bioRxiv Subject Collection: Neuroscience's Journal

The Ighmbp2-R604X mouse recapitulates the severe SMARD1 clinical symptoms of aspiration, respiratory and feeding deficits
Spinal muscular atrophy with respiratory distress type 1 (SMARD1) and Charcot Marie Tooth type 2S (CMT2S) are due to mutations in immunoglobulin mu binding protein two (IGHMBP2). We generated the Ighmbp2-R604X mouse (R605X-humans) to understand how alterations in IGHMBP2 function impact disease pathology. The IGHMBP2-R605X mutation is associated with patients with SMARD1 or CMT2S. The impact of this mutation is substantial, Ighmbp2R604X/R604X mice have a decreased lifespan (6 days) and weight, and failure to thrive consistent with SMARD1 symptoms. Significant respiratory changes were present along with disease pathology of the phrenic nerve and diaphragm muscle fibers. Ighmbp2R604X/R604X mice also presented with signs of milk aspiration and lung pathology. Interestingly, Ighmbp2R604X/R604X mice were born with visible milk spots, but demonstrated reduction of the milk spot by P3, indicating deficits in suckling. Alterations in hindlimb electrophysiology were consistent with the sciatic nerve, hindlimb neuromuscular junction and muscle pathology. Injection of the ssAAV9-WT-IGHMBP2 vector extended Ighmbp2R604X/R604X survival a few days, due to reduced expression of the vector before death ensued. Ighmbp2R604X/R604X phenotypes are consistent with the most severe SMARD1 clinical symptoms and for the first time a Ighmbp2 mouse model demonstrates that milk aspiration and loss of the ability to suckle impact survival.

Key moments in naturalistic events synchronize neural activity patterns and dominate memory reinstatement
Continuous experiences are experienced and remembered in terms of events that unfold over time. There is strong evidence that event boundaries segment experience during comprehension and that event representations are compressed in memory; however, this compression is poorly understood. We developed a novel storyboard paradigm to test the hypothesis that representations of continuous experiences are defined by a subset of key moments that capture the underlying narrative. Participants agreed on when key moments occurred; some key moments corresponded with event boundaries, but many did not. fMRI during encoding revealed that neural activity patterns throughout the default network synchronized across individuals at key moments. Further, comparing fMRI during encoding to fMRI during retrieval revealed that key moments are overrepresented in neural patterns that are reinstated during event recall in the posterior-medial cortex. These results suggest that continuous events are punctuated by a small subset of meaningful moments, which dominate neural representations during perception and memory.

Epigenetic inheritance of complex learning abilities in the mammalian brain
For several decades, the question of whether cognitive and learning capacities can be inherited through non-genetic mechanisms has been the subject of ongoing debate. Here, we provide the first evidence of transgenerational inheritance of enhanced ability to learn complex tasks in the mammalian rodent brain. These inherited learning enhancements are not limited to specific stimuli, sensory modalities, or learning paradigms. Using behavioral, cellular biophysical, methylomics, genetics and molecular methods, we find that the inherited epigenetic modifications reflect an enhanced neuronal learning state, driven by increased intrinsic neuronal excitability in most pyramidal neurons in the relevant neuronal networks. This enhancement is mediated by persistent downregulation of the muscarinic M-current and is associated with widespread changes in DNA methylation, notably within coding genes associated with the M-current, the Kv7 pathway, in the hippocampi of trained F0 rats, as well as in non-coding RNAs in their sperm samples. Remarkably, a significant portion of these DNA methylation changes were also observed in the hippocampi of their untrained F1 offspring. These findings suggest that complex learning abilities can be inherited in the mammalian brain, as the offspring of trained rodents are born with the biophysical modifications that enable them to become super-learners, the exact change that occurs in their parents' brains only after the rule learning.

Visual Regularities Underlie Hierarchical Object Representations in the Human Brain and Self-supervised DNN
NeuroAI develops the interplay of neuroscience and artificial intelligence, especially on visual processing. Human visual system organizes objects based on a representational hierarchy. However, it remains unclear whether this hierarchy arises from visual or semantic information. One hypothesis posits that the visual system is structured around statistical regularities of visual information. Here, we test this hypothesis using the THINGS datasets and pure-visual deep neural networks (DNN). We constructed a low-dimensional object space based on multiple abstract object properties, reflecting statistical patterns of visual regularities. By applying voxelwise encoding models, we identified clusters in the higher visual cortex based on their property tuning, and they were found to support specific object categories. These clusters serve as the middle level to reveal a property-cluster-object hierarchical organization. Subsequently, we investigated whether this hierarchical structure could be captured by a self-supervised DNN. Through activity similarity analysis, we mapped the brain clusters onto the DNN and independently found that the DNN's clusters exhibited distinct property tuning and influenced the classification accuracy of corresponding object categories, mirroring the effects observed in the human brain. Our results demonstrate similar hierarchical structures in the human brain and self-supervised DNN, suggesting that the visual regularities shape neural architecture of visual system. This study highlights the great potential of neural computational model in neuroscience study.

