bioRxiv Subject Collection: Neuroscience's Journal

Resting-state alpha reactivity is reduced in Parkinson disease and associated with gait variability
Background: The extent to which the cholinergic system contributes to gait impairments in Parkinson disease (PD) remains unclear. Electroencephalography (EEG) alpha reactivity, which refers to change in alpha power over occipital electrodes upon opening the eyes, has been suggested as a marker of cholinergic function. We compared alpha reactivity between people with PD and healthy individuals and explored its potential association with gait measures. Methods: Eyes-closed and eyes-open resting-state EEG data were recorded from 20 people with idiopathic PD and 19 healthy individuals with a 64-channel EEG system. Alpha reactivity was calculated as the relative change in alpha power (8-13 Hz) over occipital electrodes from eyes-closed to eyes-open. Gait spatiotemporal measures were obtained with an electronic walkway. Results: Alpha reactivity was reduced in people with PD compared to healthy individuals (U = 105, p = 0.017); the rank-biserial correlation of 0.447 indicated a moderate effect size. When controlling for global cognition (Mini Mental State Examination), the group difference in alpha reactivity was no longer significant. Alpha reactivity associated with measures of gait variability only (rho = -0.437 to -0.532). Conclusions: Reduced alpha reactivity in PD is driven by levels of global cognition and suggests impaired cholinergic function in PD. Reduced alpha reactivity was associated with greater gait variability, indicating a role of the cholinergic system in the mechanisms underlying gait variability. Therefore, the cholinergic system may represent a target for treatments aiming to reduce gait variability and alpha reactivity should be further explored as an endpoint for clinical trials.

Multi-stream predictions in human auditory cortex during natural music listening
Real-world perception involves the prediction and integration of multiple dynamic objects and features in parallel, yet most research focuses on single-stream sequences. We present PolyRNN, a recurrent neural network designed to model predictions across multiple, simultaneous information streams, using polyphonic music as a case study. We recorded neurophysiological activity non invasively (MEG) and within the human cortex (intracranial EEG) while participants listened to real piano music. Musical expectations are encoded in P2- and P3-like components in auditory regions. Compared to a state-of-the-art generative music model, we demonstrate that parallelization better reflects the brains processing of simultaneous sequences compared to serialization. Overall, our approach enables the study of predictive processing in ecologically valid polyphonic music and provides a general framework for modeling predictions in simultaneous streams.

Using deep learning to predict internalizing problems from brain structure
Internalizing problems are associated with a wide range of adverse outcomes. While we have some understanding about risk factors (e.g., neurodevelopmental conditions), biological markers are not well understood. Here, we used deep learning to predict cross-sectional (N=14,523) and worsening longitudinal trajectories (N=10,540) of internalizing problems from measures of brain structure. A stratified cross-validation scheme was used, and performance was evaluated using the area under the receiving operating characteristic curve (AUC). The cross-sectional model performed well across the sample, reaching an AUC of 0.80 [95% CI: 0.71, 0.88]. For the longitudinal model, while performance was sub-optimal for predicting worsening trajectories in a sample of the general population (AUC=0.66 [0.65, 0.67]), good performance was reached across individuals with a neurodevelopmental condition (AUC=0.73 [0.70, 0.76]). Deep learning with features of brain structure is a promising avenue for biomarkers of internalizing problems, particularly for individuals who have a higher likelihood of experiencing difficulties.

Modelling of brain dynamics reveals reduced switching between brain states in insomnia disorder - a resting state fMRI study
Insomnia disorder is the most common sleep disorder, and neuroimaging research indicates that it is related to dysfunction in large-scale brain networks. Recently developed methods have enabled the investigation of the dynamic aspects of brain activity varying over time. In the present study, we used a novel data-driven approach to evaluate time-varying brain activity in adults with insomnia disorder compared to matched controls with no sleep problems. We acquired ten minutes resting state functional magnetic resonance images and T1-weighed images in all participants. We used Hidden Markov modelling for a data-driven definition of dynamic changes in whole-brain activity. The results showed that insomnia disorder is characterised by reduced switching rates between brain states. In line with the reduced switching, the HMM analyses suggested reduced prevalence of two whole-brain states - the default mode network and a fronto-parietal network - and an increase in just one brain state - a global activation state - in insomnia patients compared to controls. The findings suggest that insomnia disorder is characterised by less flexible transitions between brain states at wakeful rest, and thus highlight the importance of evaluating the spatiotemporal dynamics of brain activity to advance the understanding of the neural underpinnings of insomnia disorder.

