bioRxiv Subject Collection: Neuroscience's Journal

Accounting for Edge Uncertainty in Stochastic Actor-Oriented Models for Dynamic Network Analysis
Stochastic Actor-Oriented Models (SAOMs) were designed in the social network setting to capture network dynamics representing a variety of influences on network change. The standard framework assumes the observed networks are free of false positive and false negative edges, which may be an unrealistic assumption. We propose a hidden Markov model (HMM) extension to these models, consisting of two components: 1) a latent model, which assumes that the unobserved, true networks evolve according to a Markov process as they do in the SAOM framework; and 2) a measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for parameter estimation. We address the computational challenge posed by a massive discrete state space, of a size exponentially increasing in the number of vertices, through the use of the missing information principle and particle filtering. We present results from a simulation study, demonstrating our approach offers improvement in accuracy of estimation, in contrast to the standard SAOM, when the underlying networks are observed with noise. We apply our method to functional brain networks inferred from electroencephalogram data, revealing larger effect sizes when compared to the naive approach of fitting the standard SAOM.

Indigenous gut microbes modulate neural cell state and neurodegenerative disease susceptibility
The native microbiome influences a plethora of host processes, including neurological function. However, its impacts on diverse brain cell types remains poorly understood. Here, we performed single nucleus RNA sequencing on hippocampi from wildtype, germ-free mice and reveal the microbiome-dependent transcriptional landscape across all major neural cell types. We found conserved impacts on key adaptive immune and neurodegenerative transcriptional pathways, underscoring the microbiome's contributions to disease-relevant processes. Mono-colonization with select indigenous microbes identified species-specific effects on the transcriptional state of brain myeloid cells. Colonization by Escherichia coli induced a distinct adaptive immune and neurogenerative disease-associated cell state, suggesting increased disease susceptibility. Indeed, E. coli exposure in the 5xFAD mouse model resulted in exacerbated cognitive decline and amyloid pathology, demonstrating its sufficiency to worsen Alzheimer's disease-relevant outcomes. Together, these results emphasize the broad, species-specific, microbiome-dependent consequences on neurological transcriptional state and highlight the capacity of specific microbes to modulate disease susceptibility.

A vectorial code for semantics in human hippocampus
As we listen to speech, our brains actively compute the meaning of individual words. Inspired by the success of large language models (LLMs), we hypothesized that the brain employs vectorial coding principles, such that meaning is reflected in distributed activity of single neurons. We recorded responses of hundreds of neurons in the human hippocampus, which has a well-established role in semantic coding, while participants listened to narrative speech. We find encoding of contextual word meaning in the simultaneous activity of neurons whose individual selectivities span multiple unrelated semantic categories. Like embedding vectors in semantic models, distance between neural population responses correlates with semantic distance; however, this effect was only observed in contextual embedding models (like BERT) and was reversed in non-contextual embedding models (like Word2Vec), suggesting that the semantic distance effect depends critically on contextualization. Moreover, for the subset of highly semantically similar words, even contextual embedders showed an inverse correlation between semantic and neural distances; we attribute this pattern to the noise-mitigating benefits of contrastive coding. Finally, in further support for the critical role of context, we find that range of neural responses covaries with lexical polysemy. Ultimately, these results support the hypothesis that semantic coding in the hippocampus follows vectorial principles.

Hyperreflexia after corticospinal tract lesion reflects 1A afferent circuit changes not increased KCC2 hyperexcitability
Hyperreflexia, spasticity, and a loss of skilled motor function are consequences of spinal cord injury (SCI). Multiple potential mechanisms underpinning hyperreflexia have been reported, including: aberrant proprioceptive afferent (PA) sprouting, which could enhance reflex signaling; reduced GABAergic inhibitory presynaptic regulation of 1A terminals (GABApre); and increased excitability produced by reduced motor neuron membrane-bound potassium-chloride co-transporter 2 (KCC2), which has only been studied after SCI and bilateral CST lesion. Here we examine ho selective CST injury allows for specific investigation of the different mechanisms to determine their contributions to hyperreflexia. Hoffmann (H)-reflex rate-dependent depression (RDD) of the forelimb and hindlimb 5th digit abductor muscles was used to assess hyperreflexia. We compared RDD in naive and unilateral-PTX animals at 7-dpi and 42-dpi, supplemented with additional timed tests to examine the time-course of hyperreflexia development. Immunohistochemistry was used to identify PA synapses (VGlut1) and GABApre (GAD65), motor neurons (ChAT), and KCC2. Following unilateral PTX, which eliminates the CST from one hemisphere, we observed significant hyperreflexia in the contralesional forelimb only. Membrane-bound KCC2 was unchanged in contralesional cervical motor neurons. Whereas both cervical and lumbar motor neurons showed increased PA sprouting contralesionally, there was a concomitant increase in GABApre terminals for the lumbar not cervical cord. Our findings show that KCC2 is dissociated from hyperreflexia in the uniPTX model. Instead, forelimb hyperreflexia can be explained by cervical motor neuron PA sprouting and an uncompensated GABApre regulation.

