bioRxiv Subject Collection: Neuroscience's Journal

Elucidating an implicit-statistical learning brain network: Coordinate-based meta-analyses and functional connectivity profiles of artificial grammar learning in healthy adults
Language rehabilitation centers on modifying its use through experience-based neuroplasticity. Implicit statistical learning of language is essential to its acquisition and likely its rehabilitation following brain injury, but its corresponding brain networks remain elusive. Coordinate-based meta-analyses were conducted to identify common and distinct brain activity across 25 studies coded for meta-data and experimental contrasts (grammatical or non-grammatical). The resultant brain regions served as seeds for profiling functional connectivity in large task-independent and task-dependent data sets. Hierarchical clustering of these profiles grouped brain regions into three subnetworks associated with grammatical/non-grammatical processes. Functional decoding clarified the mental operations associated with those subnetworks. Results support a left-dominant language sub-network and two cognitive control networks as scaffolds for grammar rule identification, maintenance, and application in healthy adults. These data suggest that cognitive control is necessary to track regularities across stimuli and imperative for rule identification and application of grammar. Future empirical investigation of these brain networks in language learning in individuals with brain injury will clarify their prognostic role in language recovery.

Activity-dependent expression of Fezf2 regulates inhibitory synapse formation in pyramidal cells
The function of the cerebral cortex relies on the precise integration of diverse neuronal populations during development, which is regulated by dynamic fine-tuning mechanisms maintaining the balance between excitation and inhibition. For instance, the development of excitatory pyramidal cells is simultaneously and precisely counterbalanced by the formation of inhibitory synapses during the maturation of neuronal circuits. Although this process relies on neuronal activity, different types of pyramidal cells likely respond to changes in activity through the expression of cell-specific genes. However, the molecular programs underlying the activity-dependent recruitment of inhibition by distinct types of pyramidal cells in the neocortex are unknown. Here, we combined neuronal activity manipulation with ribosome-associated mRNA profiling of layer 5 (L5) extratelencephalic (ET) cells to address this question in mice. We unveiled a novel function for the selector gene Fezf2 as an activity-dependent transcription factor controlling the parvalbumin inputs onto L5 ET neurons. One of the downstream effectors of FEZF2 shaping the formation of inhibitory synapses onto L5 ET pyramidal cells is the cell-surface molecule cadherin 22. Our study identifies activity-dependent factors regulating the cell type-specific assembly of inhibitory synapses onto pyramidal cells.

Spiking Neuron-Astrocyte Networks for Image Recognition
From biological and artificial network perspectives, researchers have started acknowledging astrocytes as computational units mediating neural processes. Here, we propose a novel biologically-inspired neuron-astrocyte network model for image recognition, one of the first attempts at implementing astrocytes in Spiking Neuron Networks (SNNs) using a standard dataset. The architecture for image recognition has three primary units: the pre-processing unit for converting the image pixels into spiking patterns, the neuron-astrocyte network forming bipartite (neural onnections) and tripartite synapses (neural and astrocytic connections), and the classifier unit. In the astrocyte-mediated SNNs, an astrocyte integrates neural signals following the simplified Postnov model. It then modulates the Integrateand-Fire (IF) neurons via gliotransmission, thereby strengthening the synaptic connections of the neurons within the astrocytic territory. We develop an architecture derived from a baseline SNN model for unsupervised digit classification. The Spiking Neuron-Astrocyte Networks (SNANs) display better network performance with an optimal variance-bias trade-off than SNN alone. We demonstrate that astrocytes promote faster learning, support memory formation and recognition, and provide a simplified network architecture. Our proposed SNAN can serve as a benchmark for future researchers on astrocyte implementation in artificial networks, particularly in neuromorphic systems, for its simplified design.

Devaluing memories of reward: A case for dopamine
We describe a novel role for dopamine in devaluing sensory memories of reward. Mesencephalic dopamine cells activated during a mediated devaluation phase were later chemogenetically reactivated. This retrieval of the devalued reward memory elicited a reduction in the hedonic evaluation of sucrose reward. Through optogenetic and chemogenetic manipulations, we confirm dopamine cells are both sufficient and necessary for mediated devaluation, and retrieval of these memories reflected dopamine release in the nucleus accumbens. Consistent with our computational modelling data, our findings indicate a critical role for dopamine in encoding predictive representations of the sensory features of reinforcement. Overall, we illuminate the elaborate nature of reinforcement signals encoded by dopamine and suggest novel approaches to treating a host of psychobiological disorders.

Criticality and universality in neuronal cultures during 'up' and 'down' states
The brain can be seen as a self-organized dynamical system that optimizes information processing and storage capabilities. This is supported by studies across scales, from small neuronal assemblies to the whole brain, where neuronal activity exhibits features typically associated with phase transitions in statistical physics. Such a critical state is characterized by the emergence of scale-free statistics as captured, for example, by the sizes and durations of activity avalanches corresponding to a cascading process of information flow. Another phenomenon observed during sleep, under anesthesia, and in in vitro cultures, is that cortical and hippocampal neuronal networks alternate between "up" and "down" states characterized by very distinct firing rates. Previous theoretical work has been able to relate these two concepts and proposed that only up states are critical whereas down states are subcritical, also indicating that the brain spontaneously transitions between the two. Using high-speed high-resolution calcium imaging recordings of neuronal cultures, we test this hypothesis here by analyzing the neuronal avalanche statistics in populations of thousands of neurons during "up" and "down" states separately. We find that both "up" and "down" states can exhibit scale-free behavior when taking into account their intrinsic time scales. In particular, the statistical signature of "down" states is indistinguishable from those observed previously in cultures without "up" states. We show that such behavior can not be explained by network models of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression, even when realistic noise levels, spatial network embeddings, and heterogeneous populations are taken into account, which instead exhibits behavior consistent with previous theoretical models. Similar differences were also observed when taking into consideration finite-size scaling effects, suggesting that the intrinsic dynamics and self-organization mechanisms of these cultures might be more complex than previously thought. In particular, our findings point to the existence of different mechanisms of neuronal communication, with different time scales, acting during either highactivity or low-activity states, potentially requiring different plasticity mechanisms.