Parkinson's Disease affects the contextual control, but not the expression, of a rapid visuomotor response that initiates visually-guided reaching: Evidence for multiple, interacting motor pathways and implications for motor symptoms in Parkinson's Dise
Despite significant deficits in voluntary motor control, patients with Parkinson's disease (PD) can generate reflexive or stimulus-driven movements. How are such spared capabilities realized? Here, we recorded upper limb muscle activity in patients with PD and age-matched healthy controls (HCs) as they reached either toward or away from a visual stimulus. The task promoted express visuomotor responses (EVRs), which are brief bursts of muscle recruitment time-locked (<100 ms) to stimulus presentation that are thought to originate from the midbrain superior colliculus. Across two experiments, we observed a remarkable sparing of the latency and magnitude of EVRs in patients with PD, but a decreased ability for patients with PD to contextually modulate the EVR depending on trial instruction. EVR Magnitudes were strikingly strongly correlated with PD Reaction times and Error rates, despite compromised levels of electromyographic (EMG) recruitment in subsequent phases of muscle activity, which predicted lower Peak velocities. Our results are consistent with a differential influence of PD on parallel-but-interacting subcortical and cortical pathways that converge onto brainstem and spinal circuits during reaching. This differential influence is discriminable even within a single trial in the selective sparing of stimulus-aligned but not movement-aligned muscle recruitment, and has implications for our understanding of the motor and cognitive deficits seen in PD.

Rostral ventromedial medulla (RVM) projects to the lateral hypothalamic area (LHA) to drive aversion and anxiety
Neurons in the LHA are critical drivers of behavioral and physiological responses to acute and chronic stress. However, the roles of the specific pre-synaptic inputs to the LHA in driving stress and resultant physiological effects are yet to be fully understood. Here, taking advantage of mouse viral genetics, rabies tracing, optogenetics, chemogenetics, and fiber photometry, we show that the excitatory projections from the RVM to LHA drive stress-induced anxiety. This is a surprising finding since, traditionally, RVM has been studied in the context of opioidergic pain modulation through its inhibitory projections to the spinal cord. We find that the LHA neurons receiving inputs from the RVM, when activated, do not alter the nociceptive thresholds yet are sufficient to drive anxiety-like behaviors. These LHA neurons are recruited by acute restraint, which is known to cause stress. On the other hand, the LHA-projecting RVM neurons are responsive to both noxious thermal stimuli and acute restraint, promoting stress-induced anxiety, yet with no effect on pain thresholds. Together, we found an ascending neural pathway between RVM and LHA that mediates stress-induced anxiety.

Acute restraint stress and pain modulation depend on the interaction between the periaqueductal gray and the lateral septum
Acute restraint stress is known to cause analgesia in humans and laboratory animals, but the mechanisms are unknown. Recently, we have shown that a multi-nodal circuitry between the dorsal lateral septum (dLS)-lateral hypothalamic area (LHA)-rostral ventromedial medulla (RVM) plays an instructive role in restraint stress-induced analgesia. We found that the LS neurons are activated when mice struggle to escape the restraint, and we wondered about the origin of the escape signals. Hence, we performed retrograde viral labeling from the LS and found that the ventrolateral periaqueductal gray (vlPAG), a known anatomical substrate for escape behaviors, provides inputs to the LS. Through anatomical, behavioral, and in-vivo fiber photometry, we show that the PAG and LS neurons are synaptically connected; activation of either PAG or the post-synaptic LS neurons is sufficient to cause analgesia and sufficiently cause hyperalgesia. Moreover, we found that the LS neurons that receive inputs from PAG send axonal projections to the LHA. Together, we found that the vlPAG neurons encoding nociceptive and escape behaviors provide synaptic inputs to the dLS-LHA-RVM circuitry to mediate acute restraint stress-induced analgesia.

Gamma Synchrony Mediates Figure-Ground Perception
Gamma synchrony is ubiquitous in visual cortex, but whether it contributes to perceptual grouping remains contentious based on observations that gamma frequency is not consistent across stimulus features and that gamma synchrony depends on distances between image elements. These stimulus dependencies have been argued to render synchrony among neural assemblies encoding components of the same object difficult. Alternatively, these dependencies may shape synchrony in meaningful ways. Using the theory of weakly coupled oscillators (TWCO), we demonstrate that stimulus dependence is crucial for gamma's role in perception. Synchronization among coupled oscillators depends on frequency dissimilarity and coupling strength, which in early visual cortex relate to local feature dissimilarity and physical distance, respectively. We manipulated these factors in a texture segregation experiment wherein human observers identified the orientation of a figure defined by reduced contrast heterogeneity compared to the background. Human performance followed TWCO predictions both qualitatively and quantitatively, as formalized in a computational model. Moreover, we found that when enriched with a Hebbian learning rule, our model also predicted human learning effects. Increases in gamma synchrony due to perceptual learning predicted improvements in behavioral performance across sessions. This suggests that the stimulus-dependence of gamma synchrony is adaptable to the statistics of visual experiences, providing a viable neural grouping mechanism that can improve with visual experience. Together our results highlight the functional role of gamma synchrony in visual scene segmentation and provide a mechanistic explanation for its stimulus-dependent variability.

