Publications using the PZ5

Objective. To develop and validate a machine learning framework for the classification of distinct seizure onset patterns using intracranial EEG (iEEG) recordings in a non-human primate (NHP) model of penicillin-induced seizures. Approach. iEEG data were collected from six NHPs, comprising 1, 496 frontal and 549 temporal lobe seizures. Seizure onset patterns were manually categorized into five types: Sharp Activity (5–15 Hz), Low Amplitude Fast Activity (15–30 Hz), Delta Brush (1–3 Hz with bursts), High Amplitude Spike (2–5 Hz), and Polyspike. A Random Forest classifier was trained using features extracted from optimized seizure onset segments. Feature selection and seizure segment length optimization were performed using nested cross-validation to enhance classification accuracy and generalizability. Main results. The classifier achieved strong performance with F1-scores exceeding 79% for Sharp Activity, Low Amplitude Fast Activity, and High Amplitude Spike patterns. When validated on an independent temporal lobe seizure dataset, the model demonstrated robust generalizability, achieving precision and sensitivity above 80% for Sharp Activity and High Amplitude Spike. Significance. These findings demonstrate that the suggested spectral and dynamic features can effectively distinguish seizure onset patterns and generalize in distinct brain regions. Although there are limitations due to use of manual annotations and the sample size of certain categories, the proposed approach provides a framework for automatic classification of seizure onset patterns. Further, the framework has a potential use for epilepsy research and clinical applications in future.

Neurostimulation for cardiovascular control faces challenges due to the lack of predictive modeling for stimulus-driven dynamic responses, which is crucial for precise neuromodulation via quality feedback. We address this by employing a digital twin approach that leverages computational mechanisms underlying neuro-hemodynamic responses during neurostimulation. Our results emphasize the computational role of the nucleus tractus solitarius (NTS) in the brainstem in determining these responses. The intrinsic neural circuit within the NTS harbors collective dynamics residing in a low-dimensional latent space, which effectively captures stimulus-driven hemodynamic perturbations. Building on this, we developed a digital twin framework for individually optimized predictive modeling of neuromodulatory outcomes. This framework potentially enables the design of closed-loop neurostimulation systems for precise hemodynamic control. Consequently, our digital twin based on neural computation mechanisms marks an advancement in the artificial regulation of internal organs, paving the way for precise translational medicine to treat chronic diseases.

Introduction Tinnitus is the perception of non-meaningful sound in the absence of external stimuli. Although tinnitus behavior in animal models is associated with altered central nervous system activity, it is not currently possible to identify tinnitus using neuronal activity alone. In the mouse inferior colliculus (IC), a subpopulation of neurons demonstrates a sustained increase in spontaneous activity after a long-duration sound (LDS). Methods Here, we use the “ LDS test ” to reveal tinnitus-specific differences in sound-evoked plasticity through IC extracellular recordings and the auditory brainstem response (ABR LDS ) in CBA/CaJ mice after sound exposure and behavioral tinnitus assessment. Results Sound-exposed mice showed stronger and shorter tone-evoked responses in the IC compared to unexposed controls, but these differences were not strong predictors of tinnitus. In contrast, in the LDS test, non-tinnitus mice had a significantly stronger suppression in tone-evoked spike rate compared to tinnitus and unexposed control mice. ABR peak amplitudes also revealed robust differences between tinnitus and non-tinnitus mice, with ABR peaks from non-tinnitus mice exhibiting significantly stronger suppression in the LDS test compared to tinnitus and control mice. No significant differences were seen between cohorts in ABR amplitude, latency, wave V:I ratio, wave V:III ratio, I-V intra-peak latency, and I-VI intra-peak latency. We found high-frequency tone stimuli better suited to reveal tinnitus-specific differences for both extracellular IC and ABR recordings. Discussion We successfully used the LDS test to demonstrate that tinnitus-specific differences in sound-evoked plasticity can be shown using both multi-unit near-field recordings in the IC and non-invasive far-field recordings, providing a foundation for future electrophysiological research into the causes and treatment of tinnitus.

