bioRxiv Subject Collection: Neuroscience's Journal

Population sparseness determines strength of Hebbian plasticity for maximal memory lifetime in associative networks
The brain can efficiently learn and form memories based on limited exposure to stimuli. One key factor believed to support this ability is sparse coding, which can reduce overlap between representations and minimize interference. It is well known that increased sparseness can enhance memory capacity, yet its impact on the speed of learning remains poorly understood. Here we analyze the relationship between population sparseness and learning speed -- specifically, how the learning speed that maximizes memory capacity depends on the sparseness of the neural code, and how this in turn affects the networks maximal capacity. To this end, we study a feedforward network with Hebbian and homeostatic plasticity and a two-state synapse model. The network learns to associate binary input-output pattern pairs, where sparseness corresponds to a small fraction of active neurons per pattern. The learning speed is modeled as the probability of synaptic changes during learning. The results presented in this manuscript are based on both network simulations and an analytical theory that predicts expected memory capacity and optimal learning speed. For both perfect and noisy retrieval cues, we find that the optimal learning speed indeed increases with increasing pattern sparseness -- an effect that is more pronounced for input sparseness than for output sparseness. Interestingly, the optimal learning speed stays the same across different network sizes if the number of active units in an input pattern is kept constant. While the capacity obtained at optimal learning speed increases monotonically with output sparseness, its dependence on input sparseness is non-monotonic. Overall, we provide the first detailed investigation of the interactions between population sparseness, learning speed, and storage capacity. Our findings propose that differences in population sparseness across brain regions may underlie observed differences in how quickly those regions adapt and learn.

Selective octopaminergic tuning of mushroom body circuits during memory formation
The catecholamines octopamine and tyramine undoubtedly have a major impact on the life of an insect. A wide range of physiological processes and behaviours are regulated by these neurotransmitters/hormones. Octopamine and tyramine act homologous to the adrenergic system of vertebrates, primarily adapting the organism to the given situation, by switching between the states of alertness and rest. Interestingly, higher brain functions like learning and memory are also regulated by octopamine and tyramine. About 30 years ago, initial work in Drosophila has demonstrated that dopaminergic neurons signal punishment, while octopaminergic neurons signal reward during olfactory associative learning and memory. In the meantime, however, it has become clear that distinct types of dopaminergic neurons convey both reward and punishment signals to the mushroom bodies, a central brain region responsible for the formation and storage of associative memories. Although some conflicting data remain, these findings challenge the previously established model of functional segregation and may limit the proposed role of octopamine neurons as teaching neurons during memory formation. We have therefore re-examined the role of octopamine in learning and memory in Drosophila larvae. Through a combination of Ca2+ imaging, anatomical studies and gain-of-function and loss-of-function behavioural approaches, we demonstrate that octopamine signalling plays a crucial role in larval learning by modulating dopaminergic neurons across distinct cell clusters to orchestrate memory processes.

Motoneurons can count: A cell intrinsic spike number memory compensates for deviations from rate coding
Firing rate is an important means of encoding information in many types of neurons. A prime example is asynchronous flight as used by ~600,000 insect species (Dudley, 2018), where wingbeat frequency and flight power output are controlled by a rate code of the flight power motoneurons (Hurkey et al., 2023). The five motoneurons that innervate the wing depressor muscle fibers translate different magnitudes of excitatory drive smoothly into changes of their common firing rates, which in turn, are linearly related to wing power output (Gordon and Dickinson, 2006). Such motoneuron input/output properties are called type-I excitability and are achieved by the expression of specific combinations of ion currents that linearize the frequency-input current curve. But are there additional motoneuron properties that compensate for acute perturbation of their rate code? Here we combine in vivo electrophysiology with Drosophila genetics to test for mechanisms that compensate for transient perturbation of rate coding during behavior. We show that MN intrinsic properties compensate for the occurrence of extra spikes by delaying the subsequent spikes, thus restoring rate coding fidelity. The underlying mechanism is dose and phase dependent. First, compensatory increases of subsequent interspike interval durations grow with the number of supernumerous spikes that interfere with coding. Second, the magnitude of the compensation for single extra spikes depends on when during an interspike interval these occur. This mechanism depends at least in part on axonally localized HCN channels and increases the fidelity of motoneuron rate coding in the light of perturbation during flight motor behavior.

Reversed functional gradient in primate prefrontal cortex: posterior dominance and frontopolar deactivation
The frontopolar cortex (FPC) is thought to coordinate the more posterior lateral prefrontal cortex (LPFC) during complex, non-routine behaviors through high-level functions such as management of multiple goals, exploration, and self-generated decision-making. However, direct neurophysiological comparisons with other prefrontal regions are lacking, leaving the FPC's putative dominance untested. Contrary to this view, our comparison of neuronal activity across the full anteroposterior LPFC in macaques during six distinct tasks probing these functions revealed a posterior-to-mid LPFC dominance, with resource-allocation, novelty-detection (including reward-prediction error), and modality invariant decision-monitoring signals all showing a common posterior bias. In contrast, regardless of task demands, the FPC's strongest encoding was about the most recently executed action, and it displayed minimal object selectivity, even when objects were task-critical. We identified a turning point in this graded posterior-to-anterior transition from task-positive to task-negative regions around the border between the anterior and middle thirds of the LPFC. These findings challenge the prevailing notion that the LPFC is anterior-dominant across primate species, and provide evolutionary constraints on theories of human prefrontal organization.

