bioRxiv Subject Collection: Neuroscience's Journal

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria (RDoC) Framework
Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific, or excessively broad, relative to the underlying brain circuitry it seeks to elucidate, leading to potential misrepresentation of circuit-function relations. We used a latent variable approach to address this issue, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. Furthermore, we externally validated the factor solutions from our curated training dataset using an independent set of 36 coordinate maps sourced through Neurosynth. We used RDoC constructs as seed terms for Neurosynth topic meta-analysis. We hypothesized that if boundaries of RDoC domains warrant refinement, this would be indicated by the presence of overlapping domains or domains lacking specificity. Our findings suggest that a bifactor data-driven structure fits better with the current corpus of tfMRI data, with a general domain representing task-general patterns of brain activation. The data-driven model also proposes a different group of major domains, particularly splitting the RDoC cognitive systems domain into distinct domains. Data-driven models are useful for revising the posited circuit-function relations outlined in the current RDoC framework.

Functional Connectivity Differences in Distinct Dentato-Cortical Networks in Alzheimers Disease and Mild Cognitive Impairment
Recent research has implicated the cerebellum in Alzheimers disease (AD), and cerebro- cerebellar network connectivity is emerging as a possible contributor to symptom severity. The cerebellar dentate nucleus (DN) has parallel motor and non-motor sub-regions that project to motor and frontal regions of the cerebral cortex, respectively. These distinct dentato-cortical networks have been delineated in the non-human primate and human brain. Importantly, cerebellar regions prone to atrophy in AD are functionally connected to atrophied regions of the cerebral cortex, suggesting that dysfunction perhaps occurs at a network level. Investigating functional connectivity (FC) alterations of the DN is a crucial step in understanding the cerebellum in AD and in mild cognitive impairment (MCI). Inclusion of this latter group stands to provide insights into cerebellar contributions prior to diagnosis of AD. The present study investigated FC differences in dorsal (dDN) and ventral (vDN) DN networks in MCI and AD relative to cognitively normal participants (CN) and relationships between FC and behavior. Our results showed patterns indicating both higher and lower functional connectivity in both dDN and vDN in AD compared to CN. However, connectivity in the AD group was lower when compared to MCI. We argue that these findings suggest that the patterns of higher FC in AD may act as a compensatory mechanism. Additionally, we found associations between the individual networks and behavior. There were significant interactions between dDN connectivity and motor symptoms. However, both DN seeds were associated with cognitive task performance. Together, these results indicate that cerebellar DN networks are impacted in AD, and this may impact behavior. In concert with the growing body of literature implicating the cerebellum in AD, our work further underscores the importance of investigations of this region. We speculate that much like in psychiatric diseases such as schizophrenia, cerebellar dysfunction results in negative impacts on thought and the organization therein. Further, this is consistent with recent arguments that the cerebellum provides crucial scaffolding for cognitive function in aging. Together, our findings stand to inform future clinical work in the diagnosis and understanding of this disease.

Unraveling the impact of congenital deafness on individual brain organization
Research on brain plasticity, particularly in the context of deafness, consistently emphasizes the reorganization of the auditory cortex. However, a critical question arises: to what extent do all individuals with deafness show the same level of reorganization? To address this question, we examined the individual differences in deafness functional connectivity (FC), specifically from the deprived auditory cortex. Our findings demonstrate a remarkable differentiation between individuals deriving from the absence of shared auditory experiences, resulting in heightened FC variability among deaf individuals, compared to more consistent FC in the hearing group. Notably, this increased variability is particularly pronounced in regions where FC diverges between the deaf and hearing individuals, reflecting the individual differences in how the brain reorganizes in response to sensory deprivation. Additionally, connectivity to language regions also becomes more diverse in deafness across individuals. Importantly, this does not stem from delayed language acquisition, as it is found in deaf native signers, who are exposed to rich natural language since birth. Further, comparing FC diversity between deaf native signers and deaf delayed signers who were deprived of language in early development, we show that language experience also impacts individual differences, although to a more moderate extent. Overall, our research points out the intricate interplay between brain plasticity and individual differences, shedding light on the diverse ways reorganization manifests among individuals. It further joins findings in blindness, showing that individual differences are affected by sensory experience. Finally, these findings highlight the importance of considering individual differences in personalized rehabilitation for hearing loss.

