bioRxiv Subject Collection: Neuroscience's Journal

Propofol Disrupts the Functional Core-Matrix Architecture of the Thalamus in Humans
Research into the role of thalamocortical circuits in anesthesia-induced unconsciousness is difficult due to anatomical and functional complexity. Prior neuroimaging studies have examined either the thalamus as a whole or focused on specific subregions, overlooking the distinct neuronal subtypes like core and matrix cells. We conducted a study of heathy volunteers and functional magnetic resonance imaging during conscious baseline, deep sedation, and recovery. We advanced the functional gradient mapping technique to delineate the functional geometry of thalamocortical circuits, within a framework of the unimodal-transmodal functional axis of the cortex. We observed a significant shift in this geometry during unconsciousness, marked by the dominance of unimodal over transmodal geometry. This alteration was closely linked to the spatial variations in the density of matrix cells within the thalamus. This research bridges cellular and systems-level understanding, highlighting the crucial role of thalamic core-matrix functional architecture in understanding the neural mechanisms of states of consciousness.

Sertraline modulates hippocampal plasticity and learning via sigma 1 receptors, cellular stress and neurosteroids
In addition to modulating serotonin transport, selective serotonin reuptake inhibitors (SSRIs) have multiple other effects that may contribute to clinical effects, and some of these latter actions prompt repurposing of SSRIs for non-psychiatric indications. We recently observed that the SSRIs fluvoxamine and fluoxetine prevent the acute adverse effects of pro-inflammatory stimulation on long-term potentiation (LTP) in the CA1 hippocampal region. Sertraline showed markedly different effects, acutely inhibiting LTP at a low micromolar concentration through inverse agonism of sigma 1 receptors (S1Rs). In the present studies, we pursued mechanisms contributing to sertraline modulation of LTP in rat hippocampal slices. We found that sertraline partially inhibits synaptic responses mediated by N-methyl-D-aspartate receptors (NMDARs) via effects on NMDARs that express GluN2B subunits. A selective S1R antagonist (NE-100), but not an S1R agonist (PRE-084) blocked effects on NMDARs, despite the fact that both S1R ligands were previously shown to prevent LTP inhibition. Both NE-100 and PRE-084, however, prevented adverse effects of sertraline on one-trial learning. Because of the important role that S1Rs play in modulating endoplasmic reticulum stress, we examined whether inhibitors of cellular stress alter effects of sertraline. We found that two stress inhibitors, ISRIB and quercetin, prevented LTP inhibition, as did inhibitors of the synthesis of endogenous neurosteroids, which are homeostatic regulators of cellular stress. These studies highlight complex effects of sertraline, S1Rs and neurosteroids on hippocampal function and have relevance for understanding therapeutic and adverse drug actions.

Rat anterior cingulate neurons responsive to rule or strategy changes are modulated by the hippocampal theta rhythm and sharp-wave ripples
To better understand neural processing during adaptive learning of stimulus-response-reward contingencies, we recorded synchrony of neuronal activity in anterior cingulate cortex (ACC) with hippocampal rhythms in male rats acquiring and switching between spatial and visual discrimination tasks in a Y-maze. ACC population and single unit activity responded shortly after task rule changes, or just before the rats adopted different task strategies. Hippocampal theta oscillations (associated with memory encoding) modulated an elevated proportion of rule-change responsive neurons (70%), but other neurons that were correlated with strategy-change, strategy value, and reward-rate were not. However, hippocampal sharp wave-ripples modulated significantly higher proportions of rule-change, strategy-change and reward-rate responsive cells during post-session sleep but not pre-session sleep. This suggests an underestimated mechanism for hippocampal mismatch and contextual signals to facilitate ACC detection of contingency changes for cognitive flexibility, a function that is attenuated after it is damaged.

Differential contribution of distinct neuronal populations to danger representations
The recognition of specific stimuli and contexts in dangerous situations determines the expression of behaviors needed to appropriately cope with each threatening encounter. Moreover, the detection of common features shared by different dangerous situations allows eliciting general brain states and is necessary for both the expression of preparatory reactions and adaptive behavioral responses in a timely manner. However, it is unknown how general and specific danger representations emerge from the combined activity of different neuronal populations to elicit the expression of adaptive defensive responses. Using a behavioral paradigm that exposes mice to multiple threatening situations and calcium imaging recordings in freely moving mice, we investigated the role of different dmPFC neuronal populations in the generation of general and specific neuronal representations. Our results suggest that the population of somatostatin positive (SST+) interneurons generates specific representations while those arising from parvalbumin positive (PV+) interneurons are mainly unspecific. Together, this data suggests the presence of distinct information in different dmPFC neurons allowing a collective encoding of both general and specific danger representations.