Enhanced Brain-Heart Connectivity as a Precursor of Reduced State Anxiety After Therapeutic Virtual Reality Immersion
State anxiety involves transient feelings of tension and nervousness in response to threats, which can escalate into anxiety disorders if persistent. Despite treatments, 30%-50% of individuals show limited improvement, and neurophysiological mechanisms of treatment responsiveness remain unclear, requiring the development of objective biomarkers. In this study, we monitored multimodal electrophysiological parameters: heart rate variability (high-frequency, low-frequency, LF/HF ratio), EEG beta and alpha relative power, and brain-to-heart connectivity in participants with real-life state anxiety. Participants underwent a therapeutic intervention combining virtual-reality immersion, hypnotic script, and a breath control exercise. Real-life state anxiety was captured using the STAI-Y1 scale before and after the intervention. We observed reduced anxiety immediately after the intervention in 16 out of 27 participants. While all participants, independently of their STAI-Y1 score, showed increased HRV low frequency power, only treatment-responders displayed increased overall autonomic tone (high and low frequency HRV), increased midline beta power and brain-to-heart connectivity. Notably, the LF/HF ratio showed a significant linear relationship with anxiety reduction, with higher ratios linked to greater therapeutic response. These findings suggest that increased cognitive regulation of brain-to-heart connectivity could serve as a biomarker for therapeutic efficacy, with elevated midline beta power facilitating improved cardiac tone in responders.

NaDyNet: A Toolbox for Dynamic Network Analysis of Naturalistic Stimuli
Experiments with naturalistic stimuli (e.g., listening to stories or watching movies) are emerging paradigms in brain function research. The content of naturalistic stimuli is rich and continuous. The fMRI signals of naturalistic stimuli are complex and include different components. A major challenge is isolate the stimuli-induced signals while simultaneously tracking the brain's responses to these stimuli in real-time. To this end, we have developed a user-friendly graphical interface toolbox called NaDyNet (Naturalistic Dynamic Network Toolbox), which integrates existing dynamic brain network analysis methods and their enhanced versions. The main features of NaDyNet are: 1) extracting signals of interest from naturalistic fMRI signals; 2) incorporating six commonly used dynamic analysis methods and three static analysis methods; 3) enhanced versions of these dynamic methods by adopting inter-subject analysis to eliminate the effects of non-interest signals; 4) performing K-means clustering analysis to identify temporally reoccurring states along with their temporal and spatial attributes. We then introduced the rationale for incorporating inter-subject analysis to improve existing dynamic brain network analysis methods, and presented numerous examples. We also summarized research progress in comparing methodological efficacy, offered our recommendations for method selection in dynamic network analysis, and discussed the limitations of current approaches and directions for future research. We hope that this open source toolbox will promote the development of naturalistic neuroscience. The toolbox is available at https://github.com/yuanbinke/Naturalistic-Dynamic-Network-Toolbox.

Characterizing neuronal and population responses to electrical stimulation in the human hippocampo-cortical network
Direct electrical stimulation (DES) can advance our understanding of the intricate dynamics of the human hippocampo-neocortical network, which underlies complex cognitive processes such as spatial cognition and memory. This knowledge can help Neurotechnology to more effectively interface with this network and improve its functions. Here, we investigated the effects of DES in seven epilepsy patients under medical supervision recording single neuron activity alongside local field potentials to investigate neural responses to single pulses at different levels of granularity. Our results demonstrate that (i) single neurons respond to local electrical stimulation with a stereotypical pattern of short-lived increased excitation, followed by sustained inhibition, (ii) that input into the hippocampus from neocortex takes ~100 ms, and (iii) that output from the hippocampus to the neocortex is gated by theta phase. These results are vital to inform the optimal choice of parameters for future electrical stimulation studies targeting the human memory system.

Fate (or state) of CA2 neurons in a mineralocorticoid receptor knockout.
Hippocampal area CA2 has emerged as a functionally and molecularly distinct part of the hippocampus and is necessary for several types of social behavior, including social aggression. As part of the unique molecular profile of both mouse and human CA2, the mineralocorticoid receptor (MR; Nr3c2) appears to play a critical role in controlling CA2 neuron cellular and synaptic properties. To better understand the fate (or state) of the neurons resulting from MR conditional knockout, we used a spatial transcriptomics approach. We found that without MRs, (CA2) neurons acquire a CA1-like molecular phenotype. Additionally, we found that neurons in this area appear to have a cell size and density more like that in CA1. These finding support the idea that MRs control at least CA2s state during development, resulting in a CA1-like fate.