High-level Speech Processing During Mind-Wandering: Evidence from Neural Alignment with Language Models
Speech processing involves a subjective sense of engagement with content. This experience occurs alongside continuous prediction of upcoming contents, raising questions regarding the potential relationship between these phenomena. Is the activation of predictive mechanisms sufficient for subjective experience, or can it proceed when the mind wanders away? Participants (N = 25) listened to audiobook content (>12,000 words) while providing self-reports of mind-wandering. Such reports were associated with spectral changes in the EEG signal and decreased early response to word onset, in line with current accounts of mind-wandering. In contrast, markers of word-level contextual surprise (indexed by the contextual surprise of Language Models) remained intact during mind-wandering, alongside significant encoding of semantic contents. Last, we measured the neural encoding of predictive context, measured by the correlation between EEG and the vectorial embeddings that Language Models use to predict each word given prior context. This encoding also remained strong during mind-wandering, albeit weaker compared to attentive periods. Our findings show that the brain predicts and monitors relevant inputs even when we disengage from the external environment. It suggests that the shared computational mechanisms between humans and Language Models are insufficient for experiencing the meaning of speech contents, with implications for theoretical accounts of mind-wandering and predictive processing accounts of consciousness.

The Fragile X mental retardation protein (FMRP) coordinates an epigenetic checkpoint in neural progenitor cells
Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by silencing of the FMR1 gene, which encodes the multifunctional RNA-binding protein FMRP. While FMRP is best known for its roles in RNA metabolism, it can also associate with chromatin through recognition of histone H3 lysine 79 di-methylation (H3K79me2), an epigenetic mark linked to transcriptionally active genes. However, the functional relevance of this FMRP-H3K79me2 interaction has remained largely unexplored in the context of FXS pathophysiology. We assessed H3K79me2 levels during the differentiation of induced pluripotent stem cells generated from both healthy individuals and FXS patients and discovered a global increase in H3K79me2 levels specifically in FXS neural progenitor cells (NPCs). Altered H3K79me2 landscape drives widespread transcriptional dysregulation, marked by reduced expression of neurogenesis-associated genes and aberrant upregulation of programs promoting proliferation and glial lineage commitment. Functionally, these alterations result in enhanced NPC proliferation and a biased differentiation trajectory. Together, our findings uncover an H3K79me2-dependent epigenetic checkpoint that governs NPC proliferation and lineage commitment. We further establish FMRP as a critical component of this checkpoint, linking its loss to the neurodevelopmental defects characteristic of FXS.

Developmental vitamin A deficiency induces sex-specific reward processing alterations through a dysregulation of the mesolimbic dopamine transmission in mice
Neurodevelopmental psychiatric diseases such as schizophrenia or affective disorders share common symptomatic dimensions, in particular reward processing dysfunctions, associated with dysregulation of dopamine (DA) transmission. Retinoic acid (RA) homeostasis is altered across psychiatric disorders but whether impaired developmental RA signaling impacts the functionality of DA-related reward processing at adulthood remains poorly explored. Herein, we explored in male and female mice how developmental vitamin A deficiency (VAD), as a model of blunted RA signaling, could impact motivational processes through a modulation of mesolimbic DA transmission. Behavioral performances were evaluated using operant conditioning tasks, parallel with investigations of the integrity of DA transmission through biochemical analyses of markers of DA transmission and measures of DA dynamics using DA biosensor coupled with fiber photometry. Finally, chemogenetic manipulation of the mesolimbic DA pathway was used to normalize DA transmission and assess the effect on motivational performance in VAD offspring. Developmental VAD induced sex-specific alterations of reward-related processes at adulthood. Indeed, while female behavioral performances were spared, VAD males exhibited elevated instrumental performance and impulsivity. These behavioral alterations were coherent with reduced DA transporter (DAT) expression and increased DA dynamic in the mesolimbic pathway. Strikingly, chemogenetic inhibition of the mesolimbic DA pathway normalized motivational performance in VAD males. Our results show that developmental RA hyposignaling induces sex-specific reward processing alterations in adulthood through hyperactivity of the mesolimbic DA pathway. Our data support that developmental impairment in RA signaling might be at the core of reward-related symptoms across psychiatric disorders.