Primary auditory cortex is necessary for the acquisition and expression of categorical behavior
The primary auditory cortex (ACtx) is critically involved in the association of sensory information with specific behavioral outcomes. Such sensory-guided behaviors are necessarily brain-wide endeavors, requiring a plethora of distinct brain areas, including those that are involved in aspects of decision making, motor planning, motor initiation, and reward prediction. ACtx comprises a number of distinct excitatory cell-types that allow for the brain-wide propagation of behaviorally-relevant sensory information. Exactly how ACtx involvement changes as a function of learning, as well as the functional role of distinct excitatory cell-types is unclear. Here, we addressed these questions by designing a two-choice auditory task in which water-restricted, head-fixed mice were trained to categorize the temporal rate of a sinusoidal amplitude modulated (sAM) noise burst and used transient cell-type specific optogenetics to probe ACtx necessity across the duration of learning. Our data demonstrate that ACtx is necessary for the ability to categorize the rate of sAM noise, and this necessity grows across learning. ACtx silencing substantially altered the behavioral strategies used to solve the task by introducing a fluctuating choice bias and increasing dependence on prior decisions. Furthermore, ACtx silencing did not impact the animal's motor report, suggesting that ACtx is necessary for the conversion of sensation to action. Targeted inhibition of extratelencephalic projections on just 20% of trials had a minimal effect on task performance, but significantly degraded learning. Taken together, our data suggest that distinct cortical cell-types synergistically control auditory-guided behavior and that extratelencephalic neurons play a critical role in learning and plasticity.

Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models
Typical sex differences in white matter (WM) microstructure during development are incompletely understood. Here we evaluated sex differences in WM microstructure during typical brain development using a sample of neurotypical individuals across a wide developmental age (N=239, aged 5-22 years). We used the conventional diffusion-weighted MRI (dMRI) model, diffusion tensor imaging (DTI), and two advanced dMRI models, the tensor distribution function (TDF) and neurite orientation dispersion density imaging (NODDI) to assess WM microstructure. WM microstructure exhibited significant, regionally consistent sex differences across the brain during typical development. Additionally, the TDF model was most sensitive in detecting sex differences. These findings highlight the importance of considering sex in neurodevelopmental research and underscore the value of the advanced TDF model.

Counter-regulation of RNA stability by UPF1 and TDP43
RNA quality control is crucial for proper regulation of gene expression. Disruption of nonsense mediated mRNA decay (NMD), the primary RNA decay pathway responsible for the degradation of transcripts containing premature termination codons (PTCs), can disrupt development and lead to multiple diseases in humans and other animals. Similarly, therapies targeting NMD may have applications in hematological, neoplastic and neurological disorders. As such, tools capable of accurately quantifying NMD status could be invaluable for investigations of disease pathogenesis and biomarker identification. Toward this end, we assemble, validate, and apply a next-generation sequencing approach (NMDq) for identifying and measuring the abundance of PTC-containing transcripts. After validating NMDq performance and confirming its utility for tracking RNA surveillance, we apply it to determine pathway activity in two neurodegenerative diseases, amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) characterized by RNA misprocessing and abnormal RNA stability. Despite the genetic and pathologic evidence implicating dysfunctional RNA metabolism, and NMD in particular, in these conditions, we detected no significant differences in PTC-encoding transcripts in ALS models or disease. Contrary to expectations, overexpression of the master NMD regulator UPF1 had little effect on the clearance of transcripts with PTCs, but rather restored RNA homeostasis through differential use and decay of alternatively poly-adenylated isoforms. Together, these data suggest that canonical NMD is not a significant contributor to ALS/FTD pathogenesis, and that UPF1 promotes neuronal survival by regulating alternatively poly-adenylated transcripts.

Dynamic regulation of CeA gene expression during acute and protracted abstinence from chronic binge drinking of male and female C57BL/6J mice.
Binge alcohol consumption is a major risk factor for developing Alcohol Use Disorder (AUD) and is associated with alcohol-related problems like accidental injury, acute alcohol poisoning, and black-outs. While there are numerous brain regions that have been shown to play a role in this AUD in humans and animal models, the central nucleus of the amygdala (CeA) has emerged as a critically important locus mediating binge alcohol consumption. In this study, we sought to understand how relative gene expression of key signaling molecules in the CeA changes during different periods of abstinence following bouts of binge drinking. To test this, we performed drinking in the dark (DID) on two separate cohorts of C57BL/6J mice and collected CeA brain tissue at one day (acute) and 7 days (protracted) abstinence after DID. We used qRTPCR to evaluate relative gene expression changes of 25 distinct genes of interest related to G protein-coupled receptors (GPCRs), neuropeptides, ion channel subunits, and enzymes that have been previously implicated in AUD. Our findings show that during acute abstinence CeA punches collected from female mice had upregulated relative mRNA expression of the gamma-aminobutyric acid receptor subunit alpha 2 (Gabra2), and the peptidase, angiotensinase c (Prcp). CeA punches from male mice at the same time point in abstinence had upregulated relative mRNA encoding for neuropeptide-related molecules, neuropeptide Y (Npy) and somatostatin (Sst), as well as the neuropeptide Y receptor Y2 (Npyr2) but downregulated, Glutamate ionotropic receptor NMDA type subunit 1 (Grin1). After protracted abstinence CeA punches collected from female mice had increased mRNA expression of corticotropin releasing hormone (Crh) and Npy. While CeA punches collected from male mice at the same timepoint had upregulated relative mRNA expression of Npy2r and downregulated mRNA expression of Gabra2, Grin1 and opioid receptor kappa 1 (Oprk1). Our findings support that there are differences in how the CeA of male and female respond to binge-alcohol exposure, highlighting the need to understand the implications of such differences in the context of AUD and binge drinking behavior.

