bioRxiv Subject Collection: Neuroscience's Journal

Change-resistance distinguishes the representational geometries of human spatial memory and mouse CA1 in deformed environments
Prior work has highlighted qualitative similarities between the neural instantiations of cognitive maps in rodents and memory-guided navigation in humans, suggesting a conservation of representational structure across species. Yet evidence of cross-species differences in neural coding continues to mount. Our ability to reconcile these similarities and differences has been inherently limited by the qualitative nature of our cross-species comparisons. To overcome this limitation, here we combine recent technical and theoretical advances to characterize the representational geometry of human spatial memory during a diverse set of environmental deformations and compare this geometry to that of mouse CA1. Across three untethered immersive virtual reality experiments (n > 100 participants per experiment), we find that deformations induce compounding local distortions in human spatial memory. These distortions yield a representational geometry which closely resembles a change-resistant version of that of mouse hippocampal CA1 during analogous deformations. The geometries of mouse CA1 subpopulations with higher firing rates, spatial tuning stability, and spatial tuning specificity all better resembled that of human spatial memory. The precision, but not accuracy, of human spatial memory also modulated cross-species resemblance. The local impact of deformations scaled up when humans navigated a larger environment, preserving representational geometry and cross-species resemblance. Neither geometry nor cross-species resemblance depended on the human visual advantage during retrieval. Together, these results establish a common cross-species resemblance in the representational geometry of mouse CA1 and human spatial memory during environmental deformations, with a notable difference in the resistance to change between these assays.

The impact of a psychedelic drug on olfactory search behavior by mice
Animals use their sense of smell for survival and well-being. Any disruption to olfactory perception, such as in the case of olfactory hallucinations, can lead to devastating consequences and decreased quality of life. Psychedelics interrupt and distort accurate perception, yet little is known about the impact of psychedelics on olfactory behaviors. Using an olfactory search task, we investigated the impact of the psychedelic 2,5-dimethoxy-4-iodoamphetamine (DOI) on olfactory search behavior of mice. We found that DOI decreases search accuracy, alters movement, and increases sniff rate. These findings suggest that the olfactory behaviors are altered by DOI, elucidating psychedelic-induced changes to olfactory processes.

Functional connectivity differences in adult's ADHD - a MEG study
The neurobiology of adult Attention-Deficit/Hyperactivity Disorder (ADHD), particularly functional brain network connectivity, remain poorly understood. Magnetoencephalography (MEG) can reveal frequency-specific network dynamics given its high temporal resolution. Here, we investigated intrinsic functional connectivity differences between adults with ADHD (n = 24) and healthy controls (n = 44) using MEG data from the Open MEG Archive (OMEGA) dataset. We employed source reconstruction (Destrieux atlas), weighted phase-lag index (wPLI) connectivity across six frequency bands, graph theory metrics (Characteristic Path Length (CPL), node strength, clustering coefficient), and network-based statistics (NBS). We observed widespread hypo-connectivity in the high-gamma band (50-150 Hz) in adults with ADHD compared to controls. NBS identified a significant high-gamma sub-network, predominantly involving dorsal and ventral attention networks (DAN/VAN) and default mode network (DMN) nodes centered around a left fusiform gyrus hub, where all constituent connections exhibited consistently lower connectivity in the ADHD group. Globally, this was reflected in reduced gamma network integration (longer CPL) within the DAN and VAN. Locally, reduced high-gamma clustering was observed in VAN nodes (e.g., insula) and reduced node strength in a DAN region (postcentral sulcus). Predictive modeling using ElasticNet regression confirmed the importance of high-gamma metrics, with CPL measures yielding moderate classification accuracy (AUC = 0.70-0.73). In contrast to high-gamma findings, the alpha band showed increased integration (shorter CPL) within the DMN and between the VAN and DAN in the ADHD group, alongside differences in alpha and beta band node properties in cingulate and somatomotor regions. Our findings reveal robust, frequency-dependent functional network alterations in adult ADHD, particularly highlighting disrupted high-frequency communication within and between key cognitive networks.

Cell-type-specific plasticity in synaptic, intrinsic, and sound response properties of deep-layer auditory cortical neurons after noise trauma
Peripheral damage drives auditory cortex (ACtx) plasticity, but the underlying synaptic and cellular mechanisms remain poorly understood. We used a combination of in vitro slice electrophysiology, optogenetics, and in vivo two-photon imaging to investigate layer 5 extratelencephalic (ET) and layer 6 corticothalamic (CT) neuronal plasticity in mice, following noise-induced hearing loss (NIHL). Thalamocortical (TC) input was initially balanced between CTs and ETs but shifted to CT-dominant one day post-NIHL and then normalized by day seven. This transient shift was accompanied by increased quantal size and suprathreshold excitability in CTs, with minimal changes in ETs. In vivo, CTs exhibited persistent elevation in sound intensity thresholds, while ETs showed a transient shift in frequency tuning and reduced high-frequency responsiveness that recovered within a week. These findings reveal distinct, cell-type-specific plasticity mechanisms in deep-layer ACtx neurons following peripheral damage and highlight potential targets for treating hearing loss-related disorders such as tinnitus and hyperacusis.