The cortical scene processing network emerges in infancy, prior to independent navigation experience
Sighted people rely on vision to recognize and navigate the local environment. By adulthood, human cortex contains at least three regions that respond selectively to visual scene information, but it remains unknown when or how these regions develop. One hypothesis is that scene selectivity emerges gradually in regions that initially prefer certain low-level visual features (e.g., peripheral visual input, high spatial frequencies, rectilinearity), and then exposure to the visual statistics of natural scenes drives the emergence of scene selective responses. However, both aspects of this hypothesis remain to be tested: how early scene selectivity first arises in human development, and whether it is driven by passive exposure to visual statistics. We therefore collected functional magnetic resonance imaging data from awake 2-9-month-old infants while they watched videos of real-world scenes with ego-motion, as well as faces, objects, and scrambled videos. We found stronger responses to scenes than control conditions in the location of all three scene regions. Scene-selective responses could not be explained by low-level visual properties of the stimuli, and were found in infants as young as 2-5 months old, with no evidence of age-related change. We also measured infants experience independently navigating (e.g., crawling), which was not necessary for the development of scene-selectivity. In sum, cortical regions are scene-selective in human infants prior to independent navigation, and after only limited exposure to visual scene statistics.

Compensatory Mechanisms in Visual Sequence Learning: An fMRI Study of Children with Developmental Language Disorder
Symptoms of developmental language disorder (DLD) may in part result from an underlying deficit in statistical learning (SL). This learning deficit may be related to the ability to extract probabilistic properties of events in the environment, which is based on the functions of cortical and subcortical brain regions underlying SL. Using a behavioral SL task and functional magnetic resonance imaging (fMRI), we tested SL ability in the visual domain and its neural correlates in children with DLD and their typically developing (TD) peers. During fMRI, children performed SL tasks involving sequences of two types of stimuli: easy-to-name (EN) objects and difficult-to-name (DN) objects. The children underwent a pre-training fMRI, one week of behavioural training and a post-training fMRI. Similar task performance was observed in both groups during the experimental sessions, with an improvement in performance following training in the SL tasks involving both EN and DN objects. FMRI results revealed that, after training, the DLD group presented greater involvement of the frontal cortex and temporal pole for EN objects. Furthermore, in the TD group, the left putamen, globus pallidus (GP) and thalamus were involved in the early stages of SL, whereas in the DLD group, these areas were involved in SL after training. For DN objects, after training, the DLD group presented greater involvement of the parietal and precuneus regions in the SL task performance. Our results suggest that children with DLD may employ different cognitive processes in SL than TD children, possibly as a compensatory mechanism.

Precision mapping of functional brain network trajectories during early development
Preterm birth is a known risk factor for neurodevelopmental disabilities, but early cognitive assessments often fail to predict long-term outcomes. This limitation underscores the need for alternative biomarkers that reflect early brain organization. Resting-state functional connectivity is a powerful tool to study functional brain organization during the perinatal period. However, most fMRI studies in infant populations use group-level analyses that average subject-specific data across several weeks of development, reducing sensitivity to subtle, time-sensitive deviations from typical brain trajectories. Using a novel precision functional mapping approach, we estimated individual resting-state networks (RSNs) in a large cohort of neonates (N = 352, gestational age at birth: 25.6-42.3 weeks) from the developing Human Connectome Project. RSN connectivity strength increased linearly with age at scan, especially in higher-order networks. In particular, the default mode network (DMN) exhibited marked changes in topography and connectivity strength, evolving from an immature organization in preterm infants to a more adult-like pattern in term-born infants. Longitudinal data from a subset of preterm infants (N = 15) confirmed ongoing network development shortly after birth. Despite this maturation, preterm infants did not reach the connectivity levels of term-born infants by term-equivalent age. These findings highlight the potential of individualized RSN mapping as an early marker of neurodevelopmental trajectories.