Episodic memory requires the precise coordination between the hippocampus and distributed cortical regions. This may be facilitated by bursts of brain activity called high-frequency oscillations (HFOs). We hypothesized that HFOs activate specific networks during memory retrieval and aimed to describe the electrophysiological properties of HFO-associated activity. To study this, we recorded intracranial electroencephalography while human participants performed a list learning task. Hippocampal HFOs (hHFOs) increased during encoding and retrieval, and these increases correlated with memory performance. During retrieval, hHFOs demonstrated activation of semantic processing regions that were previously active during encoding. This consisted of broadband high-frequency activity (HFA) and cortical HFOs. HFOs in the anterior temporal lobe, a major semantic hub, co-occurred with hHFOs, particularly during retrieval. These coincident HFOs were associated with greater cortical HFA and cortical theta bursts. Hence, HFOs may support synchronization of activity across distributed nodes of the hippocampal-cortical memory network.

The medial prefrontal cortex (mPFC) and hippocampus are critical for memory retrieval, decision making, and emotional regulation. While ventral CA1 (vCA1) shows direct and reciprocal connections with mPFC, dorsal CA1 (dCA1) forms indirect pathways to mPFC, notably via the thalamic reuniens nucleus (Re). Neuroanatomical tracing has documented structural connectivity of this indirect pathway through Re however, its functional operation is largely unexplored. Here, we used in vivo and in vitro electrophysiology along with optogenetics to address this question. Whole-cell patch-clamp recordings in acute mouse brain slices revealed both monosynaptic excitatory responses and disynaptic feedforward inhibition at Re–mPFC synapses. However, we also identified a prolonged excitation of mPFC by Re. These early monosynaptic and late recurrent components are in marked contrast to the primarily feedforward inhibition characteristic of thalamic inputs to the neocortex. Local field potential recordings in mPFC brain slices revealed prolonged synaptic activity throughout all cortical lamina upon Re activation, with the late excitation enhanced by blockade of parvalbumin neurons and GABA A Rs. In vivo Neuropixels recordings in head-fixed awake mice revealed a similar prolonged excitation of mPFC units by Re activation. In summary, Re output produces recurrent feedforward excitation within mPFC suggesting a potent amplification system in the Re–mPFC network. This may facilitate amplification of dCA1->mPFC signals for which Re acts as the primary conduit, as there is little direct connectivity. In addition, the capacity of mPFC neurons to fire bursts of action potentials in response to Re input suggests that these synapses have a high gain.

Dopamine (DA) signals to the striatum play critical roles in shaping and sustaining stimulus-reward associations. In primates, however, the dynamics of the DA signals remain unknown since conventional methods are not necessarily appropriate in terms of the spatiotemporal resolution or chemical specificity sufficient for detecting the DA signals. In our study, fiber photometry with a fluorescent DA sensor was employed to identify reward-related DA transients in the monkey striatum. This technique, which directly monitors local DA release, reveals a reward prediction error signal in the anterior putamen originating from midbrain DA neurons. Further, DA transients in the head of the caudate nucleus exhibit a value-based response to reward-predicting stimuli. These signals have been found to arise from two separate groups of DA neurons in the substantia nigra pars compacta. The present results demonstrate that fluorescence DA monitoring is applicable to detect DA signals in the primate striatum for investigating their roles.

Although recent work has made headway in understanding the neural temporospatial dynamics of conscious perception, much of that work has focused on visual paradigms. To determine whether there are shared mechanisms for perceptual consciousness across sensory modalities, here we test within the auditory domain. Participants completed an auditory threshold task while undergoing intracranial electroencephalography. Recordings from >2,800 grey matter electrodes were analyzed for broadband gamma power (a range which reflects local neural activity). For perceived trials, we find nearly simultaneous activity in early auditory regions, the right caudal middle frontal gyrus, and the non-auditory thalamus; followed by a wave of activity that sweeps through auditory association regions into parietal and frontal cortices. For not perceived trials, significant activity is restricted to early auditory regions. These findings show the cortical and subcortical networks involved in auditory perception are similar to those observed with vision, suggesting shared mechanisms for conscious perception.

Face processing models propose gradually more complex receptive field properties culminating in invariant representations in anterior inferotemporal cortex (aITC), leading to late socio-emotionally relevant encoding in pre- and orbitofrontal cortex (POC). Top-down facilitation models, however, predict that some lower-level POC neurons respond faster than aITC. To resolve this discrepancy, we recorded from 2,459 neurons in fMRI-defined POC and aITC face patches. POC patches are more heterogeneous, containing smaller fractions of face-selective neurons than aITC and a mixture of responses to faces and non-faces. In one POC patch, face responses are surprisingly fast, outpacing those in aITC. Moreover, its responses correlate inversely with stimulus spatial frequency. Hence, our extensive data, with a large diversity of POC neurons, support both models and suggest one POC face patch might be specialized in fast, low-level face processing, which may enable (partially) invariant face representations during subsequent processing stages in inferotemporal cortex (ITC).