Beyond the Anterior Temporal Lobe: Domain-Related Degeneration of Cortical Language Network Dynamics in Semantic Dementia
Semantic dementia (SD) is a neurodegenerative disorder marked by a progressive decline in semantic memory, mainly due to focal atrophy in the anterior temporal lobe (ATL). As the disease advances, atrophy spreads to the perisylvian regions, accompanied by non-semantic language impairments. Despite these clinical findings, the network mechanisms behind cross-domain linguistic deficits remain poorly understood. In this study, we used our recently developed meta-networking framework of cortical language network dynamics to systematically examine domain-specific network disruptions in SD. Using resting-state functional MRI (fMRI) and comprehensive neuropsychological tests across several language domains, we analyzed data from SD patients at two timepoints: baseline (n = 42) and a 2-year follow-up (n = 24). Our findings showed that the framework successfully identified domain-specific language network degeneration. Beyond the ATL, progressive atrophy disrupted the dynamic separation of language networks involved in semantic processing, phonological processing, and speech production. These disruptions were characterized by state-specific hypo- and hyper-connectivity patterns that related to distinct language impairments. At follow-up, atrophy extended to posterior temporal and prefrontal regions, worsening network function. Importantly, the patterns of language network disruption predicted individual language deficits, providing a mechanistic link between structural degeneration, functional network changes, and clinical symptoms.

From injury to recovery: Functional neuronal regeneration after traumatic brain injury in the telencephalon of the young adult African turquoise killifish
Neuronal loss caused by neurodegenerative diseases and traumatic brain injuries (TBI) often results in long-term disabilities, highlighting the urgent need for further research and effective regenerative strategies. In mammals, neurogenic capacity is inherently limited and declines further with age. In contrast, the young adult killifish demonstrates a remarkable ability to regenerate neurons in the telencephalon following TBI. However, it remains unknown whether and when these newly generated neurons functionally integrate into existing circuits, as traditional histological analysis of fixed tissue offers only static insights into this dynamic process. To this end, we optimized a retroviral vector strategy to label dividing stem cells and their progeny, including newborn neurons. By introducing a combination of novel approaches i.e., retroviral vector labeling, electrophysiology and a conditioned place avoidance test, we investigated the generation, morphology, and synaptic integration of newborn neurons following TBI in the dorsomedial (Dm) zone of the telencephalon, a region homologous to the mammalian amygdala in other teleost fish. Our results show that injury-induced adult-born neurons functionally integrate into existing circuits, and that killifish can achieve functional behavioral recovery after TBI. While previous histological assessments using a stab-wound injury suggested a 30-day recovery period, our functional data reveal that full behavioral recovery requires approximately 50 days. At this point, fish successfully relearn to avoid a conditioned place, and the new neurons exhibit mature morpho-electric characteristics, including abundant dendritic spines. Electrophysiological analysis revealed that newborn neurons in an injured environment take longer to mature when compared to neurons in naive killifish. Together, our findings demonstrate that structural regeneration aligns with functional recovery, and establish retroviral vectors as a powerful tool for birth dating injury-induced neurogenesis in teleosts. Killifish thus represent a promising model for studying interventions aimed at enhancing neuronal maturation and integration after brain injury.

Differences in LC integrity and fMRI activations in healthy aging and MCI associated with successful memory encoding
INTRODUCTION: We examined whether the decline of delayed episodic memory in old age and MCI is related to structural integrity of the locus coeruleus (LC). We also tested whether LC integrity was associated with encoding-related brain activity, controlling for emotional salience. METHODS: Participants (28 young adults, 28 older adults, 25 MCI) memorized emotional and neutral images during fMRI. Delayed memory was tested four hours later and compared to immediate memory. LC integrity was measured with a neuromelanin-sensitive sequence. RESULTS: MCI showed overall memory decline, independent of delay or emotional valence, and reduced LC integrity compared to older adults. Across participants, LC integrity correlated with delayed memory, but did not explain performance within OA or MCI. LC integrity was associated with lower activity related to encoding success and emotional salience. Groups remembered emotional better than neutral images, though memory was greatly impaired in MCI, matching with their reduce LC integrity. DISCUSSION: LC integrity was associated with lower brain activity during encoding and emotional salience processing but not clear relationship with delayed memory performance in MCI.

