bioRxiv Subject Collection: Neuroscience's Journal

Olfactory Bulb Pinprick Induction of Cortical Spreading Depolarizations
Cortical spreading depolarization (CSD) is a wave of cellular depolarization followed by prolonged depression of neuronal activity and is associated with a broad array of neurological diseases, including migraine with aura, traumatic brain injury, and stroke. Traditional CSD induction methods for animal studies have included pinprick, concentrated potassium chloride (KCl) application, and electrical stimulation. These methods are invasive and can cause injury to the cortex. Recently, a non-invasive approach using optogenetics has become available, but requires the use of transgenic mice or transfection of an optogene, which limits its wide adoption. Here, we describe a novel approach using olfactory bulb needle insertion in rodents to induce CSD. We also included KCl-induced CSDs as a comparator in the same mice. Olfactory bulb pinprick resulted in CSDs on every attempt (n = 18/18) as confirmed with optical intrinsic signal imaging. Histological analysis revealed that needle disruption in the caudal olfactory bulb, which is continuous with the cerebral cortex, may account for the propagation of CSD from the olfactory bulb to the cortex. Olfactory bulb pinprick reliably induces CSD and is non-invasive with respect to cortex. The approach may prove to be useful in rodent studies where maintenance of cortical integrity is important.

TDP-43 pathology triggers SRRM4-dependent cryptic splicing of G3BP1 in ALS/FTD
Loss of nuclear TDP-43 is a defining feature of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), yet how this leads to selective neuronal vulnerability is poorly understood. Here, using human iPSC-derived neurons and a large multi-omics dataset of ALS/FTD patients, we demonstrate that TDP-43 pathology induces the inclusion of an in-frame cryptic exon in human G3BP1. The resulting CRYPTIC G3BP1 protein contains an additional 10 amino acids within the highly conserved NTF2L domain, which acts as a dominant negative and disrupts stress granule dynamics. We further show that cryptic exon inclusion in G3BP1 upon TDP-43 loss is enriched in neurons. Mechanistically, the loss of TDP-43 unmasks a binding site for the neuron-specific splicing regulator SRRM4 within intron 2 of G3BP1, enabling the inclusion of the cryptic exon. Collectively, our findings reveal that neuron-specific regulatory mechanisms intersect with TDP-43 -mediated splicing and suggest a mechanistic basis for the increased neuronal vulnerability observed in ALS/FTD.

The Rational Irrational: Better Learners Show Stronger Frequency Heuristics
Does favoring less valuable options that deliver more frequent rewards reflect flawed decision-making or an adaptive strategy under complex environments? Frequency effects, defined as a bias toward more frequently rewarded but less valuable options, have traditionally been viewed as maladaptive decision-making deficits. In the present study, we used a within-subject design in which participants completed a four-option reinforcement learning task twice, once under a baseline condition and once with a reward frequency manipulation, to test whether better baseline learning predicts greater or lesser susceptibility to frequency-based biases. Participants were first trained on two fixed option pairs and then transferred their knowledge to novel pairings in a testing phase. Across conditions, higher training accuracy generally predicted higher test accuracy, with one critical exception: on trials where a more valuable option was pitted against a more frequently rewarded but less valuable alternative, participants with higher training accuracy exhibited a stronger bias toward the more frequent option. Moreover, baseline optimal choice rates in these specific trials were unrelated to--and even slightly negatively correlated with--optimal choice rates under the frequency condition. Computational modeling further showed that participants with better baseline learning performance were better fit by frequency-sensitive models in the frequency condition and they weighed frequency-based processing more heavily than value-based processing. Overall, these findings suggest that frequency effects, rather than signaling flawed learning, manifest more strongly in individuals with better baseline learning performance. This seemingly irrational bias may, under conditions of uncertainty, reflect a flexible, adaptive strategy that emerges among the best learners when value-based approaches are costly or unreliable.