Global Huntingtin Knockout in Adult Mice Leads to Fatal Neurodegeneration that Spares the Pancreas
Huntington's disease (HD) is a fatal neurogenerative disorder caused by an expanded glutamine-coding CAG tract in the Huntingtin (Htt) gene. HD is believed to primarily arise via a toxic gain of function, and as a result a wide range of Htt-lowering treatments are in clinical trials. The safety of these trials is contingent on the risks imposed by Htt lowering: Htt is widely conserved, ubiquitously expressed and its complete loss causes severe developmental symptoms in mice and humans. Recently, multiple labs have reported on the consequences of widespread inducible Htt loss in mice. One report describes that early induction of global Htt loss causes fatal pancreatitis, but that later onset lowering is benign. Another study did not report fatal pancreatitis but suggested that postnatal Htt loss was associated with widespread progressive phenotypes, including subcortical calcification and neurodegeneration. To better understand the risks posed by widespread inducible Htt loss we established the phenotypes of mice in which we knocked out Htt with two tamoxifen inducible Cre lines, which we have here extensively characterized. In short, we find that widespread loss of Htt at 2 months of age leads to a wide range of phenotypes, including subcortical calcification, but does not result in acute pancreatitis or histological changes in the pancreas. Additionally, we report here for the first time that Htt loss is followed by robust and sustained increases in the levels of neurofilament light chain (NfL), a peripherally accessible biomarker of neuroaxonal stress. These results confirm that complete loss of Htt in mice is associated with pronounced risks, including progressive subcortical calcification and neurodegeneration.

Single residues in the complexin N-terminus exhibit distinct phenotypes in synaptic vesicle fusion
The release of neurotransmitters at central synapses is dependent on a cascade of protein interactions, specific to the presynaptic compartment. Amongst those dedicated molecules the cytosolic complexins play an incompletely defined role as synaptic transmission regulators. Complexins are multidomain SNARE complex binding proteins which confer both inhibitory and stimulatory functions. Using systematic mutagenesis and combining reconstituted in vitro membrane fusion assays with electrophysiology in neurons, we deciphered the function of the N-terminus of complexin II (Cpx). The N-terminus (amino acid 1 - 27) starts with a region enriched in hydrophobic amino acids (1-12), which can lead to lipid binding. In contrast to mutants which maintain the hydrophobic character and the stimulatory function of Cpx, non-conservative exchanges largely perturbed spontaneous and evoked exocytosis. Mutants in the downstream region (amino acid 11-18) show differential effects. Cpx-A12W increased spontaneous release without affecting evoked release whereas replacing D15 with amino acids of different shapes or hydrophobic properties (but not charge) not only increased spontaneous release, but also impaired evoked release and surprisingly reduced the size of the readily releasable pool, a novel Cpx function, unanticipated from previous studies. Thus, the exact amino acid composition of the Cpx N-terminus fine tunes the degree of spontaneous and evoked neurotransmitter release.

Loss of postsynaptic NMDARs drives nanoscale reorganization of Munc13-1 and PSD-95
Nanoscale protein organization within the active zone (AZ) and post-synaptic density (PSD) influences synaptic transmission. Nanoclusters of presynaptic Munc13-1 are associated with readily releasable pool size and neurotransmitter vesicle priming, while postsynaptic PSD-95 nanoclusters coordinate glutamate receptors across from release sites to control their opening probability. Nanocluster number, size, and protein density vary between synapse types and with development and plasticity, supporting a wide range of functional states at the synapse. Whether or how the receptors themselves control this critical architecture remains unclear. One prominent PSD molecular complex is the NMDA receptor (NMDAR). NMDARs coordinate several modes of signaling within synapses, giving them the potential to influence synaptic organization through direct protein interactions or through signaling. We found that loss of NMDARs results in larger synapses that contain smaller, denser, and more numerous PSD-95 nanoclusters. Intriguingly, NMDAR loss also generates retrograde reorganization of the active zone, resulting in denser, more numerous Munc13-1 nanoclusters, more of which are aligned with PSD-95 nanoclusters. Together, these changes to synaptic nanostructure predict stronger AMPA receptor-mediated transmission in the absence of NMDARs. Notably, while prolonged antagonism of NMDAR activity increases Munc13-1 density within nanoclusters, it does not fully recapitulate these trans-synaptic effects. Thus, our results confirm that NMDARs play an important role in maintaining pre- and postsynaptic nanostructure and suggest that both decreased NMDAR expression and suppressed NMDAR activity may exert distinct effects on synaptic function, yet through unique architectural mechanisms.

Scalable, optically-responsive human neuromuscular junction model reveals convergent mechanisms of synaptic dysfunction in familial ALS
Neuromuscular junctions (NMJs) are specialized synapses that mediate communication between motor neurons and skeletal muscles and are essential for movement. The degeneration of this system can lead to symptoms observed in neuromuscular and motor neuron diseases. Studying these synapses and their degeneration has proven challenging. Prior NMJ studies heavily relied upon the use of mouse, chick, or isolated primary human cells, which have demonstrated limited fidelity for disease modeling. To enable the study of NMJ dysfunction and model genetic diseases, we, and others, have developed methods to generate human NMJs from pluripotent stem cells (PSCs), embryonic stem cells, and induced pluripotent stem cells. However, published studies have highlighted technical limitations associated with these complex in vitro NMJ models. In this study, we developed a robust PSC-derived motor neuron and skeletal muscle co-culture method, and demonstrated its sensitivity in modeling motor neuron disease. Our method spontaneously and reproducibly forms human NMJs. We developed multiwell-multielectrode array (MEA) parameters to quantify the activity of PSC-derived skeletal muscles, as well as measured the electrophysiological activity of functional human PSC-derived NMJs. We further leveraged our method to morphologically and functionally assess NMJs from the familial amyotrophic lateral sclerosis (fALS) PSCs, C9orf72 hexanucleotide (G4C2)n repeat expansion (HRE), SOD1A5V, and TDP43G298S to define the reproducibility and sensitivity of our system. We observed a significant decrease in the numbers and activity of PSC-derived NMJs developed from the different ALS lines compared to their respective controls. Furthermore, we evaluated a therapeutic candidate undergoing clinical trials and observed a variant-dependent rescue of functionality of NMJs. Our newly developed method provides a platform for the systematic investigation of genetic causes of NMJ neurodegeneration and highlights the need for therapeutic avenues to consider patient genotype.

Idiosyncratic pupil regulation in autistic children
Recent neuroimaging and eye tracking studies have suggested that children with autism spectrum disorder (ASD) may exhibit more variable and idiosyncratic brain responses and eye movements than typically developing (TD) children. Here we extended this research for the first time to pupillometry recordings. We successfully completed pupillometry recordings with 103 children (66 with ASD), 4.5-years-old on average, who viewed three 90 second movies, twice. We extracted their pupillary time-course for each movie, capturing their stimulus evoked pupillary responses. We then computed the correlation between the time-course of each child and those of all others in their group. This yielded an average inter-subject correlation value per child, representing how similar their pupillary responses were to all others in their group. ASD participants exhibited significantly weaker inter-subject correlations than TD participants, reliably across all three movies. Differences across groups were largest in responses to a naturalistic movie containing footage of a social interaction between two TD children. This measure enabled classification of ASD and TD children with a sensitivity of 0.82 and specificity of 0.73 when trained and tested on independent datasets. Using the largest ASD pupillometry dataset to date, we demonstrate the utility of a new technique for measuring the idiosyncrasy of pupil regulation, which can be completed even by young children with co-occurring intellectual disability. These findings reveal that a considerable subgroup of ASD children have significantly more unstable, idiosyncratic pupil regulation than TD children, indicative of more variable, weakly regulated, underlying neural activity.