Modulation of prefrontal functional connectivity by anodal tDCS over left DLPFC predicts performance enhancement in competitive swimmers: A simultaneous tDCS-fNIRS, double-blind, sham-controlled crossover study
While transcranial direct current stimulation (tDCS) has been proposed as a method to enhance physical performance in athletes, the underlying neural mechanisms and the reasons for the widely reported individual variability in its effects remain unclear. This study investigated whether prefrontal hemodynamic responses, measured by functional near-infrared spectroscopy (fNIRS), are associated with the effects of anodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) on swimming performance. In a double-blind, sham-controlled, crossover design, eight trained male swimmers performed 100 m freestyle trials under both anodal tDCS and sham conditions. We recorded prefrontal cortical activation and functional connectivity using fNIRS during a resting-state period and a subsequent stimulation period. While tDCS led to a numerical improvement in 100 m freestyle time, the overall effect was not statistically significant. The fNIRS analyses revealed that tDCS significantly reduced intra-hemispheric functional connectivity, especially in the stimulated left prefrontal cortex. Crucially, the magnitude of this connectivity reduction correlated with the degree of performance improvement, suggesting a direct brain-behavior link. Exploratory analyses further suggested that baseline functional connectivity could predict an individual's neural response to tDCS, with those having higher baseline connectivity showing a greater reduction. These findings suggest that tDCS over the left DLPFC may enhance physical performance by increasing the neural efficiency of prefrontal networks. Therefore, baseline functional connectivity is a promising physiological biomarker that could be used to personalize neuromodulation protocols in athletes.

Multiscale Metabolic Covariance Networks Uncover Stage-Specific Biomarker Signatures Across the Alzheimer's Disease Continuum.
Background Connectomics studies analyze neural connections and their roles in cognition and disease. Beyond regional comparisons, recent research has revealed inter-regional brain relationships via graph theory of brain network connectivity. Within these networks, path length measures a network's efficiency in communication. These connections can be quantified as inter-subject covariance networks related to functional connectivity, with alterations reported in neurodegenerative diseases. Methods Retrospective analysis of ADNI 18F-FDG PET images using metabolic covariance analysis and hierarchical clustering was used to assess regional brain networks in subjects from cognitively normal (CN) to AD. We evaluated AD stage changes by calculating whole brain entropy, connection strength, and clustering coefficients. Additionally, estimates of shortest path for positive and negative correlations as a measure of network efficiency. We also developed a novel region set enrichment analysis (RSEA) to detect brain functional changes based on metabolic variations. Results were aligned with transcriptomic signatures and clinical cognitive assessments. Findings In AD subjects, whole brain metabolic connectivity revealed an increase in entropy, connection strength, and clustering coefficients, which indicates brain network reorganization as compensatory mechanisms of pathological disruption. As AD advances, path lengths between brain regions decrease from CN to MCI; however, path lengths significantly increased in AD. RSEA indicated functional changes in motor, memory, language, and cognition functions related to disease progression. Interpretation Metabolic covariance analysis of whole brain, and regional connectomics, track with AD progression. Moreover, path lengths permitted AD stages determination via alterations in brain connectivity. Furthermore, RSEA facilitated the identification of functional changes based on metabolic readouts.

White matter reorganization of motor and affective-motivational networks in pain-indifferent carriers of the R221W mutation
Congenital insensitivity to pain (CIP) due to the R221W mutation on the nerve growth factor gene results in reduced peripheral C-nociceptor density and behavioural indifference to painful stimuli. While functional neuroimaging has revealed altered cortical and sub-cortical pain processing in R221W carriers, structural white matter changes remain unexplored and may suggest an anatomical basis of symptoms. Heterozygous R221W carriers (n = 11) and age-, sex-, education-matched controls (n = 11) diffusion tensor imaging data were compared using fixel-based analysis, and complimentary edge and node analyses using graph theory, and network-based statistics. Whole-brain and region of interest (ROI) fixel-based analyses revealed significantly reduced fibre density and fibre-bundle cross-section in brainstem motor tracts of R221W carriers, encompassing the corticospinal pathways, corona radiata, external capsule, cerebellar peduncles, and pontine crossing (p < 0.05). Graph theory analysis of pain-processing ROIs demonstrated reduced local efficiency in right anterior cingulate cortex (ACC) and altered betweenness centrality in bilateral insula and left ACC of R221W carriers. Despite R221W carriers showing higher node degrees in the somatosensory cortex and ACC, these connections had reduced efficiency and integration with cortical network regions. Network-based statistics identified a possible compensatory subnetwork with stronger connectivity from right thalamus to left ACC and left insula in R221W carriers (p < 0.019). These findings suggest that congenitally reduced peripheral nociception could lead to abnormalities in the thalamocortical and motor efferent pathway, but not sensory afferent pathways. The combination of reduced brainstem motor tract integrity and altered cortical network efficiency, alongside potentially compensatory thalamo-cortical connectivity, could support a model of R221W CIP as motor under-reactivity rather than sensory insensitivity.