Emergence of clustered synapses during the development of a nervous system
Synaptic organization is central for proper transmission of neural information. Studies in invertebrates and mammalian cortices show that synapses are clustered along neurite extensions, an organization that promotes key functional roles. Here we studied how these synaptic clusters emerge during the development of a nervous system. Leveraging the available C. elegans connectomes that span all larval developmental stages, we show that clustered synapses are formed at the early stages of the neural network development and that their occurrence further increases throughout development. These synaptic clusters significantly constitute small neural circuits that endow the network with important functional roles, such as feedback between mutually synapsing neurons and information transfer in mutually regulated neurons. Moreover, clustered synapses emerge early on during development of the head motor system, where they facilitate the crucial 3D head swings. Finally, the synaptic clusters within these key neural circuits are maintained throughout all developmental stages and are robustly found across different individuals, further accentuating their central functional roles in neural networks.

Suprachiasmatic Neuromedin-S Neurons Regulate Arousal
Mammalian circadian rhythms, which orchestrate the daily temporal structure of biological processes, including the sleep-wake cycle, are primarily regulated by the circadian clock in the hypothalamic suprachiasmatic nucleus (SCN). The SCN clock is also implicated in providing an arousal 'signal,' particularly during the wake-maintenance zone (WMZ) of our biological day, essential for sustaining normal levels of wakefulness in the presence of mounting sleep pressure. Here we identify a role for SCN Neuromedin-S (SCNNMS) neurons in regulating the level of arousal, especially during the WMZ. We used chemogenetic and optogenetic methods to activate SCNNMS neurons in vivo, which potently drove wakefulness. Fiber photometry confirmed the wake-active profile of SCNNM neurons. Genetically ablating SCNNMS neurons disrupted the sleep-wake cycle, reducing wakefulness during the dark period and abolished the circadian rhythm of body temperature. SCNNMS neurons target the dorsomedial hypothalamic nucleus (DMH), and photostimulation of their terminals within the DMH rapidly produces arousal from sleep. Pre-synaptic inputs to SCNNMS neurons were also identified, including regions known to influence SCN clock regulation. Unexpectedly, we discovered strong input from the preoptic area (POA), which itself receives substantial inhibitory input from the DMH, forming a possible arousal-promoting circuit (SCN->DMH->POA->SCN). Finally, we analyzed the transcriptional profile of SCNNMS neurons via single-nuclei RNA-Seq, revealing three distinct subtypes. Our findings link molecularly-defined SCN neurons to sleep-wake patterns, body temperature rhythms, and arousal control.

Subthreshold electric fields bidirectionally modulate neurotransmitter release through axon polarization
Subthreshold electric fields modulate brain activity and demonstrate potential in several therapeutic applications. Electric fields are known to generate heterogenous membrane polarization within neurons due to their complex morphologies. While the effects of somatic and dendritic polarization in postsynaptic neurons have been characterized, the functional consequences of axonal polarization on neurotransmitter release from the presynapse are unknown. Here, we combined noninvasive optogenetic indicators of voltage, calcium and neurotransmitter release to study the subcellular response within single neurons to subthreshold electric fields. We first captured the detailed spatiotemporal polarization profile produced by uniform electric fields within individual neurons. Small polarization of presynaptic boutons produces rapid and powerful bidirectional modulation of neurotransmitter release, depending on the direction of polarization. We determined that subthreshold electric fields drive this effect by rapidly altering the number of synaptic vesicles participating in neurotransmission, producing effects which resemble short-term plasticity akin to presynaptic homeostatic plasticity. These results provide key insights into the mechanisms of subthreshold electric fields at the cellular level.

Neural circuit mechanisms to transform cerebellar population dynamics for motor control in monkeys
We exploit identification of neuron types during extracellular recording to demonstrate how the cerebellar cortex's well-established architecture transforms inputs into outputs. During smooth pursuit eye movements, the floccular complex performs distinct input-output transformations of temporal dynamics and directional response properties. The responses of different interneuron types localize the circuit mechanisms of each transformation. Mossy fibers and unipolar brush cells emphasize eye position dynamics uniformly across the cardinal axes; Purkinje cells and molecular layer interneurons code eye velocity along directionally biased axes; Golgi cells show unmodulated firing. Differential directional response properties of different neuron types localize the directional input-output transformation to the last-order inputs to Purkinje cells. Differential temporal dynamics pinpoint the site of the temporal input-output transformation to granule cells. Specific granule cell population dynamics allow the temporal transformations required in the area we study and generalize to many temporal transformations, providing a complete framework to understand cerebellar circuit computation.

Opposing control of the respiratory brainstem on multiple timescales achieved by transmitter co-release from the locus coeruleus
The locus coeruleus (LC) provides widespread noradrenergic (NAergic) modulation throughout the brain to influence a wide range of functions, including breathing. Although both anatomical and physiological evidence supports the involvement of the LC in both the upstream integration and the downstream modulation of breathing, the circuitry behind the latter is unknown. Here, we show that NAergic LC neurons send projections to the K[o]lliker-Fuse nucleus (KF), a critical site in the control of breathing. Long duration activation of NAergic LC neuron terminals in pontine slices induces persistent inhibitory and excitatory NA currents or increases firing rate in postsynaptic KF neurons. Short stimulation on the other hand leads to the VGluT2-dependent release of glutamate that may be co-released with NA in a monosynaptic circuit. Together these results demonstrate that LC neurons can exert flexible, opposing effects on different timescales via glutamatergic and NAergic signaling onto a key respiratory brainstem nucleus.

