bioRxiv Subject Collection: Neuroscience's Journal
[Most Recent Entries]
[Calendar View]
Tuesday, August 19th, 2025
| Time |
Event |
| 8:36a |
Neuroanatomical Basis of Coma in Acute Ischemic Stroke
Background: Acute ischemic stroke (AIS) can lead to profound disturbances in consciousness, including coma, which is associated with poor prognosis and increased mortality. Clarifying the lesion patterns that precipitate loss of consciousness can refine pathophysiological models and guide prognosis. Objectives: In this study, we aim to identify the brain regions most commonly affected in comatose AIS and determine whether specific combinations of lesions are necessary and sufficient to produce coma. Methods: We retrospectively analyzed 476 AIS patients (52 comatose) using diffusion-weighted imaging. Infarcts were automatically segmented, manually verified, and normalized to MNI space. Support vector regression lesion-symptom mapping (SVR-LSM) quantified voxel-wise associations with coma, controlling for lesion volume. To assess the necessity and sufficiency of lesion combinations, we employed permutation-based nested logistic regression models comparing all subsets of four anatomical predictors: brainstem, thalamus, cerebellum, and the rest of brain lesions. Results: SVR-LSM revealed that coma was strongly associated with lesions involving the brainstem, thalamus, and cerebellum, whereas non-comatose patients exhibited predominantly cortical infarcts. Nested model comparisons showed that concurrent lesions to both the brainstem and thalamus were necessary and sufficient for coma. Additional involvement of the cerebellum or cerebral cortex did not improve predictive performance. Conclusions: Coma after AIS results from a dual-node subcortical lesion pattern involving both the brainstem and thalamus. Cerebellar and cortical lesions, even when extensive, did not induce coma in the absence of the dual-brainstem and thalamic lesions. These observations emphasize the predominant role of lesion location over lesion volume in the pathogenesis of coma. They also support mechanistic models that position the brainstem and thalamic hubs as central to the neural circuitry underlying arousal. Furthermore, these findings delineate a specific anatomical substrate that may serve as a strategic target for circuit-based neuroprotective and neuromodulatory therapies. | | 9:47a |
Medial prefrontal cortex input to lateral entorhinal cortex supports both encoding and retrieval of associative recognition memory
Associative recognition memory allows us to form representations of items and their environment and to judge the novelty of such representations. This memory is dependent on a brain circuit that includes interactions between medial prefrontal cortex (mPFC) and lateral entorhinal cortex (LEC); however it is unknown whether the interaction of these brain areas is required for memory encoding, retrieval or both processes. Furthermore, little is known as to whether indirect or direct mPFC-LEC connections are critical for associative recognition memory and, if the latter, in which direction information travels. To address these questions, we first performed pharmacological disconnection of mPFC and LEC, finding that mPFC-LEC interaction is required for both memory encoding and retrieval. Next, we optogenetically inhibited projections from mPFC to LEC, showing that this projection was crucial for both encoding and retrieval of both object-in-place and object-in-context recognition memory when a 1 h, but not a 5 min, memory retention delay was used. These data show that a direct connection from mPFC to LEC is critical for associative recognition memory, in a delay-dependent manner. | | 9:47a |
Proteasome mutations associated with CANDLE syndrome cause altered neuronal development by dysregulating polyamine synthesis
Genetic mutations affecting proteasome function can result in multi-organ diseases, such as Chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperature (CANDLE) syndrome. Neurological symptoms associated with CANDLE suggest that proteasomal mutations may impact neuronal development and/or function. We generated cerebral organoids (COs) from CANDLE patient induced pluripotent stem cells (iPSCs), which exhibited impaired neuronal development when compared to COs from healthy control iPSCs. Impaired neuronal maturation in CANDLE COs was correlated with increased polyamines, which were also elevated in CANDLE patient CSF. The proteasome-regulated Ornithine decarboxylase (ODC), a rate limiting enzyme for polyamines, was elevated in CANDLE neurons. Inhibition of ODC reversed polyamine overproduction and repaired neuronal maturation in CANDLE COs, suggesting a potential therapeutic avenue for intervention. These findings demonstrate that dysfunction of the proteasome affects neuronal development through overproduction of polyamines via dysregulation of ODC and offer insight into potential therapeutic strategies for CNS-related proteasomal dysfunction. | | 10:15a |