Higher-order crossmodal representations in the auditory cortex of deaf and hearing individuals
The study of early deafnes provides unique insights into how sensory experience shapes brain function and organisation. Here we investigated whether the auditory cortex of deaf individuals can implement higher-order cognitive functions when its sensory input is absent or significantly reduced. Crossmodal plasticity research has shown that auditory areas of the human brain are recruited for executive processing tasks in deaf individuals (Cardin et al., 2018; Ding et al., 2015; Manini et al., 2022; Zimmermann et al., 2021). What is the role of the auditory cortex during such executive processes in deaf individuals? One possibility is that it has a role in sensory processing, extracting information about relevant features. Alternatively, they it may represent higher-order information, such as coding task rules or modulating attentional states. To distinguish between these hypotheses, we conducted an fMRI delay-to-match experiment in either the visual or somatosensory modality in deaf (N=13) and hearing (N=18) individuals. Representational Similarity Analysis (RSA) showed that the auditory cortex of deaf individuals contains information about higher-level processes such as task and sensory modality. We also found significant representations of somatosensory frequency. Critically, task and modality representations were also found in the auditory cortex of hearing individuals. These findings suggest that crossmodal plasticity relies on the enhancement of representations that are present in hearing individuals, rather than through the implementation of novel ones. In conclusion, we show that sensory experience shapes cognitive processing and the function of sensory regions in the brain, and that the functional destiny of cortical regions can be shaped by early sensory experience.

Proximity to an SGC-DLPFC Individualized Functional Target and outcomes in large rTMS clinical trials for Treatment-Resistant Depression
Background Targeting methods for repetitive transcranial magnetic stimulation (rTMS) in patients with depression now include the use of individual functional scans to target specific functional connectivity (FC) patterns obtained from functional magnetic resonance imaging (fMRI). Potential biomarkers of rTMS response include target FC with the subgenual anterior cingulate cortex (SGC) or the causal depression circuit (CDC), each of which may be candidates for individualized functional targets (iFTs). We assessed the relationship of these two approaches to clinical outcomes in two large rTMS clinical trials. Methods 501 subjects with moderate to severe depression underwent 4-6 weeks of daily rTMS to the left dorsolateral prefrontal cortex (DLPFC), targeted using neuronavigation to a common group-based functional target. Resting-state scans acquired at baseline were used to retrospectively compute iFTs using either SGC-DLPFC or CDC-DLPFC FC. The Euclidean distance from the group-based target used in the trial to the centre of gravity of each iFT was computed and correlated with outcomes. Results Most subjects' iFTs were within 2cm of their group-based target. Proximity to either the SGC- or CDC-iFT was not associated with better outcomes. Sensitivity analyses accounting for treatment target FC, methodology, data quality, or treatment parameters did not change the results. Conclusions Proximity to SGC- or CDC-derived iFTs was not associated with better outcomes in patients who received neuronavigated rTMS to a group-based target. Prospective randomized clinical trials comparing neuronavigated group-based target to neuronavigated iFTs are needed.

ALG13 loss-of-function alters glycosylation, impairs neuronal maturation, and drives network hypoactivity in a cortical organoid model of CDG
Background: Congenital disorders of glycosylation (CDGs) are a group of rare metabolic diseases recognized for their neurological presentations, including developmental delay and seizures. However, the link between glycosylation defects and cortical brain network pathology remains elusive. Methods: To address this unmet need, we generated iPSC derived human cortical organoids (hCOs) for ALG13-CDG, which is the second most common CDG that is also X-linked. To comprehensively understand the impact of glycosylation defects on cortical pathology in CDG, we combined electrophysiological recordings using multi-electrode arrays (MEA) with comprehensive molecular profiling via multiomics, including scRNA-seq, proteomics, glycoproteomics, N-glycan imaging, lipidomics, and metabolomics. X-inactivation status was also evaluated in both iPSCs and organoids. Results: ALG13-CDG hCOs revealed reduced glycosylation of proteins critical for extracellular matrix (ECM), neuronal migration, lipid metabolism, calcium ion homeostasis, and neuronal excitability. Dysregulation in related pathways was corroborated by proteomics and scRNA-seq, which also showed altered communication patterns in these pathways. Trajectory analysis revealed an inversion in neuronal development, with early inhibitory and delayed excitatory development, indicating an excitatory and inhibitory (E/I) imbalance. MEA recordings demonstrated early network hypoactivity with reduced firing rates, immature burst dynamics, and shorter axonal extensions. Despite this, transcriptomic and proteomic data revealed upregulation of excitatory receptors suggesting latent hyperexcitability. Altered lipid and sugar (GlcNAc) metabolism and skewed X-inactivation were also observed. Conclusions: Our study provides the first evidence of glycosylation defects in an ALG13-CDG human cortical organoid (hCO) model and links these defects to disrupted neuronal developmental trajectories and dysregulation of key pathways essential for brain function. We identify mistimed neuronal maturation and an excitatory/inhibitory (E/I) imbalance as early drivers of network hypoactivity and immature burst dynamics, with downstream compensatory hyperexcitability that may contribute to seizure susceptibility. While specific to ALG13-CDG, these mechanisms likely extend to other glycosylation disorders with overlapping neurological features. This work offers new mechanistic insight into cortical dysfunction associated with impaired protein glycosylation and highlights potential targets for therapeutic intervention.