Spectral disruption of inter-hemispheric resting-state BOLD coherence at vasomotor frequency (~0.1 Hz) links vascular dysfunction to functional connectivity loss in carotid artery stenosis
Vasomotor dynamics at the infra-slow frequencies (~0.1 Hz), driven by synchronized oscillation of smooth muscle cells in vessel walls, play an important role in regulating cerebral perfusion and constitute a physiological basis for resting-state functional connectivity (FC). Invasive animal studies have demonstrated that vasomotor activity contributes to coherent blood oxygenation-dependent level (BOLD) signal fluctuations. However, in humans, it remains challenging to non-invasively detect this contribution due to the limited spatiotemporal sensitivity of functional magnetic resonance imaging (fMRI) to vasomotion. Leveraging internal carotid artery stenosis (ICAS) as a natural lesion model of impaired vasomotion, we examined whether impaired vasomotor activity influences inter-hemispheric BOLD coherence at ~0.1 Hz. Using a multi-modal fMRI framework that integrates resting-state fMRI with quantitative multi-parametric mapping of cerebral blood volume, blood flow, oxygen metabolism, and BOLD time lag, we compared BOLD coherence in patients with unliteral ICAS to healthy controls. Frequency-specific coherence analysis revealed significantly diminished inter-hemispheric BOLD coherence at the vasomotor frequency range (~0.1 Hz) across canonical resting-state networks in ICAS patients, whereas ultra-slow (<0.05 Hz) coherence remains largely preserved. This reduction was spatially widespread and particularly pronounced inside watershed areas, i.e., border zones between major vascular perfusion territories that are especially vulnerable to hypoperfusion, and associated with a significantly increased lateralization in cerebral blood volume (p < 0.01) inside watershed areas. Notably, coherence-based FC patterns at ~0.1 Hz were heterogeneous inside watershed areas and homogeneous outside watershed areas, suggesting an interplay between compensatory mechanisms and vasomotion impairment. Taken together, our findings demonstrate that frequency-resolved, region-specific analysis can capture presumably vasomotion-related oscillatory signals at ~0.1 Hz and detect subtle differences in inter-hemispheric FC, offering a non-invasive biomarker for early cerebrovascular dysfunction, particularly in patients with ICAS and other vasomotion-related neuropathologies.

Larvaworld : A behavioral simulation and analysis platform for Drosophila larva
Behavioral modeling supports theory building and evaluation across disciplines. Leveraging advances in motion-tracking and computational tools, we present a virtual laboratory for Drosophila larvae that integrates agent-based modeling with multiscale neural control and supports analysis of both simulated and experimental data. Virtual larvae are implemented as 2D agents capable of realistic locomotion, guided by multimodal sensory input and constrained by a dynamic energy-budget model that balances exploration and exploitation. Each agent is organized as a hierarchical, behavior-based control system comprising three layers: low-level locomotion, optionally incorporating neuromechanical models; mid-level sensory processing; and high-level behavioral adaptation. Neural control models can range from simple linear transfer models to rate-based or spiking neural network models, e.g. to accomodate associative learning. Simulations operate across sub-millisecond neuronal dynamics, sub-second closed-loop behavior, and circadian-scale metabolic regulation. Users can configure both larval models and virtual environments, including sensory landscapes, nutrient sources, and physical arenas. Real-time visualization is integrated into the simulation and analysis pipeline, which also allows for standardized processing of motion-tracking data from real experiments. Distributed as an open-source Python package, the platform includes tutorial experiments to support accessibility, customization, and use in both research and education.

Second Language Proficiency Modulates Hierarchical Alignment between Brain Activity and Large Language Models
Second language (L2) comprehension is thought to proceed hierarchically, from basic word recognition to complex discourse-level understanding. However, the neural mechanisms underpinning this hierarchical progression remain poorly understood. Leveraging the hierarchical nature of linguistic representations in large language models (LLMs), we investigated whether L2 proficiency modulates brain-LLM alignment in a layer-dependent manner. Using functional magnetic resonance imaging (fMRI) data collected during discourse listening from 54 participants with varying levels of L2 proficiency, we constructed individualized encoding models to quantify how proficiency shapes the brain-LLM representational correspondence. In high-proficiency individuals, LLM-based models reliably predicted neural responses in a distributed network comprising temporal, frontal, and medial-parietal cortices, while for low-proficiency participants, prediction performance decreased. Notably, alignment in the superior temporal sulcus (STS) showed a robust correlation with L2 proficiency at mid-to-deep LLM layers, suggesting that the STS encodes higher-level linguistic abstractions essential for advanced language comprehension. These findings indicate that L2 proficiency modulates the hierarchical alignment between LLMs and the brain, providing insights into how L2 learning shapes neural language representations.

Multidimensional feature tuning in category-selective areas of human visual cortex
Human high-level visual cortex has been described in two seemingly opposed ways. A categorical view emphasizes discrete category-selective areas, while a dimensional view highlights continuous feature maps spanning across these areas. Can these divergent perspectives on cortical organization be reconciled within a unifying framework? Using data-driven decomposition of fMRI responses in face-, body-, and scene-selective areas, we identified overlapping activity patterns shared across individuals. Each area encoded multiple interpretable dimensions tuned to both finer subcategory features and coarser cross-category distinctions beyond its preferred category, even in the most category-selective voxels. These dimensions formed distinct clusters within category-selective areas but were also sparsely distributed across the broader visual cortex, supporting both locally selective, category-specific, and globally distributed, feature-based coding. Together, these findings suggest multidimensional tuning as a fundamental organizing principle that integrates feature-selective clusters, category-selective areas, and large-scale tuning maps, providing a more comprehensive understanding of category representations in human visual cortex.

