Frontiers in Systems Neuroscience

Activity in the gamma frequency range (>30Hz) serves as a neural hallmark of stimulus encoding across multiple sensory modalities. Based on its distinct properties, this activity could be classified as broadband or narrowband oscillations. Visual stimuli, such as a grating, induce narrowband oscillations, which have shown promise as diagnostic markers for neurological disorders. However, such tasks are discomforting for the subjects. These can be made subject-friendly by swapping the stimulus for an auditory one; however, most auditory stimuli elicit a broadband activity. In search of an ideal candidate to induce auditory narrowband oscillations, we previously recorded responses to auditory gratings in electroencephalography (EEG) recordings (Gulati & Ray, 2024) using a passive listening paradigm. We observed a focal sustained broadband increase in high gamma activity (70-150Hz), which was not selective towards any stimulus attributes. Nevertheless, since EEG represents activity of a large population, it remained unclear whether observed activity represented a broadband increase or comprised multiple narrowband rhythms, and whether it was indeed untuned. To elucidate this, we recorded invasively from the regions on and around the superior temporal gyrus, closer to Heschl's gyrus of seven patients undergoing epilepsy resection surgery. Patients were either under anesthesia or fully awake while we presented auditory gratings through loudspeakers. We found a transient increase in broadband gamma activity (30-100Hz) followed by a sustained suppression of beta rhythm (14-28Hz). This broadband gamma was selective for slower spectro-temporal modulations. Our result compares the responses obtained across different recording scales and corroborates that auditory gratings produce broadband but not narrowband gamma activity.

Background: Understanding fetal brain responses to hypoxia-ischemia is of growing interest, as it remains unclear to what extent these responses differ from those of newborns and whether developmental adaptations during gestation contribute to such differences. Materials and methods: Using a combination of broadband near-infrared spectroscopy (bNIRS) and diffuse correlation spectroscopy (DCS), we measured brain microvascular oxygen saturation (StO2), total hemoglobin concentration (THC), relative cerebral blood flow (rCBF), and the oxidation state of cytochrome c oxidase (oxCCO) during an acute ischemic challenge in the brains of five rabbit fetuses in utero. Simultaneous measurements were acquired from four control fetuses located in the contralateral uterine horn. Results: During the hypoxia-ischemia phase, we observed a rapid reduction of CBF (47% +/- 29%) and StO2, (from 64.7% +/- 1.5% at baseline to 40.6% +/- 2.0%) together with an increase of oxCCO (3.7 uM +/- 0.5 uM). Conclusion: The simultaneous increase in oxCCO and decrease in rCBF and StO2 during hypoxia-ischemia reveals a cerebral response pattern that differs from that commonly reported in the newborn brain. These findings support the hypothesis that unique developmental adaptations influence fetal cerebral metabolism during hypoxic-ischemic stress.

Astrocytes actively regulate synaptic transmission and neuronal excitability, yet their role in orchestrating macroscopic cortical network regimes and slow-wave oscillations remains an active area of reasearch. This study investigates how bidirectional neuron astrocyte interactions shape emergent population dynamics using a computational network model of excitatory and inhibitory neurons coupled to an astrocyte. The results identify astrocytic feedback topology, rather than astrocytic coupling strength alone, as a key determinant of emergent cortical network dynamics. By systematically dissecting pathway-specific connectivity, it has been shown that the neuronal population driving astrocytic activation and the neuronal population receiving gliotransmission jointly determine whether the network occupies asynchronous irregular (AI), synchronous irregular (SI), synchronous regular(SR), asynchronous regular(AR) or quiescent regimes.Directing gliotransmission selectively onto excitatory neurons consistently promotes population synchrony regardless of the population influencing astrocytic dynamics, whereas selective modulation of inhibitory interneurons induces network quiescence via strong suppression. Under dual-target gliotransmission, network synchrony is dictated by the population driving astrocytic dynamics: excitatory-only drive promotes synchrony, while combined or inhibitory-specific drive preserves asynchronous states. Furthermore, the model reveals that astrocytic signaling kinetics provide an additional temporal control mechanism that regulates the frequency and persistence of self sustained up states.