An inductive bias for slowly changing features in human reinforcement learning
Identifying goal-relevant features in novel environments is a central challenge for efficient behaviour. We asked whether humans address this challenge by relying on prior knowledge about common properties of reward-predicting features. One such property is the rate of change of features, given that behaviourally relevant processes tend to change on a slower timescale than noise. Hence, we asked whether humans are biased to learn more when task-relevant features are slow rather than fast. To test this idea, 100 human participants were asked to learn the rewards of two-dimensional bandits when either a slowly or quickly changing feature of the bandit predicted reward. Participants accrued more reward and achieved better generalisation to unseen feature values when a bandit's relevant feature changed slowly, and its irrelevant feature quickly, as compared to the opposite. Participants were also more likely to incorrectly base their choices on the irrelevant feature when it changed slowly versus quickly. These effects were stronger when participants experienced the feature speed before learning about rewards. Modelling this behaviour with a set of four function approximation Kalman filter models that embodied alternative hypotheses about how feature speed could affect learning revealed that participants had a higher learning rate for the slow feature, and adjusted their learning to both the relevance and the speed of feature changes. The larger the improvement in participants' performance for slow compared to fast bandits, the more strongly they adjusted their learning rates. These results provide evidence that human reinforcement learning favours slower features, suggesting a bias in how humans approach reward learning.

A functional interaction between TDP-43 and USP10 reveals USP10 dysfunction in TDP-43 proteinopathies
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive degeneration of motor neurons in the cerebral cortex and spinal cord, with a rapid progression from diagnosis to death. The great majority of ALS cases and around 50% of FTD cases present with TDP-43 pathology, leading to mislocalization and cytoplasmic aggregation of TDP-43, which can result in both its loss of nuclear functions and a gain of toxicity in the cytoplasm. TDP-43 and other RNA-binding proteins accumulate in stress granules (SGs) under stress conditions. The ubiquitin-specific protease 10 (USP10) is an inhibitor of SGs assembly that has been recently linked to neurodegeneration. Here, we identified a new functional interaction between TDP-43 and USP10, in which USP10 can control multiple aspects of TDP-43 biology that are thought to play important roles in its involvement in disease pathogenesis, such as its cytoplasmic and nuclear aggregation, expression and splicing functionality. In turn, TDP-43 is also able to control diverse aspects of USP10 biology, such as its expression levels, aggregation and function. Critically, we found USP10 dysregulation in ALS and FTD patients, overall suggesting a possible role for USP10 in ALS/FTD pathogenesis.

PP2B-dependent cerebellar plasticity sets the amplitude of an innate reflex during juvenile development
Throughout life, the cerebellum plays a central role in the coordination and optimization of movements, using cellular plasticity to adapt a range of behaviors. If these plasticity processes establish a fixed setpoint during development, or continuously adjust behaviors throughout life, is currently unclear. Here, by spatiotemporally manipulating the activity of protein phosphatase 2B (PP2B), an enzyme critical for cerebellar plasticity, we examined the consequences of disrupted plasticity on the performance and adaptation of the vestibulo-ocular reflex (VOR). We find that, in contrast to Purkinje cell specific deletion starting early postnatally, acute pharmacological as well as adult-onset genetic deletion of PP2B affects all forms of VOR adaptation, but not the level of VOR itself. Next, we show that Purkinje cell-specific genetic deletion of PP2B in juvenile mice leads to a progressive loss of the protein PP2B and a concurrent change in the VOR, in addition to the loss of adaptive abilities. Finally, re-expressing PP2B in adult mice that lack PP2B expression from early in development, rescues VOR adaptation, but does not affect the performance of the reflex. Together, our results indicate that chronic or acute, genetic or pharmacological block of PP2B disrupts the adaptation of the VOR. In contrast, only the absence of plasticity during cerebellar development affects the setpoint of VOR, an effect that cannot be corrected after maturation of the cerebellum. These findings suggest that cerebellar plasticity influences behavior in two ways, through direct control of behavioral adaptation and via long-term effects initiated in the juvenile period.

Biophysical Essentials - A Full Stack Open-Source Software Framework for Conserved and Advanced Analysis of Patch-Clamp Recordings
Patch-Clamp recordings allow for in depth electrophysiological characterization of single cells, their general biophysical properties as well as characteristics of voltage- and ligand-gated ionic currents. Different acquisition modes, such as whole-cell patch-clamp recordings in the current or voltage clamp configuration, capacitance measurements or single channel recordings from cultured cells as well as acute brain slices are routinely performed for these purposes. Nevertheless, multipurpose transparent and adaptable software tools to perform reproducible state-of-the-art analysis of multiple experiment types and to manage larger sets of experimental data are currently unavailable. We therefore developed Biophysical Essentials (BPE) as an open-source python software for transparent and reproducible analysis of electrophysiological recordings. While initially designed to improve time consuming and repetitive analysis steps, BPE was optimized to cover entire workflows from data acquisition, preprocessing, visualization and normalization of single recordings up to stacked calculations and statistics of multiple experiments. BPE can operate with different file formats from different amplifier systems and producers. An in-process database logs all analysis steps for later review and serves as a central storage point for recordings. Statistical testing as well as advanced analysis functions like Boltzmann-fitting and dimensional reduction methods further support the researchers' needs in projects involving electrophysiology techniques.