Influence of social and semantic contexts in processing speech in noise
Social interactions occupy a substantial part of our life. Not only interacting in first person, but also listening to others' interactions serve critical functions in understanding our social world. Yet, these auditory social signals are very often mixed with surrounding noise, such as in a restaurant, which degrades the information and forces our brain to use strategies to compensate for this loss. Although the role of semantics in speech comprehension under noisy conditions has been extensively studied as one of these strategies, the role of social context, and how it may interact with semantics, is unknown. Here, we conducted a series of four perceptual experiments to better understand the processing of multiple-speaker conversations from a third-person viewpoint, by manipulating the social and semantic contexts of a conversation. To do so, we used an assortment of artificial intelligence (AI) tools to generate a set of auditory stimuli, that consisted of two-speaker dialogues or one-speaker monologues (social context factor) arranged in intact or sentence-scrambled order (semantic context factor). Each stimulus comprised five sentences, with the fifth sentence embedded in multi-talker babbling noise. This fifth sentence was subsequently repeated without noise, with a single word altered or unchanged. Stimuli were presented over headphones to healthy young adult listeners, who were asked whether the repeated sentence was same as or different from the previous in-noise sentence. As results, we found overall that both the manipulation of social and semantic contexts had significant effects on performance in this task. We also found a correlation between a measure of autistic traits and individual performances when processing dialogues, but not monologues. Taken together, our results suggest that both semantic and social features of a conversation can modulate speech processing and that individual performance in this task could be related to certain social traits. These results raise new questions regarding the predictive or other mechanisms that may be at play when perceiving speech in social contexts.

Gray Matters: An Efficient Vision Transformer GAN Framework for Predicting Functional Network Connectivity Biomarkers from Brain Structure
The field of brain connectivity research has undergone revolutionary changes thanks to state-of-the-art advancements in neuroimaging, particularly regarding structural and functional magnetic resonance imaging (MRI). To navigate the intricate neural dynamics, one must possess a keen comprehension of the interdependent links between structure and function. Such relationships are understudied as they are complex and likely nonlinear. To address this, we created a new generative deep learning architecture using a conditional efficient vision transformer generative adversarial network (cEViTGAN) to capture the distinct information in structural and functional MRI of the human brain. Our model generates functional network connectivity (FNC) matrices directly from three-dimensional sMRI data. Two pioneering innovations are central to our approach. First, we use a novel linear embedding method for structural MRI (sMRI) data that retains the 3D spatial detail. This embedding is best for representative learning, and when used on a consistent dataset, and shows that it is good at upstream classification assignments. To estimate neural biomarkers, we need to process much smaller patches using ViT-based architectures, which usually makes the computations more difficult because of the self-attention operations. We present a new, lightweight self-attention mechanism to address this challenge. Our mechanism not only overcomes computational shortcomings of traditional softmax self-attention but also surpasses pure linear self-attention models in accuracy and performance. This optimization enables us to analyze even the tiniest neuroanatomical details with exceptional precision. Our model allows for the identification of functional network connectivity (FNC) with 74.2% accuracy and also predicts subject differences in FNC for schizophrenia patients versus controls. The results are intriguing and suggest the links between gray matter volume and brain function may be stronger than previously considered.

Synaptic Theory of Working Memory for Serial Order
Working Memory (WM) enables the temporally-ordered maintenance of sequences of stimuli over short periods of time. This ability is critical for many cognitive and behavioral tasks. Despite its importance, however, how WM encodes, stores and retrieves information about serial order remains a major outstanding problem. Here, we extend our previously-proposed synaptic theory of WM to include synaptic augmentation, as experimentally observed at the same synapses that feature short-term facilitation. We find that synaptic augmentation leads to the emergence of a primacy gradient that can be used to reconstruct the order of presentation at recall, by an appropriate control of the background input to the WM network. The model reproduces prominent features of the behavior of human subjects recalling lists of items and makes a series of experimentally-testable predictions. Intriguingly, the model suggests that WM capacity limitations could result from a failure in retrieving, rather than encoding, information.

Response-locked theta dissociations reveal potential feedback signal following successful retrieval
Successful memory retrieval relies on memory processes to access an internal representation and decision processes to evaluate and respond to the accessed representation, both of which are supported by fluctuations in theta (4-8Hz) activity. However, the extent to which decision making processes are engaged following a memory response is unclear. Here, we recorded scalp electroencephalography (EEG) while human participants performed a recognition memory task. We focused on response-locked data, allowing us to investigate the processes that occur prior to and following a memory response. We replicate previous work and find that prior to a memory response theta power is greater for identification of previously studied items (hits) relative to rejection of novel lures (correct rejections; CRs). Following the memory response, the theta power dissociation 'flips' whereby theta power is greater for CRs relative to hits. We find that the post-response 'flip' is more robust for hits that are committed quickly, potentially reflecting a positive feedback signal for strongly remembered experiences. Our findings suggest that there are potentially distinct processes occurring before and after a memory response that are modulated by successful memory retrieval.

Node centrality in MEG resting-state networks covaries with neurotransmitter receptor and transporter density
Neuronal oscillations are central mechanisms in the regulation of neuronal processing and communication (Deco et al., 2011; Fries, 2015; S. Palva & Palva, 2012; Siegel et al., 2012; Singer, 1999), but the relationship between the emergent inter-areal synchronization of oscillations and their underlying synaptic and neuromodulatory mechanisms - the dynome - has remained poorly understood (Kopell et al., 2014). While oscillations are largely generated by fast synaptic neurotransmission among pyramidal cells and interneurons (Traub et al., 2004), these microcircuits are subject to slower neuromodulation in a frequency- and region-specific manner (Batista-Brito et al., 2018; Roopun et al., 2010). While the efferent connections, receptor densities, and neurotransmitter reuptake regulation of neuromodulatory systems are highly heterogeneous across the cortical mantle (Avery & Krichmar, 2017; Deco et al., 2017; Hansen et al., 2022), it has remained unresolved how this variability shapes inter-areal connectivity of neuronal oscillations. Here, we used source-reconstructed human magnetoencephalography (MEG) data to assess how the centrality of brain areas (nodes) in large-scale networks of phase synchrony and amplitude correlations covaries with neurotransmitter receptor and transporter densities. Node centrality strongly covaried with receptor and transporter densities in a coupling- and frequency-specific manner both at the level of individual receptors and transporters and that of principal components. In delta, theta, and gamma frequencies, node centrality in phase-synchronization networks covaried positively, and in high-alpha and beta bands negatively, with dopaminergic, GABA, NMDA, muscarinic, and most serotonergic receptor densities. In amplitude-correlation networks, node centrality in delta and gamma bands covaried positively, and in theta to beta bands negatively, with most receptor and transporter densities. These results establish the contribution of neurotransmitter receptor and transporter densities for shaping connectivity of neuronal oscillations in the human brain and demonstrate a link between coupling of neuronal oscillations with the underlying biological details.