4-Methyllumbiferone (4-MU) exerts a neuroprotective effect against cerebral ischemia/reperfusion injury by ameliorating learning and memory impairments
Background: Stroke is the sixth leading cause of death and lifelong disability for millions of people in the United States. Cerebral ischemia lead to in oxidative stress, excitotoxicity, inflammation, apoptosis and also, impairments in memory and learning occur in majority of subjects with ischemic stroke. Lack of definitive treatment has sparked extensive research into novel therapeutic strategies, including the use of 4-methylumbelliferone (4-MU), a coumarin derivative with potential neuroprotective properties. The present study examines impact of 4-MU on reducing cerebral ischemia-reperfusion (I/R) injury and learning and memory impairments in male Wistar rats. Method: The animal was exposed to middle cerebral artery occlusion (MCAO) and were cured with one dose of 4-MU (at dosage of 25 mg/kg) dissolved in DMSO 0.9%. Automated shuttle box test was employed to evaluate learning and memory impairments. Western blot assay, TTC staining and Nissl staining was used to measure protein expression, infarct volume, and cell death, respectively. Results: Results showed treatment with 4-MU reduced infarct volume and improved learning and memory impairments by down-regulation of HAS1 and HAS2. 4-MU has been shown to modulate the release of pro-inflammatory cytokines include TNF- and IL-1{beta}, as well as anti-inflammatory markers like IL-10 and also reduced oxidative stress markers in the brain. Conclusion: Generally, neuroprotective effects of 4-MU against cerebral I/R injury can be attributed to down-regulation of HAS1 and 2. Keywords: Ischemic stroke; MCAO; Learning and memory impairments; 4-methylumbelliferone; Neural death; Hyaluronan synthases

In vivo modulation of network activity drives the nanoscale reorganisation of axo-axonic synapses at the axon initial segment
Chemical synapses control their strength through the nanoscale clustering of postsynaptic receptors into sub-synaptic domains (SSDs). Despite their importance in synapse function, the properties and plasticity of these domains are not well understood in vivo, particularly in inhibitory synapses. We used direct Stochastic Optical Resolution Microscopy (dSTORM) to show that Gephyrin, the main inhibitory receptor scaffold protein, is organised into SSDs in vivo, with distinct arrangements depending on their sub-cellular location and presynaptic partner. Furthermore, chronic chemogenetic increases in cortical activity caused a reduction in Gephyrin SSD volume specifically in axo-axonic, but not axo-dendritic, synapses. Functionally, this resulted in a weakening of axo-axonic contacts. We show that the nanoscale arrangement of synapses in the brain is plastic and used to fine-tune synaptic gain in vivo.

Pleasant smells: a privileged gateway to soothing autonomic responses and improving brain-body rhythm coupling
Aromatherapy commonly uses odors to improve well-being through their evocation of positive emotions. Although knowledge in this area is often very empirical, the olfactory stimulus has different properties which, taken together, could explain why it can relax. First, olfactory sense have a direct access to the limbic system, without thalamic relay processing, which confers it a strong emotional valence. Second, when appreciated, odors can slow down breathing and cardiac rates. Third, when slow and deep, breathing can entrain brain activity, due to the mechano-sensitivity of olfactory receptors to airflows. We hypothesized that, thanks to these properties, pleasant odors could enhance the subjective feeling of relaxation, slow down body rhythms, and facilitate entrainment of brain activity by respiration. Comparing the effects of a personally pleasant odor to a personally pleasant music on psychological, physiological and neuronal responses, we showed a tendency for both odors and music to enhance subjective relaxation. However, only pleasant odors were able to 1) decrease heart rate while increasing its variability, and 2) decrease respiratory rate while enhancing the respiratory drive of brain activities, regardless of the music tempo. Overall, we demonstrated that the positive emotion evoked by a personally pleasant smell is sufficient to evoke an olfactomotor response, which, by slowing breathing, synchronizes respiration, fluctuations of heart rate and brain activity.

Dissecting the differential role of C-terminal truncations in the regulation of aSyn pathology formation and the biogenesis of Lewy bodies
Alpha-synuclein (aSyn) post-translational modifications (PTMs), particularly phosphorylation at serine 129 and C-terminal truncations, are highly enriched in Lewy bodies (LBs), Lewy neurites, and other types of aSyn pathological aggregates in the brain of patients with Parkinson's disease (PD) and other synucleinopathies. However, our knowledge about the precise role of PTMs in regulating the different stages of pathology formation, neurodegeneration, and aSyn pathology spreading remains incomplete. In this work, we applied a systematic approach to address this knowledge gap with an emphasis on mapping and elucidating the role of post-fibrillization C-terminal aSyn truncations in regulating the uptake, processing, seeding activity, and formation of LB-like inclusions and maturations in a well-established neuronal seeding model that recapitulates all the stages leading to LB formation and neurodegeneration. Our work shows that C-terminal cleavage of aSyn fibrils at multiple sites is a conserved process that occurs rapidly after and during the formation of intracellular LB-like aSyn inclusions in all neuronal seeding models. Interestingly, blocking the cleavage of internalized fibrils does not influence their seeding activity, whereas inhibiting the enzymes that regulate the cleavage of newly formed fibrils (e.g., calpains 1 and 2) significantly alters the formation of LB-like inclusions. We also show that C-terminal truncations, in combination with other PTMs, play a crucial role in regulating the interactome and remodeling of newly formed aSyn fibrils, including their shortening, lateral association, and packing during LB formation and maturation. Altogether, our results demonstrate that post-fibrillization C-terminal truncations have a differential role at different stages of aSyn aggregation and pathology formation. These insights, combined with the abundance of truncated aSyn species in LBs, have significant implications in understanding aSyn pathological diversity and developing therapeutic strategies targeting the C-terminus of aSyn or its proteolytic processing.