We investigated how auditory and visual presentation of small numerical magnitudes and non- numerical magnitudes are passively processed at the neural level. Earlier research suggests automatic processing of quantities occurs rapidly in the brain. However, there are no studies on passive, pre-attentive change detection of magnitudes utilizing mismatch negativity (MMN) across sensory modalities in same participants, important to understand amodal processing of magnitudes. We recorded EEG in passive auditory and visual multi-feature oddball and control paradigms from the same adult individuals (N=104/105, respectively). In the auditory paradigm, the standard stimulus was a 2-part sound, while the deviant stimuli varied in frequency, intensity, gap length, and number of sounds (1-part and 3-part stimuli). In the visual paradigm, the standard stimulus was a dot array containing two medium-sized dots, while the deviant stimuli varied in numerosity (1 or 3 dots) and dot size (small or large). The results for the auditory paradigm showed significant MMN across all features, including comparisons with deviant-alone control stimuli, except for the 2-part vs. 1-part contrast. The results for the visual paradigm suggested differences in processing across all features apart from large and medium-size stimuli. These results replicate earlier MMN findings that the brain distinguishes pre-attentively changes in sound frequency, amplitude and gap. Auditorily presented small numerical magnitudes also showed pre-attentive processing but less robustly to the contrast 2 vs. 1. Visual pre-attentive processing of small numerical magnitudes was also observed. Together these findings could be interpreted to support the existence of abstract, pre- attentive processing of numerical magnitudes. Highlights* Auditory and visual processing of numerical and non-numerical magnitudes was examined with EEG * Auditory non-numerical magnitudes and medium vs. small dots replicated earlier MMN studies * aMMN and vMMN showed pre-attentive processing of changes in small numerosities 2 vs. 3 and 2 vs. 1 * 2 vs. 1 tone processing was less robust than 2 vs. 3 or the processing of the visual contrasts * The brain processes changes in small non-symbolic magnitudes pre-attentively

This study presents a systematic investigation of the brain's own microwave emissions in the 0.85-5.0 GHz range, aiming to validate microwave encephalography (MWEG) as a novel tool for cognitive state assessment. Early experiments (2016-2019) using comparisons with background noise, a control phantom, and different antenna types confirmed the endogenous origin of the recorded signals. In a 2021 experiment (118 measurements on one subject, right and left occipital areas), we observed a significant functional hemispheric asymmetry: the number of "substantial spikes" was significantly higher in darkness for the right hemisphere (p<0.001) and higher in light for the left hemisphere (p<0.001). A 2025 group study (three subjects) confirmed the asymmetry (p<0.05 for right, p<0.01 for left). We describe the signal processing pipeline, including spike detection and statistical analysis. Two complementary mathematical models are presented: an energy-based model linking optical input to microwave emission via neural activity modes, and an information-based model interpreting spectral changes in terms of structural pattern overlap with memory engrams. An estimation of the physical dimensions of possible brain resonators (2-22 mm) is provided. The results are discussed in the context of hemispheric specialization and the information commutation theory. This work opens new perspectives for developing low cost, contactless brain monitoring systems.

Neural activity during performance of a cognitive or motor task is often described as lying on a low-dimensional manifold in the space of all possible activity patterns. When multiple tasks are performed, an individual brain region's activity may generate multiple such manifolds. Several models have been introduced to explain this capability, each with different assumptions concerning architecture and inputs. Here, we develop a theory of multi-tasking according to which models and experimental paradigms can be organized. We introduce a distinction between sequential and simultaneous multi-tasking, the performance of one task at a time or multiple in parallel. We show that interference between tasks often prevents multi-tasking and introduce a model of ``task barcodes" that circumvents this problem and parameterizes a neuronal population's task selectivity. Our theory reveals categorical distinctions between neural circuit mechanisms supporting multi-tasking that can be probed using physiological recordings.