Deep learning-based location decoding reveals that across-day representational drift is better predicted by rewarded experience than time
Neural representations of space in the hippocampus and related brain areas change over timescales of days-weeks, even in familiar contexts and when behavior appears stable. It is unclear whether this 'representational drift' is primarily driven by the passage of time or by behavioral experience. Here we present a novel deep-learning approach for measuring network-level representational drift, quantifying drift as the rate of change in decoder error of deep neural networks as a function of train-test lag. Using this method, we analyse a longitudinal dataset of 0.5-475 Hz broadband local field potential (LFP) data recorded from dorsal hippocampal CA1, medial prefrontal cortex and parietal cortex of six rats over ~30 days, during learning of a spatial navigation task in an unfamiliar environment. All three brain regions contained clear spatial representations which improve and drift over training sessions. We find that the rate of drift slows for later training sessions. Finally, we find that drift is statistically better explained by task-relevant rewarded experiences within the maze, rather than the passage of time or number of sessions the animal spent on the maze. Our use of deep neural networks to quantify drift in broadband neural time series unlocks new possibilities for testing which aspects of behavior drive representational drift.

Representational dissimilarity component analysis (ReDisCA)
The principle of Representational Similarity Analysis (RSA) posits that neural representations reflect the structure of encoded information, allowing exploration of spatial and temporal organization in brain information processing. Traditional RSA when applied to EEG or MEG data faces challenges in accessing activation timeseries at the brain source level due to modeling complexities and insufficient geometric/anatomical data. To address this, we introduce Representational Dissimilarity Component Analysis (ReDisCA), a method for estimating spatial-temporal components in EEG or MEG responses aligned with a target representational dissimilarity matrix (RDM). ReDisCA yields informative spatial filters and associated topographies, offering insights into the location of "representationally relevant" sources. Applied to evoked response timeseries, ReDisCA produces temporal source activation profiles with the desired RDM. Importantly, while ReDisCA does not require inverse modeling its output is consistent with EEG and MEG observation equation and can be used as an input to rigorous source localization procedures. Demonstrating ReDisCA's efficacy through simulations and comparison with conventional methods, we show superior source localization accuracy and apply the method to a real EEG dataset, revealing physiologically plausible representational structures without inverse modeling. ReDisCA adds to the family of inverse modeling free methods such as independent component analysis (Makeig et al., 1995), Spatial spectral decomposition (Nikulin et al., 2011) and Source power comodulation (Dahne et al., 2014) designed for extraction sources with desired properties from EEG or MEG data. Extending its utility beyond EEG and MEG analysis, ReDisCA is likely to find application in fMRI data analysis and exploration of representational structures emerging in multilayered artificial neural networks.

Pia-FLOW: Deciphering hemodynamic maps of the pial vascular connectome and its response to arterial occlusion
The pial vasculature is the sole source of blood supply to the neocortex. Above the pia is the skull, a vascularized bone marrow with a unique anatomical connection to the brain. Recent developments in tissue clearing have enabled unprecedented mapping of the entire pial and calvarial vasculature. However, what are the absolute flow rates values of those vascular networks? This information cannot accurately be retrieved with the commonly used bioimaging methods. Here, we introduce Pia-FLOW, a new approach based on large-scale fluorescence localization microscopy, to attain hemodynamic imaging of the whole murine pial and calvarial vasculature at frame rates up to 1000 Hz and spatial resolution reaching 5.4 um. Using Pia-FLOW, we provide detailed maps of flow velocity, direction and vascular diameters which can serve as ground-truth data for further studies, advancing our understanding of brain fluid dynamics. Furthermore, Pia-FLOW revealed that the pial vascular network functions as one unit for the robust allocation of blood after stroke.

Identifying A Neural Signature That Predicts Self-Focus
People are remarkably self-focused, disproportionately choosing to think about themselves relative to other topics. Self-focus can be adaptive, helping individuals fulfill their needs. It can also go haywire, with maladaptive self-focus a risk and maintenance factor for internalizing disorders like depression. Yet, the neural mechanism driving people to focus on themselves remains unknown. This gap is due to timing: while prior research measures neural activity the moment participants are instructed to self-reflect, a brain state that precedes, or nudges, the bias to spontaneously focus on the self remains undetermined. We identified a default network neural signature from pre-trial activity that predicts 1) multiple indicators of self-focus within our sample and 2) internalizing symptoms in a separate sample from the Human Connectome Project. This is the first work to "decode" the bias to focus on the self and paves the way towards stopping maladaptive self-focus in its course.