Efficient Coding of Spatial Frequency in Natural Images: Cross-frequency Dependence
Research suggests that spatial frequency (SF) channels in the visual system operate with a degree of independence. However, the independence model has been questioned by evidence of non-additive effects in compound gratings, indicating complex interactions between SF channels. These studies, however, typically employ artificial stimuli, leaving questions about SF processing in natural scenes. Efficient Coding hypothesis, which posits that the visual system minimizes redundancy and retains relevant information, predicts a dependence between HSF and LSF. In this study, we examined interactions between LSF and HSF using natural and phase-scrambled images to explore SF integration during perception. Participants completed an SF identification task, using both natural and scrambled images to isolate the role of phase alignment. Our results indicate that HSF and LSF interact primarily in phase-aligned conditions, with phase scrambling driving independent processing of two SFs and reducing error rates. These findings suggest that phase alignment enhances perceptual efficiency, facilitating a trade-off between accuracy and redundancy reduction in natural scene processing.

Geometric constraints on the architecture of mammalian cortical connectomes
The intricate network of axonal fibres that forms the mammalian cortical connectome has a complex topology, being organized in a way that is neither completely regular nor random, as well as a characteristic topography, in which specific anatomical locations are imbued with distinctive connectivity profiles. The mechanisms that give rise to such properties remain a mystery. Here, we formulate a simple analytic model derived from neural field theory that prioritizes physical constraints on connectome architecture by assuming that connectivity is preferentially concentrated between pairs of cortical locations that facilitate the excitation of resonant geometric modes of the cortex. We show that the model outperforms existing approaches in reproducing multiple topological and topographical properties of cortical connectomes mapped at spatial scales spanning orders of magnitude in humans, chimpanzees, macaques, marmosets, and mice, as mapped with either non-invasive diffusion magnetic resonance imaging or invasive viral tract-tracing. Our findings thus point to a fundamental role of geometry in shaping the multiscale architecture of cortical connectomes that has been conserved across 90 million years of evolution.

Internal and external contexts drive distinct dynamics in shared action-encoding striatal neurons
Animals can flexibly initiate actions guided by external cues or by internal drive. Disease states often disrupt cue-driven and self-paced actions in distinct ways, underscoring separable neural mechanisms. Such differences could arise from specialized circuits dedicated to each action mode or shared neuronal populations that shift their dynamics across contexts. To distinguish between these possibilities, we developed a task in which mice performed a lever press either spontaneously or in response to a cue, enabling direct comparison of internally and externally triggered movements. Two-photon calcium imaging in dorsolateral striatum revealed subpopulations of neurons tuned to the cue, movement, or post-movement periods. One cluster was consistently active around movement regardless of context, yet population dynamics diverged prior to action. Support vector machine decoding and subspace analyses revealed distinct context and action components within the same population. Both D1- and D2-SPNs contributed to both subspaces, with D1-SPNs more active at the time of the sensory stimulus. These results show that context shapes dynamics in shared action-encoding neurons within striatal circuits, suggesting that different initiation contexts are funneled into a common action space that flexibly supports movement execution.

Common brain network dynamics capture attention fluctuations in tasks and movies
Attentional states are highly dynamic and variable, fluctuating from moment to moment and showing stark differences across contexts. To what extent does functional brain reorganization capture variability in attentional states? In the present study we utilize a time-resolved measure of functional MRI connectivity to examine and compare the extent to which univariate activity and functional networks reflect second-to-second sustained attentional fluctuations. Sustained attention was measured objectively, using auditory and visual tasks, and subjectively while participants watched and listened to narratives. Results revealed that objective measures of sustained attention to images and sounds involved common patterns of neural activity and functional interactions. Additionally, networks related to sustained attentional performance during controlled tasks also predicted fluctuations in subjective attentional engagement while participants watched movies and listened to a podcast. Generalization between experimental and everyday task contexts highlights the robustness of time-resolved functional networks for capturing dynamic fluctuations in sustained attentional states.