Disgust propensity, not disgust sensitivity, shapes the reactivity of a subjective disgust circuit in humans
Disgust constitutes an evolutionary adaptive defensive-avoidance response, yet humans vary markedly in their dispositional tendency to experience disgust (disgust propensity) and in their negative appraisal of such experience (disgust sensitivity). Conceptual frameworks and neuroimaging studies suggest that these traits may differentially modulate neural responses to disgust-eliciting stimuli; however, methodological constraints have left their precise roles unresolved. Our comparably large fMRI study (n = 142) therefore aimed to systematically determine how trait disgust modulates neural responses to carefully selected and validated disgust-specific visual stimuli across varying levels of subjective disgust experience. The whole-brain voxel-wise regression analyses revealed a neural dissociation between the two disgust traits, with disgust propensity, but not disgust sensitivity, modulating disgust-related neural activity in the anterior, middle, and posterior insula, as well as the caudate, putamen, thalamus, hippocampus, and parahippocampal gyrus. Mediation and network-level analyses further supported this dissociation by showing that disgust propensity shapes disgust experience via insula-striatal-hippocampal pathways. Together, these findings provide evidence for a neurofunctional dissociation of disgust propensity and sensitivity and elucidate how trait disgust shapes subjective experiences. They further suggest that disgust propensity and the identified systems may represent promising targets for the regulation of disgust-related pathology.

Learning local geometry and nonlinear topology of neural manifolds via spike-timing dependent plasticity
Neural manifolds are an indispensable framework for understanding information encoded by activity in neural populations. While some neural manifolds are linear and can be recovered from population activity using standard techniques, many neural manifolds exhibit nonlinear global topology for which such tools can be less effective. Notably, circular and toroidal manifolds describe activity in neural systems across a range of species; common examples include orientation-selective simple cells in primary visual cortex, head-direction cells in thalamic circuits, and grid cells in entorhinal cortex. That such structured information appears in both sensory and deep-brain regions raises a basic question: is the propagation of nonlinear coordinate systems a generic feature of biological neural networks, or must this be learned? If learning is necessary, how does it occur? In this paper, we apply methods from topological data analysis developed to quantitatively measure propagation of such nonlinear manifolds across populations to address these problems. We identify a collection of connectivity and parameter regimes for feed-forward networks in which learning is required, and demonstrate that simple Hebbian spike-timing dependent plasticity reorganizes such networks to correctly propagate circular coordinate systems. We observe during this learning process the emergence of geometrically non-local experimentally observed receptive field types: bimodally-tuned head-direction cells and cells with spatially periodic, band-like receptive fields. These observations provide quantitative support for the hypothesis that simple biologically plausible plasticity mechanisms suffice to induce changes in the structure of neural architectures sufficient to explain the appearance of such features in real neural systems.

Molecularly defined auditory neuron subtypes show different vulnerabilities to noise- and age-related synaptopathy in mice
Neuronal subtype-specific synaptopathy is a hallmark of many forms of neurodegeneration. We examined the cellular basis for synaptic vulnerability in the auditory system, where three subtypes of spiral ganglion neurons (SGNs) - Ia, Ib, and Ic - carry acoustic information from the cochlea to the brain. In response to noise and aging, a subset of synapses between inner hair cells and SGNs are lost, but it is unclear how this loss varies across SGN subtypes. Using genetic labelling, we showed that Ia SGNs have larger post-synaptic densities (PSDs) than Ib and Ic SGNs and are the most resilient subtype. Ia PSD volumes increased with age and were unchanged after noise exposure. By contrast, average Ib/Ic PSD volumes did not change with age but decreased with noise. Genetic reprogramming of Ib/Ic neurons to a Ia-like identity provided significant protection against noise-induced synaptopathy, linking identity to resilience and providing an entry point for therapeutics.

A Neural Circuit for Modular Gating of Organ Somatosensation
End-organs such as the bladder rely on a delicate balance between internal urgency and voluntary restraint. However, the specific spinal circuits coordinating these functions remain poorly defined. Here, we identify a genetically defined population of lumbosacral spinal interneurons marked byTrpc4expression that gate bladder sensory input and regulate micturition reflexes. Single-cell transcriptomics and in situ physiology reveal molecularly distinctTrpc4subtypes. Functional manipulation reveals that Trpc4 neurons are essential for coordinating bladder-sphincter activity and gating visceral pain. Their ablation leads to bladder hypersensitivity and voiding dysfunction, while targeted activation reverses these maladaptive states. Circuit tracing reveals convergence of primary afferent and descending brainstem inputs ontoTrpc4neurons. These findings establishTrpc4interneurons are critical for bladder sensory-motor integration and extend classical spinal gating models to encompass visceral pain and organ reflex control.

