agency and perception of flow in time. These capacities define distinct degrees of information fitness in the evolution of information-powered organisms.

Accelerated signal propagation speed in human neocortical dendrites

Human-specific cognitive abilities depend on information processing in the cerebral cortex, where the neurons are significantly larger and their processes longer and sparser compared to rodents. We found that, in synaptically connected layer 2/3 pyramidal cells (L2/3 PCs), the delay in signal propagation from soma to soma is similar in humans and rodents. To compensate for the longer processes of neurons, membrane potential changes in human axons and/or dendrites must propagate faster. Axonal and dendritic recordings show that the propagation speed of action potentials (APs) is similar in human and rat axons, but the forward propagation of excitatory postsynaptic potentials (EPSPs) and the backward propagation of APs are 26 and 47% faster in human dendrites, respectively. Experimentally-based detailed biophysical models have shown that the key factor responsible for the accelerated EPSP propagation in human cortical dendrites is the large conductance load imposed at the soma by the large basal dendritic tree. Additionally, larger dendritic diameters and differences in cable and ion channel properties in humans contribute to enhanced signal propagation. Our integrative experimental and modeling study provides new insights into the scaling rules that help maintain information processing speed albeit the large and sparse neurons in the human cortex.

Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans

Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits that may contribute to the associated evoked potentials (EPs). Here we show that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the state of the targeted cortico-thalamic network. We then optogenetically dissect the underlying circuit in mice, demonstrating that the EPs late component is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with bursting frequency and synchronicity of thalamic neurons, modulated by the subject's behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that single pulse cortical stimulation engages cortico-thalamo-cortical circuits largely preserved across different species and stimulation modalities.

Collective decision making by embodied neural agents

Collective decision making using simple social interactions has been studied in many types of multiagent systems, including robot swarms and human social networks. However, existing multiagent studies have rarely modeled the neural dynamics that underlie sensorimotor coordination in embodied biological agents. In this study, we investigated collective decisions that resulted from sensorimotor coordination among agents with simple neural dynamics. We equipped our agents with a model of minimal neural dynamics based on the coordination dynamics framework, and embedded them in an environment with a stimulus gradient. In our single-agent setup, the decision between two stimulus sources depends solely on the coordination of the agent's neural dynamics with its environment. In our multiagent setup, that same decision also depends on the sensorimotor coordination between agents, via their simple social interactions. Our results show that the success of collective decisions depended on a balance of intra-agent, interagent, and agent-environment coupling, and we use these results to identify the influences of environmental factors on decision difficulty. More generally, our results illustrate how collective behaviors can be analyzed in terms of the neural dynamics of the participating agents. This can contribute to ongoing developments in neuro-AI and self-organized multiagent systems.

Impact of transcranial alternating current stimulation on psychological stress: A functional near-infrared spectroscopy study

This pilot study investigates the impact of transcranial alternating current stimulation (tACS) on psychological stress using functional near-infrared spectroscopy (fNIRS). Forty volunteers were randomly assigned to two groups: the tACS and the control. The experiment was divided into three distinct stages: pre-stimulation, stimulation, and post-stimulation. The Stroop Color-Word Task (SCWT) was employed as a validated stress-inducing paradigm to assess pre- and post-stimulation changes. During the initial phase, the participants completed the SCWT. This was followed by either tACS or sham. In the third session, the individuals solved the task again. The anode and cathode for the transcranial tACS were placed on the dorsolateral prefrontal cortex (DLPFC). tACS, was applied with current intensity of 1.5 mA at 16 Hz over the dorsolateral prefrontal cortex (DLPFC), aimed to modulate cortical activation and mitigate stress. Sham included 5-second ramp periods. Physiological data using alpha amylase and the NASA Task Load Index (NASA-TLX) were utilized. The results revealed significant hemodynamic changes and reduced stress levels in the tACS group compared to the sham group (p <  0.001). The connectivity network changed significantly (p <  0.001) following tACS. In addition, the NASA-TLX results showed a statistically significant difference between the pre-and post-tACS sessions. In contrary, no statistical significance was noticed for the sham control group. An increase in the blood flow in the prefrontal cortex region of the brain was observed, demonstrating the potential of tACS as a non-invasive neuromodulation technique for stress mitigation.

Distractor anticipation during working memory is associated with theta and beta oscillations across spatial scales

Introduction

Anticipating distractors during working memory maintenance is critical to reduce their disruptive effects. In this study, we aimed to identify the oscillatory correlates of this process across different spatial scales of neural activity.

Methods

We simultaneously recorded local field potentials (LFP) from the lateral prefrontal cortex (LPFC) and electroencephalograms (EEG) from the scalp of monkeys performing a modified memory-guided saccade (MGS) task. The monkeys were required to remember the location of a target visual stimulus while anticipating distracting visual stimulus, flashed at 50% probability during the delay period.

Results

We found significant theta-band activity across spatial scales during anticipation of a distractor, closely linked with underlying working memory dynamics, through decoding and cross-temporal generalization analyses. EEG particularly reflected reactivation of memory around the anticipated time of a distractor, even in the absence of stimuli. During this anticipated time, beta-band activity exhibited transiently enhanced intrahemispheric communication between the LPFC and occipitoparietal brain areas. These oscillatory phenomena were observed only when the monkeys successfully performed the task, implicating their possible functional role in mitigating anticipated distractors.

Discussion

Our results demonstrate that distractor anticipation recruits multiple oscillatory processes across the brain during working memory maintenance, with a key activity observed predominantly in the theta and beta bands.