Background: High-definition transcranial alternating current stimulation (HD-tACS) is favored by the neurostimulation community for its precision and ability to influence neuronal dynamics. Yet, the exact mechanism by which the underlying brain structures are being affected remains unclear. We believe that the investigation of the aperiodic nature of the electroencephalograph (EEG) could shed light on the modulatory effects of HD-tACS. Methods: We analyzed the EEG of 9 participants during a compensatory tracking task (CTT) in two sessions, each with different HD-tACS protocols. Every session consisted of an initial period of no stimulation, followed by 30 Hz HD-tACS in the left motor (M30) or frontal (F30) cortex. We then isolated the aperiodic component of the EEG and calculated its spectral slope {beta}. Results and Discussion: {beta} decreased during F30 mainly in the right frontal cortex, indicating a shift towards higher frequencies and an increase of the excitatory/inhibitory balance. Additionally, we found that despite the long monotonus task the accuracy of the participants did not decrease, which might be attributed to the ability of both M30 and F30 to sustain attention for prolonged time. Finally, the change of CTT accuracy during the stimulation correlated with the {beta} of specific channels before the stimulation. This indicates the potential of {beta} to be used as a screening biomarker in future studies. In conclusion, we showed the ability of HD-tACS to alter EEG aperiodic dynamics and paved the way for future exploration of such dynamics in the field.

Introduction: Observation of a partner in pain elicits empathy-related cortical responses and social support can modulate pain. Objective: The aim of this study was to investigate empathy and the cortical response during observation of a romantic partner in pain with and without consoling touch. Methods: A total of 40 participants (20 couples) completed validated measures of handedness, pain and empathy. Each member of the couple was randomly assigned to be either the recipient of cold pressor pain or the consoling partner undergoing EEG. EEG activity was recorded from the consoling partner during baseline and pain conditions (with and without consoling touch) to investigate how observation of a partner in pain could alter mu rhythms. Results: Observing a romantic partner in pain significantly enhanced mu at electrodes C4 and Cz, yet addition of consoling touch did not significantly change mu rhythms. Personal Distress was positively associated with mu rhythm enhancement at electrode C4 during pain without consoling touch (r = .576, p = .008). Conclusion: Viewing a romantic partner in pain significantly increased mu rhythms, that was not further changed by offering consoling touch. The association between increases in mu rhythms and Personal Distress in the current study suggests that self-oriented distress may be especially relevant to sensorimotor cortex responses when witnessing a partner in pain, particularly when consoling touch is not possible. Keywords: pain; empathy, EEG, mu rhythms, cold pressor

The basal ganglia (BG), thalamus and cortex form the BG-thalamo-cortical (BGTC) network, which is essential for voluntary movement and centrally involved in Parkinson's disease (PD). In both patients and animal models, BG neuronal activity exhibits exaggerated oscillatory synchronization in the beta frequencies (13-30 Hz). Theoretical studies have proposed multiple network mechanisms for the generation of these abnormal beta oscillations. However, key properties, such as frequency and power distribution across BG nuclei, vary substantially among patients and between animal models (rodents vs. non-human primates). This variability complicates direct comparisons between theoretical models and experimental data, and questions whether distinct neuronal mechanisms may underlie beta oscillations across species. In an experimentally constrained BGTC network model with species-specific parameters (synaptic, neuronal, and network properties), we evaluate the features of abnormal beta oscillatory activity generated by different mechanisms. The network's negative feedback loops serve as potential sources of spontaneous oscillations. Using rodent- or primate-constrained models, we compare the spectral properties of oscillatory activity across network populations for each loop and derive the expected phase relationships between populations to align predictions with existing rodent data and propose testable hypotheses for primates. We also demonstrate how oscillation frequency can be modulated when multiple generation mechanisms interact as coupled oscillators in the full network. Our results, combined with observed cross-species beta oscillations characteristics, suggest that abnormal beta oscillations likely arise from distinct mechanisms in rodents and primates.

The discrepancy between chronological age and predicted brain age, derived from neuroimaging data, serves as a biomarker for neurological health. However, the exact electroencephalography (EEG) mechanisms contributing to the variation of brain age are yet to be understood. This paper comprehensively investigated the relationship between brain age estimated from EEG-based functional connectivity and cognitive or mental manifestations of healthy adults. A mean absolute error (MAE) of 5.97 and R-squared (R2) of 0.85 were reported for training data, while an MAE of 10.73 and R2 of 0.56 were reported for the prediction data. Increased brain age was found to be negatively related to the scores of memory and attention tasks. Most young adults (19 of 26) suffering from mental symptoms such as addictions, depression, phobia, and anorexia nervosa demonstrated higher brain age than their chronological age. Alcohol consumption was also significantly correlated to the difference between brain and chronological ages. The functional connectivity of frontal and central motor regions was found to be most distinguishing between individuals with high and low brain ages. These brain regions were significantly correlated with brain age but not chronological age, suggesting a relatively stronger relationship with biological age. Ablation studies further confirmed the importance of the frontal brain and all bands EEG in the prediction of brain age. This work advances the understanding of EEG mechanisms contributing to brain age and could provide an interpretation for physiological or psychological conditions associated with brain age.