Agouti integrates environmental information to regulate natural variation in paternal behavior
Male investment in offspring rearing through paternal care is rare among mammals and the neural mechanisms governing its emergence are poorly understood. We leveraged the natural paternal behavior of African striped mice (Rhabdomys pumilio) in combination with brain-wide cFos quantification, single-nucleus RNA-sequencing, viral-mediated gene manipulation, and environmental manipulation to dissect the neural basis of natural variation in male parenting. We find that socio-environmental conditions drive individual variation in male alloparenting such that post-weaning social isolation increases paternal care while social living in higher density groups increases infanticide. This natural variation in care corresponds to neural activity in the medial preoptic area and changes in correlated activity across brain regions. Within the medial preoptic area, expression of agouti signaling protein (Agouti) in neurons is increased by group housing and is negatively associated with care, and overexpression of Agouti reduces care and enhances infanticide in previously tolerant animals. Naturalistic manipulations further reveal that Agouti integrates long-term housing conditions rather than food availability/hunger. Together, our results demonstrate that Agouti acts as a molecular integrator of socio-environmental information to drive variation in paternal care.

Column-Like Subnetwork Reconstruction in Motor Cortex from Graph-Based 3D High-Density Two-Photon Calcium Imaging
How precise 3D interactions among cortical neurons underlie layer-specific computations remains elusive. We developed a graph-framework to infer functional connectivity from fast volumetric two-photon Ca2+ imaging in the awake mouse primary motor cortex. By converting deconvolved traces into binary spike trains, removing population bursts, and applying an adaptive, layer-specific statistical threshold, we reconstructed a directed, weighted network of ~1,000 neurons. Decomposition into strongly connected components revealed ~10-cell subnetworks, predominantly in layer II/III but often bridging to layer Va. Across six 20-min recordings, we found that (1) layer II/III dominates connectivity, (2) feedback links are more numerous and stronger than the feed-forward (II/III->Va) ones, and (3) information flows in <6 synapses. We uncovered seven geometrical and dynamical motifs, ranging from compact columns to elongated diagonal, each with characteristic event sizes and durations. These findings reveal diverse, column-like microcircuits in M1 with a net ascending flow, suggesting that such subnetworks form elemental processing modules for motor control.

Assessing the degree of cortical dislamination through electrical pattern analysis
Focal cortical dysplasia (FCD) is a leading cause of pharmacoresistant epilepsy in pediatric populations, although its contribution to epileptogenesis remains incompletely understood. Recent findings indicate that hyperexcitability might stem from peripheral areas that are not dysplastic, rather than from the malformation itself. However, considering the significant variability associated with these malformations, it remains challenging to clarify whether this degree of disorganization contributes to changes in activity. In this study, we used the carmustine-induced animal model to investigate how varying degrees of cortical malformation influence neural dynamics. Local field potentials (LFP) were recorded using a multielectrode array (MEA) during both spontaneous activity and external perturbation. We developed a novel metric to quantify spatial heterogeneity in signal organization and evaluated its association with excitation-inhibition (E/I) balance. Our results reveal that alterations such as the aperiodic exponent value and the sparcity of clustering in signal classification are related to the extent and distribution of cortical abnormalities, underscoring the functional relevance of cytoarchitectural variability. This work advances the understanding of FCD-related network dysfunction and introduces analytical approaches with potential translational value for neuroscience research and pre-surgical evaluation.

Alcohol consumption drives sex- and region- specific disruption of somatostatin signaling in mice
The prefrontal cortex (PFC), which is thought to be disrupted early in the cycle of substance use and addiction [1], is comprised of a complex microcircuit of long-range glutamatergic pyramidal neurons controlled by GABAergic-expressing local inhibitory neurons [2, 3]. Somatostatin (SST)-expressing neurons are a subpopulation of these local GABAergic inhibitory cells and provide both peptidergic and GABAergic control over these PFC circuits [3, 4], and are disturbed following alcohol consumption in humans [5] and in rodent models [6, 7]. However, little is known about how endogenous SST peptide signaling is affected by alcohol. Using ex vivo electrophysiology, immunohistochemistry, in situ hybridization, and behavior, we demonstrate robust down-regulation of SST control over pyramidal output activity in the prelimbic (PL), but not infralimbic (IL), PFC after alcohol exposure. We also show this is likely mediated by changes in SST receptor expression levels and not disrupted expression or capacity for release of SST peptide, suggesting postsynaptic homeostatic changes to SST signaling following binge alcohol consumption in mice that may underlie post-alcohol dysregulation in mood. This provides insight into how voluntary alcohol consumption disrupts PFC peptide signaling and suggests a potential therapeutic target for the treatment of alcohol use disorder (AUD).

Differential reliance on sensory reinstatement and internally transformed representation during vivid retrieval of visual and auditory episodes
Auditory memory is considered less detailed yet more durable than visual memory, implying a modality-specific memory retrieval process. We used fMRI and multivoxel pattern analyses to examine how 25 participants encoded and retrieved naturalistic sounds and videos. Both auditory and visual targets reinstated item-specific fine activation patterns in the association cortex during retrieval, and reinstatement strength correlates with subjective memory vividness. However, auditory episodes showed a markedly larger reliance on internally constructed representations than visual episodes, quantified by retrieval-retrieval similarity after removing encoding traces. Sensory reinstatement correlated more to the (detail-related) posterior hippocampus, while internal representations also correlated to the (gist-related) anterior hippocampus. Furthermore, temporal voice areas preserved gist-level (human versus non-human) information from encoding to retrieval, whereas fusiform face representations degraded. These findings reveal that auditory and visual memories share a common sensory reinstatement mechanism, but differ in the neural mechanism that supports retrieval, with participants favouring gist over perceptual details during auditory memory retrieval.