Mechanisms and implications of high depolarization baseline offsets in conductance-based neuronal models
Somatic current-step injection is a routinely used protocol to characterize neurons and measure their electrophysiological properties. A signature feature of the responses of many neuronal types is an elevated baseline of action potential firing from the resting membrane potential, the depolarization baseline level offset (DBLO). We find that four key factors together account for high DBLO: Liquid Junction Potential correction, subthreshold impedance amplitude profile, fast potassium delayed rectifier kinetics, and appropriate transient sodium current kinetics. We show that simple mechanisms for DBLO, such as Ohmic depolarization due to current input, fail to explain the effect, and many sophisticated conductance-based models also do not correctly manifest DBLO. Neither low pass filtering effect of membrane nor high reversal potential of potassium channels are able to explain high DBLO. Using stochastic parameter search in conductance-based models of CA1 pyramidal neurons, we explore cellular morphology configurations and channel kinetics. Multi-compartment models which matched experimental subthreshold impedance amplitude profiles had higher DBLO than single compartment models. DBLO was further increased in models that exhibited rapid deactivation time-constants in the dominant potassium conductance. We also saw that certain transient sodium current kinetics resulted in higher DBLO than others. We emphasize that correct expression of DBLO in conductance-based models is important to make quantitative predictions about the levels of ion channels in a neuron and also to correctly predict mechanisms underlying cellular function, such as the role of persistent sodium in imparting firing bistability to a neuron.

Transcriptome Dynamics in Mouse Amygdala under Acute and Chronic Stress Revealed by Thio-labeled RNA Sequencing
Both acute and chronic stress have significant impact on brain functions. The amygdala is essential in mediating stress responses, but how its transcriptomic dynamics change under stress remains elusive. To overcome the difficulties in detecting subtle stress-induced changes by evaluating total RNA using classic RNA sequencing, we conducted thio-labeled RNA sequencing (SLAM-seq). We injected 4-thiouridine (4sU) into mouse amygdala followed by SLAM-seq to detect nascent mRNA induced by acute and chronic restraint stress, and found that SLAM-seq could label actively transcribed genes in major neuronal and glial subtypes. We also found that acute stress induced the transcription of 6 gamma-aminobutyric acid (GABA) receptors and only 1 glutamate receptor, indicating an imminent increase of inhibitory control in the stressed amygdala. Conversely, chronic stress led to transcription of more glutamate receptors, fewer GABA receptors, and genes associated with activity-dependent myelination, suggesting a release of inhibitory control (disinhibition) and hyperactivity of the amygdala. Additionally, genes detected by SLAM-seq and RNA-seq only partially overlapped, with SLAM-seq particularly sensitive to transcriptional changes in genes with high basal transcription levels. Thus, by applying SLAM-seq in vivo, we obtained a rich dataset of nascently transcribed genes in the amygdala under stress, and revealed distinct transcriptional dynamics associated with acute and chronic stress.

Dynamic Network Analysis of Electrophysiological Task Data
An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. Whilst this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches, can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.

Human spinal interneurons repair the injured spinal cord through synaptic integration
Advances in cell therapy offer promise for some of the most devastating neural injuries, including spinal cord injury (SCI). Endogenous VSX2-expressing spinal V2a interneurons have been implicated as a key component in plasticity and therapeutically driven recovery post-SCI. While transplantation of generic V2a neurons may have therapeutic value, generation of human spinal V2a neurons with rostro-caudal specificity and assessment of their functional synaptic integration with the injured spinal cord has been elusive. Here, we efficiently differentiated optogenetically engineered cervical V2a spinal interneurons (SpINs) from human induced pluripotent stem cells and tested their capacity to form functional synapses with injured diaphragm motor networks in a clinically-relevant sub-acute model of cervical contusion injury. Neuroanatomical tracing and immunohistochemistry demonstrated transplant integration and synaptic connectivity with injured host tissue. Optogenetic activation of transplanted human V2a SpINs revealed functional synaptic connectivity to injured host circuits, culminating in improved diaphragm activity assessed by electromyography. Furthermore, optogenetic activation of host supraspinal pathways revealed functional innervation of transplanted cells by host neurons, which also led to enhanced diaphragm contraction indicative of a functional neuronal relay. Single cell analyses pre- and post-transplantation suggested the in vivo environment resulted in maturation of cervical SpINs that mediate the formation of neuronal relays, as well as differentiation of glial progenitors involved in repair of the damaged spinal cord. This study rigorously demonstrates feasibility of generating human cervical spinal V2a interneurons that develop functional host-transplant and transplant-host connectivity resulting in improved muscle activity post-SCI.

Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected
HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.

EMG-to-torque models for exoskeleton assistance: a framework for the evaluation of in situ calibration
In the field of robotic exoskeleton control, it is critical to accurately predict the intention of the user. While surface electromyography (EMG) holds the potential for such precision, current limitations arise from the absence of robust EMG-to-torque model calibration procedures and a universally accepted model. This paper introduces a practical framework for calibrating and evaluating EMG-to-torque models, accompanied by a novel nonlinear model. The framework includes an in situ procedure that involves generating calibration trajectories and subsequently evaluating them using standardized criteria. A comprehensive assessment on a dataset with 17 participants, encompassing single-joint and multi-joint conditions, suggests that the novel model outperforms the others in terms of accuracy while conserving computational efficiency. This contribution introduces an efficient model and establishes a versatile framework for EMG-to-torque model calibration and evaluation, complemented by a dataset made available. This further lays the groundwork for future advancements in EMG-based exoskeleton control and human intent detection.

Simulated brain networks reflecting progression of Parkinson's disease
Neurodegenerative progression of Parkinson's disease affects brain structure and function and, concomitantly, alters topological properties of brain networks. The network alteration accompanied with motor impairment and duration of the disease is not yet clearly demonstrated in the disease progression. In this study, we aim at resolving this problem with a modeling approach based on large-scale brain networks from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover brain networks showing unexplored relationships with clinical variables. We observe that simulated brain networks exhibit significant differences between healthy controls (n=51) and patients with Parkinson's disease (n=60) and strongly correlate with disease severity and disease duration of the patients. Moreover, the modeling results outperform the empirical brain networks in these clinical measures. Consequently, this study demonstrates that utilizing simulated brain networks provides an enhanced view on network alterations in the progression of motor impairment and potential biomarkers for clinical indices.