Connectomic reconstruction from hippocampal CA3 reveals spatially graded mossy fiber inputs and selective feedforward inhibition to pyramidal cells
The mossy fiber (MF) connections to pyramidal cells in hippocampal CA3 are hypothesized to participate in pattern separation and memory encoding, yet no large-scale neuronal wiring diagram exists for these connections. We assembled a 3D electron microscopy volume (~1x1x0.1 mm3) from mouse hippocampal CA3. By proofreading and automated segmentation, we reconstructed and classified all soma-containing neurons--including 1,815 pyramidal cells and 229 inhibitory cells--and over 55,000 MFs. Pyramidal cells receive more numerous MF inputs along a proximodistal gradient. Some distal cells show surprisingly high convergence via relatively small terminals with fewer vesicles. Pyramidal cells share significantly more MF inputs than networks randomized by degree-preserving swap, and are better approximated by networks randomized by proximity-preserving swap. We identify a feedforward inhibitory circuit from MFs via perisomatic interneurons that selectively target a pyramidal subtype. We demonstrated large-scale mapping across levels in the hippocampus--from circuits to cell types to vesicles. The dataset is shared through Pyr, an online platform for hippocampal connectomics.

Alcohol disrupts long-term potentiation at hippocampus-medium spiny neuron synapses in the medial shell of the nucleus accumbens
Background: Chronic alcohol exposure is a major driver of alcohol use disorders (AUD), in part through its ability to induce maladaptive plasticity within neural circuits that regulate reward, motivation, and affect. Excitatory projections from the hippocampus (Hipp) to the nucleus accumbens (NAc) play a pivotal role in regulating reward-related behaviors, and this pathway serves as a key locus for establishing associations between rewarding stimuli and related contextual information. Regulation of the strength of Hipp-NAc synapses is critical for supporting these behaviors, and aberrant Hipp-NAc plasticity is associated with anhedonia and disrupted reward learning. Methods: To examine acute ethanol effects, we used whole-cell electrophysiology to record Hipp-NAc synaptic plasticity in acute brain slices in the presence or absence of 50mM ethanol. To examine the effects of chronic ethanol administration, mice were exposed to ethanol vapor in a 3-week chronic intermittent ethanol (CIE) paradigm. Slices from ethanol and air exposed mice were used for whole-cell electrophysiology to record Hipp-NAc synaptic plasticity. Results: Here, we demonstrate that acute ethanol application to ex vivo brain slices prevents long-term potentiation (LTP) at Hipp-NAc synapses, without altering presynaptic release probability. Furthermore, chronic intermittent exposure to ethanol abolishes LTP at these synapses, even during abstinence, indicating persistent synaptic dysfunction. Conclusions: Together, our findings demonstrate that ethanol has immediate and long-lasting effects on Hipp-NAc plasticity. Given the behavioral relevance of these synapses, this work has important implications for the mechanisms underlying ethanol-dependent effects on reward processing and negative affective states associated with AUD.

Developmental Differences in Novelty Reactivity in Adolescent and Adult Male and Female Rats
Adolescence is a time of high-risk behavior and increased exploration. This developmental period is marked by a greater probability of initiating drug use and is associated with an increased risk to develop addiction and dependency in adulthood. Human adolescents are predisposed toward an increased likelihood of risk taking behaviors, including drug use or initiation. The purpose of the study was to examine differences in developmental risk-taking behaviors. Adolescent and adult animals were exposed to a novel stimulus in a familiar environment to assess impulsive behaviors, novelty preference and exploratory behaviors. Adolescent animals had greater novelty-induced locomotor activity, greater novelty preference, and showed higher approach and exploratory behaviors compared to preadolescent and adult animals. These data support the notion that adolescents may be predisposed toward sensation seeking and consequently are more likely to engage in risk taking behaviors, such as drug use initiation.

Pde10a gates light responses in the SCN to regulate circadian photoentrainment
Light is the principal cue for synchronizing the circadian clock. A common feature of the clock among all organisms is the lack of responsiveness to light during the daytime. To understand the interaction between the circadian clock and light, we described the transcriptome of the suprachiasmatic nucleus (SCN) in mice across different circadian times under both constant darkness (DD) and in response to light exposure. In addition to classifying 10 distinct molecularly-defined SCN neuronal subtypes, we uncovered that SCN exhibits significant transcriptomic responsiveness to light during daytime, the so-called behavioral dead zone. We further identified Pde10a, a cyclic nucleotide phosphodiesterase, as the first critical component for gating SCN responsiveness to light across the day and thus maintaining robust daily oscillations under regular light-dark conditions.