Nonequilibrium dynamics elicited as the origin of perturbative complexity
Assessing the level of consciousness someone is in, is not a trivial question and physicians have to rely on behavioural evaluations instead of quantifiable metrics. Many studies have empirically investigated measures related to the complexity elicited after the brain is stimulated to quantify and assess the level of consciousness across different states. Here we hypothesized that the level of non-equilibrium dynamics of the unperturbed brain already contains the information needed to know how the system will react to an external stimulus. We created personalized whole-brain models fitted to resting state fMRI data recorded in participants in different states of reduced consciousness (such as deep sleep and disorders of consciousness) to infer the effective connections underlying their brain dynamics. We then measured the out-of-equilibrium nature of the unperturbed brain by evaluating the level of asymmetry of the inferred connectivity, the time irreversibility in each model and compared this with the elicited complexity generated after in silico perturbations. Crucially, we found that states of reduced consciousness had a lower level of asymmetry in their effective connectivities compared to control subjects, as well as a lower level of irreversibility in their simulated dynamics, and a lower complexity. We demonstrated that the asymmetry in the underlying connections drives the nonequilibrium state of the system and in turn the differences in complexity as a response to the external stimuli.

Intestinal Dysbiosis Alters Acute Seizure Burden and Antiseizure Medicine Activity in the Theiler's Virus Model of Encephalitis
Objective: Brain infection with Theiler's virus (TMEV) in C57BL/6J mice produces an etiologically relevant model of acquired seizures. Dietary changes can modify acute seizure presentation following TMEV brain infection and influence intestinal microbiome diversity and composition. Intestinal dysbiosis may thus similarly affect seizure burden and antiseizure medicine (ASM) activity in this model, independent of pharmacokinetic effects. We thus sought to define the influence of antibiotic (ABX)-induced gut dysbiosis on acute seizure presentation, anticonvulsant activity of carbamazepine (CBZ), and CBZ pharmacokinetics with TMEV infection. Methods: Male C57BL/6J mice (4-5 weeks) received oral (p.o.) ABX or saline (SAL) once daily beginning on arrival through Day 7 post-TMEV infection (p.i.). Mice were infected with TMEV or PBS on Day 0. Mice received intraperitoneal (i.p.; 20 mg/kg) CBZ or vehicle (VEH) twice daily Days 3-7 p.i. and were assessed for handling-induced seizures 30 min after treatment. Plasma was collected on Day 7 p.i. at 15 and 60 min post-CBZ administration for bioanalysis. Results: TMEV infection induced acute seizures, but ABX-induced gut dysbiosis altered seizure presentation. There were 75% SAL-VEH, 35% SAL-CBZ, 35% ABX-VEH, and 72% ABX-CBZ mice with seizures during the 7-day monitoring period. There was a significant pretreatment x ASM interaction (p=0.0001), with differences in seizure burden in SAL- versus ABX-pretreated mice (p=0.004). CBZ significantly increased latency to seizure presentation; an effect absent in ABX-CBZ mice. Plasma CBZ concentrations did not differ between SAL and ABX pretreatment groups, suggesting that ABX did not influence CBZ pharmacokinetics. Significance: ABX-induced gut dysbiosis markedly altered acute disease trajectory with TMEV-induced encephalitis, reflecting a novel contribution of the gut microbiome to seizure presentation. ABX-induced gut dysbiosis also significantly changed acute seizure control by CBZ, but did not influence plasma CBZ concentrations. The gut-brain axis is thus an under-recognized contributor to TMEV infection-induced seizures, ASM activity, and disease burden.

Lower-limb express visuomotor responses are spared in Parkinson's Disease during step initiation from a stable position
While motor impairments have been extensively studied in Parkinson's Disease, rapid visuomotor transformations for flexible interaction with the environment have received surprisingly little attention. In recent years, such rapid visuomotor transformations have been studied in the form of express visuomotor responses (EVRs), which are goal-directed bursts of muscle activity that are thought to originate from superior colliculus, reaching the periphery via the tecto-reticulospinal pathway. Here, we examined EVRs in the lower limbs during goal-directed step initiation in 20 people with Parkison's Disease (PwPD) and 20 age-matched healthy control participants (HC). As lower-limb EVRs in the young have been shown to interact with postural control - which is often affected in PwPD - we manipulated the postural demands by varying initial stance width and target location. In the low postural demand condition, EVRs were robustly present in both the PwPD (17/20) and HC (16/20) group. However, when postural demands were high, EVRs were largely absent in both groups and, instead, strong anticipatory postural adjustments (APAs) were required prior to foot off. EVR magnitudes were, on average, stronger in PwPD compared to HC, but they decreased with increasing disease severity, suggesting that the EVR network may become compromised or down-regulated in later stages of the disease. While APA magnitudes were smaller in PwPD compared to HC, subsequent stepping performance (step reaction time, duration, size, velocity) was remarkably similar between the two groups. We suggest that the EVR network may be upregulated in the early stages of Parkinson's disease in order to compensate for some of the emerging motor deficits experienced in daily life.