To comprehend the volumetric neural connectivity of a brain organoid, it is crucial to monitor the spatiotemporal electrophysiological signals within the organoid, known as intra-organoid signals. However, previous methods risked damaging the three-dimensional (3D) cytoarchitecture of organoids, either through sectioning or inserting rigid needle-like electrodes. Also, the limited numbers of electrodes in fixed positions with non-adjustable electrode shapes were insufficient for examining the complex neural activity throughout the organoid. Herein, we present a magnetically reshapable 3D multi-electrode array (MEA) using direct printing of liquid metals for electrophysiological analysis of brain organoids. The adaptable distribution and the softness of these printed electrodes facilitate the spatiotemporal recording of intra-organoid signals. Furthermore, the unique capability to reshape these soft electrodes within the organoid using magnetic fields allows a single electrode in the MEA to record from multiple points, effectively increasing the recording site density without the need for additional electrodes.

Homeostatic sleep regulation is essential for optimizing the amount and timing of sleep for its revitalizing function, but the mechanism underlying sleep homeostasis remains poorly understood. Here, we show that optogenetic activation of locus coeruleus (LC) noradrenergic neurons immediately increased sleep propensity following a transient wakefulness, contrasting with many other arousal-promoting neurons whose activation induces sustained wakefulness. Fiber photometry showed that repeated optogenetic or sensory stimulation caused a rapid reduction of calcium activity in LC neurons and steep declines in noradrenaline/norepinephrine (NE) release in both the LC and medial prefrontal cortex (mPFC). Knockdown of α 2 A adrenergic receptors in LC neurons mitigated the decline of NE release induced by repetitive stimulation and extended wakefulness, demonstrating an important role of α 2 A receptor–mediated auto-suppression of NE release. Together, these results suggest that functional fatigue of LC noradrenergic neurons, which reduces their wake-promoting capacity, contributes to sleep pressure.

In this paper we aim to examine the correlation between diffusion tensor imaging parameters of anatomical connectivity and characteristics of signal transmission obtained from patient-specific transfer function models. Here, we focused on elucidating the correlation between structural and functional neural connectivity within a cohort of pediatric patients diagnosed with dystonia. Diffusion tractography images were obtained from 12 patients with dystonia prior to the deep brain stimulation surgery. For each patient, we processed the imaging data to estimate anatomical measures including fractional anisotropy, axial diffusivity, number of fibre tracts per unit area, and fibre tract length. After the implantation of temporary depth leads for each patient as part of their treatment plan, intracranial signals were recorded. Transfer function models of local field potential recordings and the corresponding measures of functional connectivity were computed for each patient. Linear mixed effect analysis was then employed to determine the relationship between transfer function measures and diffusion tractography parameters. Our results illustrate a positive correlation between fractional anisotropy, AD, and intrinsic neural transmission measures, representing amplification and spread of intrinsic neural oscillations, obtained from the transfer functions models. However, no significant correlation was found between the functional connectivity and number of fibre tracts or fibre lengths. Our findings suggest that white matter integrity, as measured by fractional anisotropy and AD, can potentially reflect the amplification and spread of intrinsic brain signals throughout the network. This study underscores the significant relationship between structural and functional connectivity, offering valuable insights into propagation of neural activity in the brain network and potential implications for optimizing non-invasive treatments and planning for neurological disorders.

The ability of the brain to process auditory information across multiple temporal scales is crucial for perception. This study introduces an innovative experimental approach using nonlinguistic structured stimuli composed of click trains that alternate at different rates to investigate auditory processing across 3 distinct temporal scales: individual clicks (tens of milliseconds), click trains forming auditory objects (hundreds of milliseconds), and higher-order click trains in neuronal novelty detection (seconds) in both rhesus monkeys and humans. Electrocorticography recordings in the auditory cortex of rhesus monkeys unveil the primate brain’s remarkable ability to process intricate auditory temporal patterns at timescales of tens and hundreds of milliseconds. Furthermore, extracellular recordings in monkeys demonstrate pronounced responsiveness to deviant click trains at longer temporal scales in the primary auditory cortex (A1), accompanied by synchronization with both individual clicks and click trains. By contrast, neurons in the auditory thalamus prefer individual clicks only. Notably, neurons in A1 exhibit the ability to synchronize with individual clicks while simultaneously integrating these clicks into cohesive train objects, a phenomenon we term “temporal integration during synchronization”. Additional human electroencephalography recordings complement and support these findings, highlighting the paradigm’s potential for clinical applications. Our research offers novel insights into the neural mechanisms underpinning auditory information processing across various temporal scales at the neuronal level.