Parkinson's disease (PD) gait is characterized by reduced walking speed, short shuffling steps, increased double stance time, and increased step time variability. The most debilitating symptom in PD is freezing of gait (FOG), a context-dependent locomotion arrest, mostly occurring while encountering turns, obstacles, and narrow pathways. FOG is more pronounced during turning, a complex task that challenges motor planning and execution. Here, we simulate this phenomenon using a computational model inspired by the cortico-basal ganglia circuitry, based on reinforcement learning (RL) principles. In the model, the agent was trained to learn a value profile while navigating a path that involved turning. To simulate PD conditions, we clamped the temporal difference (TD) error, typically associated with dopaminergic signaling. Our model results show that compared to healthy controls (HC), PD non-freezers and PD freezers exhibit a reduction in turning velocity and step length, alongside an attenuated compensatory step-width widening at the sharpest turn, more pronounced in PD freezers, who additionally show increased step time variability. These kinematic impairments worsen during tighter turns. Ultimately, this model implicates FOG as a problem of impaired estimation of value over space and could potentially represent a generalized deficit in gait impairments in PD.

We explored the role of proprioceptive feedback from Ia afferents in intra-muscle synergies stabilizing muscle force with the help of muscle vibration, which is known to increase presynaptic inhibition of those afferents to alpha-motoneurons. Healthy young adults performed an accurate three-finger force production task by pressing with the index, middle and ring fingers of the dominant (right) hand. This was done without vibration and with vibration applied to the extrinsic finger flexors, extensors, both muscles, the palm, or biceps brachii. High-density electromyography was used to identify motor units in both finger flexors and extensors. The raster plots of motor units were converted into firing frequency functions and then subjected to principal component analysis to extract stable motor unit groups (MU-modes). The uncontrolled manifold (UCM) analysis was used to quantify the variance components affecting and not affecting force, and the synergy index in the MU-mode space. Contrary to our expectations, the synergy index did not drop under any vibration condition and showed an increase during vibration applied simultaneously to both flexor and extensor muscles. This was primarily associated with a drop in the component of variance affecting the force magnitude. The results speak against an important role of monosynaptic Ia-afferent projections to alpha-motoneurons in the formation of intra-muscle synergies. They suggest that muscle vibration can play the role of a noisy signal decreasing performance-affecting variance via stochastic resonance effects.

Recurrent cortical circuits are hypothesized to perform Bayesian posterior sampling through their stochastic dynamics. Yet identifying which sampling algorithm a circuit intrinsically implements, as opposed to mapping manually designing algorithms onto circuits, requires rigorous theoretical analysis of the biological circuit dynamics, and remains a fundamental open challenge because of the analytical intractability of nonlinear recurrent dynamics. Here, we tackle this challenge through comprehensive theoretical analysis of analytically tractable nonlinear recurrent circuit dynamics used in neuroscience research-- the continuous attractor network (CAN). Surprisingly, we find that the circuit, without any non-Gaussian components in its connectivity or internal variability, intrinsically emerges heavy-tailed subspaces dynamics described as scale-mixture Langevin posterior sampling. The mechanism emerges from the circuits own nonlinear dynamics: divisive normalization renders the overall population activation Gamma-distributed which multiplicatively modulates the time constant of the Langevin sampling in the stimulus feature subspace. Eventually, this scale-mixture structure produces a heavy-tailed (Student-t) sampling step size distribution, significantly accelerating the sampling speed. Moreover, the scale-mixture Langevin sampling approaches a Levy-like regime in the weak input limit. Our results analytically identify an intrinsic circuit algorithm embedded in subspaces of nonlinear recurrent dynamics, and establish a minimal circuit mechanism for endogenous heavy-tail statistics without any explicit non-Gaussian components.