An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation
Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.

Integrating Ideal Bayesian Searcher and Neural Networks Models for Eye Movement Prediction in a Hybrid Search Task
Visual search, where observers search for a specific item, is a crucial aspect of daily human interaction with the visual environment. Hybrid search extends this by requiring observers to search for any item from a given set of objects. While there are models proficient at simulating human eye movement in visual search tasks within natural scenes, none are able to do so in Hybrid search tasks within similar environments. In this work, we present an enhanced version of the neural network Entropy Limit Minimization (nnELM) model, which is based on a Bayesian framework and decision theory. We also present the Hybrid Search Eye Movements (HSEM) Dataset, comprising several thousands of human eye movements during hybrid search tasks in natural scenes. A key challenge in Hybrid search, absent in visual search, is that participants might search for different objects at different time points. To address this, we developed a strategy based on the posterior probability distribution generated after each fixation. By adjusting the model's peripheral visibility, we made early search stages more efficient, aligning it closer to human behaviour. Additionally, limiting the model's memory capacity reduced its success in longer searches, mirroring human performance. To validate these improvements, we compared our model against participants from the HSEM dataset and against existing models in a visual search benchmark. Altogether, the new nnELM model not only successfully explains Hybrid search tasks, but also closely replicates human behaviour in natural scenes. This work advances our understanding of complex processes underlying visual and Hybrid search while maintaining model interpretability.

Deep learning super-resolution of paediatric ultra-low-field MRI without paired high-field scans
Brain magnetic resonance imaging (MRI) is essential for diagnosis and neurodevelopmental research, but the high cost and infrastructure demands of high-field MRI scanners restrict their use to high-income settings. To address this, more affordable and energy-efficient ultra-low-field MRI scanners have been developed. However, the reduced resolution and signal-to-noise ratio of the resulting scans limit their clinical utility, motivating the development of super-resolution techniques. The current state-of-the-art super-resolution methods require either three anisotropic ultra-low-field scans acquired at different orientations (axial, coronal, sagittal) to reconstruct a higher-resolution image using multi-resolution registration (MRR), or the training of deep learning super-resolution models using paired ultra-low- and high-field scans. Since acquiring three high-quality ultra-low-field scans is not always feasible, and paired high-field data may not be available for the target population, this study explores the efficacy of using a deep learning model, the 3D UNet, to generate higher-resolution brain scans from just one ultra-low-field scan. The model was trained to receive a single ultra-low-field brain scan of 6-month-old infants and produce a scan of MRR quality. Results showed a significant improvement in the quality of output scans compared to input scans, including increased image quality metrics, stronger correlations in tissue volume estimates across participants, and greater Dice overlap of the underlying tissue segmentations to those of target scans. The study demonstrates that the 3D UNet effectively enhances the resolution of ultra-low-field infant MRI scans. Generating higher-resolution brain scans from single ultra-low-field scans, without needing paired high-field data, reduces scanning time and supports wider MRI use in low- and middle-income countries. Additionally, this approach allows for easier model training on a site- and population-specific basis, enhancing adaptability in diverse settings.

Computational constraints underlying theemergence of functional domains in thetopological map of Macaque V4
V4, an intermediate visual area in the ventral visual stream of primates, is known to contain neurons tuned to color, complex local patterns, shape, and texture. Neurons with similar visual attribute preferences are closely positioned on the cortical surface, forming a topological map. Recent studies based on multi-electrode arrays and calcium imaging revealed the macaque V4 has neuronal columns tuned to specific natural image features, and these columns are clustered into various functional domains. There are domains tuned to attributes generally associated with object surface properties such as texture or color, as well as domains associated with the shape and form of object boundaries reminiscent of the blobs and inter-blobs in the primary visual cortex. Here, we explored the computational constraints underlying the development of the V4 topological map. We found that the map learned based on self-organizing principles constrained by neuronal column's tuning and retinotopy position can account for many characteristics of the observed V4 map, including the interwoven organization of texture and shape processing clusters. These anatomical clustering, with the implied local recurrent connectivity, might facilitate a modular parallel processing of surfaces and boundaries of objects along the ventral visual system.