Multi-Scale Coupling Between LFP and EMG in Mice by Low-Intensity Pulsed Ultrasound Stimulation With Different Number of Tone-Burst

Low-intensity pulsed ultrasound stimulation (LIPUS) as a non-invasive, high-spatial resolution and high penetration depth brain modulation technology has been used for modulating neuromuscular function. However, the modulation of neural electrical signal changes in the neuromuscular system by LIPUS remains to be explored. In this study, we stimulated the mouse brain motor cortex by LIPUS with different number of tone burst (NTB) and recorded the local field potential (LFP) signals of the target region and electromyography (EMG) of tail muscle. Multi-Scale Transfer Entropy (MSTE) analysis method was used to explore the multi-scale synchronization characteristics and functional cortico-muscular coupling(FCMC) strength changes of mice LFP-EMG before and after LIPUS under different NTBs. The results show that the MSTE of LFP-EMG before and after LIPUS stimulation was higher than that of EMG-LFP. After adding multi-scale, MSTE has a significant relationship with time scales. When NTB =200, the scale of extremum is the largest. There was a fitting intersection between LFP-EMG and EMG-LFP scale 7-21 before and after stimulation. After scale averaging, the LFP-EMG after stimulation was lower than that before stimulation, and the EMG-LFP after stimulation was higher than that before stimulation. Conclusion: There is a significant correlation between NTB and time scale before and after LIPUS, as well as upward and downward. Consequently, This study used FCMC methods to study different NTBs and multi-scale relationships, provides new variables from LIPUS parameters and analysis, and provides new reference for clinical applications of LIPUS.

Structural Determinants of Signal Speed: A Multimodal Investigation of Face Processing in Autism Spectrum Disorder

Face perception is fundamental to social cognition and often disrupted in autism. However, the neurological basis for this disrupted face perception and the mechanisms underlying altered electrophysiological signaling in autism, such as increased latency of the N170-an electrophysiological marker of face processing, remain unknown. Here, we leverage multimodal neuroimaging in autistic adolescents to establish a link between MRI-measured axonal microstructure within the face processing network and EEG-measured N170 latency. We demonstrate that a novel metric of axonal signal transit time derived from axonal diameter, myelination, and length-estimated axonal latency (EAL)-predicts N170 latency during face processing. Moreover, we demonstrate that individuals with and without autism rely upon different pathways, providing a structural account for autism-related face processing differences. By establishing this relationship between EEG-based electrical function and MRI-based axonal microstructure, we provide a non-invasive, spatially-detailed estimate of neuronal processing speed that can inform understanding of brain function, development, and disorder.

Neural mechanisms underlying reduced nocifensive sensitivity in autism-associated Shank3 mutant dogs

Autistic individuals carrying mutations in SHANK3 (encoding a synaptic scaffolding protein) have been consistently reported to exhibit reduced pain sensitivity. However, the neural mechanisms underlying impaired pain processing remain unclear. To investigate the role of SHANK3 in pain processing, we conducted behavioral, electrophysiological, and pharmacological tests upon nociceptive stimulation in a Shank3 mutant dog model. Behaviorally, Shank3 mutant dogs showed reduced nocifensive sensitivity compared to wild-type (WT) dogs. Electrophysiologically, Shank3 mutant dogs exhibited reduced neural responses elicited by the activations of both Aδ- and C-fiber nociceptors. Additionally, Shank3 mutants showed a lower level of aperiodic exponents, which serve as a marker for the excitatory-inhibitory balance of neural activity. The aperiodic exponents mediated the relationship between genotype and nocifensive sensitivity as well as between genotype and neural responses elicited by nociceptive stimuli. Pharmacologically, the reduced nocifensive sensitivity and atypical excitatory-inhibitory balance were rescued by a GABAAR antagonist pentylenetetrazole. These findings highlight the critical role of Shank3 in pain processing and suggest that an impaired excitatory-inhibitory balance may be responsible for the reduced nocifensive reactivity in autism.

GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals

Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.

Electrophysiological signatures underlying variability in human memory consolidation

We experience countless pieces of new information each day, but remembering them later depends on firmly instilling memory storage in the brain. Numerous studies have implicated non-rapid eye movement (NREM) sleep in consolidating memories via interactions between hippocampus and cortex. However, the temporal dynamics of this hippocampal-cortical communication and the concomitant neural oscillations during memory reactivations remains unclear. To address this issue, the present study used the procedure of targeted memory reactivation (TMR) following learning of object-location associations to selectively reactivate memories during human NREM sleep. Cortical pattern reactivation and hippocampal-cortical coupling were measured with intracranial EEG recordings in patients with epilepsy. We found that TMR produced variable amounts of memory enhancement across a set of object-location associations. Successful TMR increased hippocampal ripples and cortical spindles, apparent during two discrete sweeps of reactivation. The first reactivation sweep was accompanied by increased hippocampal-cortical communication and hippocampal ripple events coupled to local cortical activity (cortical ripples and high-frequency broadband activity). In contrast, hippocampal-cortical coupling decreased during the second sweep, while increased cortical spindle activity indicated continued cortical processing to achieve long-term storage. Taken together, our findings show how dynamic patterns of item-level reactivation and hippocampal-cortical communication support memory enhancement during NREM sleep.

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brain's intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (α-tACS) and observed that α-tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of α-tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods.Significance Statement Alpha (8-12 Hz) oscillations and the default mode network (DMN) represent two major intrinsic activities of the brain. Prominent theories and extant findings converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Combining simultaneous electroencephalogram-functional-magnetic-resonance imaging (EEG-fMRI) with alpha-frequency transcranial alternating current stimulation (α-tACS), we demonstrated tightened coupling between alpha oscillations and DMN connectivity. These results lend credence to an inherent alpha-DMN link. Given DMN dysfunctions in multiple major neuropsychiatric conditions, the findings also highlight potential utility of α-tACS in clinical interventions by regulating the DMN.