Npbwr1 signaling mediates fast antidepressant action
Chronic stress is a major risk factor for depression, a leading cause of disability and suicide. Because current antidepressants work slowly, have common side effects, and are only effective in a minority of patients, there is an unmet need to identify the underlying molecular mechanisms. Here, we reveal the receptor for neuropeptides B and W, Npbwr1, as a key regulator of depressive-like symptoms. Npbwr1 is increased in the nucleus accumbens of chronically stressed mice and postmortem in patients diagnosed with depression. Using viral-mediated gene transfer, we demonstrate a causal link between Npbwr1, dendritic spine morphology, the biomarker Bdnf, and depressive-like behaviors. Importantly, microinjection of the synthetic antagonist of Npbwr1, CYM50769, rapidly ameliorates depressive-like behavioral symptoms and alters Bdnf levels. CYM50769 is selective, well tolerated, and shows effects up to 7 days after administration of a single dose. In summary, these findings drastically advance our understanding of mood and chronic stress and warrant further investigation of CYM50769 as a potential fast-acting antidepressant.

Transcriptional programs mediating neuronal toxicity and altered glial-neuronal signaling in a Drosophila knock-in tauopathy model
Missense mutations in the gene encoding the microtubule-associated protein tau cause autosomal dominant forms of frontotemporal dementia. Multiple models of frontotemporal dementia based on transgenic expression of human tau in experimental model organisms, including Drosophila, have been described. These models replicate key features of the human disease, but do not faithfully recreate the genetic context of the human disorder. Here we use CRISPR-Cas mediated gene editing to model frontotemporal dementia caused by the tau P301L mutation by creating the orthologous mutation, P251L, in the endogenous Drosophila tau gene. Flies heterozygous or homozygous for tau P251L display age-dependent neurodegeneration, metabolic defects and accumulate DNA damage in affected neurons. To understand the molecular events promoting neuronal dysfunction and death in knock-in flies we performed single-cell RNA sequencing on approximately 130,000 cells from brains of tau P251L mutant and control flies. We found that expression of disease-associated mutant tau altered gene expression cell autonomously in all neuronal cell types identified and non-cell autonomously in glial cells. Cell signaling pathways, including glial-neuronal signaling, were broadly dysregulated as were brain region and cell-type specific protein interaction networks and gene regulatory programs. In summary, we present here a genetic model of tauopathy, which faithfully recapitulates the genetic context and phenotypic features of the human disease and use the results of comprehensive single cell sequencing analysis to outline pathways of neurotoxicity and highlight the role of non-cell autonomous changes in glia.

Attractor-like circuits improve visual decoding and behavior in zebrafish
Attractor networks are neural circuits with stable states that represent information or memories. They play a crucial role in memory retrieval, decision-making and integrating noisy cues. In zebrafish larvae, the spontaneous dynamics of the optic tectum is structured according to topographically organized neuronal assemblies exhibiting attractor-like behavior. Here, we took advantage of the Methyl-CpG-binding protein 2 (MeCP2) deficient zebrafish mutant, which displays perturbed tectal dynamics, to study the functional role of the attractor-like circuits in visual processing. In comparison to wild-type larvae, the mecp2-mutant showed reduced functional connectivity in the optic tectum. This abnormal connectivity significantly affected the visual response, and the ability to discriminate between visual stimuli. Finally, the mutant larvae where less efficient in hunting paramecia. We argue that the attractor dynamics of the tectal assemblies improve stimulus discrimination, visual resolution, and increase the sensitivity to behaviorally relevant visual stimuli.

Psilocybin-enhanced fear extinction linked to bidirectional modulation of cortical ensembles
The serotonin 2 receptor (5HT2R) agonist psilocybin has demonstrated rapid and long-lasting efficacy across neuropsychiatric disorders characterized by cognitive inflexibility. Psilocybin may accomplish this by inducing rapid and stable dendritic plasticity. However, the impact of psilocybin on patterns of neural activity underlying sustained changes in cognitive and behavioral flexibility has not been characterized. To test the hypothesis that psilocybin enhances behavioral flexibility by rapidly and persistently altering activity in cortical neural ensembles, we performed longitudinal single-cell calcium imaging in the retrosplenial cortex across a five-day trace fear learning and extinction assay. Leveraging tensor component analysis to identify neurons that modulate activity on multiple temporal scales, we found that a single-dose of psilocybin induced cortical ensemble turnover between fear learning and extinction days while oppositely modulating activity in fear- and extinction- active neurons. The extent of suppression of fear-active neurons and recruitment of extinction-active neurons were both predictive of psilocybin-enhanced fear extinction. These results both align with hypotheses that psilocybin enhances behavioral flexibility by recruiting new populations of neurons and introduce a new mechanism involving the suppression of fear-active populations in the retrosplenial cortex.