Scalable human neuronal models of tauopathy producing endogenous seed-competent 4R tau
Pathological accumulation of four-repeat (4R) tau is central to several frontotemporal dementia (FTD) subtypes but human neuronal models amenable to high-throughput screening of 4R tau-targeting therapies remain limited. To address this gap, we developed iPSC-derived i3Neuron (i3N) lines expressing >75% 4R tau, driven by FTD splice-shifting mutations (S305N or S305N/IVS10+3). These neurons develop hyperphosphorylated tau and demonstrate somatodendritic mis-localisation. Unlike any other stem cell model of 4R tauopathy, these i3N neurons develop endogenous seed-competent tau and present pFTAA-positive tau assemblies after 28 days in culture. For scalable screening, we CRISPR-engineered a HiBiT luminescence tag at the endogenous MAPT locus into the S305N/IVS10+3 iPSC line, enabling precise quantification of tau levels and pharmacological responses. The model responded predictably to compounds affecting tau clearance, demonstrating its suitability for drug discovery. Overall, this i3N platform recapitulates key features of 4R tauopathy and provides a robust system to identify therapeutic modulators of pathological tau.

Modular dynamics of conscious and unconscious states in marmoset cortex
General anesthetics are routinely used to induce unconsciousness. While much is known about their effects on receptor function and the activity of individual neurons, much less is known about how these local effects are manifest at the level of large-scale, distributed brain networks. Using functional magnetic resonance imaging (fMRI) with the common marmoset (Callithrix jacchus) we investigated the effects of the anaesthetic isoflurane on functional brain networks and their temporal dynamics, comparing network measures during wakeful rest and induced unconsciousness. The anaesthetic condition was characterised by weak functional networks that were more similar to anatomical structure and more fragmented than during wakeful rest. Conversely, the awake condition was characterised by coordinated network reconfiguration and more distinct subnetwork composition. Our findings are consistent with the view that consciousness is an emergent property of the dynamics of functional brain networks, and that anaesthetics impoverish these dynamics by reducing the efficacy of synaptic transmission.

Mechanistic Modeling of Sleep-Wake Transitions via Circadian-Modulated Threshold Dynamics
Human sleep-wake cycles emerge from complex interactions between homeostatic sleep pressure and circadian rhythms. In this study, we extend the Phillips-Robinson model by introducing circadian-dependent dynamic thresholds for sleep and wake transitions, yielding a more physiologically grounded framework for sleep regulation. Using bifurcation analysis, we show that the transition from sustained wakefulness to rhythmic sleep-wake cycles is governed by a saddle-node on invariant circle (SNIC) bifurcation, and that these oscillations become entrained to external 24 h light-dark cues. We analytically derive circadian-modulated sleep and wake thresholds, revealing how the interaction between circadian and homeostatic drives governs sleep-wake transitions. Our model captures key physiological phenomena, including: (1) the onset and entrainment of sleep-wake rhythms, (2) immediate sleep onset and partial rebound following sleep deprivation, and (3) sleep fragmentation under shift work-like conditions. These results offer new mechanistic insights into how circadian misalignment alters sleep timing and quality. Together, our findings establish an updated theoretical framework for modeling sleep-wake regulation in both natural and disrupted environments, with implications for shift work management, sleep disorder interventions, and personalized chronotherapy.

Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging
Sex classification using neuroimaging data has the potential to revolutionize personalized diagnostics by revealing subtle structural brain differences that underlie sex-specific disease risks. Despite the promise of machine learning, traditional methods often fall short in providing both high classification accuracy and interpretable, statistically validated feature importance scores for high-dimensional imaging data. This gap is particularly evident when conventional techniques such as random forests, LIME, and SHAP are applied, as they struggle with complex feature interactions and managing noise in large datasets. We address this challenge by developing an integrated framework that combines Oblique Random Forests (ORFs) with a novel, permutation-based feature importance testing algorithm. ORFs extend traditional random forests by employing oblique decision boundaries through linear combinations of features, thereby capturing intricate interactions inherent in neuroimaging data. Our feature importance testing method, NEOFIT, rigorously quantifies the significance of each feature by generating null distributions and corrected p-values. We first validate our approach using simulated datasets, establishing its robustness and scalability under controlled conditions. We then apply our method to classify sex from both voxel-wise structural MRI and cortical thickness data in humans and macaques, facilitating direct cross-species comparisons. Our results demonstrate that the proposed framework not only enhances classification performance but also provides clear, interpretable insights into the neuroanatomical features that distinguish sexes. These methodological advancements pave the way for improved diagnostic tools and contribute to a deeper understanding of the evolutionary basis of sex differences in brain structure.