Frontal noradrenergic and cholinergic transients exhibit distinct spatiotemporal dynamics during competitive decision-making
Norepinephrine (NE) and acetylcholine (ACh) are neuromodulators that are crucial for learning and decision-making. In the cortex, NE and ACh are released at specific sites along neuromodulatory axons, which would constrain their spatiotemporal dynamics at the subcellular scale. However, how the fluctuating patterns of NE and ACh signaling may be linked to behavioral events is unknown. Here, leveraging genetically encoded NE and ACh indicators, we use two-photon microscopy to visualize neuromodulatory signals in the superficial layer of the mouse medial frontal cortex during decision-making. Head-fixed mice engage in a competitive game called matching pennies against a computer opponent. We show that both NE and ACh transients carry information about decision-related variables including choice, outcome, and reinforcer. However, the two neuromodulators differ in their spatiotemporal pattern of task-related activation. Spatially, NE signals are more segregated with choice and outcome encoded at distinct locations, whereas ACh signals can multiplex and reflect different behavioral correlates at the same site. Temporally, task-driven NE transients were more synchronized and peaked earlier than ACh transients. To test functional relevance, using optogenetics we found that evoked elevation of NE, but not ACh, in the medial frontal cortex increases the propensity of the animals to switch and explore alternate options. Taken together, the results reveal distinct spatiotemporal patterns of rapid ACh and NE transients at the subcellular scale during decision-making in mice, which may endow these neuromodulators with different ways to impact neural plasticity to mediate learning and adaptive behavior.

Nr4a2 blocks oAbeta-mediated synaptic plasticity dysfunction and ameliorates spatial memory deficits in the APPSw,Ind mouse
Alzheimer disease (AD) is associated with disruptions in neuronal communication, especially in brain regions crucial for learning and memory, such as the hippocampus. The amyloid hypothesis suggests that the accumulation of amyloid-beta oligomers (oAbeta) contributes to synaptic dysfunction by internalisation of synaptic AMPA receptors. Recently, it has been reported that Nr4a2, a member of the Nr4a family of orphan nuclear receptors, plays a role in hippocampal synaptic plasticity by regulating BDNF and synaptic AMPA receptors. Here, we demonstrate that oAbeta inhibits activity-dependent Nr4a2 activation in hippocampal neurons, indicating a potential link between oAbeta and Nr4a2 down-regulation. Furthermore, we have observed a reduction in Nr4a2 protein levels in postmortem hippocampal tissue samples from early AD stages. Pharmacological activation of Nr4a2 proves effective in preventing oAbeta-mediated synaptic depression in the hippocampus. Notably, Nr4a2 overexpression in the hippocampus of AD mouse models ameliorates spatial learning and memory deficits. In conclusion, the findings suggest that oAbeta may contribute to early cognitive impairment in AD by blocking Nr4a2 activation, leading to synaptic dysfunction. Thus, our results further support that Nr4a2 activation is a potential therapeutic target to mitigate oAbeta-induced synaptic and cognitive impairments in the early stages of Alzheimer's disease.

Balancing safety and efficiency in human decision making
The safety-efficiency dilemma describes the problem of maintaining safety during efficient exploration, and is a special case of the exploration-exploitation dilemma in the face of potentially catastrophic dangers. Conventional exploration-exploitation solutions collapse punishment and reward into a single signal, whereby early losses can be overcome by later gains. But the brain has a separate system for Pavlovian fear learning, suggesting a possible computational advantage to maintaining a specific fear memory during exploratory decision-making. In a series of simulations, we show here this promotes safe but efficient learning, and is optimised by arbitrating Pavlovian avoidance on instrumental decision-making according to uncertainty. In a human approach-withdrawal experiment, we show that this flexible avoidance model captures both choice and reaction times. These results show that the Pavlovian fear system has a more sophisticated role in decision-making that previously thought, by shaping flexible exploratory behaviour in a computationally precise manner.