Closed-loop strategies offer advanced therapeutic potential through intelligent disease management. Here, we develop a hydrogel-based, single-component, organic electronic device for closed-loop neurotherapy. Fabricated out of conductive hydrogels, the device consists of a flexible array of microneedle electrodes, each of which can be individually addressed to perform electrical recording and control chemical release with sophisticated spatiotemporal control, thus pioneering a smart antiseizure therapeutic system by combining electrical and pharmacological interventions. The recorded neural signal acts as the trigger for a voltage-driven drug release in detected pathological conditions predicted by real-time electrophysiology analysis. When implanted into epileptic animals, the device enables autonomous antiseizure management, where the dosing of antiepileptic drug is controlled in a time-sensitive, region-selective, and dose-adaptive manner, allowing the inhibition of seizure outbursts through the delivery of just-necessary drug dosages. The side effects are minimized with dosages three orders of magnitude lower than the usage in approaches simulating existing clinical treatments.

Autism Spectrum Disorders (ASDs) are a group of neurodevelopmental disorders with heterogeneous causes, and are characterized by communication deficits, impaired social interactions, and repetitive behaviors. Despite numerous studies in mouse models focused on pathophysiological circuit mechanisms of ASD in mature animals, little is known regarding ASD onset and its evolution through development. The medial prefrontal cortex (mPFC) is crucial for higher-order cognitive functions and social behavior, thus key to understanding ASD pathology. To explore early developmental disruptions in the mPFC, we used the Shank3 knockout (Shank3 −/− ) mouse model. SHANK3 is crucial for glutamatergic synapse maturation, and the Shank3 −/− mouse has been well-characterized for displaying ASD-related behavioral phenotypes. We investigated network, cellular, and synaptic changes within the mPFC and in its projections to the mediodorsal thalamus (MD) at two developmental stages, preweaning (P14) and adulthood (>P55). Our findings reveal early synaptic deficits at P14 both within the mPFC and in its projections to the MD accompanied by alterations in mPFC network activity and reduced excitability of excitatory neurons, overall suggesting hypofunction. Interestingly, behavioral deficits were already detectable by P11, preceding the observation of synaptic changes at P14. By adulthood, these early synaptic and cellular alterations progressed to global dysfunction, characterized by mPFC network hyperfunction and layer 5 pyramidal cell hyperexcitability, accompanied by augmented glutamatergic signaling to MD with enhanced action potential production. These results suggest that early synaptic changes may precede and interact with behavioral deficits, potentially leading to compensatory mechanisms that contribute to more pronounced mPFC dysfunction later in development. This study highlights the complex dynamic progression of mPFC deficits in ASD and emphasizes the potential impact of targeting early synaptic alterations to mitigate later behavioral and cognitive deficits.

How consciousness is lost in states such as sleep or anesthesia remains a mystery. To gain insight into this phenomenon, concurrent recordings of electrophysiology signals in the anterior cingulate cortex and whole‐brain functional magnetic resonance imaging (fMRI) are conducted in rats exposed to graded propofol, undergoing the transition from consciousness to unconsciousness. The results reveal that upon the loss of consciousness (LOC), there is a sharp increase in low‐frequency power of the electrophysiological signal. Additionally, fMRI signals exhibit a cascade of deactivation across a pathway including the hippocampus, thalamus, and medial prefrontal cortex (mPFC) surrounding the moment of LOC, followed by a broader increase in brain activity across the cortex during sustained unconsciousness. Furthermore, sliding window analysis demonstrates a temporary increase in synchrony of fMRI signals across the hippocampus‐thalamus‐mPFC pathway preceding LOC. These data suggest that LOC may be triggered by sequential activities in the hippocampus, thalamus, and mPFC, while wide‐spread activity increases in other cortical regions commonly observed during anesthesia‐induced unconsciousness may be a consequence, rather than a cause of LOC. Taken together, the study identifies a cascade of neural events unfolding as the brain transitions into unconsciousness, offering insight into the systems‐level neural mechanisms underpinning LOC.