Frequency-selective contrast sensitivity modulation driven by fine-tuned exogenous attention at the foveal scale
Exogenous attention is a rapid, involuntary mechanism that automatically reallocates processing resources toward salient stimuli. It enhances visual sensitivity in the vicinity of the salient stimulus, both in extrafoveal regions and within the high-acuity foveola. While the spatial frequencies modulated by exogenous attention in extrafoveal vision are well characterized, it remains unknown how this mechanism operates within the foveola, which can resolve spatial frequencies up to 30 cycles per degree (CPD). Here, we examined which spatial frequencies were enhanced by fine-grained deployments of exogenous attention within this highest-acuity region of the visual field. Using high-precision eye-tracking and gaze-contingent display control to precisely localize gaze during attentional allocation, we found that exogenous attention at the foveal scale selectively enhances contrast sensitivity for low- to mid-range spatial frequencies (4-8 CPD), with no significant benefits for higher spatial frequencies (12-20 CPD). In contrast, attention-related benefits on asymptotic performance at the highest contrast were observed across a wide range of spatial frequencies. These results indicate that, despite the high-resolution capacity of the foveola, exogenous attention remains an inflexible mechanism that, even at this scale, selectively enhances contrast gain for lower spatial frequencies-mirroring its behavior in extrafoveal vision.

Improving neuroimaging headgear placement robustness using facial-landmark guided augmented reality
Significance: Accurate and consistent probe placement is crucial in functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) experiments, especially in longitudinal and group-based studies. Both operator experience and subject head shape variability can affect placement accuracy. Aim: We aim to develop an easy-to-use software, NeuroNavigatAR (NNAR), utilizing augmented reality (AR) and machine-learning to estimate and display in real-time the subject's cranial and head landmarks to guide consistent headgear placement. Approach: By applying a facial recognition toolbox to the image frames extracted from a video camera, we can obtain and continuously track subject-specific three-dimensional (3-D) facial landmarks. Separately, we have precomputed a robust linear transformation between facial landmarks and key cranial landmarks, including nasion and preauricular points, using a large public head-model library consisting of over 1,000 subjects. These allow us to rapidly estimate subject-specific cranial landmarks and subsequently render atlas-derived head landmarks to the subject's camera stream. Results: An open-source graphical user interface implementing this AR system has achieved a speed of 15 frame-per-second using a laptop. A median 10-20 position error of 1.52 cm was found when using a general adult atlas, and is further reduced to 1.33 cm and 0.75 cm when using age-matched atlas models and subject-specific head surfaces, respectively. NNAR demonstrated consistent head-landmark prediction errors across repeated measurement sessions; there is also no statistically significant difference in accuracy across age groups. Conclusions: NNAR is an easy-to-use AR headgear placement monitoring tool that is expected to significantly enhance consistency and reduce setup time for fNIRS and EEG probe donning across a wide range of studies.

Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic Data
Alzheimer's disease and related dementias (ADRD) involve overlapping neurodegenerative and vascular pathologies -- such as amyloid-{beta} (A{beta}), tau, cerebral amyloid angiopathy (CAA), TDP-43, and alpha-synuclein -- that complicate diagnosis and treatment. While PET and CSF biomarkers are useful for detecting A{beta} and tau, they are invasive, expensive, and not widely available. In contrast, magnetic resonance imaging (MRI) is non-invasive and widely accessible, offering an opportunity for pathology prediction when combined with deep learning. Most prior studies have focused on single-pathology detection, but there remains a need for models that can jointly predict multiple co-occurring pathologies. In this work, we evaluate deep learning models that integrate structural MRI with demographic, clinical, and genetic data to classify six autopsy-confirmed neuropathologies: A{beta}, tau, CAA, TDP-43, hippocampal sclerosis, and dementia with Lewy bodies. We compare our hybrid deep learning model to AutoGluon, an automated machine learning framework. Our findings support the potential of multimodal AI to enable non-invasive, comprehensive neuropathological profiling in ADRD.

