bioRxiv Subject Collection: Neuroscience's Journal

TAF1-dependent transcriptional dysregulation underlies multiple sclerosis
Multiple sclerosis (MS) is characterized by neuroinflammation and demyelination of the central nervous system (CNS), leading to disablility. Genetic variants that confer MS risk implicate genes involved in immune function, while variants related to severity of the disease are associated with genes preferentially expressed within the CNS. Current MS therapies decrease relapse rates by preventing immune-mediated damage of myelin, but they ultimately fail to slow long-term disease progression, which apparently depends on CNS intrinsic processes. The molecular events that trigger progressive MS are still unknown. Here we report that the C-terminal region of TAF1 (the scaffolding subunit of the general transcription factor TFIID) is underrepresented in postmortem brain tissue from individuals with MS. Furthermore, we demonstrate in vivo, in genetically modified mice, that C-terminal alteration of TAF1 suffices to induce an RNA polymerase II (RNAPII)-elongation deficit that particularly affects oligodendroglial myelination-related genes and results in an MS-like brain transcriptomic signature, including increased expression of proinflammatory genes. This transcriptional profile is accompanied by CNS-resident inflammation, robust demyelination and MS-like motor phenotypes. We also identify numerous interactors of C-terminal TAF1 that participate in RNAPII-promoter escape, of which two show evidence for genetic association to MS. Our study reveals that TAF1 dysfunction converges with genetic susceptibility to cause transcriptional dysregulation in CNS cell types, such as oligodendrocytes, to ultimately trigger MS.

Input-specific localization of NMDA receptor GluN2 subunits in thalamocortical neurons
Molecular and functional diversity among synapses is generated, in part, by differential expression of neurotransmitter receptors and their associated protein complexes. N-methyl-D-aspartate receptors (NMDARs) are tetrameric ionotropic glutamate receptors that most often comprise two GluN1 and two GluN2 subunits. NMDARs generate functionally diverse synapses across neuron populations through cell-type-specific expression patterns of GluN2 subunits (GluN2A - 2D), which have vastly different functional properties and distinct downstream signaling. Diverse NMDAR function has also been observed at anatomically distinct inputs to a single neuron population. However, the mechanisms that generate input-specific NMDAR function remain unknown as few studies have investigated subcellular GluN2 subunit localization in native brain tissue. We investigated NMDAR synaptic localization in thalamocortical (TC) neurons expressing all four GluN2 subunits. Utilizing super resolution imaging and knockout-validated antibodies, we revealed subtype- and input-specific GluN2 localization at corticothalamic (CT) versus sensory inputs to TC neurons in 4-week-old male and female C57Bl/6J mice. GluN2B was the most abundant postsynaptic subunit across all glutamatergic synapses followed by GluN2A and GluN2C, and GluN2D was localized to the fewest synapses. GluN2B was preferentially localized to CT synapses over sensory synapses, while GluN2A and GluN2C were more abundant at sensory inputs compared to CT inputs. Furthermore, postsynaptic scaffolding proteins PSD95 and SAP102 were preferentially localized with specific GluN2 subunits, and SAP102 was more abundant at sensory synapses than PSD95. This work indicates that TC neurons exhibit subtype- and input-specific localization of diverse NMDARs and associated scaffolding proteins that likely contribute to functional differences between CT and sensory synapses.

Fornix and Uncinate Fasciculus Support Metacognition-Driven Cognitive Offloading
People often use external tools to offload cognitive demands in remembering future intentions. While previous research has identified the causal role of metacognition in cognitive offloading, the neural mechanisms underlying this metacognitive control process remain unclear. To address this gap, we conducted a study with 34 participants using diffusion tensor imaging (DTI) to investigate how connections between brain regions support metacognition-driven cognitive offloading. Behaviorally, we confirmed that under-confidence in using internal memory to execute delayed intentions predicts a bias towards using external reminders. At the brain level we found that the fractional anisotropy (FA) of the fornix, a memory-related white matter tract connected to the hippocampus, positively correlated with the bias in setting up reminders. Additionally, the FA of the left uncinate fasciculus, which links the hippocampus to the prefrontal cortex and is involved in memory error monitoring, negatively correlated with deviations from optimal reminder use. Furthermore, the FA of the superior longitudinal fasciculus, a tract involved in metacognitive monitoring, moderated how confidence influenced the use of reminders. Taken together, our findings reveal a temporal-frontal neural circuit underlying metacognition-driven cognitive offloading, and provide new insights into the interaction between metacognitive monitoring and control.