Timing and Amplitude of Catch-up Saccades to Accelerating Targets
To track moving targets, humans move their eyes using both saccades and smooth pursuit. If pursuit eye movements fail to accurately track the moving target, catch-up saccades are initiated to rectify the tracking error. It is well known that retinal position and velocity errors determine saccade timing and amplitude, but the extent to which retinal acceleration error influences these aspects is not well quantified. To test this, 13 adult human participants performed an experiment where they pursued accelerating / decelerating targets. During ongoing pursuit, we introduced a randomly sized target step to evoke a catch-up saccade and analyzed its timing and amplitude. We observed that retinal acceleration error was a statistically significant predictor of saccade amplitude and timing. A multiple linear regression supported our hypothesis that retinal acceleration errors influence saccade amplitude in addition to the influence of retinal position and velocity errors. We also found that saccade latencies were shorter when retinal acceleration error increased the tracking error and vice versa. In summary, our findings support a model in which retinal acceleration error is used to compute a predicted position error ~100ms into the future to trigger saccades and determine saccade amplitude.

Obesity Facilitates Sex-Specific Improvement In Cognition And Neuronal Function In A Rat Model Of Alzheimer's Disease
Obesity reduces or increases the risk of developing Alzheimer's disease (AD) depending on whether it is assessed in mid-life or late-life. There is currently no consensus on the relationship between obesity and AD or the mechanism or their interaction. Here, we aim to differentiate the cause-and-effect relationship between obesity and AD in a controlled rat model of AD. We induced obesity in 9-month-old TgF344-AD rats, that is pathology-load wise similar to early symptomatic phase of human AD. To more accurately model human obesity, we fed both TgF344-AD and non-transgenic littermates a varied high-carbohydrate-high-fat diet consisting of human food for 3 months. Obesity increased overall glucose metabolism and slowed cognitive decline in TgF344-AD rats, specifically executive function, without affecting non-transgenic rats. Pathological analyses of prefrontal cortex and hippocampus showed that obesity in TgF344-AD rats produced varied effects, with increased density of myelin and oligodendrocytes, lowered density and activation of microglia that we propose contributes to the cognitive improvement. However, obesity also decreased neuronal density, and promoted deposition of amyloid-beta plaques and tau inclusions. After 6 months on the high-carbohydrate-high-fat diet, detrimental effects on density of neurons, amyloid-beta plaques, and tau inclusions persisted while the beneficial effects on myelin, microglia, and cognitive functions remained albeit with a lower effect size. By examining the effect of sex, we found that both beneficial and detrimental effects of obesity were stronger in female TgF344-AD rats indicating that obesity during early symptomatic phase of AD is protective in females.

PyLossless: A non-destructive EEG processing pipeline
EEG recordings are typically long and contain large amounts of data, making manual cleaning a time-consuming and error-prone task. Automated pre-processing pipelines can facilitate the efficient and objective extraction of artifacts, enabling standardized and reproducible analyses. However, automated pre-processing pipelines typically remove data considered artifact, and return a subset of irreversibly transformed signals. This approach obfuscates pre-processing decisions, and often makes it impossible to recover the original data or modify the pre-processing steps. Further, it complicates collaboration between research teams working on a common dataset, because different analyses may require specific pre-processing routines. Given the large amount of resources that are devoted to collecting EEG, tools that can help efficiently and transparently pre-process data are greatly needed. PyLossless addresses this need by creating a non-destructive, automated pre-processing pipeline that maintains the continuous EEG structure. It offers a user-friendly API, it is well documented, tested through continuous integration, easily deployable, and integrates with the popular MNE-Python environment. The pipeline further provides a browser-based quality control review (QCR) dashboard that allows researchers to visualize and edit the automated artifact annotations on sensors, time-periods, and independent components. The end product of PyLossless is a lossless annotated data state that can be shared and used with analysis-specific artifact rejection policies, allowing for an optimal balance between flexibility and standardization.

Connectomic and behavioral alterations in creatine transporter deficiency are partially normalized by gene therapy
Creatine Transporter Deficiency (CTD) is an X-linked disease due to the loss of SLC6A8 gene and presenting with low brain creatine, intellectual disability, autistic-like behavior and seizures. No treatments are available yet for CTD, and little is known about the brain circuit alterations underlying its pathological endophenotypes. Here, we tracked brain network and behavioral dysfunction in a murine model of CTD at two stages of disease progression. fMRI mapping revealed widespread disruption of brain connectivity in Slc6a8-KO mutants, with prominent somato-motor dysconnectivity in juvenile mice, and weaker and more focal cortical and subcortical hypoconnectivity in adults. Notably, perinatal AAV-mediated expression of human SLC6A8 in Slc6a8-KO mutants significantly rescued juvenile fMRI hypoconnectivity. This effect was paralleled by a regression of translationally relevant phenotypes, including a reduction in stereotyped movements and increased body weight which persisted into adulthood. Cognitive deficits and residual fMRI hypoconnectivity in adult mice were instead not reverted by gene therapy. Finally, multivariate modeling in adult mice revealed a basal forebrain network whose activity was associated with behavioral performance, and modulated by brain creatine levels. This brain-behavior relationship was disrupted in Slc6a8-KO mutants. Our results document robust network disruption in CTD and demonstrate that CTD pathology can be partially reversed by perinatal genetic expression of SLC6A8, thus laying the basis for the development of experimental therapies for this genetic disorder.

Neuronal microstructural changes in the human brain are associated with neurocognitive aging
Gray matter (GM) alterations play a role in aging-related disorders like Alzheimer's disease and related dementias, yet MRI studies mainly focus on macroscopic changes. Although reliable indicators of atrophy, morphological metrics like cortical thickness lack the sensitivity to detect early changes preceding visible atrophy. Our study aimed at exploring the potential of diffusion MRI in unveiling sensitive markers of cortical and subcortical age-related microstructural changes and assessing their associations with cognitive and behavioral deficits. We leveraged the Human Connectome Project-Aging cohort that included 707 unimpaired participants (394 female; median age = 58, range = 36-90 years) and applied the powerful mean apparent diffusion propagator model to measure microstructural parameters, along with comprehensive behavioral and cognitive test scores. Both macro- and microstructural GM characteristics were strongly associated with age, with widespread significant microstructural correlations reflective of cellular morphological changes, reduced cellular density, increased extracellular volume, and increased membrane permeability. Importantly, when correlating MRI and cognitive test scores, our findings revealed no link between macrostructural volumetric changes and neurobehavioral performance. However, we found that cellular and extracellular alterations in cortical and subcortical GM regions were associated with neurobehavioral performance. Based on these findings, it is hypothesized that increased microstructural heterogeneity and decreased neurite orientation dispersion precede macrostructural changes, and that they play an important role in subsequent cognitive decline. These alterations are suggested to be early markers of neurocognitive performance that may distinctly aid in identifying the mechanisms underlying phenotypic aging and subsequent age-related functional decline.