Astrocytes tune neuronal excitability through the calcium-activated potassium current sIAHP
Neurons have the unique ability to integrate synaptic information by modulating the function of the voltage-gated membrane ion channels, which govern their excitability. Astrocytes play active roles in synaptic function, from synapse formation and maturation to plasticity processes. However, it remains elusive whether astrocytes can impact the neuronal activity by regulating membrane ion conductances that control the intrinsic firing properties. Here, we found that astrocytes, by releasing adenosine, enhance the slow calcium-activated potassium current (sIAHP) in CA1 hippocampal pyramidal neurons. Remarkably, we showed that interneuron activity was involved in the astrocyte-mediated sIAHP modulation. Indeed, both synaptically activated and optogenetically stimulated hippocampal interneurons evoked coordinated signaling in astrocytes and pyramidal neurons, which relied on GABAB and adenosine A1 receptors activation. In addition, the selective genetic ablation of GABAB receptors in CA1 astrocytes prevented the spike frequency adaptation in pyramidal cells after interneuron activation. Therefore, our data reveal the astrocyte capability to modulate the intrinsic membrane properties that dictate neuronal firing rate and hippocampal networks activity.

A Deep Learning Pipeline for Mapping in situ Network-level Neurovascular Coupling in Multi-photon Fluorescence Microscopy
Functional hyperaemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the activity of the surrounding neurons. Although functional hyperemia has been extensively studied at the level of both tissue and individual vessels, vascular network-level coordination remains largely unknown. To bridge this gap, we developed a deep learning-based computational pipeline that uses two-photon fluorescence microscopy images of cerebral microcirculation to enable automated reconstruction and quantification of the geometric changes across the microvascular network, comprising hundreds of interconnected blood vessels, pre and post-activation of the neighbouring neurons. The pipeline's utility was demonstrated in the Thy1-ChR2 optogenetic mouse model, where we observed network-wide vessel radius changes to depend on the photostimulation intensity, with both dilations and constrictions occurring across the cortical depth, at an average of 16.1{+/-}14.3 m (mean{+/-}stddev) away from the most proximal neuron for dilations; and at 21.9{+/-}14.6 m away for constrictions. We observed a significant heterogeneity of the vascular radius changes within vessels, with radius adjustment varying by an average of 24 {+/-} 28% of the resting diameter, likely reflecting the heterogeneity of the distribution of contractile cells on the vessel walls. A graph theory-based network analysis revealed that the assortativity of adjacent blood vessel responses rose by 152 {+/-} 65% at 4.3 mW/mm2 of blue photostimulation vs. the control, with a 4% median increase in the efficiency of the capillary networks during this level of blue photostimulation in relation to the baseline. Interrogating individual vessels is thus not sufficient to predict how the blood flow is modulated in the network. Our computational pipeline, to be made openly available, enables tracking of the microvascular network geometry over time, relating caliber adjustments to vessel wall-associated cells' state, and mapping network-level flow distribution impairments in experimental models of disease.

BETA-HYDROXYBUTYRATE COUNTERACTS THE DELETERIOUS EFFECTS OF A SATURATED HIGH-FAT DIET ON SYNAPTIC AMPA RECEPTORS AND COGNITIVE PERFORMANCE
The ketogenic diet, high-fat and low-carbohydrates, has witnessed a significant rise in popularity, not merely as a bodyweight management strategy but also as a regimen that demonstrated efficacy in delaying cognitive decline associated with neurodegenerative diseases and the aging process. This dietary approach triggers the liver's production of ketone bodies, mainly {beta}-hydroxybutyrate, used as an alternative energy source for neurons, crucial for maintaining glutamatergic synapses. However, it remains to be established whether {beta}-hydroxybutyrate can counteract impaired AMPA receptor trafficking, synaptic dysfunction, and cognitive decline induced by metabolic challenges like saturated fatty acids. Here, we report that palmitic acid, a saturated fatty acid, decreased surface glutamate GluA1-type AMPA receptor levels in cultured cortical neurons, while the unsaturated fatty acids (oleic acid and {omega}-3 docosahexaenoic acid) and {beta}-hydroxybutyrate increased them. Furthermore, this ketone body counteracted the adverse effects of palmitic acid on synaptic GluA1 levels and synaptic transmission, excitability, and plasticity. Again, daily intragastrical administration of {beta}-hydroxybutyrate for two months was able to reverse the cognitive impairment mediated by a saturated high-fat diet in a mouse experimental model. Overall, our findings not only support the pivotal role of nutrients on synaptic function and neuroplasticity but provide novel insights into the potential of {beta}-hydroxybutyrate to delay cognitive impairments linked to metabolic diseases.