Cholinergic basal forebrain neurons regulate vascular dynamics and cerebrospinal fluid flux
Waste from the brain is cleared via a cerebrospinal fluid (CSF) exchange pathway, the dysfunction of which is suggested to underlie the pathogenesis of many brain conditions. Coherent cerebrovascular oscillation that couples with pulsatile CSF inflow is suggested to drive the fluid flux. However, how this coupling is regulated, whether it mediates waste clearance, and why fluid flux is impaired in disease status remain unclear. Here we show that vascular-CSF coupling correlates with cortical cholinergic activity in non-demented aged humans. The causal role of basal forebrain cholinergic neurons that project to the cortex is then verified by specific lesioning in mice, revealing correlated changes in vascular-CSF coupling, arterial pulsation and glymphatic flux, which can be altered by an acetylcholinesterase inhibitor. These results suggest a neurovascular mechanism by which CSF/glymphatic flux is modulated by cholinergic neuronal activity, thereby providing a conceptual basis for the development of diagnostics and treatments for glymphatic dysfunction.

Medial Entorhinal VIP-expressing interneurons receive direct input from Anterior Dorsal Thalamus and are critical for spatial memory
Head-direction (HD) cells are found across several regions in the brain, including the anterodorsal thalamic nucleus (ADN), the subicular complex, and the medial entorhinal cortex (MEC). A fundamental role of head direction cells is to provide input to MEC grid cells, which are thought to translate information about head direction into a metric code for spatial location. However, classic anatomical studies indicate that most thalamic HD projections pass indirectly to the MEC via the post- and para-subiculum, with only a small subset of ADN fibers terminating in the MEC. To further investigate the smaller and direct projection to the MEC, we use rabies-mediated retrograde tracing in mice to determine if this projection explicitly targets a subset of MEC neurons. Our findings reveal that ADN neurons specifically project onto MEC interneurons, with a preference for MEC VIP-expressing cells. Additionally, MEC VIP cells receive input from the hippocampus, the subicular complex, and the retrosplenial cortex - key centers for spatial memory - suggesting a specialized role for MEC VIP cells in spatial memory. Indeed, we find that MEC VIP cells exhibit increased c-Fos expression in a spatial memory task and show that chemogenetic inhibition of these neurons impairs task performance. Together, these data uncover a specific projection of head direction information onto MEC interneurons and confirm that MEC VIP-expressing cells are critical for spatial memory.

Movement-independent representation of reward-predicting cues in the medial part of the primate premotor cortex
Neural activity across the dorsal neocortex of rodents is dominated by orofacial and limb movements, irrespective of whether the movements are task-relevant or task-irrelevant. To examine the extent to which movements and a primitive cognitive signal, i.e., reward expectancy, modulate the activity of multiple cortical areas in primates, we conducted unprecedented wide-field one-photon calcium imaging of frontoparietal and auditory cortices in common marmosets while they performed a classical conditioning task with two auditory cues associated with different reward probabilities. Licking, eye movement, and hand movement strongly modulated the neuronal activity after cue presentation in the motor and somatosensory cortices in accordance with the somatotopy. By contrast, the posterior parietal cortex and primary auditory cortex did not show much influence from licking. Licking increased the activity in the caudal part of the dorsal premotor cortex, but decreased the activity in the central and lateral parts of the rostral part of the dorsal premotor cortex (PMdr). Reward expectancy that was separable from both spontaneous and goal-directed movements was mainly represented in the medial part of PMdr. Our results suggest that the influence of movement on primate cortical activity varies across areas and movement types, and that the premotor cortex processes motor and cognitive information in different ways within further subdivided areas.