Erroneous compensation for long-latency feedback delays as origin of Essential Tremor
Essential tremor (ET), a movement disorder characterized by involuntary oscillations of the limbs during movement, remains to date not well understood. It has been recently suggested that the tremor originates from impaired delay compensation, affecting movement representation and online control. Here we tested this hypothesis directly with ET patients (N=24) and neurologically intact (NI) volunteers (N=28) in an upper limb postural perturbation task. After maintaining their hand in a visual target, participants experienced perturbations of unpredictable direction and magnitude, and were instructed to counter the perturbation and steer their hand back to the starting position. In comparison with NI volunteers, ET patients early muscular responses (Short and Long Latency Responses, 20-50 ms and 50-100 ms respectively) were preserved or even slightly increased. However, they exhibited perturbation-dependent deficits when stopping and stabilizing their hand in the final target supporting the hypothesis that the tremor was generated by the feedback controller. We show in a computational model that errors in delay compensation accumulating over time produced the same small increase in initial feedback response followed by oscillations that scaled with the perturbation magnitude as observed in ET population. Our experimental results therefore validate the computational hypothesis that inaccurate delay compensation in long-latency pathways could be the origin of the tremor.

Through the lens of causal inference: Decisions and pitfalls of covariate selection
The critical importance of justifying the inclusion of covariates is a facet often overlooked in data analysis. While the incorporation of covariates typically follows informal guidelines, we argue for a comprehensive exploration of underlying principles to avoid significant statistical and interpretational challenges. Our focus is on addressing three common yet problematic practices: the indiscriminate lumping of covariates, the lack of rationale for covariate inclusion, and the oversight of potential issues in result reporting. These challenges, prevalent in neuroimaging models involving covariates such as reaction time, demographics, and morphometric measures, can introduce biases, including overestimation, underestimation, masking, sign flipping, or spurious effects. Our exploration of causal inference principles underscores the pivotal role of domain knowledge in guiding covariate selection, challenging the common reliance on statistical measures. This understanding carries implications for experimental design, model-building, and result interpretation. We draw connections between these insights and reproducibility concerns, specifically addressing the selection bias resulting from the widespread practice of strict thresholding, akin to the logical pitfall associated with "double dipping." Recommendations for robust data analysis involving covariates encompass explicit research question statements, justified covariate inclusions/exclusions, centering quantitative variables for interpretability, appropriate reporting of effect estimates, and advocating a "highlight, don't hide" approach in result reporting. These suggestions are intended to enhance the robustness, transparency, and reproducibility of covariate-driven analyses, encompassing investigations involving consortium datasets such as ABCD and UK Biobank. We discuss how researchers can use a transparent depiction of the covariate relationships to enhance the ethos of open science and promote research reproducibility.

Phase Resetting Curves Determine Stability of Synchrony in One and Two Clusters of Pulse Coupled Oscillators with Delays
Previously, our group used phase response curves under a pulsatile coupling assumption to determine the stability of synchrony within a cluster of neural oscillators and between two clusters of oscillators. The interactions of the within and between cluster terms were considered, demonstrating how an alternating firing pattern between clusters could stabilize within cluster synchrony- even in clusters unable to synchronize themselves in isolation. In addition, criteria were derived for synchrony between two pulse coupled oscillators with synaptic delays. In this study, we update our previous work on one and two clusters of coupled oscillators to include delays and demonstrate the validity of the results using a map of the firing intervals based on the phase resetting curve. We use self-connected neurons to represent clusters and derive conditions under which an oscillator can phase-lock itself with a delayed input. Although this analysis only strictly applies to identical neurons receiving identical synapses from the same number of neurons, the principles are general and can be used to understand how to promote or impede synchrony in physiological networks of neurons. Heterogeneity can be interpreted as a form of frozen noise, and approximate synchrony can be sustained despite heterogeneity. The pulse-coupled oscillator model can not only be used to describe biological neuronal networks but also cardiac pacemakers, lasers, fireflies, artificial neural networks, social self-organization, and wireless sensor networks.

Analgesic targets identified in mouse sensory neuron somata and terminal pain translatomes
The relationship between transcription and protein expression is complex. We identified polysome-associated RNA transcripts in the somata and central terminals of mouse sensory neurons in control, painful (+ Nerve Growth Factor (NGF)) and pain-free conditions (Nav1.7 null mice). The majority (98%) of translated transcripts are shared between male and female mice in both the somata and terminals. Some transcripts are highly enriched in the somata or terminals. Changes in the translatome in painful and pain-free conditions include novel and known regulators of pain pathways. Antisense knockdown of selected somatic and terminal polysome-associated transcripts that correlate with pain states diminished pain behaviour. Terminal-enriched transcripts encoding synaptic proteins (e.g. Synaptotagmin), non-coding RNAs, transcription factors (e.g. Znf431), proteins associated with trans-synaptic trafficking (HoxC9), GABA generating enzymes (Gad1 and Gad2) and neuropeptides (Penk). Thus, central terminal translation may well be a significant regulatory locus for peripheral input from sensory neurons.

Do microsaccades vary with discriminability around the visual field?
Microsaccades, or tiny fixational eye movements, improve discriminability in high acuity tasks in the foveola. To investigate whether they help compensate for low discriminability at perifovea, we examined MS characteristics relative to the adult visual performance field, which is characterized by two perceptual asymmetries: Horizontal Vertical Anisotropy (better discrimination along the horizontal than vertical meridian), and Vertical Meridian Asymmetry (better discrimination along the lower than upper vertical meridian). We investigated whether and to what extent microsaccade directionality varies when stimuli are at isoeccentric locations along the cardinals under conditions of heterogeneous discriminability (Experiment 1) and homogeneous discriminability, equated by adjusting stimulus contrast (Experiment 2). Participants performed a two-alternative forced-choice orientation discrimination task. In both experiments, performance was better on trials without microsaccades between ready signal onset and stimulus offset than on trials with microsaccades. Across the trial sequence the microsaccade rate and directional pattern were similar across locations. Our results indicate that microsaccades were similar regardless of stimulus discriminability and target location, except during the response period, once the stimuli were no longer present and target location no longer uncertain, when microsaccades were biased toward the target location. Thus, this study reveals that microsaccades do not flexibly adapt as a function of varying discriminability in a basic visual task around the visual field.