Elevated Activity in Left Homologous Music Circuits is Maladaptive for Music Perception but Mediated by Decoupled Structure and Function
Music is inherent in human life and is a significant topic of cognitive neuroscience. Previous studies focused on amusia suggested that two frontotemporal circuits engage in music processing. Structure-function coupling is an important feature of human brain, which is associated with cognition and allows for a more sensitive investigation of brain-behavior association. However, we still have limited knowledge about the relation between structure-function coupling, music processing and other regional neural profiles. We recruited 106 participants (43 subjects were diagnosed with congenital amusia) and measured their music perception by Montreal Battery of Evaluation of Amusia (MBEA). Then we utilized support vector regression algorithm and mediation analysis, and employed amplitude of low frequency fluctuation (ALFF), structural/functional degree centrality (DC) and structure-function coupling to explore their relation with global averaged MBEA score. We found structure-function coupling of widespread brain regions in both hemispheres, rather than ALFF or DC, contributed to predict MBEA score. Left middle frontal gyrus, bilateral inferior temporal gyrus and right insula were most predictive regions, and these regions were involved in memory and cognitive control according to meta-analysis. Further, coupling of left middle frontal gyrus, a region that is homologous to and is connected with typical music circuits, fully mediated the negative relation between ALFF and MBEA score. Our findings provide further understanding for the neural basis of music, and have implications for neural plasticity, neuromodulation therapy and cognitive causes of amusia.

μSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modeling
How does the motor cortex (MC) produce purposeful and generalizable movements from the complex musculoskeletal system in a dynamic environment? To elucidate the underlying neural dynamics, we use a goal-driven approach to model MC by considering its goal as a controller driving the musculoskeletal system through desired states to achieve movement. Specifically, we formulate the MC as a recurrent neural network (RNN) controller producing muscle commands while receiving sensory feedback from biologically accurate musculoskeletal models. Given this real-time simulated feedback implemented in advanced physics simulation engines, we use deep reinforcement learning to train the RNN to achieve desired movements under specified neural and musculoskeletal constraints. Activity of the trained model can accurately decode experimentally recorded neural population dynamics and single-unit MC activity, while generalizing well to testing conditions significantly different from training. Simultaneous goal- and data- driven modeling in which we use the recorded neural activity as observed states of the MC further enhances direct and generalizable single-unit decoding. Finally, we show that this framework elucidates computational principles of how neural dynamics enable flexible control of movement and make this framework easy-to-use for future experiments.

Optogenetic Locus Coeruleus Stimulation Improves Pupil Size Tracking of Cortical State
Brain state heavily influences our perception, cognition, and behavior. Multiple neuromodulatory systems, including the locus coeruleus - norepinephrine (LC-NE) system, contribute to the regulation of brain state. This research harnesses machine learning and optogenetics technologies to probe the impact of the LC on cortical state and pupillary responses in awake mice. Our integrative approach combines EEG recordings with pupillometry to capture the LC's optogenetic activation, evoking notable EEG spectral power shifts synchronized with pupil dilation. These changes provide a noninvasive glimpse into the cortical states, modulated by the LC's activity. Central to our methodology is the application of Support Vector Regression (SVR) modeling, which robustly correlates LC-induced pupillary changes with EEG power fluctuations. These insights help reinforce the LC's crucial role in modulating prefrontal activity and its regulatory influence over arousal states. Beyond advancing our comprehension of the LC's function, our work also highlights the potential for developing closed-loop stimulation systems. These systems, integrated with machine-learning techniques and multi-modal data, could offer precise therapeutic interventions for neurological disorders characterized by abnormal arousal states.

The timescale and functional form of context-dependence during human value-learning
Contrary to the predictions of normative theories, choices between two high-value alternatives can be biased by the introduction of a third low-value alternative (dubbed the distractor effect). Normalization-based theories, like divisive and range normalization, explain different forms of the distractor effect by suggesting that the value of each alternative is normalized by a summary statistic of the values encountered in a particular decision context. The decision context can include alternatives encountered over an extended timeframe (temporal context); and alternatives that are available for choice on a given instance (immediate context). To date, the extent to which the immediate and temporal context (co-) shape context-dependent value representations remains unclear. To investigate this, we designed a task in which participants learned the values associated with three different alternatives and provided explicit value estimates before making a series of choices among ternary and binary combinations of those alternatives. We show that context-dependence already emerges in the pre-choice value estimates and is equally present in binary and ternary choice trials. Based on these findings, we conclude that the temporal (and not the immediate) context modulates subjective value representations. Interestingly, the functional form of context-dependence we report runs against both divisive and range normalization theories. Instead, our data are best explained by a stochastic rank-based model, according to which the value of an alternative is distorted by a series of memory-based binary comparisons with previously encountered alternatives.