EEG connectivity and BDNF correlates of fast motor learning in laparoscopic surgery

This paper investigates the neural mechanisms underlying the early phase of motor learning in laparoscopic surgery training, using electroencephalography (EEG), brain-derived neurotrophic factor (BDNF) concentrations and subjective cognitive load recorded from n = 31 novice participants during laparoscopy training. Functional connectivity was quantified using inter-site phase clustering (ISPC) and subjective cognitive load was assessed using NASA-TLX scores. The study identified frequency-dependent connectivity patterns correlated with motor learning and BDNF expression. Gains in performance were associated with beta connectivity, particularly within prefrontal cortex and between visual and frontal areas, during task execution (r = - 0.73), and were predicted by delta connectivity during the initial rest episode (r = 0.83). The study also found correlations between connectivity and BDNF, with distinct topographic patterns emphasizing left temporal and visuo-frontal links. By highlighting the shifts in functional connectivity during early motor learning associated with learning, and linking them to brain plasticity mediated by BDNF, the multimodal findings could inform the development of more effective training methods and tailored interventions involving practice and feedback.

High-Frequency rTMS Improves Visual Working Memory in Patients With aMCI: A Cognitive Neural Mechanism Study

BACKGROUND

Visual working memory (VWM), which is an essential component of higher cognitive processes, declines with age and is associated with the progression from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD). Cognitive impairment, particularly in VWM, is prominent in aMCI and may indicate disease progression. This study investigates the cognitive neural mechanisms responsible for VWM impairment in aMCI, with a focus on identifying the VWM processing stages affected. The study targets the dorsolateral prefrontal cortex (DLPFC) for repetitive transcranial magnetic stimulation (rTMS) to investigate its influence on VWM in aMCI patients. The role of the DLPFC in the top-down control of VWM processing is central to understanding rTMS effects on the stages of information processing in aMCI-related VWM impairments.

METHODS

A 7-day rTMS intervention was performed in 25 aMCI patients and 15 healthy elderly controls to investigate its effects on VWM and cognitive functions. Tasks included VWM change detection, digital symbol transformation, and the Stroop task for attention and executive functions. EEG analyses consisting of ERP, ERSP, and functional connectivity (wPLI) were integrated. The first part of the study addressed the cognitive neural mechanism of VWM impairment in aMCI and differentiated the processing stages using EEG. The second part investigated the effects of rTMS on EEG processing at different VWM stages and revealed cognitive neural mechanisms that improve visual working memory in aMCI.

RESULTS

The results indicated a significant deterioration of VWM tasks in aMCI, especially in accuracy and memory capacity, with prolonged reaction time and increased duration of the Stroop task. In the VWM memory encoding phase, N2pc amplitude, α-oscillation in the parieto-occipital region, and θ-band synchronization in the frontoparietal connectivity decreased. Conversely, rTMS improved N2pc amplitude, α-oscillation, and θ-band synchronization, which correlated with improved frontoparietal connectivity, parieto-occipital α-oscillation, and attentional capacity.

CONCLUSIONS

Patients with aMCI experience significant deterioration in VWM function, particularly during the encoding phase. This deterioration manifests in reduced accuracy and capacity of memory performance, accompanied by a significant decrease in N2pc amplitude, alpha oscillations, and theta-band connectivity in frontoparietal and fronto-occipital brain regions. rTMS proves to be a promising intervention that improves VWM, attention, and executive functions. In particular, it supports attention during target selection by increasing N2pc amplitude during encoding, enhancing alpha oscillations for better suppression of irrelevant information, and increasing synchronization in frontoparietal and occipital functional connectivity, which ultimately improves visual working memory.

Exploring the role of dimensionality transformation in episodic memory

Episodic memory must accomplish two adversarial goals: encoding and storing a multitude of experiences without exceeding the finite neuronal structure of the brain, and recalling memories in vivid detail. Dimensionality reduction and expansion ('dimensionality transformation') enable the brain to meet these demands. Reduction compresses sensory input into simplified, storable codes, while expansion reconstructs vivid details. Although these processes are essential to memory, their neural mechanisms for episodic memory remain unclear. Drawing on recent insights from cognitive psychology, systems neuroscience, and neuroanatomy, we propose two accounts of how dimensionality transformation occurs in the brain: structurally (via corticohippocampal pathways) and functionally (through neural oscillations). By examining cross-species evidence, we highlight neural mechanisms that may support episodic memory and identify crucial questions for future research.

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brain's intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (α-tACS) and observed that α-tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of α-tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods.

Resting state EEG in young children with Tuberous Sclerosis Complex: associations with medications and seizures

BACKGROUND

Tuberous Sclerosis Complex (TSC) is a rare genetic condition caused by mutation to TSC1 or TSC2 genes, with a population prevalence of 1/7000 births. TSC manifests behaviorally with features of autism, epilepsy, and intellectual disability. Resting state electroencephalography (EEG) offers a window into neural oscillatory activity and may serve as an intermediate biomarker between gene expression and behavioral manifestations. Such a biomarker could be useful in clinical trials as an endpoint or predictor of treatment response. However, seizures and antiepileptic medications also affect resting neural oscillatory activity and could undermine the utility of resting state EEG features as biomarkers in neurodevelopmental disorders such as TSC.

METHODS

This paper compares resting state EEG features in a cross-sectional cohort of young children with TSC (n = 49, ages 12-37 months) to 49 age- and sex-matched typically developing controls. Within children with TSC, associations were examined between resting state EEG features, seizure severity composite score, and use of GABA agonists.

RESULTS

Compared to matched typically developing children, children with TSC showed significantly greater beta power in permutation cluster analyses. Children with TSC also showed significantly greater aperiodic offset (reflecting nonoscillatory neuronal firing) after power spectra were parameterized using SpecParam into aperiodic and periodic components. Within children with TSC, both greater seizure severity and use of GABAergic antiepileptic medication were significantly and independently associated with increased periodic peak beta power.