Red means heavy: Action intentions reactivate representations of task-relevant cognitive cues
Recent research shows that the intention to act on an object alters its neural representation in ways as afforded by underlying sensorimotor processes. For example, the intention to grasp and pick up an object results in representations of the objects weight. But these representations become grasp-specific only immediately before object lift if weight information is relayed through object material. This feature triggers earlier representations regardless of intention probably because material-weight contingencies are overlearned. By contrast, recently learned weight cues should be recalled deliberately during grasp planning resulting in early grasp-specific representations. Here, we examined how action intentions affect the representation of newly acquired colour-weight contingencies. We recorded electroencephalography while human participants grasped or reached for objects that varied in shape and density as indicated by their colour. Multivariate analyses revealed a grasp-specific reactivation of colour during planning that was mirrored in beta band. This suggests that task-relevancy influences the representation of colour such that previously encoded colour-weight contingencies may be reactivated as required for grasping, mediated top-down via working memory. Grasp-specific representations of shape and colour were also present in theta band, perhaps reflecting attentional activity. These results provide novel insights into the interplay between cognition and motor planning processes.

Dual-acting gene therapy targeting HIF1A and HIF2A by RNA interference mitigates retinal degeneration in two mouse models of AMD
BackgroundThe combination of reduced choroidal blood flow, increased Bruchs membrane (BM) thickness and drusen formation leads to reduced oxygenation of the outer retina in the aging eye and contributes to the pathology of age-related macular degeneration (AMD). This implies that the molecular response of photoreceptors to hypoxia, with chronic activation of hypoxia-inducible factors (HIFs) at its core, impacts disease development and progression.

MethodsWe used the shRNAmiR system to develop a dual-acting gene therapy based on a single AAV that reduces activity of HIF1 in photoreceptors and HIF2 in the retinal pigment epithelium (RPE). The virus was injected subretinally in two models of pseudo (Rod{Delta}Vhl) or true (RPE{Delta}Vegfa) hypoxia-related retinal degeneration and treated mice were followed for up to 61 weeks post-injection. Light microscopy, fluorescence funduscopy, and optical coherence tomography were used to quantify the therapeutic effect. In situ hybridization, real-time PCR, Western blotting, immunofluorescence, and flatmounts of the retina, RPE, and choroid were used to investigate the disease models and therapeutic effects of the treatment.

ResultsNo adverse effects were noted after subretinal injection of the AAV expressing shRNAs targeting Hif1a in photoreceptors and Hif2a in the RPE. The virus preserved ONL thickness and photoreceptor segment length in Rod{Delta}Vhl mouse retinas up to 22 weeks and in RPE{Delta}Vegfa mice up to 61 weeks after injection demonstrating a long-lasting rescue of the phenotype. The dual-acting virus showed significantly higher efficacy than single-acting viruses targeting solely Hif1a in photoreceptors or Hif2a in the RPE.

ConclusionThis study introduces a novel dual-acting AAV vector that effectively downregulates two different genes in two specific cell types, offering a promising therapeutic strategy for complex diseases such as AMD. By simultaneously targeting Hif1a in photoreceptors and Hif2a in the retinal pigment epithelium, this gene-agnostic therapy shows significant potential to protect retinal tissues from chronic hypoxic conditions. By targeting a common and conserved disease pathway in AMD, it is applicable to a wide range of patients.

Semi-automated navigation for efficient targeting of electron tomography to regions of interest in volume correlative light and electron microscopy
Electron microscopy is essential for the quantitative study of synaptic ultrastructure. At present, the correlation of functional and structural properties of the same synapse is extremely challenging. We introduce a novel integrated workflow designed to simplify sample navigation across spatial scales, allowing the identification of individual synapses from optical microscopy mouse brain image stacks that can be targeted for analysis using electron tomography imaging. We developed a software which has a function to register multimodal images using a novel segmentation-based image registration algorithm as well as a function to visualize all the registration results. Using our newly designed software we streamline mapping of high-resolution optical imaging onto reference maps using blood vessels as endogenous fiducial marks. Further we demonstrate significant improvements on the ultramicrotomy stage of volume Correlative Light and Electron Microscopy (vCLEM) workflows, providing real time guidance to targeted trimming to match previously acquired Regions Of Interest (ROIs), and reliable estimates of cutting depth relative to ROI, based on fluorescence imaging of TEM ready ultrathin sections. Using this workflow, we successfully targeted TEM tomography to the proximal axonal region containing the Axon Initial Segment identified using fluorescent light microscopy.

Anatomical and functional examination of superior colliculus projections to the inferior olivary neurons in mice
The Inferior olive (IO) is an important region for motor learning and movement coordination. Its climbing fibers projection to the Purkinje neurons in the cerebellar cortex is a sole source of the complex spikes, characterized by a strong depolarization in the Purkinje neuron's dendrites. To generate spikes, the IO relies on inputs from various regions of the brain, including the superior colliculus (SC), a midbrain structure known for its role in orienting behaviors. This study investigates SC projections to the IO using viral tracers, calcium imaging, and optogenetic stimulation. We reveal that, in addition to the known projections to the medial accessory olive (MAO), SC axons also project to the ventral principal olive (PO). Despite projecting to different parts of the IO, SC-MAO and SC-PO neurons are intermingled within the lateral part of the SC with similar gross morphology. We show that SC axons terminate on both dendritic shafts and spines of IO neurons, potentially influencing not only spiking probability, but also the network synchronization mediated by gap junctions coupling on the dendritic spines. As a proof of principle, we recorded the in-vivo activity of neurons in ventral PO using calcium indicators and show that optogenetic activation of SC inputs can evoke spiking and enhance synchronization in IO neurons.