Here, we characterized the dynamics of sleep spindles, focusing on their damping, which we estimated using a metric called oscillatory-Quality (o-Quality), derived by fitting an autoregressive model to electrophysiological signals, recorded from the cortex in mice. The o-Quality of sleep spindles correlates weakly with their amplitude, shows marked laminar differences and regional topography across cortical regions, reflects the level of synchrony within and between cortical networks, is strongly modulated by sleep-wake history, reflects the degree of sensory disconnection, and correlates with the strength of coupling between spindles and slow waves. As most spindle events are highly localized and not detectable with conventional low-density recording approaches, o-Quality thus emerges as a valuable metric that allows us to infer the spread and dynamics of spindle activity across the brain and directly links their spatiotemporal dynamics with local and global regulation of brain states, sleep regulation, and function.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by ‘electrophysiology-invisible’ signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Visual object memory is a fundamental element of various cognitive abilities, and the underlying neural mechanisms have been extensively examined especially in the anterior temporal cortex of primates. However, both macroscopic large-scale functional network in which this region is embedded and microscopic neuron-level dynamics of top-down regulation it receives for object memory remains elusive. Here, we identified the orbitofrontal node as a critical partner of the anterior temporal node for object memory by combining whole-brain functional imaging during rest and a short-term object memory task in male macaques. Focal chemogenetic silencing of the identified orbitofrontal node downregulated both the local orbitofrontal and remote anterior temporal nodes during the task, in association with deteriorated mnemonic, but not perceptual, performance. Furthermore, imaging-guided neuronal recordings in the same monkeys during the same task causally revealed that orbitofrontal top-down modulation enhanced stimulus-selective mnemonic signal in individual anterior temporal neurons while leaving bottom-up perceptual signal unchanged. Furthermore, similar activity difference was also observed between correct and mnemonic error trials before silencing, suggesting its behavioral relevance. These multifaceted but convergent results provide a multiscale causal understanding of dynamic top-down regulation of the anterior temporal cortex along the ventral fronto-temporal network underpinning short-term object memory in primates.

Sleep is indispensable for health and wellbeing, but its basic function remains elusive. The locus coeruleus (LC) powerfully promotes arousal by releasing noradrenaline. We found that noradrenaline transmission is reduced by prolonged wakefulness and restored during sleep. Fiber-photometry imaging of noradrenaline using its biosensor showed that its release evoked by optogenetic LC neuron activation was strongly attenuated by three hours of sleep deprivation and restored during subsequent sleep. This is accompanied by the reduction and recovery of the wake-promoting effect of the LC neurons. The reduction of both LC evoked noradrenaline release and wake-inducing potency is activity dependent, and the rate of noradrenaline transmission recovery depends on mammalian target of rapamycin (mTOR) signaling. The decline and recovery of noradrenaline transmission also occur in spontaneous sleep-wake cycles on a timescale of minutes. Together, these results reveal an essential role of sleep in restoring transmission of a key arousal-promoting neuromodulator.

Converging electrophysiological, molecular and ultrastructural evidence supports the hypothesis that sleep promotes a net decrease in excitatory synaptic strength, counteracting the net synaptic potentiation caused by ongoing learning during waking. However, several outstanding questions about sleep‐dependent synaptic weakening remain. Here, we address some of these questions by using two established molecular markers of synaptic strength, the levels of the AMPA (alpha‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazole propionic acid) receptors containing the GluA1 subunit and the phosphorylation of GluA1 at serine 845 (p‐GluA1(845)). We previously found that, in the rat cortex and hippocampus, these markers are lower after 6–8 h of sleep than after the same time spent awake. Here, we measure GluA1 and p‐GluA1(845) levels in synaptosomes of mouse cortex after 5 h of either sleep, sleep deprivation, recovery sleep after sleep deprivation or selective REM sleep deprivation (32 C57BL/B6 adult mice, 16 females). We find that relative to after sleep deprivation, these synaptic markers are lower after sleep independent of whether the mice were allowed to enter REM sleep. Moreover, 5 h of recovery sleep following acute sleep deprivation is enough to renormalize their expression. Thus, the renormalization of GluA1 and p‐GluA1(845) expression crucially relies on NREM sleep and can occur in a few hours of sleep after acute sleep deprivation.