CONCLUSIONS

The elevated peak beta power observed in children with TSC compared to matched typically developing controls may be driven by both seizures and GABA agonist use. It is recommended to collect seizure and medication data alongside EEG data for clinical trials. These results highlight the challenge of using resting state EEG features as biomarkers in trials with neurodevelopmental disabilities when epilepsy and anti-epileptic medication are common.

Major individual and regional variations in unit entrainment by oscillations of different frequencies

In vitro studies have shown that a neuron's electroresponsive properties can predispose it to oscillate at specific frequencies. In contrast, network activity in vivo can entrain neurons to rhythms that their biophysical properties do not predispose them to favor. However, there is limited information on the comparative frequency profile of unit entrainment across brain regions. Therefore, this study aimed to characterize the frequency profile of unit entrainment in cortex, thalamus, striatum, and basolateral amygdala (BLA) in rats of either sex. Neurons recorded simultaneously in a given brain region and behavioral state generally had very similar frequency profiles of unit entrainment. While cortical, striatal, and thalamic neurons were more strongly entrained by low than high local field potential (LFP) frequencies, increases in the power of these oscillations were linked to decreased firing rates for low frequencies versus increased firing rates for high frequencies. Deviating from this general trend, BLA neurons were more strongly entrained by high gamma than all other frequency bands in all subjects and states. By contrast, neurons in other regions displayed marked inter-individual variability. That is, although neurons in some regions had exceptionally high entrainment values in particular frequency bands, these were not observed consistently across rats. Based on these findings, some might infer that oscillations play a minor role or that different oscillatory patterns can support the same functions. Alternatively, the oscillations critical to brain function could be those not investigated here, namely those arising transiently in response to specific task variables or contexts. Perhaps those are less susceptible to genetic variations. While our findings do not allow us to determine which explanation is correct, they do highlight the perils of averaging.

Developmental axon diameter growth of central nervous system axons does not depend on ensheathment or myelination by oligodendrocytes

Myelination facilitates the rapid conduction of action potentials along axons. In the central nervous system (CNS), myelinated axons vary over 100-fold in diameter, with conduction speed scaling linearly with increasing diameter. Axon diameter and myelination are closely interlinked, with axon diameter exerting a strong influence on myelination. Conversely, myelinating Schwann cells in the peripheral nervous system can both positively and negatively affect axon diameter. However, whether axon diameter is regulated by CNS oligodendrocytes is less clear. Here, we investigated CNS axon diameter growth in the absence of myelin using mouse (Mbp shi/shi and Myrf conditional knockout) and zebrafish (olig2 morpholino) models. We find that neither the ensheathment of axons, nor the formation of compact myelin are required for CNS axons to achieve appropriate and diverse diameters. This indicates that developmental CNS axon diameter growth is independent of myelination, and shows that myelinating cells of CNS and PNS differentially influence axonal morphology.

Individualized Spectral Features in First-Episode and Drug-Naïve Major Depressive Disorder: Insights From Periodic and Aperiodic Electroencephalography Analysis

BACKGROUND

The detection of abnormal brain activity plays an important role in the early diagnosis and treatment of major depressive disorder (MDD). Recent studies have shown that the decomposition of the electroencephalography (EEG) spectrum into periodic and aperiodic components is useful for identifying the drivers of electrophysiologic abnormalities and avoiding individual differences.

METHODS

In this study, we aimed to elucidate the pathological changes in individualized periodic and aperiodic activities and their relationships with the symptoms of MDD. EEG data in the eyes-closed resting state were continuously recorded from 97 first-episode and drug-naïve patients with MDD and 90 healthy control participants. Both periodic oscillations and aperiodic components were obtained via the fitting oscillations and one-over f (FOOOF) algorithm and then used to compute individualized spectral features.

RESULTS

Patients with MDD presented higher canonical alpha and beta band power but lower aperiodic-adjusted alpha and beta power. Furthermore, we found that alpha power was strongly correlated with the age of patients but not with disease symptoms. The aperiodic intercept was lower in the parieto-occipital region and was positively correlated with Hamilton Depression Rating Scale scores after accounting for age and sex. In the asymmetry analysis, alpha activity appeared asymmetrical only in the healthy control group, whereas aperiodic activity was symmetrical in both groups.

CONCLUSIONS

The findings of this study provide insights into the role of abnormal neural spiking activity and impaired neuroplasticity in MDD progression and suggest that the aperiodic intercept in resting-state EEG may be a potential biomarker of MDD.

Caregiver-child neural synchrony: Magic, mirage, or developmental mechanism?

Young children transition in and out of synchronous states with their caregivers across physiology, behavior, and brain activity, but what do these synchronous periods mean? One body of two-brain studies using functional near-infrared spectroscopy (fNIRS) finds that individual, family, and moment-to-moment behavioral and contextual factors are associated with caregiver-child neural synchrony, while another body of literature finds that neural synchrony is associated with positive child outcomes. Taken together, it is tempting to conclude that caregiver-child neural synchrony may act as a foundational developmental mechanism linking children's experiences to their healthy development, but many questions remain. In this review, we synthesize recent findings and open questions from caregiver-child studies using fNIRS, which is uniquely well suited for use with caregivers and children, but also laden with unique constraints. Throughout, we highlight open questions alongside best practices for optimizing two-brain fNIRS to examine hypothesized developmental mechanisms. We particularly emphasize the need to consider immediate and global stressors as context for interpretation of neural synchrony findings, and the need for full inclusion of socioeconomically and racially diverse families in future studies.