Neuropeptidergic regulation of neuromuscular signaling in larval zebrafish alters swimming behavior and synaptic transmission
The regulation of synaptic transmission is crucial for plasticity, homeostasis and learning. Chemical synaptic transmission is thus modulated to accommodate different activity levels, which also enables homeostatic scaling in pre- and postsynaptic compartments. In nematodes, cAMP signaling enhances cholinergic neuron output, and these neurons use neuropeptide signaling to modulate synaptic vesicle content. To explore if this mechanism is conserved in vertebrates, we studied the involvement of neuropeptides in cholinergic transmission at the neuromuscular junction of larval zebrafish. Optogenetic stimulation by photoactivated adenylyl cyclase (bPAC) resulted in elevated locomotion as measured in behavioural assays. Furthermore, post-synaptic patch-clamp recordings revealed that in bPAC transgenics, the frequency of miniature excitatory postsynaptic currents (mEPSCs) was increased after photostimulation. These results suggested that cAMP-mediated activation of ZF motor neurons leads to increased fusion of SVs, consequently resulting in enhanced neuromuscular activity. We generated mutants lacking the neuropeptide processing enzyme carboxypeptidase E (cpe), and the most abundant neuropeptide precursor in motor neurons, tachykinin (tac1). Both mutants showed exaggerated locomotion after photostimulation. cpe mutants exhibit lower mEPSC frequency during photostimulation and less large-amplitude mEPSCs. In tac1 mutants mEPSC frequency was not affected but amplitudes were significantly smaller. Exaggerated locomotion in the mutants thus reflected upscaling of postsynaptic excitability. cpe and tac1 mutant muscles expressed more nicotinic acetylcholine receptors (nAChR) on their surface. Thus, neuropeptide signaling regulates synaptic transmitter output in zebrafish motor neurons, and muscle cells homeostatically regulate nAChR surface expression, compensating reduced presynaptic input. This mechanism may be widely conserved in the animal kingdom.

Neuronal subtypes and connectivity of the adult mouse paralaminar amygdala
The paralaminar nucleus of the amygdala (PL) is comprised of neurons which exhibit delayed maturation. PL neurons are born during gestation but mature during adolescent ages, differentiating into excitatory neurons. The PL is prominent in the adult amygdala, contributing to its increased neuron number and relative size compared to childhood. However, the function of the PL is unknown, as the region has only recently begun to be characterized in detail. In this study, we investigated key defining features of the adult PL; the intrinsic morpho-electric properties of its neurons, and its input and output connectivity. We identify two subtypes of excitatory neurons in the PL based on unsupervised clustering of electrophysiological properties. These subtypes are defined by differential action potential firing properties and dendritic architecture, suggesting divergent functional roles. We further uncover major axonal inputs to the adult PL from the main olfactory network and basolateral amygdala. We also find that axonal outputs from the PL project reciprocally to major inputs, and to diverse targets including the amygdala, frontal cortex, hippocampus, hypothalamus, and brainstem. Thus, the adult PL is centrally placed to play a major role in the integration of olfactory sensory information, likely coordinating affective and autonomic behavioral responses to salient odor stimuli.

Training interoceptive awareness with real-time haptic vs. visual heartbeat feedback
The perception of signals from within the body, known as interoception, is increasingly recognized as a prerequisite for physical and mental health. The study is dedicated to the development of effective technological approaches for enhancing interoceptive awareness. We provide evidence of the effectiveness and practical feasibility of a novel real-time haptic heartbeat supplementation technology combining principles of biofeedback and sensory enhancement. In a randomized controlled study, we applied the developed naturalistic haptic feedback on a group of 30 adults, while another group of 30 adults received more traditional real-time visual heartbeat feedback. A single session of haptic, but not visual heartbeat feedback resulted in increased interoceptive accuracy and confidence, as measured by the heart rate discrimination task, and in a shift of attention towards the body. Participants rated the developed technology as more helpful and pleasant than the visual feedback, thus indicating high user satisfaction. The study highlights the importance of matching sensory characteristics of the feedback to the natural bodily prototype. Our work suggests that real-time haptic feedback might be a superior approach to strengthen mind-body interaction in interventions for physical and mental health.

Neural variability compresses with increasing belief precision during Bayesian inference
To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that moment-to-moment neural variability provides a signature that scales with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SDBOLD, as measured with functional MRI) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SDBOLD compression primarily in Default Mode Network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SDBOLD changes. Our results provide first evidence that moment-to-moment neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.

Sexually dimorphic effects of Amylin 1 receptor activation in trigeminovascular neurons
Background: Migraine is more prevalent in women, and although the mechanisms involved in this disparity remain poorly understood, an interaction between the trigeminovascular system and cycling estrogen levels in biologically-predisposed women has been suggested. We investigated the role of amylin 1 (AMY1) receptor activation in the modulation of the trigeminal nociceptive system in female rats across the estrous cycle in cycle stages with falling and rising estrogen levels and compared these to the responses in males. Methods: We recorded neuronal activity in vivo within the trigeminocervical complex (TCC) and examined the effects of targeting AMY1 receptors on ongoing spontaneous and dural stimulus evoked firing rates of trigeminovascular neurons. The selective AMY1 receptor agonist pramlintide and AMY1 receptor antagonist AC 187 were used. Estrous cycle stages were identified via cytology from vaginal smears. Results: Administration of pramlintide increased the spontaneous activity and dural stimulus-evoked neuronal responses in the TCC, only during falling estrogen phases of the female estrous cycle. Moreover, the administration per se of AC 187 decreased spontaneous evoked firing rates of central trigeminovascular neurons in females and males, whereas pretreatment with AC 187 prevented pramlintide-induced increases in spontaneous activity and dural stimulus-evoked responses in females with falling estrogen levels. Conclusion: AMY1 receptors modulate the trigeminal nociceptive system. The facilitating effect is most pronounced in female rats during falling estrogen phases of the estrous cycle. Our data also supports selective AMY1 receptor antagonists as potentially effective targets for the treatment of migraine.

Deciphering the astrocytic contribution to learning and relearning
Astrocytes play a key role in the regulation of synaptic strength and are thought to orchestrate synaptic plasticity and memory. Yet, how specifically astrocytes and their neuroactive transmitters control learning and memory is currently an open question. Recent experiments have uncovered an astrocyte-mediated feedback loop in CA1 pyramidal neurons which is started by the release of endocannabinoids by active neurons and closed by astrocytic regulation of the D-serine levels at the dendrites. D-serine is a co-agonist for the NMDA receptor regulating the strength and direction of synaptic plasticity. Activity-dependent D-serine release mediated by astrocytes is therefore a candidate for mediating between long-term synaptic depression (LTD) and potentiation (LTP) during learning. Here, we show that the mathematical description of this astrocytic regulation is consistent with the classic Bienenstock Cooper Munro (BCM) model for synaptic plasticity, which postulated the existence of an activity-dependent LTP/LTD threshold. We show how the resulting mathematical framework can explain the experimentally observed behavioral effects of astrocytic cannabinoid receptor knock-out on mice during a place avoidance task and give rise to new testable predictions about the learning process advancing our understanding of the functional role of neuron-glia interaction in learning.

Interneuron-like features of microglia in the mouse outer retina
Microglia have recently been associated with complex functionalities both in the brain and the retina, including in synaptic function and plasticity. While investigating the morphology, protein expression profile and connectivity of microglia in the mouse outer retina, we have found that they closely resemble interneurons in the outer retina of other species. We argue that functional studies are needed to reveal whether they also perform an interneuron-like role in vision.