Hybrid Neural Networks for Volitional Movement
Massive interconnectivity in large-scale neural networks is the key feature underlying their powerful and complex functionality. We have developed hybrid neural network (HNN) models that allow us to find statistical structure in this connectivity. Describing this structure is critical for understanding biological and artificial neural networks. The HNNs are composed of artificial neurons, a subset of which are trained to reproduce the responses of individual neurons recorded experimentally. The experimentally observed firing rates came from populations of neurons recorded in the motor cortices of monkeys performing a reaching task. After training, these networks (recurrent and spiking) underwent the same state transitions as those observed in the empirical data, a result that helps resolve a long-standing question of prescribed vs ongoing control of volitional movement. Because all aspects of the models are exposed, we were able to analyze the dynamic statistics of the connections between neurons. Our results show that the dynamics of extrinsic input to the network changed this connectivity to cause the state transitions. Two processes at the synaptic level were recognized: one in which many different neurons contributed to a buildup of membrane potential and another in which more specific neurons triggered an action potential. HNNs facilitate modeling of realistic neuron-neuron connectivity and provide foundational descriptions of large-scale network functionality.

Odor-evoked respiratory responses throughout development in sighted and blind mice
Congenital blindness affects olfactory function depending on developmental stage. However, when studying the ontogeny of olfactory abilities, not all behaviors are expressed at every age making the longitudinal comparisons difficult. Odor-evoked respiratory responses, which are unlearned and do not require complex motor coordination, may serve as sensitive measures of olfactory abilities throughout ontogeny. Using a non-invasive measure of respiration in an olfactory perceptual paradigm, we assessed odor-evoked respiratory responses in a model of congenital blindness at 3 ages, infant, juvenile and adult, in the same mice and in both males and females. We demonstrated the differential outcome of two respiratory parameters (i.e. frequency and amplitude) in a mouse model of congenital blindness. We showed that blind mice have similar olfactory abilities than sighted mice throughout ontogeny but display enhanced sniffing frequency and amplitude, starting at the juvenile age for the latter one, that may help them better explore their environment. We also demonstrated that respiratory frequency is a robust index of age and of olfactory detection, habituation and discrimination at all ages. On the other side, respiratory amplitude does not provide a proxy of olfactory performance at all ages, however, it does highlight differences between sexes and phenotypes associated with visual deprivation. To conclude, our data highlight that respiratory parameters can be used as a complementary approach to assess olfactory performance throughout development and provide an index of olfactory plasticity.

Perinatal environmental enrichment affects murine neonates' brain structure before their active engagement with environment
Early life experiences shape individuals. Environmental enrichment, an experimental paradigm used to study the effect of increased environmental complexity and novelty in animal models, has long been recognised for its broad effect on nervous system function and behaviour. In adult rodents, structural changes in the brain due to enriched environments are well documented, notably in the hippocampus. However, the effect of environmental enrichment on the developing brain during early life is not well understood. This study aims to investigate how environmental enrichment affects brain development during the critical perinatal period, and how such effects compare to those observed during adulthood. We use high-resolution MRI to measure the brain structure of mouse neonates at postnatal day 7, born either in an enriched or a standard environment. We show that rodents exhibit brain structure differences as early as postnatal day 7. However, the regional changes observed differ from those in adulthood: hippocampal changes are limited, but changes in the hindbrain, the dorsal striatum, and the medial habenula are strong. Given the lack of direct interaction between neonates and the environment at P7, we hypothesised that maternal care may mediate these effects. We show that maternal care differs between enriched and standard environments, that maternal care correlates with brain structure changes in the neonates, and that maternal care and enriched environment affect brain structure similarly. This suggests that early changes in brain structure due to environmental enrichment are at least partly mediated by maternal care. This study provides novel insight into the differential effect of enriched environment on early brain development in rodents.

Machine Learning-Based Model for Behavioral Analysis in Rodents: Application to the Forced Swim Test
The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy, studying stress response, and evaluating depressive-like behaviours in rodents. Over the last 10 years, more than 5,500 scientific articles reporting the use of the FST have been published. Despite its widespread use, the FST behaviours are still manually scored, resulting in a labor-intensive and time-consuming process that is prone to human bias and variability. Despite eliminating some biases, existing automated systems are costly and typically only able to distinguish between immobility and active behaviours. Therefore, they are often unable to accurately differentiate the major subtypes of movement patterns, such as swimming and climbing. To address these limitations, we propose a novel approach based on machine learning (ML) using a three-dimensional residual convolutional neural network (3D RCNN) that processes video pixels directly, capturing the spatiotemporal dynamics of rodent behaviour. Our ML model was validated against manual scoring in rats treated with fluoxetine and desipramine, two antidepressants known to induce distinct behavioural patterns. The ML model successfully differentiated among swimming, climbing, and immobility behaviours, demonstrating its potential as a standardized and unbiased tool for automatized behavioural analysis in the FST. Subsequently, we successfully validated our model by testing its ability to distinguish between drugs that predominantly evoke climbing (i.e., amitriptyline), those that preferentially facilitate swimming (i.e., paroxetine), and those that evoke both in a more balanced manner (i.e., venlafaxine). This approach represents a significant advancement in preclinical research, providing a more accurate and efficient method to analyze forced swimming data in rodents. We anticipate that due to its adaptability, in addition to the FST, the model could be applied to various behavioural tests in laboratory animals.