Prefrontal cortex contribution in transitive inference task through the interplay of beta and gamma oscillations

Transitive inference allows people to infer new relations between previously experienced premises. It has been hypothesized that this logical thinking relies on a mental schema that spatially organizes elements, facilitating inferential insights. However, recent evidence challenges the need for these complex cognitive processes. To dig into the neural substrate driving TI cognitive processes, we examine the role of beta and gamma local field potential bands in the prefrontal cortex of 2 monkeys. During the inferential problem-solving period, we discover a tight link between beta and gamma bands modulation and TI complexity. This correlation diminishes its strength before initiating the motor response, indicating the chosen item. Notably, while the beta band maintains a constant relationship with TI performance throughout the trial, the gamma band shows a flexible relationship. This research highlights the role of beta and gamma interplay in cognitive computations when solving TI problems.

Physiological Entrainment: A Key Mind-Body Mechanism for Cognitive, Motor and Affective Functioning, and Well-Being

BACKGROUND

The human sensorimotor system can naturally synchronize with environmental rhythms, such as light pulses or sound beats. Several studies showed that different styles and tempos of music, or other rhythmic stimuli, have an impact on physiological rhythms, including electrocortical brain activity, heart rate, and motor coordination. Such synchronization, also known as the "entrainment effect", has been identified as a crucial mechanism impacting cognitive, motor, and affective functioning.

OBJECTIVES

This review examines theoretical and empirical contributions to the literature on entrainment, with a particular focus on the physiological mechanisms underlying this phenomenon and its role in cognitive, motor, and affective functions. We also address the inconsistent terminology used in the literature and evaluate the range of measurement approaches used to assess entrainment phenomena. Finally, we propose a definition of "physiological entrainment" that emphasizes its role as a fundamental mechanism that encompasses rhythmic interactions between the body and its environment, to support information processing across bodily systems and to sustain adaptive motor responses.

METHODS

We reviewed the recent literature through the lens of the "embodied cognition" framework, offering a unified perspective on the phenomenon of physiological entrainment.

RESULTS

Evidence from the current literature suggests that physiological entrainment produces measurable effects, especially on neural oscillations, heart rate variability, and motor synchronization. Eventually, such physiological changes can impact cognitive processing, affective functioning, and motor coordination.

CONCLUSIONS

Physiological entrainment emerges as a fundamental mechanism underlying the mind-body connection. Entrainment-based interventions may be used to promote well-being by enhancing cognitive, motor, and affective functions, suggesting potential rehabilitative approaches to enhancing mental health.

Change in aperiodic activity over first year of life is associated with later autism diagnosis and 18-month language development in infants with family history of autism

Delays in language often co-occur among toddlers diagnosed with autism. Despite the high prevalence of language delays, the neurobiology underlying such language challenges remains unclear. Prior research has shown reduced EEG power across multiple frequency bands in 3-to-6-month-old infants with an autistic sibling, followed by accelerated increases in power with age. Here we apply new methods to decompose the power spectra into aperiodic (broad band neural firing) and periodic (oscillations) activity to explore possible links between aperiodic changes in the first year of life and later language outcomes. Combining EEG data across two longitudinal studies of infants with and without autistic siblings, we assessed whether infants with an elevated familial likelihood (EFL) exhibit altered changes in both periodic and aperiodic EEG activity at 3 and 12 months of age, compared to those with a low likelihood (LL), and whether developmental change in activity is associated with language development. At 3-months of age, we observed that EFL infants have significantly lower aperiodic activity from 6.7-55Hz (p<0.05). However, change in aperiodic activity from 3 to 12 months was significantly increased in infants with a later diagnosis of autism, compared to EFL infants without an autism diagnosis. In addition, greater increases in aperiodic offset and slope from 3-to12-months were associated with worse language development measured at 18 months. Findings suggest that early age-dependent changes in EEG aperiodic power may serve as potential indicators of autism and language development in infants with family history of autism.

Multiscale organization of neuronal activity unifies scale-dependent theories of brain function

Brain recordings collected at different resolutions support distinct signatures of neural coding, leading to scale-dependent theories of brain function. Here, we show that these disparate signatures emerge from a heavy-tailed, multiscale functional organization of neuronal activity observed across calcium-imaging recordings collected from the whole brains of zebrafish and C. elegans as well as from sensory regions in Drosophila, mice, and macaques. Network simulations demonstrate that this conserved hierarchical structure enhances information processing. Finally, we find that this organization is maintained despite significant cross-scale reconfiguration of cellular coordination during behavior. Our findings suggest that this nonlinear organization of neuronal activity is a universal principle conserved for its ability to adaptively link behavior to neural dynamics across multiple spatiotemporal scales while balancing functional resiliency and information processing efficiency.

On Emergence of Spontaneous Oscillations in Kombucha and Proteinoids

An important part of studying living systems is figuring out the complicated steps that lead to order from chaos. Spontaneous oscillations are a key part of self-organisation in many biological and chemical networks, including kombucha and proteinoids. This study examines the spontaneous oscillations in kombucha and proteinoids, specifically exploring their potential connection to the origin of life. As a community of bacteria and yeast work together, kombucha shows remarkable spontaneous oscillations in its biochemical parts. This system can keep a dynamic balance and organise itself thanks to metabolic processes and complex chemical reactions. Similarly, proteinoids, which may have been primitive forms of proteins, undergo spontaneous fluctuations in their structure and function periodically. Because these oscillations happen on their own, they may play a very important part in the development of early life forms. This paper highlights the fundamental principles governing the transition from chaos to order in living systems by examining the key factors that influence the frequency and characteristics of spontaneous oscillations in kombucha and proteinoids. Looking into these rhythms not only helps us understand where life came from but also shows us ways to make self-organising networks in synthetic biology and biotechnology. There is significant discussion over the emergence of biological order from chemical disorder. This article contributes to the ongoing discussion by examining at the theoretical basis, experimental proof, and implications of spontaneous oscillations. The results make it clear that random oscillations are an important part of the change from nonliving to living matter. They also give us important information about what life is all about.