Population Bursts in a Modular Neural Network as a Mechanism for Synchronized Activity in KNDy Neurons
The pulsatile activity of gonadotropin-releasing hormone neurons (GnRH neurons) is a key factor in the regulation of reproductive hormones. This pulsatility is orchestrated by a network of neurons that release the neurotransmitters kisspeptin, neurokinin B, and dynorphin (KNDy neurons), and produce episodic bursts of activity driving the GnRH neurons. We show in this computational study that the features of coordinated KNDy neuron activity can be explained by a neural network in which connectivity among neurons is modular. That is, a network structure consisting of clusters of highly-connected neurons with sparse coupling among the clusters. This modular structure, with distinct parameters for intracluster and intercluster coupling, also yields predictions for the differential effects of the co-released transmitters neurokinin B and dynorphin. In particular, it provides one possible explanation for how the excitatory neurotransmitter neurokinin B and the inhibitory neurotransmitter dynorphin can both increase the degree of synchronization among KNDy neurons.

Cell-type-specific origins of spinal rhythmicity at different locomotor speeds in larval zebrafish
Different speeds of locomotion require heterogeneous spinal populations, but a common mode of rhythm generation is presumed to exist. Here, we explore the cellular versus synaptic origins of spinal rhythmicity at different speeds by performing electrophysiological recordings from premotor excitatory interneurons in larval zebrafish. Chx10-labeled V2a neurons are divided into at least two subtypes proposed to play distinct roles in timing and intensity control. Consistent with distinct rhythm generating and output patterning functions within the spinal V2a population, we find that one subtype is recruited exclusively at slow or fast speeds and exhibits intrinsic cellular properties suitable for rhythmogenesis at those speeds, while the other subtype is recruited more reliably at all speeds and lacks appropriate rhythmogenic cellular properties. Unexpectedly, however, phasic firing patterns during locomotion in rhythmogenic and non-rhythmogenic subtypes are best explained by distinct modes of synaptic inhibition linked to cell-type and speed. At fast speeds reciprocal inhibition in rhythmogenic V2a neurons supports phasic firing, while recurrent inhibition in non-rhythmogenic V2a neurons helps pattern motor output. In contrast, at slow speeds recurrent inhibition in rhythmogenic V2a neurons supports phasic firing, while non-rhythmogenic V2a neurons rely on reciprocal inhibition alone to pattern output. Our findings suggest cell-type-specific, not common, modes of rhythmogenesis generate and coordinate different speeds of locomotion.

The relationship between white matter architecture and language lateralisation in the healthy brain
Interhemispheric anatomical asymmetries have long been thought to be related to language lateralisation. Previous studies have explored whether asymmetries in the diffusion characteristics of white matter language tracts are consistent with language lateralisation. These studies, typically with smaller cohorts, yielded mixed results. This study investigated whether connectomic analysis of quantitative anisotropy (QA) and shape features of white matter tracts across the whole brain are associated with language lateralisation. We analysed 1040 healthy individuals from the Human Connectome Project database. Hemispheric language dominance for each participant was quantified using a laterality quotient (LQ) derived from fMRI activation in regions of interest (ROIs) associated with a language comprehension task compared against a math task. A linear regression model was used to examine the relationship between structural asymmetry and functional lateralisation. Connectometry revealed that LQs were significantly negatively correlated with QA of corpus callosum tracts, including forceps minor, body, tapetum, and forceps major, indicating that reduced language dominance (more bilateral language representation) is associated with increased QA in these regions. The QA of the left arcuate fasciculus, cingulum, and right cerebellar tracts was positively associated with LQ, suggesting that stronger structural asymmetry in these tracts may identify left language dominance. Language lateralisation was not significantly associated with the shape metrics (including length, span, curl, elongation, diameter, volume, and surface area) of all white matter tracts. These results suggest that diffusion measures of microstructural architecture, and not the geometric features of reconstructed white matter tracts, are associated with lateralisation of language comprehension functions. People with increased dependence on both cerebral hemispheres for language processing may have more developed commissural fibres, which may support more efficient interhemispheric communication.

Beyond Glycolysis: Aldolase A is a Novel Effector in Reelin Mediated Dendritic Development
Reelin, a secreted glycoprotein, plays a crucial role in guiding neocortical neuronal migration, dendritic outgrowth and arborization, and synaptic plasticity in the adult brain. Reelin primarily operates through the canonical lipoprotein receptors apolipoprotein E receptor 2 (Apoer2) and very low-density lipoprotein receptor (Vldlr). Reelin also engages with non-canonical receptors and unidentified co-receptors; however, the effects of which are less understood. Using high-throughput tandem mass tag LC-MS/MS-based proteomics and gene set enrichment analysis, we identified both shared and unique intracellular pathways activated by Reelin through its canonical and non-canonical signaling in primary murine neurons during dendritic growth and arborization. We observed pathway crosstalk related to regulation of cytoskeleton, neuron projection development, protein transport, and actin filament-based process. We also found enriched gene sets exclusively by the non-canonical Reelin pathway including protein translation, mRNA metabolic process and ribonucleoprotein complex biogenesis suggesting Reelin fine-tunes neuronal structure through distinct signaling pathways. A key discovery is the identification of aldolase A, a glycolytic enzyme and actin binding protein, as a novel effector of Reelin signaling. Reelin induced de novo translation and mobilization of aldolase A from the actin cytoskeleton. We demonstrated that aldolase A is necessary for Reelin-mediated dendrite growth and arborization in primary murine neurons and mouse brain cortical neurons. Interestingly, the function of aldolase A in dendrite development is independent of its known role in glycolysis. Altogether, our findings provide new insights into the Reelin-dependent signaling pathways and effector proteins that are crucial for actin remodeling and dendritic development.

Altered firing output of VIP interneurons and early dysfunctions in CA1 hippocampal circuits in the 3xTg mouse model of Alzheimer's disease
Alzheimer's disease (AD) leads to progressive memory decline, and alterations in hippocampal function are among the earliest pathological features observed in human and animal studies. GABAergic interneurons (INs) within the hippocampus coordinate network activity, among which type 3 interneuron-specific (I-S3) cells expressing vasoactive intestinal polypeptide and calretinin play a crucial role. These cells provide primarily disinhibition to principal excitatory cells (PCs) in the hippocampal CA1 region, regulating incoming inputs and memory formation. However, it remains unclear whether AD pathology induces changes in the activity of I-S3 cells, impacting the hippocampal network motifs. Here, using young adult 3xTg-AD mice, we found that while the density and morphology of IS-3 cells remain unaffected, there were significant changes in their firing output. Specifically, I-S3 cells displayed elongated action potentials and decreased firing rates, which was associated with a reduced inhibition of CA1 INs and their higher recruitment during spatial decision-making and object exploration tasks. Furthermore, the activation of CA1 PCs was also impacted, signifying early disruptions in CA1 network functionality. These findings suggest that altered firing patterns of I-S3 cells might initiate early-stage dysfunction in hippocampal CA1 circuits, potentially influencing the progression of AD pathology.