The contribution of vestibular and proprioceptive signals to trunk stabilization varies between postural tasks and between walking speeds.
Stabilizing the upright posture of the trunk relies on vestibular and proprioceptive afference. Previous studies found that the feedback responses to sensory afference vary between postures and tasks. We investigated whether and how vestibular and proprioceptive afference contribute to trunk stabilization during different postural tasks, and during walking at different speeds. Twelve healthy adults performed tasks in a random order: sitting, standing on the right foot or both feet, and treadmill walking at five speeds: 0.8, 2.0, 3.2, 4.3 and 5.5 km/h, while exposed to unilateral muscle vibration on the right paraspinal muscles at the level of the second lumbar vertebra, or to a step-like electrical vestibular stimulation (EVS) with the anode behind the left ear. The mediolateral displacements of markers at the sixth thoracic level and sacrum in the global coordinate system were used to evaluate the responses to sensory stimulation. No significant responses to EVS at T6 and sacrum level were found in sitting and standing. Responses to muscle vibration were significant and differed between unipedal standing compared to sitting and bipedal standing. The latter suggests a different interpretation of the sensation of muscle lengthening in these postures. The magnitude of the responses to both stimuli increased from very slow speeds to moderate speeds. This may indicate that a different control strategy is adopted in walking at slow speeds compared to walking at faster speeds. From moderate to high speeds, the responses decreased, suggesting a decreased demand for feedback control at higher speeds.

Developing a Sensory Representation of an Artificial Body Part
Somatosensory feedback is crucial for motor control, yet artificial limbs are thought to lack such feedback. We investigated whether the physical interaction of artificial limbs with the body generates informative sensory signals, and whether such naturally-mediated feedback can drive a distinct representation of an artificial limb. We examined the perceptual, neural and phenomenological bases of this "natural" somatosensory representation using an extra robotic digit ('Third Thumb'; Dani Clode Design), used for hand augmentation. We first compared the perceptual representation of naturally mediated somatosensory signals via the Third Thumb (D6) against state-of-the-art artificial touch feedback systems. Across material discrimination tasks, participants performed comparably, or outperformed, with "natural" feedback, demonstrating versatile and intuitive interpretation of natural sensory signals. To then study the emergence and refinement of D6's neural representation, we examined representational similarity patterns in primary somatosensory cortex (S1) using fMRI. Even with minimal motor experience to use D6, the brain immediately organised tactile input from D6 topographically relative to the hand, and distinctively from the palm (where it is attached). Following seven days of D6-biological hand collaboration motor training, this representation refined as it became more similar to the biological digits. This integration was also reflected in increased subjective somatosensory embodiment post-training. Our findings demonstrate that wearable devices naturally provide a powerful source of behaviourally-relevant feedback, which is immediately integrated with our hand representation. While such a representation is already accessible with minimal experience, motor training contributes to stronger integration and embodiment of the artificial device with our somatosensory body representation.

The precision of attention controls attraction of population receptive fields
We alter our sampling of visual space not only by where we direct our gaze but also by where and how we direct our attention. Attention attracts receptive fields toward the attended position, but our understanding of this process is limited. Here we show that the degree of this attraction towards the attended locus is dictated not just by the attended position, but also by the precision of attention. We manipulated attentional precision while using 7T fMRI to measure population receptive field (pRF) properties. Participants performed the same color-proportion detection task either focused at fixation (0.1{ring} radius) or distributed across the entire display (more than 5{ring} radius). We observed BOLD response amplitude increases as a function of the task, with selective increases in foveal pRFs for the focused attention task and vice versa for the distributed attention task. Furthermore, cortical spatial tuning changed as a function of attentional precision. Specifically, focused attention more strongly attracted pRFs towards the attended locus compared to distributed attention. This attraction also depended on the degree of overlap between a pRF and the attention field. A Gaussian attention field model with an offset on the attention field explained our results. Together, our observations indicate the spatial distribution of attention dictates the degree of its resampling of visual space.

Innervation and cargo-specific axonal transport impairments in FUS-ALS mice with gain and loss of function
Mutations in the RNA-binding protein FUS lead to nuclear depletion and cytoplasmic mislocalisation of the protein and cause amyotrophic lateral sclerosis (ALS). Using a novel FUS-ALS mouse model, we found that adult mutant mice develop loss of function transcriptomic alterations, along with aberrant cytoplasmic partitioning associated with translatome deficits. Neuromuscular junction innervation was selectively impaired in FUS-ALS females; however, reinnervation following sciatic nerve crush was equally perturbed in both sexes. Additionally, we observed cargo-specific axonal transport alterations, a process critical for neuronal maintenance. In vivo mitochondrial transport was impaired across FUS-ALS mice, whereas the transport of signalling endosomes was selectively disrupted in mutant females. Altogether, our findings identify broader and sex-dependent motor neuron dysfunction in FUS-ALS, emphasise the link between endosomal transport impairments and denervation in disease, and establish FUS-ALS mice as a valuable model for investigating early cellular impairments driving ALS pathology.