Linking brain-heart interactions to emotional arousal in immersive virtual reality

The subjective experience of emotions is linked to the contextualized perception and appraisal of changes in bodily (e.g., heart) activity. Increased emotional arousal has been related to attenuated high-frequency heart rate variability (HF-HRV), lower EEG parieto-occipital alpha power, and higher heartbeat-evoked potential (HEP) amplitudes. We studied emotional arousal-related brain-heart interactions using immersive virtual reality (VR) for naturalistic yet controlled emotion induction. Twenty-nine healthy adults (13 women, age: 26 ± 3) completed a VR experience that included rollercoasters while EEG and ECG were recorded. Continuous emotional arousal ratings were collected during a video replay immediately after. We analyzed emotional arousal-related changes in HF-HRV as well as in BHIs using HEPs. Additionally, we used the oscillatory information in the ECG and the EEG to model the directional information flows between the brain and heart activity. We found that higher emotional arousal was associated with lower HEP amplitudes in a left fronto-central electrode cluster. While parasympathetic modulation of the heart (HF-HRV) and parieto-occipital EEG alpha power were reduced during higher emotional arousal, there was no evidence for the hypothesized emotional arousal-related changes in bidirectional information flow between them. Whole-brain exploratory analyses in additional EEG (delta, theta, alpha, beta and gamma) and HRV (low-frequency, LF, and HF) frequency bands revealed a temporo-occipital cluster, in which higher emotional arousal was linked to decreased brain-to-heart (i.e., gamma→HF-HRV) and increased heart-to-brain (i.e., LF-HRV → gamma) information flow. Our results confirm previous findings from less naturalistic experiments and suggest a link between emotional arousal and brain-heart interactions in temporo-occipital gamma power.

Beyond-local neural information processing in neuronal networks

While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is integrated and combined across neuronal networks to encode higher order brain functions. One challenge lies in the large number of complex neural interactions. Neural networks use oscillating activity for information exchange between distributed nodes. To better understand building principles underlying the observation of synchronized oscillatory activity in a large-scale network, we developed a reductionistic neuronal network model. Fundamental building principles are laterally and temporally interconnected virtual nodes (microcircuits), wherein each node was modeled as a local oscillator. By this building principle, the neuronal network model can integrate information in time and space. The simulation gives rise to a wave interference pattern that spreads over all simulated columns in form of a travelling wave. The model design stabilizes states of efficient information processing across all participating neuronal equivalents. Model-specific oscillatory patterns, generated by complex input stimuli, were similar to electrophysiological high-frequency signals that we could confirm in the primate visual cortex during a visual perception task. Important oscillatory model pre-runners, limitations and strength of our reductionistic model are discussed. Our simple scalable model shows unique integration properties and successfully reproduces a variety of biological phenomena such as harmonics, coherence patterns, frequency-speed relationships, and oscillatory activities. We suggest that our scalable model simulates aspects of a basic building principle underlying oscillatory, large-scale integration of information in small and large brains.

Theta oscillations optimize a speed-precision trade-off in phase coding neurons

Theta-band oscillations (3-8 Hz) in the mammalian hippocampus organize the temporal structure of cortical inputs, resulting in a phase code that enables rhythmic input sampling for episodic memory formation and spatial navigation. However, it remains unclear what evolutionary pressures might have driven the selection of theta over higher-frequency bands that could potentially provide increased input sampling resolution. Here, we address this question by introducing a theoretical framework that combines the efficient coding and neural oscillatory sampling hypotheses, focusing on the information rate (bits/s) of phase coding neurons. We demonstrate that physiologically realistic noise levels create a trade-off between the speed of input sampling, determined by oscillation frequency, and encoding precision in rodent hippocampal neurons. This speed-precision trade-off results in a maximum information rate of ∼1-2 bits/s within the theta frequency band, thus confining the optimal oscillation frequency to the low end of the spectrum. We also show that this framework accounts for key hippocampal features, such as the preservation of the theta band along the dorsoventral axis despite physiological gradients, and the modulation of theta frequency and amplitude by running speed. Extending the analysis beyond the hippocampus, we propose that theta oscillations could also support efficient stimulus encoding in the visual cortex and olfactory bulb. More broadly, our framework lays the foundation for studying how system features, such as noise, constrain the optimal sampling frequencies in both biological and artificial brains.

Filtered Point Processes Tractably Capture Rhythmic And Broadband Power Spectral Structure in Neural Electrophysiological Recordings

Neural electrophysiological recordings arise from interacting rhythmic (oscillatory) and broadband (aperiodic) biological subprocesses. Both rhythmic and broadband processes contribute to the neural power spectrum, which decomposes the variance of a neural recording across frequencies. Although an extensive body of literature has successfully studied rhythms in various diseases and brain states, researchers only recently have systematically studied the characteristics of broadband effects in the power spectrum. Broadband effects can generally be categorized as 1) shifts in power across all frequencies, which correlate with changes in local firing rates and 2) changes in the overall shape of the power spectrum, such as the spectral slope or power law exponent. Shape changes are evident in various conditions and brain states, influenced by factors such as excitation to inhibition balance, age, and various diseases. It is increasingly recognized that broadband and rhythmic effects can interact on a sub-second timescale. For example, broadband power is time-locked to the phase of <1 Hz rhythms in propofol induced unconsciousness. Modeling tools that explicitly deal with both rhythmic and broadband contributors to the power spectrum and that capture their interactions are essential to help improve the interpretability of power spectral effects. Here, we introduce a tractable stochastic forward modeling framework designed to capture both narrowband and broadband spectral effects when prior knowledge or theory about the primary biophysical processes involved is available. Population-level neural recordings are modeled as the sum of filtered point processes (FPPs), each representing the contribution of a different biophysical process such as action potentials or postsynaptic potentials of different types. Our approach builds on prior neuroscience FPP work by allowing multiple interacting processes and time-varying firing rates and by deriving theoretical power spectra and cross-spectra. We demonstrate several properties of the models, including that they divide the power spectrum into frequency ranges dominated by rhythmic and broadband effects, and that they can capture spectral effects across multiple timescales, including sub-second cross-frequency coupling. The framework can be used to interpret empirically observed power spectra and cross-frequency coupling effects in biophysical terms, which bridges the gap between theoretical models and experimental results.