A tactile discrimination task to study neuronal dynamics in freely-moving mice
Sensory discrimination tasks are valuable tools to study neuronal mechanisms of perception and learning. Most of the previously developed discrimination tasks for electrophysiological and imaging studies in rodents require the animals to be head-fixed. However, implementing neurophysiological recordings into more ethologically realistic settings with unrestrained animals has been challenging, especially for somatosensory studies. This study introduces a tactile discrimination task for freely moving mice, integrating electrophysiology and calcium imaging with cellular resolution. In this go/no-go paradigm, mice learn to discriminate between different aperture widths within days in order to forage for food rewards on a linear platform. We demonstrate that the task is whisker-dependent and that mice reliably discriminate aperture differences as small as 6 mm. The setup's versatility enables exploration into diverse behavioral aspects, including tactile discrimination thresholds, valence-dependent behavior, and cognitive flexibility following repeated task rule reversals. Rule learning was highly stereotypical, fast and reproducible across individual mice, with approximately 500 trials to attain expert level performance and approximately 1000 trials to relearn the first rule reversal. We further demonstrate that electrophysiological recordings and calcium imaging can be conducted in the same paradigm such that multiple behavioral read-outs (learning progression, whisker motion, whisker touch, reward licking) can be synchronized with respective electrophysiological and imaging data, providing a new versatile tool to elucidate neural mechanisms of cognition and sensory processing.

Therapeutically targeting the classical complement pathway with antisense oligonucleotides in Alzheimer's disease
The complement classical pathway (CP) is a key mediator of synapse loss and neurodegeneration in mouse models of Alzheimer's (AD) and other neurodegenerative diseases. We analyzed human brain proteomics and found consistent elevations of all CP proteins, but not other complement pathways, in AD patient brains. We performed human genetics analysis that identified a rare variant in the C1S gene within the Finnish population that is associated with AD and we found that a common AD-associated C1S variant correlates with increased C1S protein levels. A targeted assay detected elevated C1S activation in AD patient CSF. Given this specific implication of the CP in AD, we next evaluated the therapeutic approach of targeting the CP in the brain using antisense oligonucleotides (ASOs). To identify promising CP targets for knockdown using ASOs we first tested for rescue of synapse loss in an AD mouse model using heterozygous and homozygous complement knockout mice and examined the relative brain expression levels of different CP genes. Based on these experiments we prioritized C1r, C1s and C4 as promising targets for therapeutic knockdown using ASOs. We then screened for ASOs for each target, evaluating in vitro and in vivo knockdown and toxicity, and identified optimal ASOs targeting C1r, C1s and C4. Experiments with AD model mice demonstrated significant rescue of synapse loss following treatment with C1r, C1s or C4 ASOs. Overall, our findings provide proof of concept for using nucleic acid-based medicine to target the CP in AD and demonstrate the translational potential of this approach.

Population coding under the scale-invariance of high-dimensional noise
High-dimensional neural activities exhibiting scale-invariant, power-law noise spectra are ubiquitously observed across various brain regions and species. However, their impact on information coding remains unclear. We provide the scaling conditions for noise covariance that clarify the boundedness of information and establish a quantitative relation between information capacity and population size, based on the properties of scale-invariant noise covariance observed in stimulus-evoked activities of mouse V1 neurons. Our analysis reveals that sublinearly scaling small noise components align sufficiently with the signal direction, enabling neurons to convey stimulus information unboundedly as population size increases. These findings demonstrate that the quasi-universal scaling of neural noise covariance lays the foundation for understanding the scaling and boundedness of population codes, highlighting the critical need to consider the full spectrum of high-dimensional noise.