The elusive neural signature of emotion regulation capabilities: evidence from a large-scale consortium
Cognitive reappraisal is a fundamental emotion regulation strategy for mental and physical well-being, but how its neural mechanisms relate to individual differences remains poorly understood. In a consortium effort analyzing 40 fMRI datasets (N=2,175), we examined the relationship between neural activation during reappraisal tasks and three core individual difference indices of reappraisal capabilities: (1) trait questionnaires, (2) task-based affective ratings, and (3) amygdala down-regulation. Strikingly, there was no shared overlap across these three common indices. Only a very weak correlation emerged between amygdala down-regulation and task-based affective ratings. Whole-brain analyses revealed no reliable neural associations with trait questionnaires, and associations with task-based affective ratings fell outside canonical emotion regulation networks (e.g., prefrontal circuitry). Moreover, amygdala down-regulation, often interpreted as a stable individual marker, was confounded by person-specific whole-brain responses - a limitation extending to fMRI research beyond the emotion regulation domain. These findings challenge the assumption that an individual's prefrontal activity is a valid indicator of their reappraisal capabilities and suggest that common trait, behavioral, and neural measures might capture distinct facets of emotion regulation. More broadly, our results highlight concrete methodological challenges for fMRI research on individual differences, with implications extending beyond emotion regulation to the neuroscience of personality, psychopathology, and general well-being. | | 10:15a |
Competitive pre-ordering during planning persists in kinematically fused sequential movements
Results in human and non-human primates have shown that elements of a movement sequence are pre-ordered in parallel competitively before execution, a process known as competitive queueing (CQ). However, it is unclear whether the preplanning of individual movements persists in continuous skilled actions that involve greater biomechanical integration and is associated with the formation of new motor primitives (neural fusion). We investigated how kinematics impact sequence planning in a handwriting-like task asking whether fusing velocity curves between adjacent movements affects movement preparation. Participants were trained and tested for two days to perform two sequences of four sequential centre-out-and-back movements from memory in a delayed sequence production task using a stylus on a Wacom tablet. To manipulate kinematic fusion between subsequent strokes, participants were assigned to one of three groups that were trained to perform the sequences either with acute, right or obtuse angles between sequential targets. Probe trials assessed the availability of constituent movement elements for fast and accurate execution towards each target during planning. Movement elements associated with later sequence positions were less available than earlier movements, regardless of kinematic fusion, in line with CQ findings for discrete typing sequences in humans. Importantly, a more pronounced CQ gradient was associated with higher fusion, faster initiation and greater accuracy of sequence production. These findings indicate that kinematically fused sequential actions do not result in the formation of new movement primitives (neural fusion) with a single movement plan. Instead, they continue to be planned separately and are associated with skilled performance. | | 10:46a |
Brain functional connectivity initiates structured reorganization at a critical oxygen threshold during hypoxia
The human brain dynamically adapts to hypoxia, a reduction in oxygen essential for metabolism. The brain's adaptive response to hypoxia, however, remains unclear. We investigated dynamic functional connectivity (FC) in healthy adults under acute hypoxia (FiO2 = 7.7%, 11.8%) using BOLD fMRI, physiological monitoring (PetO2, PetCO2, SpO2), and a Go/No-Go task. Principal component analysis identified a hypoxia-responsive FC component involving 400 cerebral parcels. This component emerged with a critical drop in PetO2 (~53 mmHg), preceding changes in SpO2, BOLD signals, and behavior. These FC changes were network-specific and centered on the default mode network (DMN), which selectively synchronized with other high-level cognitive networks. In contrast, visual networks remained stable and segregated from the DMN. These results suggest that the brain proactively reorganizes its functional architecture in anticipation of oxygen decline, rather than in response to it. FC-based markers may offer early indicators of vulnerability in neurological or neurodegenerative conditions. | | 10:46a |