Early changes in the properties of CA3 engram cells explored with a novel viral tool
Forming new memories after a one-time experience requires initial encoding then consolidation over time. During learning, multimodal information converges onto the hippocampus, activating sparse neuronal assemblies which are thought to form a memory representation through concerted activity and synaptic interconnectivity. In this work, we use a novel tool for fast-labeling of engram neurons (FLEN). FLEN is based on c-Fos activity-dependent transient expression of a destabilized fluorescent marker ZsGreen1 rapidly after one-trial learning. With FLEN, we explore the electrophysiological properties of c-Fos activated CA3 pyramidal neurons a few hours following one-trial learning of an episodic-like memory. In parallel, we employ the Robust Activity Marker (RAM) system, which provides activity-dependent labelling 24 hours following a novel experience. Comparing FLEN+ and RAM+ neurons allows to characterize how the properties of neuronal assemblies evolve during an initial phase of consolidation. Whereas no difference was observed in the excitability of FLEN+ vs. FLEN-neurons, RAM+ neurons were more excitable than RAM-neurons. This suggests that CA3 pyramidal neurons recruited in an engram progressively acquire increased excitability as compared to neurons which were not activated by the one-trial contextual memory task. In contrast, FLEN+ CA3 neurons show an increased number of excitatory inputs. Overall, with the FLEN strategy, we can show that both the intrinsic excitability and the synaptic properties of CA3 pyramidal neurons undergo progressive plastic changes over the first day following a one-trial memory task.

Inferring global exponents in subsampled neural systems
In systems displaying an activity charaterized by avalanches, critical exponents may give informations on the mechanisms underlying the observed behaviour or on the topology of the connections. However, when only a small fraction of the units composing the system are observed and sampled, the measured exponents may differ significantly from the true ones. We show that some of the exponents, namely the ones governing the power spectrum and the detrended fluctuation analysis of the system activity, are more robust and are unaffected in some intervals of frequencies by the subsampling. They may be used therefore to extract in a simple and unbiased way some of the exponents of the unobserved full system.

Gray matter abnormalities in sight deprivation and sight restoration
Blindness provides a unique model for investigating brain plasticity in response to sensory deprivation. While structural changes in both gray and white matter have been widely documented, particularly in cases of early or congenital visual deprivation, gray matter studies have traditionally focused on cortical thickness, often finding cortical thickening in posterior regions. However, other aspects of gray matter integrity, such as cortical myelin content, remain underexplored. In this study, we examined the effects of visual deprivation on cortical structure in a cohort of congenitally blind individuals who received eye surgery during adolescence, expanding beyond conventional measures to include cortical thickness, curvature, and T1-weighted signal intensity. This multi-faceted approach offers a more comprehensive view of cortical adaptations to congenital sensory deprivation. While blindness offers valuable insights into sensory-driven brain plasticity, an intriguing and unresolved question is whether structural plasticity reverses after sight restoration, enabling typical visual processing circuits to develop despite the initial period of deprivation. To address this, we assessed the effect of sight-recovering eye surgery on gray matter changes. Critically, individuals in this cohort received surgery after the closure of the sensitive period for visual development. We did not find evidence of gray matter changes after surgery. However, in a previous study conducted on the same cohort, we reported that notable plasticity in white matter emerged in this same population. These results suggest that white matter alterations, rather than gray matter changes, may potentially serve as a biomarker of structural plasticity following sight restoration, even beyond the sensitive developmental window.

Kernel-based LFP estimation in detailed large-scale spiking network model of mouse visual cortex
Simulations of large-scale neural activity are powerful tools for investigating neural networks. Calculating measurable brain signals like local field potentials (LFPs) bridges the gap between model predictions and experimental observations. However, accurately simulating LFPs from large-scale models has traditionally required highly detailed multicompartmental neuron models, posing significant computational challenges. Here, we demonstrate that a kernel-based method can efficiently and accurately estimate LFPs in a state-of-the-art multicompartmental model of the mouse primary visual cortex (V1). Beyond its computational efficiency, the kernel method aids analysis by disentangling contributions of individual neuronal populations to the LFP. Using this approach, we found that LFPs in the V1 model were dominated by external synaptic inputs, with local synaptic activity playing a minimal role. Our findings establish the kernel method as a powerful tool for LFP estimation in large-scale network models and for uncovering the synaptic mechanisms underlying brain signals.