Unlocking new avenues for non-invasive brain monitoring with combined electroencephalography and functional magnetic resonance imaging at ultra-high field
The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) at ultra-high field (7 Tesla) offers unprecedented possibilities to probe human brain function non-invasively with high coverage, millisecond temporal precision and sub-millimeter spatial precision, unraveling cortical layers and small subcortical structures. Unfortunately, this technique has remained largely inaccessible at 7T, due to prohibitive cross-modal interference effects and physical constraints. Here, we developed a first-of-its-kind EEG-fMRI acquisition framework on a clinical 7T system combining key improvements from previous works: compact EEG transmission chain to reduce artifact incidence, reference sensors for artifact correction, and adapted leads for compatibility with a dense radiofrequency receive-array allowing state-of-the-art fMRI sensitivity and acceleration. Two implementations were tested: one using an EEG cap adapted in-house, and another using a recently-designed prototype from an industrial manufacturer, intended to be further developed into a commercial product accessible to the broader community. A comprehensive evaluation in humans showed that simultaneous acquisitions, including with sub-millimeter fMRI resolution, could be conducted without detectable safety issues or major practical constraints. The EEG exerted relatively mild perturbations on fMRI quality (6-11% loss in temporal SNR), without measurably affecting the detection of resting-state networks and visual responses. The artifacts induced on EEG could be corrected to a degree where the spatial, spectral and temporal characteristics were comparable to outside recordings, and hallmark features such as resting-state alpha and eyes-closing alpha modulation could be clearly detected. Altogether, these findings indicate excellent prospects for neuroimaging applications, that can leverage the unique possibilities achievable at 7T.

Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specificneuromodulation
Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.

Optimization of Seizure Prevention by Cannabidiol (CBD)
Objective: Cannabidiol (CBD) is one of the most prominent non-psychotropic cannabinoids with known therapeutic potentials. Based on its anti-seizure efficacy, the first cannabis derived, pharmaceutical grade CBD-based medication was approved in the USA in 2018 for the treatment of seizures in patients 2 years and older. Despite the effectiveness in reducing seizures, there remain several major questions on the optimization of CBD therapy for epilepsy such as the optimal dosage, composition, and route of delivery, which are the main objective of this current study. Methods: We evaluated the antiseizure effects of CBD through different compositions, routes of delivery, and dosages in a pre-clinical model. We used a kainic acid-induced epilepsy model in C57BL/6 mice, treated them with placebo and/or CBD through inhalation, oral and injection routes. We used CBD broad spectrum (inhaled and injection) versus CBD isolate formulations. We employed the Racine scaling system to evaluate the severity of the seizures, flow cytometry for measuring Immune biomarkers and neurotrophic factors, and histologic analysis to examine and compare the groups. Results: Our findings showed that all forms of CBD reduced seizures severity. Among the combination of CBD tested. CBD broad spectrum via inhalation was the most effective in the treatment of epileptic seizures (p<0.05) compared to other forms of CBD treatments. Conclusion: Our data suggest that route and CBD formulations affect its efficacy in the prevention of epileptic seizures. Inhaled broad spectrum CBD showed a potential superior effect compared to other delivery routes and CBD formulations in the prevention of epileptic seizures, warrants further research.

Ischemic lesions to inferior frontal cortex alter the dynamics of conscious visual perception
Whether the prefrontal cortex is part of the neural correlates of conscious visual perception has been subject to longstanding debate. Recent work, using functional magnetic resonance imaging (fMRI) and repetitive transcranial magnetic stimulation (rTMS) to induce virtual lesions, has illustrated a key role of the right inferior frontal cortex (IFC) in the detection and resolution of perceptual ambiguities. Here, we sought to validate an active role of the IFC in conscious perception by evaluating how loss-of-function in patients with ischemic stroke in this region influences processing of ambiguous visual information. To this end, twenty-three patients (11 female, mean age 70.65 {+/-} 1.2 yrs) with chronic (>6 months), right-hemispheric ischemic stroke lesions within the MCA-territory (9 patients with IFC lesions, 14 without) performed a bistable perception task, having to report the perceived direction of rotation of an ambiguous random-dot-kinematogram (RDK). As hypothesized, patients with IFC lesions showed significantly fewer perceptual changes compared to patients without IFC lesions (IFC 44.9 {+/-} 46.3 s, non-IFC 28.2 {+/-} 37.1 s, T(182.9) = 3.1, p = 2.2 x 10-3). Importantly, this effect remained significant when controlling for age, sex, stroke severity and lesion volume (T(5.97) = -2.9, p =0.026). Our results support the notion that the IFC is crucial for resolving perceptual ambiguities, suggesting an active role of frontal cortex in shaping conscious visual experience.