Whole-body central processing of lateral line inputs encodes flow direction relative to the center-of-mass
From shifting visual scenes to tactile deformations and fluid motion, animals must interpret patterns of sensory flow around their body to construct stable internal models and produce adaptive behavior. Understanding of how such transformations are encoded within the brain remains incomplete. To tackle this question, we leverage the lateral line of larval zebrafish as a tractable sensory system sensitive to fluid motion that is used to steer navigation, feed, and avoid predators. By presenting stimuli of either direction to neuromasts along the body, we used high-resolution calcium imaging to map hindbrain responses. Unexpectedly, our findings challenge the notion that central lateral line processing lacks topographic structure by revealing a simple yet powerful principle centered on a egocentric spatial framework: the direction and location of local flow motions are encoded in reference to the animals center-of-mass. This simple representation enables the brain to register complex flow patterns and provides a robust basis for subsequent behavioral action selection. MON neurons that encode flow toward the center-of-mass broadly project to form bilateral connections onto reticulospinal neurons that coordinate forward locomotion while MON neurons that encode flow away from the center-of-mass displayed a more selective and unilateral projection profile to command neurons for turns. Our discovery represents a shift from purely somatotopic encoding toward an integrative representation of axial position and directionality combined, revealing a novel principle of encoding spatio-directional cues in the hindbrain. This study advances our understanding of how complex mechanosensory inputs select appropriate motor outputs via simple egocentric neural maps in the hindbrain.

Significance StatementSpatial and directional cues are essential to select appropriate actions in response to changes in the environment. How information from broadly distributed mechanosensors across the entire body occurs in the brain enables motor selection remains elusive. By directionally stimulating each and virtually all neuromasts distributed along the lateral line of larval zebrafish, our study uncovers that spatial and directional inputs from each flow sensors is encoded in the medial octavolateralis nuclei relative to the animals center-of-mass in order to subsequently recruit reticulospinal neurons driving forward and turn bouts. These results establish a new framework for understanding how broadly distributed inputs get integrated to recruit motor command neurons responsible for producing diverse behaviors.

Lysergic Acid Diethylamide extends lifespan in Caenorhabditis elegans
Aging is modulated by nutrient-sensing pathways that integrate metabolic and hormonal cues to regulate growth, stress resilience, and lifespan. Caloric restriction (CR), a well-established intervention, extends longevity in diverse species primarily through inhibition of the TOR signaling pathway. Lysergic acid diethylamide (LSD), a classic serotonergic psychedelic with emerging therapeutic applications, remains largely unexplored in the context of aging. Here, we show that LSD treatment significantly extends lifespan in Caenorhabditis elegans and reduces age-associated lipofuscin accumulation, indicative of delayed cellular aging. LSD reproduces several CR-like phenotypes, including decreased reproductive output and increased nuclear localization of the transcription factor PHA-4/FOXA, without affecting food intake. Moreover, LSD treatment reduces lipid stores and downregulates global protein synthesis, both hallmark signatures of TOR inhibition. These findings establish LSD as a modulator of evolutionarily conserved longevity pathways and suggest that psychedelic signaling can mimic a caloric restriction-like metabolic state, paving the way for the development of novel geroprotective strategies.

Transient recurrent dynamics shape representations in mice
In the brain, different stimuli are represented by responses of a neural population. How is this representation reshaped over time by the dynamics of local recurrent circuits? We investigate this question in Neuropixels recordings of awake behaving mice and recurrent neural network models. We derive a mean-field theory that reduces the dynamics of complex networks to only three relevant dynamical quantities: the mean population activity and two overlaps that reflect the variability of responses within and across stimulus classes, respectively. This theory enables us to quantitatively explain experimental observables and reveals how the three quantities shape the separability of stimulus representations through a dynamic interplay. We measure the information transmitted from multiple stimuli to the responses with an optimally trained readout on the population signal. This reveals a trade-off between more information conveyed with an increasing number of stimuli, and stimuli becoming less separable due to their larger overlap in the finite-dimensional neuronal space. We find that the experimentally observed small population activity lies within a regime where information increases with the number of stimuli, sharply separated from a second regime in which information asymptotically converges to zero -- revealing a crucial advantage of sparse coding.

Metabolic resilience rules sex-specific pain recovery during hormonal aging: a multi-omics analysis of neuropathy in mice.
Biological aging and sex interact to shape systemic metabolism, yet their role in chronic pain resolution remains unexplored. We hypothesized that metabolic resilience -- the ability to flexibly switch fuel sources and maintain energy homeostasis -- rules successful recovery from nerve injury in a sex-dependent manner during aging.