Predicting Neuroplasticity Effects of Continuous Theta Burst Stimulation with Biomarkers from the Motor Evoked Potential TMS Input-Output Curve
The field of neuromodulation lacks predictors of individual differences in plasticity that influence responses to repetitive transcranial magnetic stimulation (rTMS). Continuous theta burst stimulation (cTBS), a form of rTMS known for its inhibitory effects, shows variable responses between individuals, potentially due to differences in neuroplasticity. Predicting individual cTBS effects could vastly enhance its clinical and experimental utility. This study explores whether motor evoked potential (MEP) input-output (IO) parameters measured prior to neuromodulation can predict motor cortex responses to cTBS. IO curves were sampled from healthy adults by recording MEPs over a range of single pulse TMS intensities to obtain parameters including MEPmax and S50 (midpoint intensity). Subjects later received cTBS over the same location of motor cortex and their MEPs before and after stimulation were compared. Both MEPmax and S50 predicted responses, significantly correlating (p<0.05, R2>0.25) with individuals' MEP changes at 10, 20, and 30 minutes after cTBS. Further, we introduced and validated an easily implementable biomarker that does not require the time-consuming sampling of full IO curve: MEP130RMT (median of 10 MEPs at 130% RMT). MEP130RMT was also a strong predictor of cTBS response (p<0.005, R2>0.3). Head-to-head comparison against a previously studied genetic biomarker of rTMS responses (BDNF polymorphism) showed that IO based predictors had a superior performance in explaining more response variability. Thus, IO curves derived prior to cTBS administration can reliably predict cTBS-induced changes in cortical excitability. This work points toward an accessible strategy for tailoring stimulation procedures in both diagnostic and therapeutic applications of rTMS, and potentially boosting response rate to other brain stimulation approaches.

Biplanar nulling coil system for OPM-MEG using printed circuit boards
Optically pumped magnetometers (OPMs) are a promising sensor technology for non-invasive measurement of human electrophysiological signals, in particular the magnetoencephalogram (MEG). OPMs do not need cryogenic cooling and can be placed conformal to the subject's scalp, thus greatly reducing the sensor-to-source distance and improving signal sensitivity. OPMs, however, require near-zero background magnetic field to achieve linearity and minimize signal distortion. Prior work has proposed the use of biplanar field nulling coils to remove the uniform and gradient components of the background magnetic field. Biplanar coils have been expensive to construct, involving tedious error-prone manual winding of over 1000 m of copper wire. In this work, we designed and fabricated background field nulling coils (three uniform and three gradient components) on two-layer Printed Circuit Boards (PCBs). We used an open-source software (bfieldtools) to determine the current loops needed to produce the target magnetic field in a 50-cm-diameter spherical volume. We developed a software-based approach to connect the discrete current loops into a continuous conducting path traversing the two layers of the PCB. For ease of manufacture, the designed (1.5 x 1.5 m2) coils were cut along the symmetry axis and printed as pairs of 1.5 x 0.75 m2 PCBs (2 oz Cu), soldered together and mounted on a sliding aluminum frame. The efficiency of the coils (1.3 - 7.1 nT/mA) was similar or higher than previously reported in the literature. We mapped the field inside the target region after field nulling inside our single-layer shielded room and were able to reduce the largest component of the background field from 21 to 2 nT. Using our nulling coil system, we were able to operate OPMs in a lightly shielded room (background field varying from 6.5 to 108 nT in the floor-to-ceiling direction) to record somatosensory evoked fields (SEFs) comparable to those measured using SQUID-based MEG in a 3-layer shielded room. We disseminate the software and hardware as an open-source package opmcoils. This work will facilitate access to more affordable field nulling coils for OPM-MEG and help to realize the potential of OPM-MEG as an accessible sensor technology for use in human neuroscience.

Object representations drive emotion schemas across a large and diverse set of daily-life scenes
The rapid emotional evaluation of objects and events is essential in daily life. While visual scenes reliably evoke emotions, it remains unclear whether emotion schemas evoked by daily-life scenes depend on object processing systems or are extracted independently. To explore this, we collected emotion ratings for 4913 daily-life scenes from 300 participants, and predicted these ratings from representations in deep neural networks and fMRI activity patterns in visual cortex. AlexNet, an object-based model, outperformed EmoNet, an emotion-based model, in predicting emotion ratings for everyday scenes, while EmoNet excelled for explicitly evocative stimuli. Emotion information was processed hierarchically within the object recognition system, consistent with the visual cortex's organization. Activity patterns in the lateral occipital complex (LOC), an object-selective region, reliably predicted emotion ratings and outperformed other visual regions. These findings suggest that emotion processing in everyday scenes follows visual object recognition, with additional mechanisms engaged when object content is uninformative.