Multimodal 3D Image Registration for Mapping Brain Disorders
We introduce an AI-driven approach for robust 3D brain image registration, addressing challenges posed by diverse hardware scanners and imaging sites. Our model trained using an SSIM-driven loss function, prioritizes structural coherence over voxel-wise intensity matching, making it uniquely robust to inter-scanner and intra-modality variations. This innovative end-to-end framework combines global alignment and non-rigid registration modules, specifically designed to handle structural, intensity, and domain variances in 3D brain imaging data. Our approach outperforms the baseline model in handling these shifts, achieving results that align closely with clinical ground-truth measurements. We demonstrate its efficacy on 3D brain data from healthy individuals and dementia patients, with particular success in quantifying brain atrophy, a key biomarker for Alzheimer's disease and other brain disorders. By effectively managing variability in multi-site, multi-scanner neuroimaging studies, our approach enhances the precision of atrophy measurements for clinical trials and longitudinal studies. This advancement promises to improve diagnostic and prognostic capabilities for neurodegenerative disorders.

Amyloid-β deposition in basal frontotemporal cortex is associated with selective disruption of temporal mnemonic discrimination
Cerebral amyloid-beta (A{beta}) accumulation, a hallmark pathology of Alzheimer's disease (AD), precedes clinical impairment by two to three decades. However, it is unclear whether A{beta} contributes to subtle memory deficits observed during the preclinical stage. The heterogenous emergence of A{beta} deposition may selectively impact certain memory domains, which rely on distinct underlying neural circuits. In this context, we tested whether specific domains of mnemonic discrimination, a neural computation essential for episodic memory, exhibit specific deficits related to early A{beta} deposition. We tested 108 cognitively unimpaired human older adults (66% female) who underwent 18F-florbetapir positron emission tomography (A{beta}-PET), and a control group of 35 young adults, on a suite of mnemonic discrimination tasks taxing object, spatial, and temporal domains. We hypothesized that A{beta} pathology would be selectively associated with temporal discrimination performance due to A{beta}'s propensity to accumulate in the basal frontotemporal cortex, which supports temporal processing. Consistent with this hypothesis, we found a dissociation in which generalized age-related deficits were found for object and spatial mnemonic discrimination, while A{beta}-PET levels were selectively associated with deficits in temporal mnemonic discrimination. Further, we found that higher A{beta}-PET levels in medial orbitofrontal and inferior temporal cortex, regions supporting temporal processing, were associated with greater temporal mnemonic discrimination deficits, pointing to the selective vulnerability of circuits related to temporal processing early in AD progression. These results suggest that A{beta} accumulation within basal frontotemporal regions may disrupt temporal mnemonic discrimination in preclinical AD, and may serve as a sensitive behavioral biomarker of emerging AD progression.

Functional imaging and connectome analyses reveal organizing principles of taste circuits in Drosophila
Taste is crucial for many innate and learned behaviors. In the fly, taste impacts feeding, oviposition, locomotion, mating, and memory formation, to name a few. These diverse roles may necessitate the apparent distributed nature of taste responses across different circuits in the fly brain, leading to complexity that has hindered attempts to deduce unifying principles of taste processing and coding. Here, we combine information from the whole brain connectome with functional calcium imaging to examine the neural representation of taste at early steps of processing. We find that the representation of taste quality remains largely segregated in cholinergic and GABAergic local interneurons (LNs) that are directly postsynaptic to taste sensory neurons of the labellum. Although some taste projection neurons (TPNs) projecting to superior protocerebrum receive direct inputs from sensory neurons, many receive primarily indirect taste inputs via cholinergic LNs. Moreover, we found that cholinergic interneurons appear to function as nodes to convey feedforward information to dedicated sets of morphologically similar TPNs. Examining a small number of representative TPNs suggests that taste information remains mostly segregated at this level as well. Together, these studies suggest a previously unappreciated logic in the organization of fly taste circuits.