Altered cortical synaptic lipid signaling leads to intermediate phenotypes of mental disorders

Excitation/inhibition (E/I) balance plays important roles in mental disorders. Bioactive phospholipids like lysophosphatidic acid (LPA) are synthesized by the enzyme autotaxin (ATX) at cortical synapses and modulate glutamatergic transmission, and eventually alter E/I balance of cortical networks. Here, we analyzed functional consequences of altered E/I balance in 25 human subjects induced by genetic disruption of the synaptic lipid signaling modifier PRG-1, which were compared to 25 age and sex matched control subjects. Furthermore, we tested therapeutic options targeting ATX in a related mouse line. Using EEG combined with TMS in an instructed fear paradigm, neuropsychological analysis and an fMRI based episodic memory task, we found intermediate phenotypes of mental disorders in human carriers of a loss-of-function single nucleotide polymorphism of PRG-1 (PRG-1R345T/WT). Prg-1R346T/WT animals phenocopied human carriers showing increased anxiety, a depressive phenotype and lower stress resilience. Network analysis revealed that coherence and phase-amplitude coupling were altered by PRG-1 deficiency in memory related circuits in humans and mice alike. Brain oscillation phenotypes were restored by inhibtion of ATX in Prg-1 deficient mice indicating an interventional potential for mental disorders.

Harnessing the sensing and stimulation function of deep brain-machine interfaces: a new dawn for overcoming substance use disorders

Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.

Advances and challenges in neuroimaging-based pain biomarkers

Identifying neural biomarkers of pain has long been a central theme in pain neuroscience. Here, we review the state-of-the-art candidates for neural biomarkers of acute and chronic pain. We classify these potential neural biomarkers into five categories based on the nature of their target variables, including neural biomarkers of (1) within-individual perception, (2) between-individual sensitivity, and (3) discriminability for acute pain, as well as (4) assessment and (5) prospective neural biomarkers for chronic pain. For each category, we provide a synthesized review of candidate biomarkers developed using neuroimaging techniques including functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalography (EEG). We also discuss the conceptual and practical challenges in developing neural biomarkers of pain. Addressing these challenges, optimal biomarkers of pain can be developed to deepen our understanding of how the brain represents pain and ultimately help alleviate patients' suffering and improve their well-being.

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.

Challenges and Approaches in the Study of Neural Entrainment

When exposed to rhythmic stimulation, the human brain displays rhythmic activity across sensory modalities and regions. Given the ubiquity of this phenomenon, how sensory rhythms are transformed into neural rhythms remains surprisingly inconclusive. An influential model posits that endogenous oscillations entrain to external rhythms, thereby encoding environmental dynamics and shaping perception. However, research on neural entrainment faces multiple challenges, from ambiguous definitions to methodological difficulties when endogenous oscillations need to be identified and disentangled from other stimulus-related mechanisms that can lead to similar phase-locked responses. Yet, recent years have seen novel approaches to overcome these challenges, including computational modeling, insights from dynamical systems theory, sophisticated stimulus designs, and study of neuropsychological impairments. This review outlines key challenges in neural entrainment research, delineates state-of-the-art approaches, and integrates findings from human and animal neurophysiology to provide a broad perspective on the usefulness, validity, and constraints of oscillatory models in brain-environment interaction.

Dog-human vocal interactions match dogs' sensory-motor tuning

Within species, vocal and auditory systems presumably coevolved to converge on a critical temporal acoustic structure that can be best produced and perceived. While dogs cannot produce articulated sounds, they respond to speech, raising the question as to whether this heterospecific receptive ability could be shaped by exposure to speech or remains bounded by their own sensorimotor capacity. Using acoustic analyses of dog vocalisations, we show that their main production rhythm is slower than the dominant (syllabic) speech rate, and that human-dog-directed speech falls halfway in between. Comparative exploration of neural (electroencephalography) and behavioural responses to speech reveals that comprehension in dogs relies on a slower speech rhythm tracking (delta) than humans' (theta), even though dogs are equally sensitive to speech content and prosody. Thus, the dog audio-motor tuning differs from humans', and we hypothesise that humans may adjust their speech rate to this shared temporal channel as means to improve communication efficacy.

Alterations in aperiodic and periodic EEG activity in young children with Down syndrome

Down syndrome (DS) is the most common cause of intellectual disability, yet little is known about the neurobiological pathways leading to cognitive impairments. Electroencephalographic (EEG) measures are commonly used to study neurodevelopmental disorders, but few studies have focused on young children with DS. Here we assess resting state EEG data collected from toddlers/preschoolers with DS (n = 29, age 13-48 months old) and compare their aperiodic and periodic EEG features with both age-matched (n = 29) and developmental-matched (n = 58) comparison groups. DS participants exhibited significantly reduced aperiodic slope, increased periodic theta power, and decreased alpha peak amplitude. A majority of DS participants displayed a prominent peak in the theta range, whereas a theta peak was not present in age-matched participants. Overall, similar findings were also observed when comparing DS and developmental-matched groups, suggesting that EEG differences are not explained by delayed cognitive ability.