Task-irrelevant phase but not contrast variability unlocks generalization in visual perceptual learning
Performance on visual tasks can be improved by practice, a process called visual perceptual learning. However, learning-induced performance improvements are often limited to the specific stimuli and visual field locations used during training. Recent research has shown that variability along task-irrelevant stimulus dimensions during training can reduce this specificity. This has been related to higher stages of visual processing that harbor neurons which are invariant to the task-irrelevant dimension. Here, we test whether task-irrelevant trial-by-trial variability in two visual features for which invariances arise at different stages of processing, contrast and spatial phase, results in different degrees of generalization in space in an orientation discrimination task. We find that randomizing spatial phase results in complete generalization of learning to a new spatial location, contrary to randomizing contrast. Our results thus suggest that the neural population undergoing plasticity in visual perceptual learning is determined by the training task, which, in turn, affects generalization. This lends further support to the hypothesis that task-irrelevant variability is an independent factor in determining the specificity of perceptual learning.

TMEM63B functions as a mammalian thirst receptor
Thirst drives animals to reinstate water homeostasis by fluid intake. An increase of blood osmolality is thought to induce thirst by activating a thirst receptor expressed in the subfornical organ (SFO), but the molecular identity of this receptor remains elusive. Here, we provide behavioral and functional evidence to show that TMEM63B functions as a mammalian thirst receptor in the SFO and mediates osmotic and dehydrated thirst. First, we showed that TMEM63B is expressed in SFO excitatory neurons and required for the neuronal responses to hypertonic stimulation. Heterologously expressed TMEM63B is activated by hypertonic stimuli and point mutations can alter the reversal potential of the channel. More importantly, purified TMEM63B in liposomes establishes osmolarity-gated currents. Finally, Tmem63b knockout mice have profound deficits in thirst, and deleting TMEM63B within the SFO neurons recapitulated this phenotype. Taken together, these results provide a molecular basis for thirst and demonstrate TMEM63B is the long-sought mammalian thirst receptor.

Identifying the factors governing internal state switches during nonstationary sensory decision-making
Recent work has revealed that mice do not rely on a stable strategy during perceptual decision-making, but switch between multiple strategies within a single session [1, 2]. However, this switching behavior has not yet been characterized in non-stationary environments, and the factors that govern switching remain unknown. Here we address these questions using an internal state model with input-driven transitions. Our approach relies on a hidden Markov model (HMM) with two sets of per-state generalized linear models (GLMs): a set of Bernoulli GLMs for modeling the animal state- and stimulus-dependent choice on each trial, and a multinomial GLM for modeling input-dependent transitions between states. We used this model to analyze a dataset from the International Brain Laboratory (IBL), in which mice performed a binary decision-making task with non-stationary stimulus statistics. We found that mouse behavior in this task was accurately described by a four-state model. This model contained two "engaged" states, in which performance was good despite slight left and right biases, and two "disengaged" states, where performance was low and exhibited with larger left and right biases, respectively. Our analyses revealed that mice preferentially used left-bias strategies during left-bias stimulus blocks, and right-bias strategies during right-bias stimulus blocks, meaning that they could achieve reasonably high performance even in disengaged states simply by biasing choice toward the side with greater prior probability. Our model showed that past choices and past stimuli predicted transitions between left- and right-bias states, while past rewards predicted transitions between engaged and disengaged states. In particular, greater past reward predicted transition to disengaged states, suggesting that disengagement may be associated with satiety.

Aging hastens locomotor decline in PINK1 knockout rats in association with decreased nigral, but not striatal, dopamine and tyrosine hydroxylase expression
Parkinsons disease (PD) rodent models provide insight into the relationship between nigrostriatal dopamine (DA) signaling and locomotor function. Although toxin-based rat models produce frank nigrostriatal neuron loss and eventual motor decline characteristic of PD, the rapid nature of neuronal loss may not adequately translate premotor traits, such as cognitive decline. Unfortunately, rodent genetic PD models, like the Pink1 knockout (KO) rat, often fail to replicate the differential severity of striatal DA and tyrosine hydroxylase (TH) loss, and a bradykinetic phenotype, reminiscent of human PD. To elucidate this inconsistency, we evaluated aging as a progression factor in the timing of motor and non-motor cognitive impairments. Male PINK1 KO and age-matched wild type (WT) rats were evaluated in a longitudinal study from 3 to 16 months old in one cohort, and in a cross-sectional study of young adult (6-7 months) and aged (18-19 months) in another cohort. Young adult PINK1 KO rats exhibited hyperkinetic behavior associated with elevated DA and TH in the substantia nigra (SN), which decreased therein, but not striatum, in the aged KO rats. Additionally, norepinephrine levels decreased in aged KO rats in the prefrontal cortex (PFC), paired with a higher DA content in young and aged KO. Although a younger age of onset characterizes familial forms of PD, our results underscore the critical need to consider age-related factors. Moreover, the results indicate that compensatory mechanisms may exist to preserve locomotor function, evidenced by increased DA in the SN early in the lifespan, in response to deficient PINK1 function, which declines with aging and the onset of motor impairment.