Frequency-dependent communication of information innetworks of non-oscillatory neurons in response to oscillatory inputs
Understanding how neuronal networks process oscillatory inputs is key for deciphering the brain's information processing dynamics. Neuronal filters describe the frequency-dependent relationship of neuronal outputs (e.g., membrane potential amplitude, firing rate) and their inputs for the level of neuronal organization (e.g., cellular, network) considered. Band-pass filters are associated to the notion of resonance and reflect the system's ability to respond maximally to inputs at a nonzero (resonant) frequency or a limited (resonant) frequency band. The complementary notion of phasonance refers to the ability of a system to exhibit a zero-phase response for a nonzero (phasonant) input frequency. The biophysical and dynamic mechanisms that shape neuronal filters and give raise to preferred frequency responses to oscillatory inputs are poorly understood beyond single cells. Moreover, the mechanisms that control the frequency-dependent communication of information across cells in a network remain unclear. Here, we use mathematical modeling, analytical calculations, computational simulations and dynamical systems tools to investigate how the complex and nonlinear interaction of the systems's biophysical properties and interacting time scales shape neuronal filters in minimal network models receiving oscillatory inputs with frequencies () within some range. The minimal networks consist of one directly stimulated cell (cell 1) connected to another (not directly stimulated) cell (cell 2) via graded chemical synapses. Individual cells are either passive or resonators and chemical synapses are either excitatory or inhibitory. The network outputs consist of the voltage peak envelopes and the impedance amplitude and phase profiles (as a function of ) for the two cells. We introduce the frequency-dependent amplitude () and phase {Delta}{Phi}() communication coefficients, defined as the ratio of the amplitude responses of the indirectly and directly stimulated cells and the phase difference between these two cells, respectively. Extending previous work, we also introduce the -curve, parametrized by , in the phase-space diagram for the voltage variables of the two participating cells. This curve joins the peak voltage values of the two cells in response to the oscillatory inputs and is a geometric representation of the communication coefficient. It allows to interpret the results and explain the dependence of the properties of the communication coefficient in terms of the biophysical and dynamic properties of the participating cells and synaptic connectivity when analytical calculations are not possible. We describe the conditions under which one or the two cells in the network exhibit resonance and phasonance and the conditions under which the network exhibits -resonance and {Delta}{Phi}-phasonance and more complex network responses depending as the complexity of the participating cells increases. For linear networks (linear nodes and linear connectivity), is proportional to the impedance of the indirectly activated cell 2 and {Delta}{Phi} is equal to the phase of the indirectly stimulated cell 2, independent of the directly stimulated cell 1 in both cases. We show that the presence of nonlinear connectivity in the network creates (nonlinear) interactions between the two cells that give rise to -resonance, {Delta}{Phi}-phasonance and more complex responses that are absent in the corresponding linear networks. The results and methods developed in this paper have implications for the processing of information in more complex networks.

Emotion Regulation in the Gradient Framework: Large-Scale Brain Organization Shapes Individual Differences in Reappraisal Success
Emotion regulation is essential for well-being and mental health, yet individuals vary widely in their emotion regulation success. Why? Traditional neuroimaging studies of emotion regulation often focus on localized neural activity or isolated networks, overlooking how large-scale brain organization may shape the integration of distributed systems and sub-processes supporting regulatory success. Here, we applied a novel system-level framework based on spatial gradients of macroscale brain organization to study variance in emotion regulation success. Using two large fMRI datasets (n=358, n=263), we projected global activation patterns from a laboratory emotion regulation task onto principal gradients derived from independent resting-state fMRI data from the Human Connectome Project. These gradients capture low-dimensional patterns of neural variation, providing a topographical framework within which complex mental phenomena, such as emotion regulation, emerge. In both datasets, individual differences in regulation success were predicted by systematic reconfiguration along Gradient 1, a principal axis differentiating unimodal and heteromodal brain areas. This gradient-based neural reconfiguration also predicted lower negative affect in daily life, as measured via smartphone-based experience sampling in a subset of participants (n=55). Meta-analytic decoding via Neurosynth revealed that Gradient 1 and regulation success align with multiple psychological processes, including social cognition, memory, attention, and negative emotion, suggesting this gradient reflects diverse, integrative demands during effective emotion regulation. These findings advance a network-level account of regulatory success, offering a biologically grounded, ecologically valid framework for understanding adaptive emotional functioning. Such gradient-based dynamics may serve as predictive biomarkers of regulatory success and inform targeted interventions in clinical populations.

Role of the 5HT2C receptor in anxiety-like behavior in zebrafish (Danio rerio)
The present study aims to describe the acute effects of 5-HT2C receptor agonists and antagonists in behavioral tests related to anxiety in adult zebrafish (Danio rerio). For this, three groups of fish (n = 12/group) were used: a group treated with the drug MK-212 (2 mg/kg), a 5- HT2C receptor agonist; another group treated with the drug RS-102221 (2 mg/kg), a 5-HT2C receptor antagonist; and a third group, the control group, using a vehicle solution. The three groups were exposed to two behavioral tests: novel tank test (NTT) and light-dark preference (LDT). MK-212 produced no effects on the NTT, while RS-102221 decreased geotaxis (d = 0.99, 95%CI[0.3, 1.7]) and erratic swimming (d = 0.87, 95%CI[0.18, 1.56]) in this test; these effects are consistent with a tonic facilitation of defensive behavior in the NTT. Conversely, in the LDT, MK-212 increased scototaxis (d = 0.96, 95%CI[0.25, 1.65]), risk assessment (d = -1.31, 95%CI[-2.02, -0.59]), and thigmotaxis (d = -1.48, 95%CI[-2.21, -0.75]), consistent with an anxiogenic-like effect of this drug. Thus, tonic and phasic effects of the activation of this receptor are observed depending on the type of behavioral test used.

Fever induces long-term synaptic enhancement and protects learning in an accelerated aging model
Physiological impact of fever in the brain remains poorly understood. Here, we demonstrate that induction of fever by yeast injection in rats (N=9) and by whole-body hyperthermia in mice (N=7) triggers structural synaptic enhancement in the prefrontal cortex involving AMPA-type glutamate receptor signalling and protein translation (N=6). Repeated fever induction in juvenile rats (N=9) results in synaptic strengthening that persists into adulthood, mitigating learning deficits and synaptic loss in a D-galactose model of accelerated aging (N=11). Our results show how common environmental conditions may shape brain function in the long-term via synaptic plasticity, warranting further exploration of thermal treatment for cognitive protection in aging.