Epilepsy-Associated SCN2A-L1342P Mutation Drives Network Hyperexcitability and Widespread Transcriptomic Changes in Human Cortical Organoids
Objective: SCN2A pathogenic mutations, such as the recurrent heterozygous Nav1.2-L1342P, are monogenic causes of epilepsy. In this human-induced pluripotent stem cell model system, we aim to investigate the molecular and cellular mechanisms underlying the SCN2A-L1342P-associated pathology. Methods: Using a human male iPSC reference line (KOLF) carrying the Nav1.2-L1342P mutation, we generated 3D cortical organoids for functional studies. Patch clamp and multi-electrode array (MEA) recordings, immunocytochemistry, and RNA sequencing were used to characterize the disease phenotypes. Results: Nav1.2-L1342P organoid neurons displayed increased intrinsic excitability, and amplified excitatory post-synaptic currents, which are consistent with an increase in excitatory synapse formation revealed by PSD95/SYN1 immunostaining. Moreover, elevated network firing activity, as demonstrated by MEA, indicates a pronounced network hyperexcitability. Transcriptomic profiling of organoids carrying the Nav1.2-L1342P mutation further revealed significant alterations in synaptic, glutamatergic, and developmental pathways. Significance: Our findings demonstrate that the Nav1.2-L1342P mutation drives a multifaceted disease phenotype, including network hyperexcitability and disruption of pathways related to neuronal and synaptic functions. These results advance our understanding of SCN2A-related Developmental and Epileptic Encephalopathy (DEE), laying a foundation for personalized interventions. | | 10:46a |
Learning Amidst Noise: The Complementary Roles of Neural Predictive Activity and Representational Changes
The ability to extract structured patterns from a noisy environment is fundamental to cognition, yet how the brain learns complex, non-adjacent regularities remains unclear. Using magnetoencephalography (MEG) during a visuomotor task, we tracked the neural dynamics as humans learned non-adjacent dependencies embedded in noise. We reveal that learning is supported by two temporally dissociable mechanisms. A rapid emergence of predictive activity, where neural patterns of expected stimuli appear before their onset, precedes measurable behavioral improvements. This is followed by a much slower representational change, characterized by an increased neural pattern similarity between linked, non-adjacent elements. Both processes are supported by a distributed consortium of sensorimotor, dorsal attention, salience, central executive, and cerebellar networks. These findings establish a temporal hierarchy for the neural mechanisms of learning, suggesting that fast predictions guide online behavior, which in turn facilitates the gradual consolidation of knowledge into stable neural representations. | | 1:30p |
Distinct and Combined Interferon-α/β-receptor-1 Loss in Neurons and Astrocytes Disrupt Brain Energy Metabolism and Drive Parkinsonian Dementia
Dysregulated interferon-alpha/beta-receptor 1 (IFNAR1) signaling was recently identified to contribute to the development of sporadic Parkinson's Disease (PD) into PD with Dementia (PDD). The molecular, cellular, and phenotypic impacts of brain IFNAR1 loss in aging have not been explored in vivo, which may reveal novel disease mechanisms and therapeutic targets. Here it is shown that baseline IFNAR1 expression varies in the major brain cell types, including neurons and astrocytes, and is differentially affected in PD and Lewy Body Dementia patients compared to unaffected controls. Neuron- and astrocyte-specific transcriptomic and proteomic alterations in Ifnar1-/- mice implicate mitochondrial defects and synergistic dysfunctional neurotransmission upon IFNAR1 loss, leading to glucose hypermetabolism measured by functional metabolic analysis. Consequently, Ifnar1-/- mice exhibited PDD-like pathogenesis, including dopaminergic cell loss in the substantia nigra, cortical neurodegeneration, Lewy-body-like inclusions, neuroinflammation, and progressive PDD-like behavior deficits. Brain cell-specific IFNAR1 loss examined in vivo revealed delayed but distinct development of PDD-like phenotypes, where neuropathology, motor, and cognitive behavior deficits were specifically recapitulated only in mice lacking neuronal IFNAR1, and behavior resembling neuropsychiatric abnormalities recapitulated only in mice lacking astrocytic IFNAR1. This work supports a crucial role of IFNAR1 in brain homeostasis and emphasizes a need for understanding neurodegenerative pathophysiology in cell-specific contexts. Highlights {middle dot} IFNAR1 and related type-I IFN genes are