Emergent modularity in large language models: Insights from aphasia simulations
Recent large language models (LLMs) have demonstrated remarkable proficiency in complex linguistic tasks and have been shown to share certain computational principles with human language processing. However, whether LLMs' internal components perform distinct functions, like semantic and syntactic processing in human language systems, remains unclear. Here, we systematically disrupted components of LLMs to simulate the behavioral profiles of aphasia--a disorder characterized by specific language deficits resulting from brain injury. Our findings showed that lesioning specific components of LLMs could replicate behaviors characteristic of different aphasia subtypes. Notably, while semantic deficits as those observed in Wernicke's and Conduction aphasia, were relatively straightforward to simulate, reproducing syntactic and lexical impairments, as seen in Broca's and Anomic aphasia, proved more challenging. Together, these results highlight both parallels and discrepancies between emergent modularity in LLMs and the human language system, providing new insights into how information is represented and processed in artificial and biological intelligence.

Mutations in ASH1L cause a neurodevelopmental disorder with sex differences in epilepsy and autism
To understand brain phenotypes associated with ASH1L, we used a mouse model, performing analysis in different genetic backgrounds: C57BL/6 and CD-1. We found that in both lines, ASH1L mutations result in seizures. Mice from both lines have microcephaly, and also less complex dendritic morphology. We also found differential effects of ASH1L between C57BL/6 and CD-1 strains on a number of different measures identifying aspects of ASH1L function that may be influenced by genetic background. When we analyzed human subjects based on biological sex, for epilepsy, intellectual disability, and ASD, we found sex differences in epilepsy and autism, with epilepsy predominantly in female and ASD in male subjects. Functional evaluation by whole cell patch clamp electrophysiology in mice demonstrated hyperexcitability female compared with male hippocampal CA1 neurons. Thus, the role of ASH1L in specific circuits may be sex-dependent leading to sexual dimorphic effects from haploinsufficiency of this gene.

'SpikeNburst' and 'Nicespike': Advanced Tools for Enhancing and Accelerating In Vitro High-Density Electrophysiology Analysis
High-density multi-electrode arrays (HD-MEAs) have revolutionized electrophysiology by enabling the recording of neuronal activity with unprecedented spatial and temporal resolution. However, analysing these large-scale datasets poses significant challenges, including artefact removal, spike sorting, and accurate assessments of neuronal synchronization. Here, we present two Python-based tools, 'spikeNburst' and 'nicespike', designed to address these challenges and provide a scalable solution for the comprehensive analysis of HD-MEA recordings. The spikeNburst tool integrates advanced methodologies for spike train filtering, burst and network burst detection, and synchronization analysis, and we implemented a full analysis pipeline in the nicespike tool, which includes GPU-accelerated spike sorting using template matching with Kilosort to accurately identify neuronal units spanning multiple electrodes. Together, these tools enable more precise analyses by mitigating redundancy and overestimation inherent in single-channel approaches. Their graphical user and command-line interfaces ensure accessibility for diverse user needs. We validated the tools on neuronal culture recordings, demonstrating their ability to identify somatic and dendritic features of neuronal units, characterize bursting behaviour, and quantify synchronization at both unit and network levels. By addressing critical limitations of existing methods, spikeNburst and nicespike provide a robust, scalable, and user-friendly framework for HD-MEA data analysis, advancing our ability to study neural network dynamics and single-cell activity in detail.

Long-Term Functional Rescue of Trauma-Induced Vision Loss by a Novel, Small Molecule TrkB Activator
Brain-derived neurotrophic factor (BDNF) signaling through the tropomyosin-related kinase B (TrkB) receptor promotes neuronal growth and survival following an injury. However, its short half-life and pleiotropic effects limit the clinical use of BDNF as a therapy in neurodegenerative disorders. Identification of novel and selective TrkB activators may ameliorate the damage caused to retinal neurons during eye-related injuries, and may reduce adverse visual outcomes associated with visual trauma. We previously described a selective TrkB agonist, N-[2-(5-hydroxy-1H-indol-3-yl) ethyl]-2-oxopiperidine-3-carboxamide (HIOC), that reduces the decline in visual function in a mouse model of ocular trauma (1). Using the lead optimization approach, we subsequently synthesized a fluoropyridine analog of HIOC, 2-fluoro-N-(2-(5-hydroxy-1H-indol-3-yl) ethyl) nicotinamide (HIFN), which also successfully activates TrkB. HIFN is a more potent TrkB activator than the parent compound, HIOC. Further, treatment with HIFN demonstrated neuroprotection in an animal model of overpressure ocular blast injury, ameliorating blast-related visual functional decline. Mice treated with HIFN had better visual acuity, contrast sensitivity, and retinal function supported by enhanced survival of retinal ganglion cells compared to vehicle-treated animals. Moreover, HIFN exhibited better protective effects than HIOC. The therapeutic effects of HIFN were attributed to TrkB activation, as blocking the receptor with a selective receptor antagonist (ANA-12) abrogated the neuroprotection. Together, our results identify HIFN, a novel TrkB receptor activator, as a strategy for decreasing retinal degeneration and progressive vision loss associated with traumatic ocular injury. In addition, this compound may have broader applications treating other diseases with altered TrkB activity.