Fishexplorer: A multimodal cellular atlas platform for neuronal circuit dissection in larval zebrafish
Understanding how neural circuits give rise to behavior requires comprehensive knowledge of neuronal morphology, connectivity, and function. Atlas platforms play a critical role in enabling the visualization, exploration, and dissemination of such information. Here, we present FishExplorer, an interactive and expandable community platform designed to integrate and analyze multimodal brain data from larval zebrafish. FishExplorer supports datasets acquired through light microscopy (LM), electron microscopy (EM), and X-ray imaging, all co-registered within a unified spatial coordinate system which enables seamless comparison of neuronal morphologies and synaptic connections. To further assist circuit analysis, FishExplorer includes a suite of tools for querying and visualizing connectivity at the whole-brain scale. By integrating data from recent large-scale EM reconstructions (presented in companion studies), FishExplorer enables researchers to validate circuit models, explore wiring principles, and generate new hypotheses. As a continuously evolving resource, FishExplorer is designed to facilitate collaborative discovery and serve the growing needs of the teleost neuroscience community.

Beta burst waveform extraction using novel 4He-OPMs
Studying the electrophysiology of motor preparation and execution is challenging due to the restrictions often placed on the experimental paradigm by the imaging modality. MEG is well suited to track temporal brain dynamics while offering good spatial resolution, but requires an absence of head motion due to the fixed helmet and associated cryogenic cooling system. Here we used novel, room temperature, wearable optically pumped magnetometers using Helium to measure the MEG in a motor preparation and execution task and compare this with 'classic' SQUID-MEG. Beta band activity (13-30Hz) is widely associated with motor activity, and it has become widely accepted that beta activity occurs in bursts rather than sustained activity. Here we used a novel beta burst extraction pipeline to look at not only the occurrence of the beta bursts across the timecourse of motor preparation and execution, but at the specific waveforms of the beta burst that evolve over time. Results show that specific beta burst waveforms show strong task relevant modulations of burst rate. Beta burst waveforms extracted using Helium-OPMs were very similar to those extracted using SQUID-MEG, with comparable modulations of burst rate over time for specific waveforms. This shows a promising step to obtaining high quality electrophysiological data in less restricted paradigms.

Role of chrna5 in multi-substance preference and phenotypes comorbid with the development of substance dependence
Addiction to nicotine and alcohol continues to be a leading cause of death and loss of productivity as measured in disability-adjusted life years. Polymorphisms in the nicotinic acetylcholine receptor subunit 5 (CHRNA5) have been identified as risk factors associated with nicotine dependence in human genetic studies and rodent models. Whether the chrna5 function is also important for phenotypes associated with comorbid disorders independently is a question of interest. We generated a stable mutant line in zebrafish using the CRISPR/Cas-9 technique. We found that the chrna5 mutant fish exhibit an increased acute preference to both nicotine and alcohol in the Self-Administration Zebrafish Assay (SAZA). When subjected to multi-day exposures to either, chrna5 mutants exhibited greater behavioural change, but reduced transcriptomic changes compared to wild type siblings, suggesting an impaired homeostatic regulation following drug exposure. Further, chrna5 mutants exhibited drug-independent changes in appetite and circadian rhythms, suggesting a genetic predisposition to disorders often comorbid with substance dependence. We expect these results to give new insights into the operation of genes whose normal function modulates vulnerability to multi-substance use and comorbid disorders.

Mapping Dendritic Spines Using 2D Two-Photon Laser Scanning
Neurons transform complex spatiotemporal patterns of synaptic input into structured sequences of action potentials that relay meaningful information. Excitatory inputs converging onto dendrites engage or interact with local non-linear regenerative events, adding a computational layer that expands the variety and complexity of input-output transformations. In conjunction with non-linear conductance-mediated mechanisms, the spatial arrangement of active synapses along dendrites strategically shapes this local computation. Mapping synaptic input organization is thus critical to fully uncover neuronal input-output function. Spine calcium imaging offers a direct functional readout of the location of active contacts, but such mapping requires access to spines distributed on intricate three-dimensional dendritic trees. We present a modular software pipeline for targeted imaging and analysis of dendrites using sequential two-dimensional scanning on standard two-photon microscopes. Designed to work with conventional two-photon microscopy setups, the method is fully compatible with ScanImage. It includes a pre-acquisition tool (ROIpy) and a post-acquisition analysis suite (Spyne). ROIpy generates dendrite-aligned region-of-interests for scattered depth-specific acquisition of neuronal arborizations. Spyne includes deep-learning modules for spine identification (using DeepD3) and within-spine calcium events detection (via a custom- classifier). This method is compatible with a range of experimental designs, including simultaneous two-photon imaging and patch-clamp recordings, as well as fully optical setups. The acquisition pipeline supports plane-by-plane imaging of the whole-arbor, targeted to specific compartments or to user-defined branches of interest. Our work provides a versatile strategy for targeted dendritic imaging using two-dimensional scanning multiphoton microscopy. While ROIpy allows adaptation to diverse experimental goals beyond synaptic mapping, Spyne provides an analysis strategy for functional mapping of active synapses at single-cell resolution, offering a basis for modeling how the spatial organization of synaptic inputs shapes dendritic integration.