differentially expressed among major brain cell types in Parkinson's Disease, Lewy Body Dementia, and unaffected controls {middle dot} Early molecular alterations in Ifnar1-/- mice show lack of immunomodulation contributing to neuroinflammation, mitochondrial defects, and dysregulated energy metabolism {middle dot} Ifnar1-/- mice develop a progressive Parkinsonian-like disease phenotype, including dopaminergic cell loss in substantia nigra, cortical neurodegeneration, phosphorylated (p)alpha-synuclein+ and pTau+ Lewy-body-like inclusions, neuroinflammation, and progressive motor, cognitive, and neuropsychiatric disturbance-like behavior deficits {middle dot} Neuropathologies, motor, and cognitive deficits are recapitulated in mice lacking neuronal IFNAR1 (Syn1Cre;Ifnar1fl/fl) whereas neuropsychiatric abnormalities are recapitulated in mice lacking astrocytic IFNAR1 (GFAPCre;Ifnar1fl/fl) | | 2:45p |
Visuomotor mismatch EEG responses in occipital cortex of freely moving human subjects
Likely the strongest predictor of visual feedback is self-motion. In mice, the coupling between movement and visual feedback is learned with first visual experience of the world (Attinger et al., 2017), and brief perturbations of the coupling result in strong visuomotor mismatch responses in visual cortex that possibly reflect prediction errors (Keller et al., 2012; Zmarz and Keller, 2016). In humans, predictive coding has primarily been studied using oddball paradigms which rely on violations of stimulus probability based on recent sensory history. It was still unclear, however, whether humans exhibit visuomotor mismatch responses similar to those observed in mice. This question was important for two reasons. First, visuomotor mismatch responses in humans constitute a basis to start translating the mechanistic understanding of the circuit that computes these responses from mouse to human cortex. Second, a paradigm that can trigger strong prediction error responses and consequently requires shorter recording times would simplify experiments in a clinical setting. Here, by combining a wireless EEG recording system with virtual reality headset, we found robust visuomotor mismatch responses in human cortex that were characterized by a reversed polarity relative to visual evoked responses and a greater signal power than both visual responses and oddball mismatch responses. | | 4:49p |
Causal necessity of human hippocampus for structure-based inference in learning
When meeting new individuals or encountering known individuals in new circumstances, we intuitively map out their relationships, not merely by direct experience, but by quickly inferring new connections based on prior relational knowledge. Using a novel task, we demonstrated that participants indeed employ knowledge of relational structures to facilitate learning of new relationships in a changing environment. Computational modelling revealed that participants leveraged relational knowledge to support inference, thus facilitating learning. Whole brain neuroimaging identified a uniquely robust representation of relational structure in the hippocampus. Neural networks trained on similar tasks demonstrated the emergence of relational structure representations, resembling those found in hippocampus. Lesioning network units sustaining such representations disrupted structure-based inference and predicted hippocampus's essential role. Transcranial ultrasound stimulation of human hippocampus, transiently modulating its activity without affecting overlying tissue, produced similar disruption effects, empirically confirming the causal necessity of hippocampal representations for structure-based inference in learning. | | 4:49p |
Distinct brain mechanisms support trust violations, belief integration, and bias in human-AI teams
This study provides an integrated electrophysiological and behavioral account of the neuro-cognitive markers underlying trust evolution during human interaction with artificial intelligence (AI). Trust is essential for effective collaboration and plays a key role in realizing the benefits of human AI teaming in information rich and decision-critical contexts. Using electroencephalography (EEG), we identified neural signatures of dynamic shifts in human trust during a face classification task involving an AI agent. Viewing the AI classification elicited an N2 P3a P3b event-related potential (ERP) complex that was sensitive to agreement with the participants own judgment and modulated by individual response biases. In addition, we observed a centro-parietal positivity (CPP) prior to participants responses, and found that ongoing EEG activity in this time window covaried with subsequent changes in AI trust ratings. These neural effects showed substantial individual variability, indicating the use of diverse metacognitive strategies. Together, these findings suggest that trust in AI is shaped by internal confidence signals and evaluative processing of feedback. | | 4:49p |