Using Diffusion Transformers to Generate Synthetic Diffusion Scalar Maps for Data Augmentation
Generation of high-quality synthetic brain MRI data could be beneficial for advancing neuroimaging research, particularly when access to large-scale, labeled datasets is limited. In this work, we leverage a pretrained Diffusion Transformer (DiT) architecture to synthesize 3D mean diffusivity (MD) scalar maps from the Cam-CAN dataset. To adapt the DiT model -- originally trained on 2D natural images -- for 3D neuroimaging data, we implemented a preprocessing strategy that tiles 2D slices from 3D volumes into composite 2D images, enabling effective finetuning. The quality of the generated synthetic images was evaluated using Multi-Scale Structural Similarity (MS-SSIM) and Maximum Mean Discrepancy (MMD) metrics, demonstrating high fidelity and anatomical coherence. To assess the utility of synthetic data in downstream tasks, we conducted transfer learning experiments for dementia classification on the ADNI dataset. A sex classification model, trained on both real and synthetic Cam-CAN data, was repurposed for this task, showing that synthetic samples can enhance model performance. These results highlight the potential of diffusion-based generative models for augmenting neuroimaging datasets and supporting clinical applications.

Mice navigate scent trails using predictive policies
Animals actively sense their environment to extract features of interest to guide behaviors. For mammals, odors are prominent environmental features which are sampled by active modulation of sniffing and orofacial orientation. We sought to understand the strategies that mice use to navigate surface-bound odor cues. We presented mice with dynamic, non-repeating odor trails using a paper treadmill, and observed their behaviors as they collected rewards offered randomly along the trail. By combining high-speed videography over long distances with quantitative behavioral analyses, we find that mice rapidly learn to track odor trails persistently and precisely. Mice with a single nostril blocked can track odor trails, but with a lateral bias and lower precision than control animals. Tracking is severely impaired in animals with both nostrils intact but with interhemispheric communication disrupted by anterior commissure transection. Respiration measurements revealed that a sniff close to the trail triggers a rapid turn towards the trail, a reaction that is lost in commissure-cut animals. Importantly, trail tracking is not simply reactive but involves adaptation to and retention of a short-term memory of the trail geometry and statistics. Our results, recapitulated by a Bayesian inference model, indicate that mice combine immediate sensory information with an internal model of the odor environment to follow odor trails efficiently.

Median Preoptic Astrocytes: Role in Sleep Regulation and Potential Mediators of Sex Differences
One in three Americans suffer from chronic sleep disorders, and women are 40% more likely than men to experience sleep disorders. This disparity emerges at puberty and is strongly associated with fluctuations in the ovarian hormone, estrogen (E2), suggesting that E2 and biological sex are a risk factor for sleep disorders. Previous work in the lab has demonstrated that E2 suppresses sleep in female rats, including in sleep deprived rats whose homeostatic need for sleep is increased. However, the specific mechanism for E2 induced decrease in sleep remains unknown. Work in the lab suggests a role for adenosine in mediating E2s sleep suppressive effects; E2 significantly increases Median Preoptic Nucleus (MnPO) extracellular adenosine and attenuates the action of specific agonists on the sleep promoting A2A-Receptor. Astrocytes represent a major source of adenosine in the CNS and have been shown to influence neuronal activity and downstream behaviors. In this project, we tested the hypothesis that astrocytes mediate E2s sleep suppressive effects. We used Gq-linked designer receptors exclusively activated by designer drugs (DREADDs) to evaluate the Gq pathway, which represents a core signaling mechanism in astrocyte activity. We found that, in female rats, activation of Gq signaling in astrocytes decreased sleep and inhibited homeostatic need for sleep. We further expressed the Pleckstrin Homology domain of PLC-like protein (p130PH), which has been shown to attenuate astrocyte activity and functions, in median preoptic nucleus (MnPO) astrocytes. We found that p130PH expression in MnPO astrocytes raised homeostatic sleep pressure to the same extent as 6 hours of sleep deprivation. We further report that inhibiting astrocytic function did not prevent E2s sleep suppressing effects suggesting that astrocytes may not play a role in estrogenic modulation of sleep. However, we did discover that MnPO astrocyte effects on sleep are sex-dependent. p130PH expression in MnPO astrocytes increased sleep and homeostatic sleep drive in female rats but showed a trend towards decreasing sleep and homeostatic sleep need in males. Further, while astrocyte effects on homeostatic sleep need are relegated to the dark phase in female rats, astrocytes appear to influence homeostatic sleep need in both the dark and light phase. To our knowledge, this is the first demonstration of a sex-based difference in astrocyte effects on sleep and homeostatic sleep pressure.