Bayesian evidence for the neural dissociation between finger and hand imitation skills
Introduction: For limb apraxia - a heterogeneous disorder of higher motor cognition following stroke - an enduring debate has arisen regarding the existence of dissociating neural correlates for finger and hand gestures in the left hemisphere. We re-assessed this question asking whether previous attempts analysing pooled samples of patients with deficits in only one and patients with deficits in both imitation types might have led to systematically biased results. Methods: We conducted frequentist and Bayesian, voxelwise and regionwise lesion symptom mappings on a pooled sample (N=96) and subsamples containing only shared and only isolated hand and finger imitation deficits and respective controls. Results: Anatomical analyses on the isolated sample reinforced a cortical dissociation of finger deficits (located more anteriorly) and hand deficits (located more posteriorly). The presence of patients with shared deficits did indeed dilute associations that appeared stronger in the respective isolated samples. Also, brain regions truly associated with hand imitation deficits showed a positive bias for finger imitation deficits, when the sample contained patients with shared deficits. In addition, our frequentist parameters uncovered that some of our Bayesian evidence supported reverse associations (damage protecting from rather than increasing the deficit). Discussion: Joint analyses of patients with shared and isolated imitation deficits do indeed lead to biases, which may explain why some previous studies have failed to detect the actual neural dissociation between hand and finger imitation deficits.

The immunological profile of RC17 hESC-derived dopaminergic neural progenitor cells in vitro: implications for the STEM-PD clinical trial.
Parkinsons Disease involves the progressive loss of dopaminergic neurons (DAn), prompting clinical trials replacing cell loss with neural grafts. This includes the transplantation of pluripotent stem cell-derived DAn progenitor cells (NPC) currently under investigation in the STEM-PD trial. To determine the likelihood of immune rejection post-grafting, we characterised the immunogenicity of the STEM-PD product (RC17-hESC-derived NPCs), comparing them to human foetal ventral mesencephalic tissue (hfVM) previously tested in trials, including our own TRANSEURO trial. Despite MHC-Class I expression, upregulated by proinflammatory cytokines, no immune response to NPCs was detected in vitro. Instead, they were immunosuppressive. Transcriptomic analysis revealed similarities between RC17-NPCs and hfVM, both strongly upregulating antigen processing and presentation pathways in response to IFNgamma. Furthermore, immunosuppressant mycophenolate mofetil detrimentally affected NPC survival and differentiation in vitro. Overall, our data suggest that aggressive immunosuppression is not required following hESC-NPC transplantation and that caution should be exercised when selecting the immunosuppressive regimen.

Brain-phenotype predictions can survive across diverse real-world data
Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.

Subject-specific maximum entropy model of resting state fMRI shows diagnostically distinct patterns of energy state distributions
Objective: Existing neuroimaging studies of psychotic and mood disorders have reported regional brain activation differences (first-order properties) and alterations in functional connectivity based on pairwise correlations in activation (second-order properties). This study used a generalized Ising model, also called a pairwise maximum entropy model (MEM), to integrate first- and second-order properties to provide a comprehensive picture of BOLD patterns and a system-wide summary measure called energy. This study examines the usefulness of individual level MEMs, attempts to identify image-derived counterparts of the model, and explores potential applications to psychiatry. Method: MEMs are fit to resting state fMRI data of each individual of a sample of 132 participants consisting of schizophrenia/schizoaffective disorder, bipolar disorder, and major depression, and a demographically matched 132 participants without these diagnoses from the UK Biobank to examine the default mode network (DMN). Results: The model explained observed brain state occurrence probabilities well across all participants, and model parameters were highly correlated to image-derived parameters for all groups. Within clinical groups, schizophrenia/schizoaffective disorder and bipolar disorder patients showed significant differences in averaged energy distribution compared to controls for all sub-systems of the DMN except for depression, where differences in the energy distributions were only detected in the DMN of the regions from the right hemisphere. Conclusions: Subject-specific Ising modeling may offer an improved measure of biological functional correlates relative to traditional approaches. The observation of distinct patterns of energy distribution among the three clinical groups compared to controls suggests relative diagnostic specificity and potential for clinical translation.