Neurons as biosensors for discriminating neurological disorders in a brain-on-chip platform: Application to Alzheimer s Disease using patient CSF
Alzheimer s disease (AD) is characterized by the accumulation of aggregated amyloid beta peptide (A{beta}) leading to progressive neuronal loss and dysfunction. Current AD diagnosis involves biomarkers assays in cerebrospinal fluid (CSF) as A{beta} to validate the diagnosis. However, these methods are time-consuming, expensive, and can result in inaccurate diagnoses by not accounting for differential diagnose. To overcome these challenges, researchers are exploring new technologies for detecting AD biomarkers in biological fluids, though progress is hindered by an incomplete understanding of AD mechanisms and CSF composition. In this study, we used a standardized microfluidic platform to investigate the effects of synthetic A{beta} peptides and cerebrospinal fluid (CSF) from AD and healthy patients on neuronal functional activity. First, human neurons derived from induced pluripotent stem cells (iPSCs) were characterized. Then, to modulate the functional activity of neurons, tetrodotoxin (TTX), a specific sodium channel blocker, was used as a control for inhibiting neuronal activity. Subsequently, glutamatergic neurons were chronically exposed to A{beta}O and patients CSF. MEA recordings were performed before and after the treatments to assess changes in network activity. Our results demonstrated that extracting key electrophysiological metrics allows for discrimination between healthy and AD CSF samples. This system could offer the potential for differential diagnosis and development of personalized therapeutic strategies.

Lamellar Schwann cells in the Pacinian corpuscle potentiate vibration perception
Pacinian corpuscles are among the most sensitive mechanoreceptors found in vertebrates and they are tuned to vibrations in the highest perceptible frequency range (100-2000Hz). One of their anatomical hallmarks is the onion-like cell layers surrounding the central axon. The innermost layers consist of ~60 densely packed lamellar Schwann cells (LSCs), whose function remains largely unknown. Using high-resolution 3D electron microscopy we found that LSCs in Pacinian corpuscles of the mouse hindlimb do not form concentric rings, but complex, multilayered and intertwining assemblies that are connected via an estimated 5805.1 desmosomes and 4142.5 gap-junctions. LSCs make multiple converging contacts with the afferent axon and its protrusions with desmosomes. Using optogenetic manipulations of LSCs we demonstrate that their activation does not only drive reliable time-locked spiking in the axon, but that their inactivation significantly elevates the thresholds in-situ and increases perceptual thresholds behaviorally. Together these findings provide evidence that LSCs are a key element of somatosensory processing, actively potentiating mechanosensitivity in Pacinian corpuscles.

SynPull: a novel method for studying neurodegeneration-related aggregates in synaptosomes using super-resolution microscopy
Synaptic dysfunction is one of the primary hallmarks of both Alzheimer's and Parkinson's disease, leading to cognitive and behavioural decline. While alpha-synuclein, beta-amyloid, and tau are involved in the physiological functioning of synapses, their pathological aggregation has been linked to synaptic dysfunction. However, the methodology for studying the small (sub-diffraction limit) and soluble aggregates -often called oligomers, formed by these proteins is limited. Here we describe SynPull, a novel method combining single-molecule pulldown, super-resolution microscopy, and advanced computational analyses, in order to reliably study the quantity and morphology of the oligomeric alpha-synuclein, beta-amyloid, and AT8-positive tau aggregates in synaptosomes harvested from post-mortem human brain samples and mouse models. Using SynPull, we show that AT8-positive tau is the predominant aggregate type in AD, with significantly more aggregates compared to the control samples, yet the aggregate size does not differ between disease and control samples. Meanwhile, the relatively smaller amount of alpha-synuclein and beta-amyloid aggregates found in the synapses are larger than the extra-synaptic ones. Collectively, these results show the utility of SynPull to study pathological aggregates in dementia, which can help further understand the disease mechanisms causing synaptic dysfunction.

Glymphatic clearance is enhanced during sleep
A recent publication questioned the existing literature by reporting that glymphatic clearance is enhanced by wakefulness. We show here that this is an erroneous conclusion, in that it was based on the assumption that tracer infusion is independent of the brain activity state. Utilizing dynamic magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and fluorescent fiber photometry, we report that less tracer enters the brains of awake animals, such that after adjusting for the injected tracer dose, brain glymphatic clearance is found to be enhanced by both sleep and anesthesia, and sharply suppressed by wakefulness.