The TRP-channel painless mediates substrate stiffness sensing in the legs during Drosophila oviposition
The distinct textural properties of fruits in varying stages of ripening present unique ecological opportunities for several species of fruit flies, resulting, over evolutionary times, in specialized egg-laying behaviors. In this study we identified a TrpA channel-dependent mechanosensory pathway in the legs, through the gene painless, that modulates the discernment of softer patches for oviposition in gravid D. melanogaster females. We report that the stiffness-sensing role of tarsi is mediated through external sensory organs housed, namely ventral mechanosensory bristles and subsets of campaniform sensilla present primarily at the joints between tarsomeres. Our findings provide new evidence that campaniform sensilla function as indirect stiffness sensors of oviposition substrates, owing to their placement at joints that experience maximal cuticular distortion. We show that Painless is expressed in mechanosensory neurons innervating peripheral organs where it likely participates in the transduction of stiffness-evoked stimuli. Furthermore, we observed that overexpression of painless in both campaniform sensilla and mechanosensory bristles partially rescues preference for the softer substrates in painless mutants, indicating that painless activity in these organs is necessary to mediate the preference. We propose that different interactions with a soft vs. a hard substrate (compression of the cuticle, distribution of contacts) results in differential mechanotransduction in painless-expressing neurons, determining oviposition preferences. | | 4:49p |
Revisiting alpha-theta cross-frequency dynamics during working memory
Prior Electroencephalography (EEG) research has shown that during working memory delay, alpha (8-14 Hz) and theta (4-8 Hz) oscillations tend to form a 2:1 frequency ratio. According to the Binary Hierarchy Brain Body Oscillation Theory (BHBBOT), a recent model grounded in mathematical analysis, such harmonic (2:1) alpha-theta frequency configurations reflect enhanced connectivity between brain regions generating these rhythms. However, this prediction has not yet been empirically tested. In this study, we leveraged Information Theory and the Theory of Weakly Coupled Oscillators (TWCO) to examine whether the previously observed frequency modulations in alpha and theta rhythms during working memory are accompanied by changes in inter-areal connectivity. Contrary to the BHBBOT predictions, both Information Theory metrics and TWCO parameters showed that connectivity between frontal theta and parietal alpha rhythms was significantly reduced during the working-memory delay period (while the proportion of 2:1 ratios increased). In addition, phase locking value, a standard measure of synchrony, was also significantly reduced during working memory delay and was negatively associated with behavioural performance. In conclusion, our results show that the increased occurrence of 2:1 alpha:theta cross-frequency ratios during working memory reflects functional segregation (rather than integration) between frontal and parietal regions. | | 11:16p |
A neural entero-pancreatic pathway that regulatesinsulin secretion and glucose tolerance
Signals from the gut enhance pancreatic secretion of insulin and thus influence glucose metabolism. This phenomenon, known as the incretin effect, is thought to be mediated by hormones secreted from enteroendocrine cells. The endocrine model, however, does not fully capture the complexity of gut-pancreas interactions. Anatomical studies identified a direct neural connection between the gut and the pancreas, known as the entero-pancreatic plexus. The role of this connection in regulating glucose metabolism remains unknown. Here we identify and functionally characterize a subpopulation of nitridergic myenteric neurons in the proximal duodenum that project directly to the pancreas. The anatomical and transcriptomic signature of these neurons places them downstream of glutamatergic and enkephalinergic enteric interneurons that indirectly respond to luminal nutrient stimuli. Their axonal projections to the pancreas contact neuro-insular ganglia and reach into the islet parenchyma. When activated chemogenetically, this circuit increases Ca2+ responses in pancreatic beta cells, enhances insulin secretion, and improves glucose tolerance in vivo. Our findings reveal a direct gut-pancreas neural pathway that complements incretin signaling in potentiating insulin secretion. This unexpectedly strong neuronal modulation of beta cell function could be harnessed to improve glycemic control in diabetes. |
|