In 12-month-old male and female mice -- corresponding to the perimenopausal phase in females and the onset of hormonal decline in both sexes -- we induced sciatic nerve chronic constriction injury and performed multi-omics profiling during Wallerian degeneration, a phase known to trigger long-term neurobiological remodeling. Aging females exhibited early activation of fatty acid oxidation, increased resting energy expenditure, upregulation of mitochondrial redox enzymes and circulating progesterone and corticosterone. Proteomic and metabolomic analysis revealed pentose phosphate pathway enrichment and gluconeogenesis, supporting redox balance and metabolic flexibility. Conversely, males displayed persistent glycolytic reliance, long-chain acylcarnitine accumulation, suppression of adiponectin and PPAR{gamma}, indicating metabolic inflexibility.

Longitudinal behavioral analysis revealed that aging females recovered earlier and more fully than aging males, reversing the pattern previously shown in our adult mouse study, where females developed persistent pain and males recovered rapidly. These patterns highlight a non-linear, sex-specific interaction between biological aging and injury response, where hormonal decline reprograms the metabolic trajectory and reshapes pain outcomes.

Metabolic resilience governs sex-specific recovery following nerve injury by directing early systemic adaptations that precede and predict long-term pain trajectories. These results define mechanistically anchored, sex- and age-specific biomarkers, and propose preclinical targets for timely, personalized interventions in age-associated neuropathic pain.

The Human LRRK2-R1441G Mutation Drives Age-Dependent Oxidative Stress and Mitochondrial Dysfunction in Dopaminergic Neurons
Mitochondrial dysfunction and oxidative stress are central to Parkinsons disease (PD) pathogenesis, particularly affecting substantia nigra pars compacta (SNc) dopamine (DA) neurons. Here, we investigate how the R1441G mutation in leucine-rich repeat kinase 2 (LRRK2), a key genetic contributor to familial and sporadic PD, impacts mitochondrial function in midbrain DA neurons. Using a BAC transgenic mouse model overexpressing human LRRK2-R1441G, we crossed these mice with TH-mito-roGFP mice, enabling mitochondria-targeted redox imaging in DA neurons. The two-photon imaging of acute brain slices from 3-, 6-, and 10-month-old mice revealed a progressive elevated oxidative stress in SNc DA neurons and their striatal projections, accompanied with reduced respiratory complex activity and decline in mitochondrial health. Spatial transcriptomics via GeoMx Digital Spatial Profiler identified molecular changes linked to dysregulated mitochondrial uncoupling protein function and calcium homeostasis. These findings demonstrate age-dependent mitochondrial dysfunction in LRRK2-mutant SNc DA neurons, highlighting calcium channels and uncoupling proteins as potential therapeutic targets to slow PD progression.

Human Neural Synergy when combining Stevia with a Flavor Modifer and the Neural effects of Sucrose vs Stevia
There is a drive to improve the acceptability of sweeteners like stevia by reducing their off-tastes. The main aim was to examine the synergistic neural effects of combining stevia with a flavor modifier and secondly to examine stevia vs. sucrose due to limited human neuroimaging data and concerns that sweeteners may be more addictive than sugar.

In a within-subjects fMRI study, 34 healthy adults (Mean age = 25) tasted four conditions: stevia, stevia plus a flavor modifier, the modifier alone, and sucrose. We analyzed whole-brain responses and focused on regions of interest (ROIs) including the insula, postcentral gyrus, and hypothalamus (identified via meta-analysis of sweet taste processing), as well as the nucleus accumbens (NAcc) and amygdala due to their roles in reward and aversion.

Stevia combined with the modifier evoked super-additive responses in the postcentral gyrus, parietal cortex, and occipital gyrus (p < 0.05, FWE-corrected). Compared to stevia alone, the stevia-modifier combination elicited reduced hypothalamic activity (p = 0.008) and the hypothalamus tracked pleasantness and mouth fullness only in this condition. The NAcc tracked mouth fullness more for the modified stevia than for stevia alone, and the amygdala tracked bitterness only in the plain stevia condition. Sucrose elicited higher postcentral gyrus activation than stevia (p = 0.01).

We provide first evidence that combining stevia with a flavour modifier reveals synergistic neural activity associated with taste sensation, intensity and multisensory integration. Adding a modifier to stevia could increase unconscious desirability for stevia by masking its bitterness and increasing its mouth fullness.

Hemispheric Dissociation Revealed by Attentional Isolation and tRNS
Prolonged sensory imbalance, induced by directing attention to one visual field, can paradoxically enhance performance in the opposite, non-attended visual field. This effect is likely driven by the brains homeostatic mechanisms that regulate excitation and inhibition between hemispheres in homotopic attention processing regions. Here, we employed transcranial random noise stimulation (tRNS) to modulate cortical excitability and probe its role in interhemispheric dynamics controlling visual attention. Specifically, we used a procedure called attentional isolation, where neurotypical participants covertly focused their visual attention in one hemifield (the attended visual field) for 30 minutes. Performance changes in both the unattended (opposite) visual field and the attended visual field were measured following this manipulation. We applied transcranial random noise stimulation (tRNS) over the right or left frontoparietal cortex to modulate the excitability of one hemisphere relative to the other during attention isolation, probing the neural mechanisms underlying the observed contralateral performance shift. Our results showed improved performance in the previously unattended visual field following the attentional isolation period after sham stimulation. However, tRNS revealed a functional dissociation between the hemispheres: right hemisphere active stimulation abolished the performance improvement, while left hemisphere stimulation preserved it. These findings suggest distinct roles for the left and right hemispheres in modulating paradoxical visual performance shifts and may inform the development of novel neurorehabilitation strategies for clinical populations.