Solaris: a panel of bright and sensitive hybrid voltage indicators for imaging membrane potential in cultured neurons
Dynamic changes in the membrane potential underlie neuronal activities. Fluorescent voltage indicators allow optical recording of electrical signaling across a neuronal population with cellular precision and at millisecond-level temporal resolution. Here we report the design and characterization of a chemigenetic hybrid voltage indicator, Solaris, in which a circularly permuted HaloTag is inserted into the first extracellular loop of Acetabularia rhodopsin. Solaris is compatible with fluorogenic HaloTag ligands JF525, JF549, JF552, JF585, and JF635. The most sensitive conjugate, Solaris585, has more than 2-fold higher voltage sensitivity than the spectrally similar Voltron2585 ({Delta}F/F0 = -28.1 {+/-} 1.3% versus -12.3 {+/-} 0.7% per action potential in cultured neurons). Solaris585 supports the measurement of optogenetically evoked spike activity or dual-color imaging in conjunction with green-emitting calcium or glutamate indicators. Solaris indicators are also applicable to fluorescence lifetime imaging, which probes the absolute membrane potential. This new hybrid voltage indicator is a valuable tool for imaging neuronal electrophysiological activities in cultured cells with substantially improved dynamic range compared to previous hybrid indicators.

Cell lipotypes localization in brain by mass spectrometry imaging
The study investigates brain lipid super-specialization by defining characteristic spectral lipotypic profiles for the five primary cerebral cell-types. Utilizing a computational approach, the research visualizes the anatomical distribution of these profiles in high spatial resolution brain tissues. This method unveils cellular stereotypic lipidic signatures within the CNS, providing a new framework for exploring the physiological roles of lipids in diverse cell types present in brain or in any other tissue.

Expression of novel androgen receptors in three GnRH neuron subtypes in the cichlid brain
Within a social hierarchy, an individual's social status determines its physiology and behavior. In A. burtoni, subordinate males can rise in rank to become dominant, which is accompanied by the upregulation of the entire HPG axis, including activation of GnRH1 neurons, a rise in circulating androgen levels and the display of specific aggressive and reproductive behaviors. Cichlids possess two other GnRH subtypes, GnRH2 and GnRH3, the latter being implicated in the display of male specific behaviors. Interestingly, some studies showed that these GnRH neurons are responsive to fluctuations in circulating androgen levels, suggesting a link between GnRH neurons and androgen receptors (ARs). Due to a teleost-specific whole genome duplication, A. burtoni possess two AR paralogs, AR and AR{beta}, that are encoded by two different genes, ar1 and ar2, respectively. Even though social status has been strongly linked to androgens, whether AR and/or AR{beta} are present in GnRH neurons remains unclear. Here, we used immunohistochemistry and in situ hybridization chain reaction (HCR) to investigate ar1 and ar2 expression specifically in GnRH neurons. We find that all GnRH1 neurons intensely express ar1 but only a few of them express ar2, suggesting the presence of genetically-distinct GnRH1 subtypes. Very few ar1 and ar2 transcripts were found in GnRH2 neurons. GnRH3 neurons were found to express both ar genes. The presence of distinct ar genes within GnRH neuron subtypes, most clearly observed for GnRH1 neurons, suggests differential control of these neurons by androgenic signaling. These findings provide valuable insight for future studies aimed at disentangling the androgenic control of GnRH neuron plasticity and reproductive plasticity across teleosts.

Neural substrates underlying the expectation of rewards resulting from effortful exertion
Expectations as a reference point shape our decisions to motivate effortful activity. Despite the important role of reference points in human performance, little is known about how the brain processes expectations to guide motivated exertion. Participants completed a reward-based effort task in an MRI scanner. During each trial, participants were presented with a risky option that would either result in a fixed monetary payment, regardless of their effort exertion, or a piece-rate payment where the payment was proportional to the amount of effort exerted. We found that participants exerted more effort as the fixed payment increased, suggesting that the fixed payment influenced the reference point for effort exertion. The strength of this reference-dependent behavior was correlated with neural activity in the ventral striatum, which was significantly modulated by the deviation from the payoff to reward expectations after effort exertion. Our results suggest that value-related brain areas, particularly the ventral striatum, encode expectations of reward as a reference point to motivate effort exertion.

Distractor suppression operates exclusively in retinotopic coordinates
Our attention is influenced by past experiences, and recent studies have shown that individuals learn to extract statistical regularities in the environment, resulting in attentional suppression of locations that are likely to contain a distractor (high-probability location). However, little is known as to whether this learned suppression operates in retinotopic (relative to the eyes) or spatiotopic (relative to the world) coordinates. In the current study, two circular search arrays were presented side by side. Participants learned the high-probability location from a learning array presented on one side of the display (e.g., left). After several trials, participants shifted their gaze to the center of the other search array (e.g., located on the right side) and continued searching without any location probability (labelled as "test array"). Due to the saccadic eye movement, the test array contained both a spatiotopic matching and a retinotopic matching location relative to the original high-probability location. The current findings show that, following saccadic eye movements, the learned suppression remained in retinotopic coordinates only, with no measurable transfer to spatiotopic coordinates. Even in a rich environment, attentional suppression still operated exclusively in retinotopic coordinates. We speculate that learned suppression may be resolved by changing synaptic weights in early visual areas.