A groove brain-music interface for enhancing individual experience of urge to move
When we listen to music, we often feel a pleasurable urge to move to music, known as groove. While previous studies have identified musical features that contribute to the groove experience, such as syncopation and tempo, they also report individual differences in which kinds of music people experience groove. Therefore, recommending groove-eliciting music requires accounting for individual differences. In this study, we aimed to develop a groove brain-music interface (G-BMI) that generates personalized playlists to maximize each individual's groove experience, using a neurofeedback system based on in-ear EEG. Twenty-four participants listened to three high-groove and three low-groove musical excerpts and rated their "urge to move." Using these ratings and the recorded EEG, we trained two LASSO models to build the G-BMI. Model 1 predicted urge to move from acoustic features extracted with VGGish, a pretrained neural network. Model 2 classified EEG data as recorded during listening to high-groove or low-groove music. Using Model 1, we ranked 7,225 candidate songs by predicted groove and assembled one groove-augmenting and one groove-diminishing playlist. Using Models 1 and 2, we created two additional playlists that updated Model 1 and the ranking in real-time based on in-ear EEG. Participants then listened to all four playlists and rated them on items including "urge to move." The groove-augmenting playlist that incorporated EEG achieved the highest "urge to move" ratings. These findings suggest that a personalized neurofeedback system employing EEG can help maximize individual groove experience.

Methylphenidate-induced reductions in choice impulsivity are associated with alterations in cortico-striatal beta oscillations.
Impulsive choice- preferring small, immediate rewards over larger, delayed reward- is a hallmark of many psychiatric disorders. Methylphenidate, a dopamine and noradrenaline transporter inhibitor, reduces impulsivity, but the neural mechanisms underlying this effect remain unclear. Identifying a neurophysiological signature of methylphenidate action could be used clinically to predict treatment response and guide drug development. To this end, we recorded local field potentials (LFP) from 32 electrodes spanning cortico-striatal circuits in rats performing a temporal discounting task, under saline or methylphenidate treatment conditions. Methylphenidate decreased beta frequency (15-30 Hz) power and connectivity for small, immediate rewards while simultaneously increasing beta power for large, delayed rewards. Reduced connectivity in cortico-striatal networks during small rewards predicted increased preference for delayed rewards. These findings suggest that methylphenidate influences discounting behavior by modulating reward-evoked beta oscillations in cortico-striatal networks. Thus, beta oscillations may serve as a translational biomarker of treatment responsivity to monoaminergic drugs, providing a circuit-level assay to measure and predict changes in impulsive behavior.

A hardwired neural circuit for temporal difference learning
The neurotransmitter dopamine plays a major role in learning by acting as a teaching signal to update the brain's predictions about rewards. A leading theory proposes that this process is analogous to a reinforcement learning algorithm called temporal difference (TD) learning, and that dopamine acts as the error term within the TD algorithm (TD error). Although many studies have demonstrated similarities between dopamine activity and TD errors, the mechanistic basis for dopaminergic TD learning remains unknown. Here, we combined large-scale neural recordings with patterned optogenetic stimulation to examine whether and how the key steps in TD learning are accomplished by the circuitry connecting dopamine neurons and their targets. Replacing natural rewards with optogenetic stimulation of dopamine axons in the nucleus accumbens (NAc) in a classical conditioning task gradually generated TD error-like activity patterns in dopamine neurons by specifically modifying the task-related activity of NAc neurons expressing the D1 dopamine receptor (D1 neurons). In turn, patterned optogenetic stimulation of NAc D1 neurons in naive animals drove dopamine neuron spiking according to the TD error of the stimulation pattern, indicating that TD computations are hardwired into this circuit. The transformation from D1 neurons to dopamine neurons could be described by a biphasic linear filter, with a rapid positive and delayed negative phase, that effectively computes a temporal difference. This finding suggests that the time horizon over which the TD algorithm operates--the temporal discount factor--is set by the balance of the positive and negative components of the linear filter, pointing to a circuit-level mechanism for temporal discounting. These results provide a new conceptual framework for understanding how the computations and parameters governing animal learning arise from neurobiological components.