Effect of Electrode Distance and Size on Electrocorticographic Recordings in Human Sensorimotor Cortex

Subdural electrocorticography (ECoG) is a valuable technique for neuroscientific research and for emerging neurotechnological clinical applications. As ECoG grids accommodate increasing numbers of electrodes and higher densities with new manufacturing methods, the question arises at what point the benefit of higher density ECoG is outweighed by spatial oversampling. To clarify the optimal spacing between ECoG electrodes, in the current study we evaluate how ECoG grid density relates to the amount of non-shared neurophysiological information between electrode pairs, focusing on the sensorimotor cortex. We simultaneously recorded high-density (HD, 3 mm pitch) and ultra-high-density (UHD, 0.9 mm pitch) ECoG, obtained intraoperatively from six participants. We developed a new metric, the normalized differential root mean square (ndRMS), to quantify the information that is not shared between electrode pairs. The ndRMS increases with inter-electrode center-to-center distance up to 15 mm, after which it plateaus. We observed differences in ndRMS between frequency bands, which we interpret in terms of oscillations in frequencies below 32 Hz with phase differences between pairs, versus (un)correlated signal fluctuations in the frequency range above 64 Hz. The finding that UHD recordings yield significantly higher ndRMS than HD recordings is attributed to the amount of tissue sampled by each electrode. These results suggest that ECoG densities with submillimeter electrode distances are likely justified.

Physiology-inspired bifocal fronto-parietal tACS for working memory enhancement

Aging populations face significant cognitive challenges, particularly in working memory (WM). Transcranial alternating current stimulation (tACS) offer promising avenues for cognitive enhancement, especially when inspired by brain physiology. This study (NCT04986787) explores the effect of multifocal tACS on WM performance in healthy older adults, focusing on fronto-parietal network modulation. Individualized physiology-inspired tACS applied to the fronto-parietal network was investigated in two blinded cross-over experiments. The first experiment involved monofocal/bifocal theta-tACS to the fronto-parietal network, while in the second experiment cross-frequency theta-gamma interactions between these regions were explored. Participants have done online WM tasks under the stimulation conditions. Network connectivity was assessed via rs-fMRI and multichannel electroencephalography. Prefrontal monofocal theta tACS modestly improved WM accuracy over sham (d = 0.30). Fronto-parietal stimulation enhanced WM task processing speed, with the strongest effects for bifocal in-phase theta tACS (d = 0.41). Cross-frequency stimulations modestly boosted processing speed with or without impairing task accuracy depending on the stimulation protocol. This research adds to the understanding of physiology-inspired brain stimulation for cognitive enhancement in older subjects.

Changes in electrophysiological aperiodic activity during cognitive control in Parkinson's disease

Cognitive symptoms in Parkinson's disease are common and can significantly affect patients' quality of life. Therefore, there is an urgent clinical need to identify a signature derived from behavioural and/or neuroimaging indicators that could predict which patients are at increased risk for early and rapid cognitive decline. Recently, converging evidence identified that aperiodic activity of the EEG reflects meaningful physiological information associated with age, development, cognitive and perceptual states or pathologies. In this study, we aimed to investigate aperiodic activity in Parkinson's disease during cognitive control and characterize its possible association with behaviour. Here, we recorded high-density EEG in 30 healthy controls and 30 Parkinson's disease patients during a Simon task. We analysed task-related behavioural data in the context of the activation-suppression model and extracted aperiodic parameters (offset, exponent) at both scalp and source levels. Our results showed lower behavioural performances in cognitive control as well as higher offsets in patients in the parieto-occipital areas, suggesting increased excitability in Parkinson's disease. A small congruence effect on aperiodic parameters in pre- and post-central brain areas was also found, possibly associated with task execution. Significant differences in aperiodic parameters between the resting-state, pre- and post-stimulus phases were seen across the whole brain, which confirmed that the observed changes in aperiodic activity are linked to task execution. No correlation was found between aperiodic activity and behaviour or clinical features. Our findings provide evidence that EEG aperiodic activity in Parkinson's disease is characterized by greater offsets, and that aperiodic parameters differ depending on arousal state. However, our results do not support the hypothesis that the behaviour-related differences observed in Parkinson's disease are related to aperiodic changes. Overall, this study highlights the importance of considering aperiodic activity contributions in brain disorders and further investigating the relationship between aperiodic activity and behaviour.

High-Density, Conformable Conducting Polymer-Based Implantable Neural Probes for the Developing Brain

Neurologic and neuropsychiatric disorders substantially impact the pediatric population, but there is a lack of dedicated devices for monitoring the developing brain in animal models, leading to gaps in mechanistic understanding of how brain functions emerge and their disruption in disease states. Due to the small size, fragility, and high water content of immature neural tissue, as well as the absence of a hardened skull to mechanically support rigid devices, conventional neural interface devices are poorly suited to acquire brain signals without inducing damage. Here, the authors design conformable, implantable, conducting polymer-based probes (NeuroShanks) for precise targeting in the developing mouse brain without the need for skull-attached, rigid mechanical support structures. These probes enable the acquisition of high spatiotemporal resolution neurophysiologic activity from superficial and deep brain regions across unanesthetized behavioral states without causing tissue disruption or device failure. Once implanted, probes are mechanically stable and permit precise, stable signal monitoring at the level of the local field potential and individual action potentials. These results support the translational potential of such devices for clinically indicated neurophysiologic recording in pediatric patients. Additionally, the role of organic bioelectronics as an enabling technology to address questions in developmental neuroscience is revealed.