The posterior parietal cortex integrates somatosensory information with signals supporting action, spatial cognition, and multisensory processing, yet the fine-scale topographic relationship between the intraparietal sulcus (IPS) and primary somatosensory cortex (S1) remains poorly understood. Here, we used resting-state fMRI in 10 awake rhesus macaques to characterize functional connectivity between four S1 subregions (areas 3a, 3b, 1, and 2) and 223 single-voxel seeds densely sampling an IPS-centered parietal territory. Across S1 subregions and hemispheres, IPS-S1 connectivity exhibited a highly structured spatial organization characterized by systematic anteroposterior and mediolateral gradients and a prominent semi-oval topology following the geometry of the intraparietal sulcus. Connectivity was generally stronger toward the cortical banks and convexities and weaker near the sulcal fundus, a pattern that could not be explained by reduced local temporal signal-to-noise ratio. Unsupervised hierarchical clustering independently recovered this spatial organization and identified connectivity-defined subdivisions that only partially corresponded to classical cytoarchitectonic boundaries. Along the S1 mediolateral axis, prominent troughs in cluster-averaged connectivity profiles corresponded to several previously described somatotopic transitions, allowing to consistently map putative boundaries between tongue and face representations and between hand and trunk-leg representations. Based on these boundaries, individual IPS clusters exhibited connectivity with multiple S1 body-part sectors, with the pattern of connectivity across these sectors varying across IPS clusters and S1 subregions. Together, our findings reveal that IPS-S1 functional connectivity is organized by a large-scale sulcal topology while preserving fine-grained features of S1 somatotopic organization. These findings suggest that the functional organization of the IPS is shaped by multiple spatial dimensions, including large-scale sulcal topology and the organization of S1 subregions and body-part representations, which may together contribute to the functional specialization of individual IPS regions.

Efficient processing of complex acoustic signals within noisy environments is a fundamental challenge for the auditory system. This study investigates the cortical representation of structured noise and vocalizations embedded in it using widefield calcium imaging with the high-speed Calcium indicator jGCaMP8m in the mouse auditory cortex. The spectrotemporal statistics of the structured noise were controllable, including its temporal variance and cross-frequency correlations. We find that structured noise invokes spatially and temporally complex neural responses across the auditory cortex that reflect the spectrotemporal properties of the noise. We identified partially distinct noise response components: noise onset responses showed stronger overlap with primary than secondary areas, while later suppression components did not show a significant primary-secondary bias. Vocalization-evoked responses were significantly attenuated in the presence of noise compared to silent contexts, exhibiting reduced onset magnitude and altered adaptation dynamics. Using decoding we find the neural representation of vocalizations to become more discriminable with longer preceding noise exposure. Furthermore, structured noise cochleograms could be reconstructed from neural population activity, with performance depending on stimulus statistics, tending to be higher in primary than secondary auditory cortex while remaining substantial across both. These findings suggest that the mouse auditory cortex represents acoustic targets in noise through context-dependent and spatially distributed population activity, shaped by both the statistical structure and temporal history of the acoustic background.

The mammalian neocortex is a complex structure with many interacting regions and cell types. Multiple efforts have recently generated large datasets from multiple cortical regions to determine generalizing principles of cortical organization and activity. One such generalizing principle is the concept of a cortical hierarchy - low-dimensional macroscopic gradients of biological properties across cortical areas and a hierarchy of timescales for information processing. Such gradients in properties along a cortical hierarchy may explain how different cortical regions serve different computational roles (i.e. sensory processing versus working memory). We set out to investigate such principles using biologically realistic simulations. Starting from our previously published model of the mouse primary visual cortex (VISp) with updated connectivity reflecting recent large-scale connectomics and synaptic physiology datasets, we generated multiple versions of the cortical circuit model with increased connectivity as measured along the cortical hierarchy. We then measure the resulting neural activity in response to a simulated single-cell photostimulation paradigm and thalamocortical inputs. We found that overall circuit stability increased across the cortical hierarchy, due to both increases in connection density and increases in the relative proportion of SST cells. The results of this study link cortical cell types to population-level activity, generating insight into the function of local circuits across cortex.