Morphological heterogeneity of human astrocytes in cerebral organoids
Astrocytes are a type of glial cell in the central nervous system responsible for modulating synaptic transmissions, tissue repair, maintaining homeostasis, and are therefore implicated in many neurological diseases. Human cortical astrocytes are more structurally complex, larger, and have unique subtypes in comparison to the commonly studied rodent cortical astrocytes. As access to human cortical tissue is sparse, cerebral organoids (COs) derived from human pluripotent stem cells have emerged as a promising in vitro model for studying the human cortex. Astrocyte subtypes unique to humans are recapitulated in COs but have not been quantitatively assessed (1, 2). In this study, we characterized human astrocytes in situ in sliced COs cultured at the air-liquid interface (ALI-COs). By 4 months of age, ALI-COs express many mature astrocyte markers and showed increasing levels of GFAP with longer culture durations. By employing immunostaining, tissue clearing, morphological reconstruction, and unsupervised clustering analysis, three major GFAP+ astrocyte subtypes were identified in ALI-COs. All subtypes exhibited greater morphological complexity than their mouse counterparts, as revealed by increased branching and longer branch extensions. However, consistent with the mid-gestation fetal stage of the ALI-COs, astrocytes did not fully recapitulate the complexity observed in adult human astrocytes, which are known to continue maturation postnatally.

No evidence of neural feature-specific pre-activation during the prediction of an upcoming stimulus
Our brains constantly make predictions about upcoming events based on prior knowledge of the environment. Although several neural mechanisms have been proposed to support this capacity, it is not yet clear how the brain makes such predictions. A compelling hypothesis is that the brain pre-activates a sensory template of a predictable stimulus before it appears. In a recent study, Demarchi et al. had participants listen to sequences of sounds with different levels of predictability. For some sequences, participants could anticipate the next sound (in regular sequences), for others not (in random sequences). Using magnetoencephalography recordings and machine-learning methods to decode sounds from brain signals, Demarchi et al. concluded that auditory predictions pre-activate tone-specific neural templates before the sound onset. In our reanalysis of their data, we demonstrate that their results can be fully explained by a bias induced by the structure of the sequences: because the most likely stimulus also happens to be physically close to the previous one, spurious higher-than-chance decoding performance arises before the sound onset. We provide general criteria to assess whether a study is affected by this confound and requires a reexamination. We conclude that there is no evidence of anticipatory predictive perception in the Demarchi et al. dataset, and that existing evidence for feature-specific pre-activation during prediction in humans remains inconclusive.

Opposite Priming Effects on Identity vs. Category Recognition Require Conscious Awareness
How conscious and unconscious priming differentially modulate object recognition at different levels of abstraction (identity vs. category) remains incompletely understood, despite extensive research. We used a binocular rivalry paradigm with Continuous Flash Suppression (CFS) to manipulate conscious awareness with image or word primes while participants performed a name-picture verification task probing identity and category recognition for faces and animal bodies. Behavioral results revealed a striking dissociation: consciously perceived primes facilitated identity recognition but impaired category recognition. This effect was most pronounced with image primes, face targets, and right visual field presentations. Under unconscious priming, no such effects were observed. To elucidate the underlying mechanism, we applied a Drift Diffusion Model, which revealed that conscious priming selectively enhanced the efficiency of evidence accumulation and introduced a pre-decisional bias for identity decisions, with no reliable change for category decisions. Our findings demonstrate a double dissociation where conscious awareness is required for priming to exert robust and opposite effects on identity and category recognition. This finding challenges the view of priming as a uniformly facilitatory process, providing a new mechanistic framework for understanding how consciousness and abstraction level interact to shape visual perception.

sEEG-Suite: An Interactive Pipeline for Semi-Automated Contact Localization and Anatomical Labeling with Brainstorm
Stereoelectroencephalography (sEEG) is a critical tool for mapping epileptic networks in patients with drug-resistant epilepsy. Accurate localization and labeling of sEEG contacts are essential for identifying the seizure onset zone (SOZ) and ensuring optimal resective surgery. Traditional methods for localizing and labeling sEEG contacts rely on manual processing, which is prone to human error and variability. To address these challenges, we developed and integrated a semi-automatic sEEG contact localization and labeling pipeline within Brainstorm, an open-source software platform for multimodal brain imaging analysis, widely adopted in the neuroscience community with over 50,000 registered users and an active online user forum. The software has been supported by the National Institute of Health (NIH) for over two decades. The pipeline presented in this paper performs three key steps: (1) import and apply rigid co-registration of post-implantation Computed Tomography (CT) or post-CT with pre-implantation Magnetic Resonance Imaging (pre-MRI), (2) post-CT image segmentation and semi-automatic detection of sEEG contacts using GARDEL, which has been integrated as a Brainstorm plugin, and (3) automatic anatomical labeling of contacts using standard and commonly used brain anatomy templates and atlases. Integrating this pipeline into Brainstorm brings the best of both worlds: GARDEL's automation and Brainstorm's multimodal data compatibility, and rich library of visualization and advanced analysis tools at both sensor and source level. This sEEG-Suite tool facilitates reproducible research, supports clinical workflows, and accelerates sEEG-based investigations of invasive brain recordings.