NGN2 Expression and Regional Patterning Allow Rapid Differentiation from hiPSCs to DRG-Like Neurons Responsive to Type 2 Cytokines
Itch or pruritus, is a sensation that elicits scratching behaviour and is a major symptom and cause of morbidity in skin diseases such as atopic dermatitis (AD), allergic contact dermatitis (ACD), prurigo nodularis (PN), and urticaria. Itch is often triggered by inflammatory stimuli in the skin including type 2 cytokines such as IL-4, IL-13, and/or IL-31. Several therapies targeting type 2 immune pathways have been developed to treat pruritus; however, itch improvement in many patients remains to be improved. Thus, additional approaches to modulate sensory neuron activity are needed. Ex vivo or even in vitro study of the molecular mechanisms underlying primary sensory neuron activation is challenging since harvesting neurons from dorsal root ganglia (DRG) in patients can only be done from cadavers. Herein, we describe rapid human sensory neurons generation (2 days to precursor cells) by in vitro differentiation of human induced pluripotent stem cells (hiPSC) from simultaneous application of patterning factors with NGN2 overexpression. We show that these hiPSC-derived sensory neurons possess key characteristics of primary sensory neurons. They express key neuronal markers, such as TRKA receptors, TRPV1 and TRPA1 channels, and functionally respond to the TRPV1 agonist capsaicin. In addition, they express key type 2 cytokine receptors such as interleukin (IL)-4R and IL31-R, known to promote itch in AD and PN. Moreover, these cells are functional as our sensory neurons respond to IL-4, IL-13 and IL-31 stimulation. Collectively, these data demonstrate that our protocol generates a phenotypic profile consistent with native somatosensory neurons that can facilitate development of novel approaches to model and treat pruritic disease.

Separating error from bias: A new framework for facial age estimation in humans and AIs
Apparent facial age plays an important role in social interactions, serving a meaningful marker of biological aging. Although both humans and AIs achieve reasonable accuracy in estimating age from a person's face, performance remains imprecise, leaving substantial room for errors and biases. Drawing on principles from classical psychophysics, we demonstrate that the existing literature on age estimation suffers from a critical theoretical and methodological shortcoming, which casts doubt on established findings. We show that the conventional measure used to benchmark the accuracy of human and AI performance is fundamentally confounded by response bias. Consequently, we introduce a novel measure that eliminates this confound. A revised framework based on simulated data, reanalysis of existing data, and new experimental results, reveals fresh insights into how facial age is processed by humans and AIs. Our structure opens up new directions for future research and applications in the study of aging.

Direction of motion decoding in mouse V1: Neuron predictive power relates to functional connectivity organization
Variability in single neuron responses presents a challenge in establishing reliable representations of visual stimuli essential for driving behavior. To enhance accuracy, integration of responses from multiple neurons is imperative. This study leverages simultaneous recordings from a large population (tens of hundreds) of neurons, achieved through in vivo mesoscopic 2-photon calcium imaging of the primary visual cortex (V1) in mice, under visual stimulus conditions as well as in resting state (absence of stimulus). The visual stimulus consisted of 16 distinct randomly shuffled directions of motion presented to the mice. We employed mutual information to identify neurons that contain the most significant information about the stimulus direction. As expected, neurons displaying high predictive power (HPP) in stimulus decoding exhibit elevated firing event rates during stimulus presentation. Furthermore, functional connectivity among HPP neurons during visual stimulation is denser and stronger compared to functional connectivity among other visually responsive neurons. Functional connections among HPP neurons appear to form independently of distance, suggesting a distributed yet highly coordinated network. In contrast, HPP neuronal activity and functional connectivity differed significantly at resting state. Specifically, during the resting state, HPP neurons exhibited lower event rates and functional connectivity structure that was not significantly different from that of other visually responsive neurons. This suggests that HPP neurons are less susceptible to being driven simultaneously by internal brain states in the absence of a stimulus. Finally, the tuning properties of HPP neurons were unexpectedly diverse: while some were sharply tuned, others conveyed a similar amount of mutual information, despite exhibiting much weaker tuning. This study sheds light on the organization of neuronal ensembles important for decoding visual motion direction in mouse area V1, contributing to the understanding of information processing in mouse visual cortex.

Optimized Quantitative Susceptibility Mapping at 7T MRI for Assessing Iron Deposition in Alzheimers Disease
INTRODUCTION: Elevated brain iron levels are common in Alzheimers disease (AD). Quantitative Susceptibility Mapping (QSM) is an advanced MRI technique for assessing iron accumulation. The optimized QSM at 7 Tesla (7T) MRI may further improve the sensitivity to detect subtle susceptibility changes in AD. METHODS: We optimized a QSM processing pipeline for 7T MRI by systematically comparing multiple reconstruction algorithms. Evaluation criteria included image quality, artifact suppression, and anatomical clarity. The finalized pipeline was applied to individuals with AD and healthy controls (HCs). RESULTS: The results revealed significantly elevated magnetic susceptibility values in the globus pallidus and dentate nucleus of the AD group compared to HCs. These findings were confirmed through both visual inspection and quantitative analysis of high-resolution QSM maps. DISCUSSION: Our results highlight the importance of optimizing QSM pipelines at 7T for accurate susceptibility quantification. We identified an optimal pipeline suitable for future applications in patients with AD and other neurological conditions.