Deep neural networks trained for estimating albedo and illumination achieve lightness constancy differently than human observers.
Lightness constancy, the ability to create perceptual representations that are strongly correlated with surface albedo despite variations in lighting and context, is a challenging computational problem. Indeed, it has proven difficult to develop image-computable models of how human vision achieves a substantial degree of lightness constancy in complex scenes. Recently, convolutional neural networks (CNNs) have been developed that are proficient at estimating albedo, but little is known about how they achieve this, or whether they are good models of human vision. We examined this question by training a CNN to estimate albedo and illumination in a computer-rendered virtual world, and evaluating both the CNN and human observers in a lightness matching task. In several conditions, we eliminated cues potentially supporting lightness constancy: local contrast, shading, shadows, and all contextual cues. We found that the network achieved a high degree of lightness constancy, outperforming three classic models, and substantially outperforming human observers as well. However, we also found that eliminating cues affected the CNN and humans very differently. Humans had much worse constancy when local contrast cues were made uninformative, but were minimally affected by elimination of shading or shadows. The CNN was unaffected by local contrast, but relied on shading and shadows. These results suggest that the CNN followed an effective strategy of integrating global image cues, whereas humans used a more local strategy. In a follow-up experiment, we found that the CNN could learn to exploit noise artifacts that were correlated with illuminance in ray-traced scenes, whereas humans did not. We conclude that CNNs can learn an effective, global strategy of estimating lightness, which is closer to an optimal strategy for the ensemble of scenes we studied than the computation used by human vision.

Biological profits of irrational computations in the orbitofrontal cortex
Making good decisions is essential for survival and success, yet humans and animals often exhibit perplexing irrational decision-making whose biological origin remains poorly understood. Recent theoretical work suggests that some forms of irrational decisions may arise from limited coding precision or metabolic budget in individual orbitofrontal neurons. Here, we consider the alternative possibility that systematic errors in decision-relevant computations are the inevitable consequence of the internal connectivity structure within orbitofrontal networks, which was molded under more distal biological constraints. We first trained cohorts of artificial neural networks to perform rational decision-relevant computations. Remarkably, they exhibited most electrophysiological coding properties of orbitofrontal neurons recorded in monkeys engaged in a preference-based decision task. We then distorted their internal connectivity to reproduce monkeys' irrational choices. This induced systematic interferences in decision-relevant computations that generalize across individuals, at both the behavioral and neural level. Importantly, irrational networks also display enhanced behavioral resilience to neural loss when compared to their rational counterparts. This suggests that irrational behavior may be the incidental outcome of distal evolutionary pressure on the tolerance to orbitofrontal circuit's damage.

Auditory-cognitive determinants of speech-in-noise perception: structural equation modelling of a large sample
Problems understanding speech-in-noise (SIN) are commonly associated with peripheral hearing loss. But pure tone audiometry (PTA) alone fails to fully explain SIN ability. This is because SIN perception is based on complex interactions between peripheral hearing, central auditory processing (CAP) and other cognitive abilities. We assessed interaction between these factors and age using a multivariate approach that allows the modelling of directional effects on theoretical constructs: structural equation modelling. We created a model to explain SIN using latent constructs for sound segregation, auditory (working) memory, and SIN perception, as well as PTA, age and measures of non-verbal reasoning. In a sample of 207 participants aged 18-81 years old, age was the biggest determinant of SIN ability, followed by auditory memory. PTA did not contribute to SIN directly, although it modified sound segregation ability, which covaried with auditory memory. A second model, using a CAP latent structure formed by measures of sound segregation, auditory memory, and temporal processing, revealed CAP to be the largest determinant of SIN ahead of age. Furthermore, we demonstrated the impact of PTA and non-verbal reasoning on SIN are mediated by their influence on CAP. Our results highlight the importance of central auditory processing in speech-in-noise perception.

Distinct resting-state connectomes for face and scene perception predict individual task performance
Face and scene perception rely on distinct neural networks centered on the Fusiform Face Area (FFA) and Parahippocampal Place Area (PPA). However, how these regions interact with broader brain networks remains unclear. Using resting-state fMRI and MEG data, we mapped the spatial and frequency-specific functional connectivity of the FFA and PPA. We found that the FFA showed predominant fMRI connectivity with lateral occipitotemporal, inferior temporal, and temporoparietal regions, while the PPA connected more strongly with ventral medial visual, posterior cingulate, and entorhinal-perirhinal areas. MEG analyses further revealed this network segregation was reflected in beta and gamma bands. Importantly, connectome-based predictive modeling showed that the strength of these intrinsic fMRI connectivity patterns predicted individual reaction times on corresponding face and scene perception tasks. Our findings demonstrate that the FFA and PPA anchor distinct intrinsic networks with unique spatio-temporal profiles that provide a functional architecture supporting their specialized roles in face and scene perception.