Maternal fluoxetine impairs synaptic transmission and plasticity in the medial prefrontal cortex and alters the structure and function of dorsal raphe nucleus neurons in offspring mice
Selective serotonin (5-HT) reuptake inhibitors (SSRIs) are a class of antidepressant drugs commonly prescribed to women during pregnancy and breastfeeding to treat depression. There is evidence that prenatal exposure to SSRIs may be associated with a higher risk of adverse cognitive outcomes and affective disorders in later life. In animal models, exposure to SSRIs during brain development results in behavioral alterations as well as structural abnormalities of cerebral cortical neurons. Little is known about the consequences of SSRI-induced excess of 5-HT during development on the brain serotonergic system itself. In this study, an SSRI - fluoxetine (FLX) - was administered to C57BL/6J mouse dams during pregnancy and lactation. We found that maternal FLX decreased field potentials, impaired long-term potentiation, facilitated induction of long-term depression and tended to increase the density of 5-HTergic fibers in the medial prefrontal cortex (mPFC) of female but not male adolescent offspring. These effects were accompanied by deteriorated performance in the temporal order memory task and reduced sucrose preference with no change in marble burying behavior in FLX-exposed female offspring. We also found that maternal FLX reduced the axodendritic tree complexity of 5-HT dorsal raphe nucleus (DRN) neurons in female but not male offspring. Whole-cell recordings demonstrated no changes in the excitability of DRN 5-HT neurons in FLX-exposed offspring of either sex. While no effects of maternal FLX on inhibitory postsynaptic currents (sIPSCs) in DRN neurons were found, we observed a significant influence of FLX exposure on kinetic characteristics of spontaneous excitatory postsynaptic currents (sEPSCs) in DRN neurons. Finally, we report that no changes in field potentials and synaptic plasticity were evident in the mPFC of the offspring after maternal exposure during pregnancy and lactation to a new antidepressant, vortioxetine. These findings show that in contrast to the mPFC, long-term consequences of maternal FLX exposure on the structure and function of DRN 5-HT neurons are mild and suggest a sex-dependent, distinct sensitivity of cortical and brainstem neurons to FLX exposure in early life. Regarding side effects on brain development, vortioxetine might be a safer alternative to FLX.

Multisensory strategies for postural compensation after lateral line loss
To control elevation underwater, aquatic vertebrates integrate multisensory information (e.g., vestibular, visual, proprioceptive) to guide posture and swim kinematics. Here we characterized how larval zebrafish changed posture and locomotive strategies after imposed instability (decreased buoyancy) in the presence and absence of visual cues. We discovered that larvae sank more after acute loss of lateral line (flow-sensing) hair cells. In response, larvae engaged different compensatory strategies, depending on whether they were in the light or dark. In the dark, larvae swam more frequently, engaging their trunk to steer their nose up and climb more effectively. However, in the light, larvae climbed more often, engaging both pectoral fins and trunk to elevate. We conclude that larvae sense instability and use vestibular and visual information as available to control posture and trajectory. Our work is a step towards understanding the multisensory neural computations responsible for control strategies that allow orientation and navigation in depth.

Measuring excitation-inhibition balance through spectral components of local field potentials
The balance between excitation and inhibition is critical to brain functioning, and dysregulation of this balance is a hallmark of numerous psychiatric conditions. Measuring this excitation-inhibition (E:I) balance in vivo has remained difficult, but theoretical models have proposed that characteristics of local field potentials (LFP) may provide an accurate proxy. To establish a conclusive link between LFP and E:I balance, we recorded single units and LFP from the prefrontal cortex (mPFC) of rats during decision making. Dynamic measures of synaptic coupling strength facilitated direct quantification of E:I balance and revealed a strong inverse relationship to broadband spectral power of LFP. These results provide a critical link between LFP and underlying network properties, opening the door for non-invasive recordings to measure E:I balance in clinical settings.

High-Resolution Laminar Identification in Macaque Primary Visual Cortex Using Neuropixels Probes
Laminar electrode arrays allow simultaneous recording of activity of many cortical neurons and assignment to correct layers using current source density (CSD) analyses. Electrode arrays with 100-micron contact spacing can estimate borders between layer 4 versus superficial or deep layers, but in macaque primary visual cortex (V1) there are far more layers, such as 4A which is only 50-100 microns thick. Neuropixels electrode arrays have 20-micron spacing, and thus could potentially discern thinner layers and more precisely identify laminar borders. Here we show that CSD signals lack the spatial resolution required to take advantage of high density Neuropixels arrays and describe the development of approaches based on higher resolution electrical signals and analyses, including spike waveforms and spatial spread, unit density, high-frequency action potential (AP) power spectrum, temporal power change, and coherence spectrum, that afford far higher resolution of laminar distinctions, including the ability to precisely detect the borders of even the thinnest layers of V1.