Generating diffusion MRI scalar maps from T1-weighted images using Reversible GANs
Diffusion tensor imaging (DTI) provides valuable insights into brain tissue microstructure, but acquiring high-quality DTI data is time-intensive and not always feasible. To mitigate data scarcity and enhance accessibility, we investigate the generation of synthetic DTI scalar maps -- specifically mean diffusivity (MD) -- from structural 3D volumetric T1-weighted brain MRI using a reversible generative adversarial network (RevGAN). Unlike conventional pipelines requiring multiple steps, our approach enables a single-step translation from T1 to diffusion-derived measures. We assess the quality and utility of the synthetic maps in two downstream tasks: sex classification and Alzheimer's disease classification. Performance comparisons between models trained on real and synthetic DTI maps demonstrate that RevGAN-generated images retain meaningful microstructural features and offer competitive accuracy, underscoring their potential for data augmentation and analysis in neuroimaging workflows. We also examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort).

Complex Patterns of Altered White Matter Structural Connectivity within a 'subjective valuation network' in Treatment-Resistant Depression
Background: Treatment-resistant depression (TRD) poses a significant clinical challenge, demanding a deeper understanding of its neurobiological underpinnings to improve therapeutics. We examined white matter microstructure and structural connectivity in TRD, focusing on the "subjective valuation network" (SVN), which captures motivated behavior, reward processing, and emotional regulation circuits commonly altered in depression. This allowed us to identify potential neuroimaging biomarkers associated with treatment resistance. Methods: Diffusion tensor imaging (DTI) data were acquired from a sample of age- and gender-balanced individuals with TRD (n = 44; female = 24, male = 11, non-binary/unchecked = 7) and non-depressed controls (n = 42; female = 27, male = 12, non-binary/unchecked = 5). Tract-based spatial statistics were used to compare whole-brain white matter integrity differences between groups. Probabilistic tractography was then used to assess fractional anisotropy (FA) and white matter structural connectivity within the SVN across groups. SVN was defined a priori based on converging functional connectivity studies and included key regions such as the ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), ventral striatum, and insula and their connecting white matter tracts. Additionally, correlations between clinical measures of depression severity and cognition and structural features of the network's white matter fibers (i.e., FA and structural connectivity) were explored. Results: Compared to non-depressed controls, individuals with TRD exhibited reduced FA within the left uncinate fasciculus, left inferior fronto-occipital fasciculus, and anterior cingulum, supporting widespread white matter integrity degradations in TRD. No correlations were found between FA and depression severity, suggesting a more specific association with anhedonic features. Whole-network measures of FA and structural connectivity of the SVN did not differ between groups. However, specific subcircuits' structural connectivity within the SVN differed between groups; namely, the white matter tracts connecting the insula-vmPFC, striatum-insula, and striatum-vmPFC. Hyperconnectivity emerged for patients with TRD for tracts connecting the insula-vmPFC and striatum-vmPFC region-pairs, with hypoconnectivity observed between the striatum-insula. Exploratory analyses for the TRD group indicated the subcircuits with altered structural connectivity within the SVN correlated with depressive severity. This indicates subcircuit network alterations may associate with greater difficulty experiencing pleasure -- a core symptom of depression and a potential marker of treatment resistance. Conclusions: This study provides evidence for widespread disruption of white matter microstructure and altered structural connectivity within specific subcircuits of the SVN in TRD. These findings point to an intricate pattern of structural hyper- and hypoconnectivity within the subcircuits of the SVN which may underlie the core symptoms of TRD. The altered structural connectivity within the SVN may contribute to the pathophysiology of TRD, especially concerning the motivational and emotional deficits associated with anhedonia. Future research employing multimodal neuroimaging techniques and longitudinal designs is warranted to further elucidate the functional consequences of these structural abnormalities and their potential as predictive biomarkers for personalized treatment interventions in TRD. Specifically, investigating how these white matter alterations change with successful treatment or targeted interventions aimed at improving anhedonia could inform more effective therapies for this challenging condition.