Image-Based Meta- and Mega-Analysis (IBMMA): A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis
The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA produced stronger effect sizes and revealed findings in brain regions that traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

Dorsal Striatum Parvalbumin interneurons translatome unveiled
Parvalbumin (PV) interneurons in the dorsal striatum (DS) are fast-spiking GABAergic cells critical for feedforward inhibition and synaptic integration within basal ganglia circuits. Despite their well-characterized electrophysiological roles, their molecular identity remains incompletely defined. Using the Ribotag approach in Pvalb-Cre mice, we profiled the translatome of DS PV interneurons and identified over 2,700 transcripts significantly enriched (fold-change > 1.5) in this population. Our data validate established PV markers and reveal a distinct molecular signature of DS PV neurons compared to PV interneurons from the nucleus accumbens. Gene ontology analyses highlight prominent expression of genes related to extracellular matrix components, cell adhesion molecules, synaptic organization, ion channels, and neurotransmitter receptors, particularly those mediating glutamatergic and GABAergic signaling. Notably, perineuronal net markers were robustly expressed in DS PV interneurons and confirmed by immunofluorescence. Transcriptomic analysis of DS PV neurons following repeated d-amphetamine exposure identified Gm20683 as the only differentially expressed transcript between treated groups. Furthermore, RNAseq analysis of mice subjected to an operant behavior paradigm with two types of food reward (high-palatable diet or standard chow) identified over 1,000 and 100 genes enriched in DS PV neurons from standard and high-palatable masters, respectively. These findings provide a comprehensive molecular profile of DS PV interneurons, distinguishing them from other striatal PV populations, and reveal specific gene expression changes associated with psychostimulant exposure and reward-driven behaviors. Our findings deepen insight into the molecular mechanisms of PV interneuron activity in striatal circuits and their potential roles in neuropsychiatric, motor and reward-related disorders.

Global hypoperfusion leads to a mismatch in oxygen delivery and consumption in the cerebral watershed area
Despite the pivotal role of pial collaterals in maintaining cerebral blood flow during focal brain ischemia, it is largely unexplored how the microvascular blood flow and oxygenation in the watershed "pial-collateral territory" differ from those in the territory supplied by the major arteries during chronic global hypoperfusion. To answer this question, we applied 2-photon microscopy and Doppler optical coherence tomography to investigate the changes in cerebral microvascular blood flow and partial pressure of oxygen (PO2), induced by bilateral common carotid artery stenosis (BCAS). The measurements were performed in the somatosensory cortex that is supplied by the middle cerebral artery (MCA), and in the adjacent watershed area in the awake, head-restrained C57BL/6 mice, via the chronic cranial window. The results showed that the BCAS induced a larger decrease in capillary red blood cell (RBC) flux in the watershed area than in the MCA territory, especially in the subcortical white matter. Besides, PO2 in the pial collaterals was significantly lower than that in the upstream MCA segments under control conditions. However, the PO2 changes in the arteries and veins under global hypoperfusion displayed different trends in the two interrogated regions, resulting in a significant increase in oxygen extraction fraction in the watershed area. These findings suggest a mismatch between oxygen supply and demand in the watershed area due to global hypoperfusion and increased subcortical white matter vulnerability. We have also observed dilation of the pial collaterals after BCAS, which might suggest a compensatory mechanism to improve the blood flow in the watershed under hypoperfusion.

Meteorin resolves nociceptive hypersensitivity by reducing connexin-mediated coupling in satellite glial cells
Neuropathic pain, a persistent condition arising from injury to the nervous system, involves complex interactions between neurons and non-neuronal cells, including satellite glial cells (SGCs) in the dorsal root ganglia (DRG). In this study, we examined the glial-targeting effects of meteorin, a neurotrophic protein with gliogenic properties, using mouse models of neuropathic and inflammatory pain. Systemic meteorin administration reversed mechanical hypersensitivity across diverse neuropathic and inflammatory pain models, with therapeutic effects persisting beyond the treatment period. We identified SGCs as the principal site of meteorin expression and action in the DRG, where it selectively activated SGCs and altered their functional state. Proteomic profiling revealed meteorin-mediated downregulation of gap junction proteins in SGCs, particularly connexin 43, which was corroborated by immunohistochemical analyses. Functional assessments demonstrated that meteorin treatment normalized injury-induced increases in intercellular coupling between SGCs, establishing a mechanistic link between glial network modulation and pain resolution. These findings identify meteorin as a regulator of SGC communication through connexin-dependent mechanisms. The sustained therapeutic effects and multi-model efficacy highlight meteorin as a potential intervention for neuropathic pain while advancing our understanding of SGC plasticity in sensory processing.