Argon neuroprotection in a non-human primate model of transient endovascular ischemic stroke.
Background: Previous studies have demonstrated the efficacy of argon neuroprotection in rodent models of cerebral ischemia. The objective of the present study was to confirm a potential neuroprotective effect of argon in a non-human primate model of endovascular ischemic stroke as an essential step before considering the use of argon as a neuroprotective agent in humans. Methods: Thirteen adult monkeys (Macaca mulatta) were allocated to two groups: a control group (n=8) without neuroprotection and an argon group (n=5) in which argon inhalation (90 min) was initiated 30 minutes after onset of ischemia. Animals in both groups underwent brain MRI (pre-ischemic) at least 7 days before the intervention. The monkeys were subjected to focal cerebral ischemia induced by a transient (90 min) middle cerebral artery occlusion (tMCAO). After tMCAO, MRI was performed 1 hour after cerebral reperfusion. The ischemic core volume was defined by the apparent diffusion coefficient (aDC) and edema in fluid attenuated inversion recovery (FLAIR) acquisitions. MRI masks were applied to distinguish between cortical and subcortical abnormalities. In addition, a modified version of the Rankin scale was used to neurologically assess post-tMCAO. Results: Despite variability in the ischemic core and edema volumes in the control group, argon significantly reduced ischemic core volume after ischemia compared to the control group (1.1 - 1.6 cm3 vs. 8.5 - 8.1 cm3; p=0.03). This effect was limited to cortical structures (0.6 - 1.1 cm3 vs. 7.4 - 7.2 cm3; p=0.03). No significant differences were observed in the edema volumes. Measures of neurological clinical outcome suggested a better prognosis in argon-treated animals. Conclusions: In the tMCAO macaque model, argon induced effective neuroprotective effects, leading to a reduced ischemic core in cortical areas. These results support the potential use of this therapeutic approach for future clinical studies in stroke patients.

Reduced Capsaicin-Induced Mechanical Allodynia and Neuronal Responses in the DRG in the Presence of Shp1 Overexpression
Transient Receptor Potential Vanilloid 1 (TRPV1) is a nonselective cation channel expressed by pain-sensing neurons and has been an attractive target for the development of drugs to treat pain. Recently, Src homology region 2 domain-containing phosphatase-1 (SHP-1) was shown to dephosphorylate TRPV1 in dorsal root ganglia (DRG) neurons, which was linked with alleviating different pain phenotypes. These previous studies were performed in male rodents only and did not directly investigate the role of SHP-1 in TRPV-1 mediated sensitization. Therefore, our goal was to determine the impact of Shp1 overexpression on TRPV1-mediated neuronal responses and capsaicin-induced pain behavior in mice of both sexes. Twelve-week-old male and female mice overexpressing Shp1 (Shp1-Tg) and their wild type (WT) littermates were used. Shp1 overexpression was confirmed in the DRG of Shp1-Tg mice by RNA in situ hybridization and RT-qPCR. Trpv1 and Shp1 were found to be co-expressed in DRG sensory neurons in both genotypes. Functionally, this overexpression resulted in lower magnitude intracellular calcium responses to 200 nM capsaicin stimulation in DRG cultures from Shp1-Tg mice compared to WTs. In vivo, we tested the effects of Shp1 overexpression on capsaicin-induced pain through a model of capsaicin footpad injection. While capsaicin injection evoked nocifensive behavior (paw licking) and paw swelling in both genotypes and sexes, only WT mice developed mechanical allodynia after capsaicin injection. We observed similar level of TRPV1 protein expression in the DRG of both genotypes, however, a higher amount of tyrosine phosphorylated TRPV1 was detected in WT DRG. These experiments suggest that, while SHP-1 does not mediate the acute swelling and nocifensive behavior induced by capsaicin, it does mediate a protective effect against capsaicin-induced mechanical allodynia in both sexes. The protective effect of SHP-1 might be mediated by TRPV1 dephosphorylation in capsaicin-sensitive sensory neurons of the DRG.

Plasticity in inhibitory networks improves pattern separation in early olfactory processing
Distinguishing between nectar and non-nectar odors presents a challenge for animals due to shared compounds in complex mixtures, where changing ratios often signify differences in reward. Changes in nectar production throughout the day and potentially many times within a forager's lifetime add to the complexity. The honeybee olfactory system, containing less than a 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. We used a computational network model and live imaging of the honeybee's AL to explore the neural mechanisms and functions of the AL plasticity. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise and efficient neural code. Our Calcium imaging data support our model's predictions. Furthermore, we applied these contrast enhancement principles to a Graph Convolutional Network (GCN) and found that similar mechanisms could enhance the performance of artificial neural networks. Our model provides insights into how plasticity at the inhibitory network level reshapes coding for efficient learning of complex odors.