Chronic sleep curtailment expediates brain aging by activating the complement and coagulation cascades in mice
Chronic sleep insufficiency is prevalent in modern society and has been associated with age-related neurodegenerative diseases. Loss of sleep accelerates the progression of neurodegeneration in animal models of neurological diseases. Here, we study whether chronic sleep curtailment leads to brain aging in wild-type animals without a genetic predisposition. We used a wild-type mouse model to simulate modern-day conditions of restricted sleep and compared the brain (cortex) proteome of young sleep-restricted animals with different aged control groups. We report the alteration of 149 proteins related to sleep and 1269 related to age with 96 proteins common between them. Through pathway analysis of proteins common to sleep restriction and aging, we discovered that the complement and coagulation cascade pathways were enriched by alterations of complement component 3 (C3), alpha-2-macroglobulin (A2M), fibrinogen alfa and beta chain (FGA and FGB). This is the first study indicating the possible role of the complement and coagulation pathways in brain aging and by chronic sleep restriction (CSR) in mice.

Tracking changes in corticospinal excitability during visuomotor paired associative stimulation to predict motor resonance rewriting
Mirror properties of the action observation network (AON) can be modulated through Hebbian-like associative plasticity using paired associative stimulation (PAS). We recently introduced a visuomotor protocol (mirror-PAS, m-PAS), which pairs transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) with visual stimuli of ipsilateral (to TMS) movements, leading to atypical corticospinal excitability (CSE) facilitation (i.e., motor resonance) during PAS-conditioned action observation. While m-PAS aftereffects are robust, little is known about markers of associative plasticity during its administration and their predictive value for subsequent motor resonance rewriting. In the present study, we analyzed CSE dynamics in 81 healthy participants undergoing the m-PAS before and after passively observing left- or right-hand index finger movements. Here, typical and PAS-conditioned motor resonance was assessed with TMS over the right M1. We examined CSE changes during the m-PAS and used linear regression models to explore their relationship with motor resonance modulations. Results showed that the m-PAS transiently reshaped both typical and PAS-conditioned motor resonance. Importantly, we found a gradual increase of CSE during m-PAS, which predicted the loss of typical motor resonance but not the emergence of atypical responses after the protocol's administration. Our findings suggest that the motor resonance reshaping induced by the m-PAS is not fully predictable by CSE online modulations. Likely, this rewriting is the product of a large-scale reorganization of the AON rather than a phenomenon restricted to the PAS-stimulated motor cortex. This study underlines that monitoring CSE during non-invasive brain stimulation protocols could provide valuable insight into some, but not all, their plastic outcomes.

The activation of fibroblast growth factor receptor 1 provides therapeutic benefit in a mouse model of Alzheimer's disease
Alzheimer's disease (AD) is a neurodegenerative disease that is characterized by the accumulation of amyloid plaque and neurofibrillary tangles, ultimately impairing multiple cognitive domains. Both plaques and tangles cause neuronal damage and stimulate inflammatory responses in glial cells. The fibroblast growth factor receptor 1 (FGFR1)-mediated signaling pathways support the function of damaged neurons and modulate inflammatory response. The FGFR1 agonists, including Fibroblast growth factor 1 (FGF1) and FG loop peptide (FGL), have been implicated in multiple disease therapy. Whether FGFR1 agonists can improve pathology and cognitive function in AD remains unknown. Here, we showed that administration of FGF1 and FGL to the AD mouse model reversed spatial memory impairment, enhanced neurogenesis, suppressed reactive astrogliosis, and restricted dystrophic neurite. However, only FGF1 treatment reduced the deposition of senile plaque. In microglial culture studies, FGF1 improves the phagocytosis ability of microglia, which contributes to the clearance of plaques. Together, our findings suggested that FGFR1 agonists alleviate pathology and cognitive impairment via immunomodulatory and improve neuronal health in the AD mouse model.

Alterations in retinal tau phosphorylation in Alzheimer's disease patients identified by mass spectrometry
Background: Most neurodegenerative diseases, including Alzheimer's disease (AD) and multiple sclerosis (MS), are associated with abnormal post-translational phosphorylation of tau (p-tau) in the brain. Studies using immunostaining techniques have revealed an accumulation of p-tau also in the AD retina and suggested this p-tau accumulation may reflect tau pathology in the brain. However, immunostainings are dependent on antibody specificity and tissue processing. Hence, further validation using additional methods is needed to identify the p-tau species in the retina and verify their relationship to AD pathology. Methods: We used mass spectrometry to measure p-tau peptides in retinal and hippocampal samples from non demented controls (NC, n=8), AD ( n=12), and MS (n=4). Peptides were first extracted with Lysis buffer to capture extracellular components and cytoplasm, and then with Ripa buffer to isolate nuclear and organelle proteins. We then analysed the differences in p-tau levels between diagnoses and explored how retinal p-tau variants correlate with hippocampal p-tau and neuropathological changes. Results: The mass spectrometry analysis of the retina revealed peaks corresponding to tau peptides phosphorylated at T181, S199/S202, T231, S396 + T403/S404, and T403/S404. These p-tau peptides were also detected in the hippocampal samples, along with additional p-tau peptides such as T217 and S262. Total tau phosphorylation and phosphorylation at S199/S202 and T231 were significantly higher in the retina of AD cases compared to NC. These two peptides, along with peptides phosphorylated at S396+T403/S404 and T403/S404, were also higher in cases with high amyloid-beta (A{beta}) Braak stages compared to those with low A{beta} Braak stages. Further analysis showed that higher A{beta} Braak stages were associated with higher mass spectrometry peak intensities of peptides phosphorylated at S199/S202 and S396+T403/S404. Additionally, retinal p-tau peptides at S396+T403/S404 and T403/S404 2 correlated with neurofibrillary tangle (NFT) Braak stages, and p-tau peptides S396+T403/S404 in the retina were linked to corresponding phosphorylaion in the CA1 region. Conclusions: These findings underscore the connection between retinal and brain tau pathology and highlight the potential of retinal tau as a biomarker for AD diagnosis and monitoring while also deepening our understanding of tauopathies in both the retina and brain.