Discrete and sequential critical periods organise the development of task-specific sensorimotor circuits in mice
Somatosensory circuits in early life must maintain stable, task-selective pathways while behavioural repertoires undergo rapid change. How such circuits construct these behaviours under evolving functional demands has remained unclear. Here we show that sensorimotor behaviours are shaped through sequential, experience-dependent critical periods rather than a single global window of plasticity. Using transient perturbations of somatosensory input across postnatal development in mice, we identify three discrete life stages that exert lasting effects on adult behaviour. Perturbation during postnatal days 8-12 selectively increases adult sensitivity to dynamic touch. The same manipulation during days 13-17 produces persistent deficits in motor coordination. Perturbation during days 18-22 instead results in lifelong impairments in skilled locomotion. These findings reveal that somatosensory circuits undergo multiple phases of refinement, each aligned with the changing functional needs of the developing organism. This framework of sequential, task-specific critical periods offers a new model for building lifelong sensorimotor function.

Submedius Thalamus Modulates Orbitofrontal Cortex Representations During Maternal Behavior in Mice
The orbitofrontal cortex (OFC) is central to cognitive and social functions, yet its presynaptic partners remain incompletely defined. In female mice, the OFC modulates infant-directed maternal caregiving behaviors essential for offspring survival in an experience-dependent manner. Here, we identify the submedius thalamus (SMT) as a major presynaptic partner of the OFC. Trans-synaptic tracing revealed intensive inputs from both the SMT and mediodorsal thalamus (MD) to OFC layer 5 excitatory neurons. A mouse line Tnnt1-Cre enabled selective targeting of these higher-order thalamic nuclei. Axonal tracing demonstrated complementary projection patterns of SMT and MD across prefrontal regions. Microendoscopic Ca2+ imaging demonstrated pup retrieval encoding in SMT and MD, but only SMT exhibited learning-related plasticity, characterized by enhanced anticipatory responses as acquired maternal behaviors. Projection-specific chemogenetic silencing demonstrated that only SMT modulates OFC activity during pup retrieval. These findings demonstrate SMT as a previously uncharacterized thalamic hub shaping cortical representations of maternal behavior.

The energetic cost of human standing balance and gait initiation over a range of natural postures
Humans typically select movements that minimize energetic cost, a principle most clearly observed during locomotion. Whether such optimization of energy expenditure also governs standing balance remains unclear because its energetic cost has not been systematically quantified across a range of natural postures. Moreover, because standing is often the resting state from which most walking begins, the optimization of posture may also reflect the energetic demands of initiating gait. In this study, we use a combination of indirect calorimetry and musculoskeletal simulations to characterize the energetic cost of standing and gait initiation across natural standing postures and investigate whether humans optimize energy expenditure under these conditions. In Experiment 1 (N = 13), we measured metabolic cost at preferred and six different prescribed whole-body orientations. Energy expenditure was lowest at a slight anterior orientation (1.15{degrees}) and increased monotonically with whole-body angle, rising twice as fast posteriorly compared to anteriorly. This asymmetry challenges the common modeling simplification that effort is symmetric and linear or quadratic with lean angle. Furthermore, participants preferred body orientations (1.50 {+/-} 0.73{degrees}) with similar energy expenditure to the minimum-cost orientation but with significantly more postural variability, suggesting that strict postural regulation was not necessary for energy-optimal control. In Experiment 2 (N = 20), participants initiated forward and backward walking from preferred or prescribed lean orientations. Participants did not alter their standing posture before expected gait initiations in the forward or backward direction, consistent with musculoskeletal simulations showing that leaning further in the anticipated direction did not significantly improve gait initiation time or energetic costs. Together, these findings suggest that postural strategies optimize energy efficiency when permitted by the demands of movement readiness. Our study quantifies the energetic cost landscape that governs human postural control, challenges widely used inverted pendulum estimations of this cost, and offers an empirical foundation for developing more accurate simulations of posture and energy expenditure.