Chronic intermittent alcohol yields sex-specific disruptions in cortical-striatal-limbic oscillations
Background: While the neurobiology of alcohol use disorder (AUD) has been extensively researched, the vast majority of these studies included only male organisms. However, there are significant sex differences in both the causes and consequences of alcohol misuse and dependence, suggesting sex-specific neurobiological mechanisms. The current study used a rodent model to determine whether chronic alcohol exposure impacts sex-specific neural circuits, and whether these changes contribute to the development of alcohol misuse. Methods: Male and female Sprague-Dawley rats were trained to self-administer 10% alcohol before implanting bilateral electrodes into the infralimbic medial prefrontal cortex (IL), nucleus accumbens shell (NAcSh), and central nucleus of the amygdala (CeA). Half of the rats were then exposed to four weeks of chronic intermittent alcohol (CIA) vapor (14 hours on/10 hours off). During acute withdrawal (6-8 after the vapor turns off), local field potentials (LFPs) were recorded from the IL, NAcSh, and CeA during 30-minute self-administration sessions. Using an unbiased machine learning approach, we built predictive models to determine whether/which LFP features could distinguish CIA-exposed from control rats in each sex, as well as if any of these LFP features correlated with rates of alcohol self-administration. Results: Female rats self-administered more alcohol in general compared to males, but only males exposed to CIA showed increased alcohol intake during acute withdrawal. LFPs predicted CIA exposure in both sexes better than chance estimates, but models built on IL and NAcSh oscillations performed the best in males, while models built on IL and CeA LFPs performed best in females. High {gamma} LFPs recorded in the NAcSh correlated with rates of alcohol self-administration in males exposed to CIA, while only left-right NAcSh {beta} coherence correlated with drinking in control females. Conclusions: These data provide support for the hypothesis that the neural circuits driving alcohol dependence development are sex-specific, and that high frequency oscillations in the NAcSh may be related to the increased drinking observed in males exposed to CIA. Overall, these data add to our understanding of the neurobiological underpinnings behind the sex differences observed in AUD and offer promising biomarkers for future therapeutic research.

Structural and functional dissection of the Pacinian corpuscle reveals an active role of the inner core in touch detection
Pacinian corpuscles are rapidly adapting mechanoreceptor end-organs that detect transient touch and high-frequency vibration. In the prevailing model, these properties are determined by the outer core, which acts as a mechanical filter limiting static and low-frequency stimuli from reaching the afferent terminal-the sole site of touch detection in corpuscles. Here, we determine the detailed 3D architecture of corpuscular components and reveal their contribution to touch detection. We show that the outer core is dispensable for rapid adaptation and frequency tuning. Instead, these properties arise from the inner core, composed of gap junction-coupled lamellar Schwann cells (LSCs) surrounding the afferent terminal. By acting as additional touch sensing structures, LSCs potentiate mechanosensitivity of the terminal, which detects touch via fast-inactivating ion channels. We propose a model in which Pacinian corpuscle function is mediated by an interplay between mechanosensitive LSCs and the afferent terminal in the inner core.

Segment AnyNeuron
Image segmentation plays an integral part in neuroimage analysis and is crucial for understanding brain disorders. Deep Learning (DL) models have shown exponential success in computer vision tasks over the years, including image segmentation. However, to achieve optimal performance, DL models require extensive annotated data for training, which is often the bottleneck to expediting brain-wide image analysis. For segmenting cellular structures such as neurons, the annotation process is cumbersome and time-consuming due to the inherent structural, intensity, and background variations present in the data caused by genetic markers, imaging techniques, etc. We propose an Active Learning-based neuron segmentation framework (Segment AnyNeuron), which incorporates state-of-the-art image segmentation modules - Detectron2 and HQ SAM, and requires minimal ground truth annotation to achieve high precision for brain-wide segmentation of neurons. Our framework can classify and segment completely unseen neuronal data by selecting the most representative samples for manual annotation, thus avoiding the cold-start problem common in Active Learning. We demonstrate the effectiveness of our framework for automated brain-wide segmentation of neurons on a variety of open-source neuron imaging datasets, acquired from different scanners and a variety of transgenic mouse lines.