Corticospinal, reticulospinal, and motoneuronal contributions to fatigability during a sustained contraction of the elbow flexors
Synaptic input to the motoneuron pool is altered during fatiguing muscle contractions. In humans, the corticospinal tract is often studied, with equivocal findings regarding its role in the reduction of force. To date, the involvement of the reticulospinal tract during states of fatigue has not been explored. Fourteen participants (28[6] years, nine males) visited the laboratory twice, first for a familiarisation, then an experimental trial. Participants completed a 5-min sustained elbow flexor contraction at an intensity eliciting 40% of the EMG recorded during a maximal isometric voluntary contraction (MVC). Before, during, and after the contraction, transcranial magnetic stimulation and electrical cervicomedullary stimulation were used to elicit motor evoked potentials (MEPs) and cervicomedullary evoked potentials during the silent period (SP-CMEPs) respectively, with CMEPs also being evoked in combination with a startling acoustic sound (CMEPcon). Electrical stimulation of the brachial plexus was used to evoke maximal compound action potentials of the elbow flexors (Mmax). The 5-min contraction induced a 53% loss of force (p<0.001), with no change in background EMG (~4% Mmax, p=0.293). Neither MEP amplitude (p=0.246) nor CMEPcon ratio (p=0.489) were altered during the contraction. Whereas CMEP and SP-CMEP amplitudes were reduced by ~20 and 50%, respectively (p<0.001) and remained depressed post-task. The results suggest that neither corticospinal nor reticulospinal tract excitability was altered during a 5-min constant-EMG task at 40% maximal EMG. Instead, the aetiology of the neural contribution to fatigability appeared to be primarily related to the loss of motoneuron excitability.

Reduced plasma hexosylceramides in frontotemporal dementia are a biomarker of white matter integrity
INTRODUCTION: Blood biomarkers are needed to facilitate new therapeutic trials and improve management of behavioural variant frontotemporal dementia (bvFTD). Since altered white matter integrity is characteristic of bvFTD, this study aimed to determine if plasma levels of myelin-enriched glycolipids are altered in bvFTD and correlate with white matter integrity. METHODS: Nineteen glycolipids were quantified in bvFTD (n=31) and control (n=26) plasma samples. White matter integrity was assessed using MRI-derived fibre tract density and cross-section (FDC). RESULTS: Eleven lipids were significantly lower in bvFTD compared to control subjects, seven were inversely correlated with disease duration, and twelve were positively correlated with cognitive performance, with C22:0 hexosylceramide most strongly correlated. FDC was lower in frontotemporal white matter tracts of bvFTD compared to control subjects, and plasma C22:0 hexosylceramide was significantly correlated with FDC in these tracts. DISCUSSION: Circulating glycolipids may be a valuable biomarker of myelin integrity and disease progression in FTD.

Building Multivariate Molecular Imaging Brain Atlases Using the NeuroMark PET Independent Component Analysis Framework
Molecular imaging analyses using positron emission tomography (PET) data often rely on macro-anatomical regions of interest (ROI), which may not align with chemo-architectural boundaries and obscure functional distinctions. While methods such as independent component analysis (ICA) have been useful to address this limitation, the fully data-driven nature can make it challenging to compare results across studies. Here, we introduce the NeuroMark PET approach, utilizing spatially constrained independent component analysis to define overlapping regions that may reflect the brain's molecular architecture. We first generate an ICA template for the PET radiotracer florbetapir (FBP), targeting amyloid-{beta} (A{beta}) accumulation in the brain, using blind ICA on large datasets to identify replicable independent components. Only components that targeted A{beta} were included in this study, defined as A{beta} networks (A{beta}Ns), by omitting components targeting myelin or other non-A{beta} targets. Next, we use the A{beta}Ns as priors for spatially constrained ICA, resulting in a fully automated ICA pipeline called NeuroMark PET. This NeuroMark pipeline, including its A{beta}Ns, was validated against a standard neuroanatomical PET atlas, using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The study included 296 cognitively normal participants with FBP PET scans and 173 with florbetaben (FBB) PET scans, an analogue radiotracer also targeting A{beta} accumulation. Our results show that NeuroMark PET captures biologically meaningful, participant-specific features, such as subject specific loading values, consistent across individuals, and also shows higher sensitivity and power for detecting age-related changes compared to traditional atlas-based ROIs. Using this framework, we also highlight some of the advantages of using ICA analysis for PET data. In this study, an A{beta}N consists of weighted voxels and forms a pattern throughout the entire brain. For example, components may have weighted values at every voxel and can overlap with one another, enabling the separation of artifacts which may coincide with the A{beta}Ns of interest. In addition, this approach allows for the differentiation, separating white matter components, which may overlap in complex ways with the A{beta}Ns, mainly residing in the neighboring gray matter. Results also showed that the most age associated A{beta}N (representing the cognitive control network, CC1) exhibited a stronger association with age compared with macro-anatomical regions of interest. This may suggest that each NeuroMark FBP A{beta}N represents a spatial network following chemo-architectural uptake with greater biological relevance compared with anatomical ROIs. In summary, the proposed NeuroMark PET approach offers a fully automated framework, providing accurate and reproducible brain A{beta}Ns. This approach enhances our ability to investigate the molecular underpinnings of brain function and pathology, offering an alternative to traditional ROI-based analyses.