Ultrastructural Analysis of Human Uncinate Fasciculus with Spectral-Focusing Coherent Anti-Stokes Raman Spectroscopy
Characterizing the ultrastructure of myelin in the human brain is key to understanding the neurobiology of both health and disease. In postmortem human brain tissue, electron microscopy is often technically unfeasible due to poorer tissue quality. The uncinate fasciculus (UF) is a long-range white matter association tract that connects the anterior temporal lobe with the orbitofrontal cortex. The UF is not present in rodents yet is highly expanded in humans and non-human primates. As such, its molecular and ultrastructural properties are virtually unknown. Here, we develop and validate a novel spectral-focusing Coherent Anti-Stokes Raman Spectroscopy (sf-CARS) system coupled with a custom AxonDeepSeg segmentation model to characterize UF ultrastructure in the human postmortem brain (n=6). We provide a proof of concept of this new methodological pipeline in the UF temporal segment and observe that the mean axon diameter detected is 0.93 m {+/-} 0.54 and mean myelin thickness is 0.48 m {+/-} 0.14. We also observe that the UF axons are thicker than those in the anterior cingulate cortex white matter. We detail and validate the full methodology, including tissue fixation and sectioning, sf-CARS acquisition settings, as well as the AxonDeepSeg deep learning model parameters such that this pipeline can be utilized by others in the field.

Temporal Requirement for Stearoyl-CoA Desaturase 1 in Oligodendrocyte Development but Not Myelin Maintenance
Stearoyl-CoA desaturase 1 is a rate-limiting enzyme in monounsaturated fatty acid synthesis, which is crucial for membrane biosynthesis. Here we show an early requirement for Scd1 in oligodendroglial cells during developmental myelination. Using oligodendrocyte progenitor cell (OPC) specific conditional knockout model of Scd1, we observed a myelination delay during CNS development. Genetic ablation of OPC-specific Scd1 resulted in oligodendrocyte maturation delay and hypomyelination within forebrain white matter tracts and optic nerve. Interestingly, although expressed at high levels within the mature oligodendrocytes, Scd1 was dispensable in maintenance of oligodendrocytes and axonal myelination, as loss of mature oligodendrocyte specific Scd1 showed no effect on myelin maintenance or oligodendrocyte survival. Together, our results suggest that Scd1 function is temporally restricted to the developmental period when oligodendrocytes undergo differentiation and active myelination but becomes dispensable for maintaining established myelin.

Resting-State fMRI and the Risk of Overinterpretation: Noise, Mechanisms, and a Missing Rosetta Stone
Resting-state fMRI is widely used to describe spontaneous neural activity via correlation-based synchronization measures, yet it faces two fundamental obstacles: pervasive non-neural noise and the absence of a definitive "Rosetta Stone" linking the measured BOLD signals to underlying neural events. Although "correlation does not imply causation" has become a cliche, leveraging correlations effectively--and understanding their inferential pitfalls--remains a nuanced challenge. Correlation-based analyses are typically not able to yield causal conclusions, yet they are frequently used to underpin causal narratives in neuroscience research and especially in clinical contexts, which represent a problematic case of overinterpretation. Using causal inference reasoning, simulations and analytic methods, we address three critical questions when performing resting-state fMRI: (1) How reliable are correlation estimates for capturing cross-regional synchrony? (2) What are the consequences of inaccuracies in estimated correlations? (3) To what extent do estimated correlations reflect causal neural interactions? We identify two principal pitfalls. First, correlation estimates are systematically distorted by diverse noise sources, including variability in neurovascular coupling, which can generate spurious, suppressed, or even reversed effects. Second, graph-based approaches lack causal interpretability, meaning that even large samples and strong statistical evidence may obscure fundamental ambiguities in what the correlations represent. In light of these challenges, we advocate for three priorities: (1) cautious interpretation that avoids causal overreach, (2) multimodal validation to cross-check findings against independent measures, and (3) enhanced methodological rigor, particularly in biomarker discovery and clinical trials, to ensure that resting-state fMRI provides meaningful insights.