Neural response latency measures when stimulus-evoked activity becomes detectable, but it does not reveal when the brain assigns the causal contribution of that activity to events in the external world. We define this temporal adjustment as projected delay and measure it in the macaque inferotemporal cortex using optogenetic stimulation and behavior. Two rhesus macaques performed a cortical perturbation detection task in which a visual masker appeared at different times relative to stimulation. Detection was selectively impaired when the masker began 200 ms before stimulation. We then tested this timing using Perceptography, a method that optimizes image perturbations to mimic stimulation-evoked visual events. Image perturbations presented 200 ms before stimulation elicited higher false-alarm rates than perturbations presented at stimulation onset. Here, we show that stimulation-evoked visual events in inferotemporal cortex depend preferentially on visual information originating approximately 200 ms before the cortical perturbation. This projected delay exceeds typical inferotemporal response latencies and suggests temporally extended readout mechanisms relevant to causal models of visual perception and the timing of cortical visual prostheses.

The vestibular system consists of peripheral end organs that provide the brain with temporally precise information about head position in space required for motor coordination and postural stability. Irregular vestibular afferents of the inner ear are particularly sensitive to rapid changes in acceleration, or jerk, which is necessary for making quick, corrective movements and postural adjustments. Vestibular short-latency evoked potentials (VsEPs) are waveforms that provide a measure of synchronous irregular afferent activity originating from peripheral (early peaks) and central (later peaks) vestibular pathways. These VsEPs are elicited by head jerk stimuli, commonly used preclinically. Previous studies have shown that early VsEP waves are dependent upon jerks of an adequate intensity, number, and duration. However, less is known about jerk characteristics necessary for generation of reliable VsEP signals centrally (later peaks). The present study examines the precise temporal dynamics of peripheral and central vestibular centers across a broad range of mechanical vestibular load in rats. We found differential changes in synchrony and timing driven by jerk repetition, intensity, and duration between early and late VsEP waves representing distinct anatomical components of the vestibular circuit. Our results suggest that central components are uniquely sensitive to jerk duration, intensity, and repetition, indicating a disconnect between peripheral and central neuronal recruitment, adaptive plasticity, and time-frequency dynamics. This work provides a foundation for using VsEP to assess jerk-induced physiological vestibular dysfunction and to understand how central relays differentially adapt to variations of mechanical vestibular stress compared with the precise, high-fidelity transmission observed in the periphery.

Background: Nervous system regeneration requires creation of new neurons and their integration into new and existing networks. How new neurons find their targets and reconnect in the environment of regenerating tissue remains a major gap in knowledge. The planarian Schmidtea mediterranea is well suited to the study of neural regeneration due to the remarkable ability of planarians to regenerate their entire body, including the nervous system. However, we have not yet been able to explore synaptogenesis in regeneration using planarians due to a lack of tools to study synapses. Here we investigate potential players in planarian synapse, focusing on the conserved syntaxin protein family, which functions in neurotransmitter vesicle fusion at the synapse. Results: We found that reduction of either syntaxin-1 (stx1) or syntaxin-binding protein 1-1 (stxbp1-1) decreased locomotion and impeded brain regeneration, specifically in synapse-rich regions. Conclusions: Our observations support STX-1 and STXBP1-1 as bona fide synapse components, providing a starting point for studies neuronal connectivity during homeostatic remodeling and regeneration.

Humans exhibit head movements synchronized with musical rhythms, frequently observed during instrument playing or dancing (e.g., hip hop). We conjectured that vestibular input from head movements could aid motor execution anticipatory of musical rhythms, known as sensorimotor synchronization (SMS). To examine this, we applied subthreshold galvanic vestibular stimulation (GVS) simultaneously with musical beats while healthy adults performed SMS to those beats. We designed a GVS waveform where the current rose steeply before each beat onset, which was expected to trigger intense vestibular afferent firing before the beats (as actual head movements would). Participants flexed their index finger in synchrony with the beats of syncopated auditory rhythms. In GVS trials, the SMS timing variability was significantly lower than in trials without GVS, suggesting that vestibular input stabilizes SMS to musical beats. Additionally, a control experiment using a waveform with a gradually increasing current revealed no significant difference in SMS variability between trials with and without GVS. This study provides causal evidence that the vestibular system is involved in synchronized movement to music. In addition, the results suggest that a specific parameter of the GVS waveform could influence predictive motor execution anticipatory of upcoming auditory events.