Lack of motor defects and ALS-like neuropathology in heterozygous Sptlc1 Exon 2 deletion mice
Mutations in the human SPTLC1 gene have recently been linked to early onset amyotrophic lateral sclerosis (ALS), characterized by global atrophy, motor impairments, and symptoms such as tongue fasciculations. All known ALS-linked SPTLC1 mutations cluster within exon 2 and a specific variant, c.58G>T, results in exon 2 skipping. However, it is unclear how the exon 2 deletion affects SPTLC1 function in vivo and contributes to ALS pathogenesis. Leveraging the high genomic sequence similarity between mouse and human SPTLC1, we created a novel mouse model with a CRISPR/Cas9-mediated deletion of exon 2 in the endogenous murine Sptlc1 locus. While heterozygous mice did not develop motor defects or ALS-like neuropathology, homozygous mutants died prematurely. These findings indicate that Sptlc1 {triangleup}Exon2 heterozygous mice do not replicate the disease phenotype but provide valuable insights into SPTLC1 biology and serve as a useful resource for future mechanistic studies.

Target-Specificity and Repeatability in Neuro-Cardiac-Guided TMS for Heart-Brain Coupling
The dorsolateral prefrontal cortex (DLPFC) is a principal target for repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder, with therapeutic effects hypothesized to be mediated by the connectivity between the DLPFC and the subgenual anterior cingulate cortex (sgACC). Interestingly, these depression-related hubs are also part of the heart-brain axis, thus potentially rendering the stratification of individual depression targets possible by tapping into short-term heart rate modulation after DLPFC stimulation. Recently, a set of stimulation protocols has been proposed to objectively quantify these downstream effects. While these neuro-cardiac-guided TMS protocols (NCG-TMS) are promising to improve clinical responses, rigorous, third-party assessments of these approaches including replicability, robustness, and the impact of stimulation side-effects are critically missing. Here, we employed a 10 Hz TMS protocol (NCG-TMS 2.0) across three sessions to evaluate the effects of stimulation intensity and DLPFC target specificity on heart-brain coupling (HBC) in a cohort of healthy participants. Our results demonstrate a significant nonlinear increase in HBC with higher stimulation intensity, with the F3 lateral and F3 posterior targets eliciting more robust effects than the sham condition. For the first time, we are able to demonstrate reliable target- and intensity-specific HBC modulation across multiple NCG-TMS 2.0 sessions. Although the repeatability within subjects was limited when including the initial session, subsequent sessions yielded consistent results for the F3 anterior and F3 lateral targets at higher intensities. Although pain and other side effects influenced HBC, they did not fully account for the observed modulation of HBC. These findings underscore the critical role of spatial specificity and stimulation intensity in modulating heart-brain interactions and offer a potential framework for optimizing individualized rTMS treatment protocols for depression.

Block-Champagne: A novel Bayesian framework for Imaging E/MEG Source
Estimating the extents of E/MEG source activities is vital for exploring brain dynamics at high spatiotemporal resolution. However, conventional ESI models exclusively overemphasize on the accurate estimation of the source locations and struggle to reconstruct the extents due to the ill-posed nature of the problem, which requires effective integration of biophysical constraints and proficiency in signal processing methods. In this study, a novel Bayesian framework Block-Champagne is introduced to provide accurate estimation of extended sources (i.e., both locations and extents). Specifically, a block-sparsity constraint is employed to model the local homogeneity of sources, which can be updated automatically to reconstruct source with arbitrary extents. Furthermore, prior constraints from other modalities, such as fMRI maps, can be incorporated to model interactions between remote sources to further enhance source reconstruction. The performance of Block-Champagne is quantitatively evaluated through simulation experiments, demonstrating its overall superiority under various complex scenarios (i.e., SNR, extent size, & number of sources) compared to benchmark algorithms (including LORETA, EBI-Convex, ts-Cham, L21-Sissy, & BESTIES). Moreover, validation results obtained from deep-brain stimulation EEG, epilepsy data, and face-processing multi-modal data further prove the practical feasibility of Block-Champagne. In conclusion, our study reveals the superiority of the proposed Block-Champagne in accurate reconstruction of extended source, positioning Block-Champagne as a highly promising tool for realistic applications where source locations and extents are of equivalent importance.