Validating genuine changes in Heartbeat Evoked Potentials using Pseudotrials and Surrogate Procedures
The brain continuously receives interoceptive information about the state and function of our internal organs. For instance, each time the heart beats, the brain responds by generating time-locked activity, known as heartbeat evoked potentials (HEP). When investigating HEPs, it is essential to adequately control for heartbeat-independent confounding activity to avoid false interpretation. In the present study, we highlight the pitfalls of uncontrolled analyses and advocate for the use of surrogate heartbeat analysis and pseudotrial correction, which are promising tools to control for spurious results. Surrogate heartbeat analysis involves shuffling the timing of heartbeats to verify the time-locking of HEP effects. Pseudotrial correction works by subtracting heartbeat-independent activity from HEPs. In this study we employ both procedures, validate them in simulations and apply them to real EEG data. Using EEG recordings obtained during the performance of an auditory novelty oddball task in a large population, we show that, without control analyses, pre-stimulus HEPs appear inversely related to task-related measures such as P300 event-related potential amplitudes and reaction time speed. However, these effects disappear after carefully controlling for heartbeat-unrelated EEG activity. Additionally, in real and simulated data, we show that pseudotrial correction has the potential to remove task-related confounds from HEPs, thereby uncovering real heartbeat-related effects that otherwise could be missed. This study therefore highlights issues that can arise when analyzing HEPs during tasks, provides solutions to overcome them, and gives recommendations for future studies to avoid pitfalls when analyzing and designing behavioral paradigms with HEPs.

NeuroAtlas: An Artificial Intelligence-based Framework for Annotation, Segmentation and Registration of Large Scale Biomedical Imaging Data
With increasing neuroimaging modalities and data diversity, mapping brain regions to a standard atlas template has become a challenging problem. Machine learning in general and deep learning, in particular, have been providing robust solutions for several neuroimaging tasks, including brain image registration and segmentation. However, these methods require a large amount of data for ground-truth labels, annotated by human experts, which is time-consuming. In this work, we introduce NeuroAtlas, an AI-based framework for atlas generation and brain region segmentation. We showcase an end-to-end solution for brain registration and segmentation by providing i) a deep learning modeling suite with a variety of high-performing model architectures to map a brain atlas onto the input brain section and ii) a Graphical User Interface (GUI)-based plugin for large-scale data annotation with a feature of modifying the predicted labels for active learning. We demonstrate a robust performance of our framework on the human brains, captured through various imaging modalities and age groups, and demonstrate its application for mouse brains as well. NeuroAtlas tool will be open-sourced and entirely compatible with both local as well as cloud-based computing so that users can easily adapt to their neuroimaging custom datasets.

Metastable Dynamics Emerge from Local Excitatory-Inhibitory Homeostasis in the Cortex at Rest
The human cortex displays highly metastable dynamics at rest, underlying the spontaneous exploration of large-scale network states. This metastability depends on edge-of-bifurcation dynamics at the circuit level, which emerge due to the local control of firing rates through multiple mechanisms of excitatory-inhibitory (E-I) homeostasis. However, it is unclear how the distinct forms of homeostasis contribute to the metastability of large-scale cortical networks. Here, we propose that individual mechanisms of E-I homeostasis contribute uniquely to the emergence of metastable dynamics and resting-state functional networks and test that hypothesis in a large-scale model of the human cortex. We show that empirical networks and dynamics can only be reproduced when accounting for multiple mechanisms of E-I homeostasis. More specifically, while the homeostasis of excitation and inhibition enhances metastability, the complementary regulation of intrinsic excitability ensures moderate levels of synchrony, maximizing the complexity of functional networks. Furthermore, the modulation of distance-to-bifurcation by the homeostasis of excitation and intrinsic excitability supports collective dynamics by compensating for strong input fluctuations in strongly connected areas. Altogether, our results show that cortical networks self-organize toward maximal metastability through the multi-factor homeostatic regulation of E-I balance, which controls local edge-of-bifurcation dynamics. Therefore, the functional benefits of combining multiple homeostatic mechanisms transcend the circuit level, supporting the rich spontaneous dynamics of large-scale cortical networks.