Instability of excitatory synapses in experimental autoimmune encephalomyelitis and the outcome for excitatory circuit inputs to individual cortical neurons
Synapses are lost on a massive scale in the brain and spinal cord of people living with multiple sclerosis (PwMS), and this synaptic loss extends far beyond demyelinating lesions. Post-mortem studies show the long-term consequences of multiple sclerosis (MS) on synapses but do not inform on the early impacts of neuroinflammation on synapses that subsequently lead to synapse loss. How excitatory circuit inputs are altered across the dendritic tree of individual neurons under neuroinflammatory stress is not well understood. Here, we directly assessed the structural dynamics of labeled excitatory synapses in experimental autoimmune encephalomyelitis (EAE) as a model of immune-mediated cortical neuronal damage. We used in vivo two-photon imaging and a synthetic tissue-hydrogel super-resolution imaging technique to reveal the dynamics of excitatory synapses, map their location across the dendritic tree of individual neurons, and examine neurons at super-resolution for synaptic loss. We found that excitatory synapses are destabilized but not lost from dendritic spines in EAE, starting with the earliest imaging session before symptom onset. This led to dramatic changes in excitatory circuit inputs to individual cells. In EAE, stable synapses are replaced by synapses that appear or disappear across the imaging sessions or repeatedly change at the same location. These unstable excitatory inputs occur closer to one another in EAE than in healthy controls and are distributed across the dendritic tree. When imaged at super-resolution, we found that a small proportion of dendritic protrusions lost their presynapse and/or postsynapse. Our finding of diffuse destabilizing effects of neuroinflammation on excitatory synapses across cortical neurons may have significant functional consequences since normal dendritic spine dynamics and clustering are essential for learning and memory.

Automatic Geometry-based Estimation of the Locus Coeruleus Region on T1-Weighted Magnetic Resonance Images
The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. We also test the hypothesis that using the phase image as input can improve the robustness of out-of-sample segmentation. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report contributes and provides an evaluation of two computational methods estimating neural structure.

The control costs of human brain dynamics
The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using network control theory, here we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from positron emission tomography, providing insight into the bioenergetic footing of resting state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

Dissociable neural signals for reward and emotion prediction errors
Reinforcement learning models focus on reward prediction errors (PEs) as the driver of behavior. However, recent evidence indicates that deviations from emotion expectations, termed affective PEs, play a crucial role in shaping behavior. Whether there is neural separability between emotion and reward signals remains unknown. We employ electroencephalography during social learning to investigate the neural signatures of reward and affective PEs. Behavioral results reveal that while affective PEs predict choices when little is known about how a partner will behave, reward PEs become more predictive overtime as uncertainty about a partners behavior diminishes. This functional dissociation is mirrored neurally by engagement of distinct event-related potentials. The FRN indexes reward PEs while the P3b tracks affective PEs. Only the P3b predicts subsequent choices, highlighting the mechanistic influence of affective PEs during social learning. These findings present evidence for a neurobiologically viable emotion learning signal that is distinguishable--behaviorally and neurally--from reward.

Heparan sulfate modified proteins affect cellular processes central to neurodegeneration and modulate presenilin function
Mutations in presenilin-1 (PSEN1) are the most common cause of familial, early-onset Alzheimer's disease (AD), typically producing cognitive deficits in the fourth decade. A variant of APOE, APOE3 Christchurch (APOE3ch), was found associated with protection from both cognitive decline and Tau accumulation in a 70-year-old bearing the disease-causing PSEN1-E280A mutation. The amino acid change in ApoE3ch is within the heparan sulfate (HS) binding domain of APOE, and purified APOEch showed dramatically reduced affinity for heparin, a highly sulfated form of HS. The physiological significance of ApoE3ch is supported by studies of a mouse bearing a knock-in of this human variant and its effects on microglia reactivity and A{beta}-induced Tau deposition. The studies reported here examine the function of heparan sulfate-modified proteoglycans (HSPGs) in cellular and molecular pathways affecting AD-related cell pathology in human cell lines and mouse astrocytes. The mechanisms of HSPG influences on presenilin-dependent cell loss and pathology were evaluated in Drosophila using knockdown of the presenilin homolog, Psn, together with partial loss of function of sulfateless (sfl), a homolog of NDST1, a gene specifically affecting HS sulfation. HSPG modulation of autophagy, mitochondrial function, and lipid metabolism were shown to be conserved in cultured human cell lines, Drosophila, and mouse astrocytes. RNAi of Ndst1 reduced intracellular lipid levels in wild-type mouse astrocytes or those expressing humanized variants of APOE, APOE3, and APOE4. RNA-sequence analysis of human cells deficient in HS synthesis demonstrated effects on the transcriptome governing lipid metabolism, autophagy, and mitochondrial biogenesis and showed significant enrichment in AD susceptibility genes identified by GWAS. Neuron-directed knockdown of Psn in Drosophila produced cell loss in the brain and behavioral phenotypes, both suppressed by simultaneous reductions in sfl mRNA levels. Abnormalities in mitochondria, liposome morphology, and autophagosome-derived structures in animals with Psn knockdown were also rescued by simultaneous reduction of sfl. sfl knockdown reversed Psn-dependent transcript changes in genes affecting lipid transport, metabolism, and monocarboxylate carriers. These findings support the direct involvement of HSPGs in AD pathogenesis.