Preparatory activity during visual search reflects attention-guiding objects rather than search targets
Efficient behavior requires the rapid attentional selection of task-relevant objects. Previous research has shown that target-selective neurons in visual cortex increase their baseline firing rate when participants are cued to search for a target object. Such preparatory activity represents a key finding for theories of visual search, as it may reflect a top-down bias that guides spatial attention, favoring processing of target-matching input for subsequent report. However, in daily life, visual search is often guided by non-target objects that are neither externally cued nor reported. For instance, when looking for a pen, we may direct our attention to the office desk where we expect the pen to be. These 'anchor objects' (e.g., the desk) thereby guide search for associated objects (e.g., the pen) in scenes. Here, we used fMRI and eye tracking to test whether preparatory activity during visual search represents the target (the pen), the guiding anchor object (the desk) or both. In an anchor-guided search task, participants (N=34) learned associations between targets and anchors and searched for these targets in scenes. To fully dissociate target from anchor processing, target-anchor associations were reversed across different scene contexts. Participants' first fixations were reliably guided towards the target-associated anchor. Importantly, preparatory fMRI activity patterns in lateral occipital cortex (LOC) represented the target-associated anchor rather than the target. Whole-brain analyses additionally identified a region in the right intraparietal sulcus that represented the anchor. Our results show that preparatory activity in visual cortex represents a self-generated guiding template, supporting visual search in structured daily-life environments.

Aperiodic and Hurst EEG exponents across early human brain development: a systematic review
In electroencephalographic (EEG) data, power-frequency slope exponents (1/f{beta}) can provide non-invasive markers of in vivo neural activity excitation-inhibition (E:I) balance. E:I balance may be altered in neurodevelopmental conditions; hence, understanding how 1/f{beta} evolves across infancy/childhood has implications for developing early assessments/interventions. This systematic review (PROSPERO-ID: CRD42023363294) explored the early maturation (0-26yrs) of resting-state EEG 1/f measures (aperiodic [AE], power law [PLE] and Hurst [HE] exponents), including studies containing [≥]1 1/f measures and [≥]10 typically developing participants. Five databases (including Embase and Scopus) were searched during March 2023. Forty-two studies were identified (N participants=3478). Risk of bias was assessed using the Quality Assessment with Diverse Studies tool. Narrative synthesis of HE data suggests non-stationary EEG activity occurs throughout development. Age-related trends were complex, with rapid decreases in AEs during infancy and heterogenous changes thereafter. Regionally, AE maxima shifted developmentally, potentially reflecting spatial trends in maturing brain connectivity. This work highlights the importance of further characterising the development of 1/f measures to better understand how E:I balance shapes brain and cognitive development.

Rapid Invisible Frequency Tagging (RIFT) in a novel setup with EEG
Steady-State Visual Evoked Potentials (SSVEPs) provide a report-free and continuous measure of neural processing. Recent progress in display technology has allowed for the tagging of multiple stimuli simultaneously at >60Hz frequencies - high enough to evade perceptibility, while still evoking an oscillatory neural response. Known as Rapid Invisible Frequency Tagging (RIFT), this technique has currently only been used in combination with Magnetoencephalography (MEG), which is less accessible compared to Electroencephalography (EEG). Although responses to LEDs flickering at similar frequencies have been shown in EEG, it is currently unclear whether RIFT, using a more conventional stimulus display, can sufficiently evoke a response in EEG, and therefore whether it is worth adding the RIFT-EEG pairing to the cognitive neuroscientist's toolkit. Here, we successfully implement the first RIFT-EEG setup. We show that the oscillatory input is measurable in the EEG trace, what its topographical spread is, a rough range of applicable frequencies, and that this response is comparable to that evoked in MEG.

Circadian regulation of endoplasmic reticulum calcium response in mouse cultured astrocytes
The circadian clock, an internal time-keeping system orchestrates 24-hour rhythms in physiology and behavior by governing rhythmic transcription within cells. Astrocyte, the most abundant glial cell type, play crucial roles in central nervous system functions. However, a detailed understanding of how the circadian clock impacts functions of astrocyte remains largely unexplored. In this study, utilizing circadian clock- synchronized mouse cultured cortical astrocytes and RNA sequencing, we identified 412 circadian rhythmic transcripts with a distinct astrocyte-specific expression pattern. A Gene Ontology analysis of these rhythmic transcripts highlighted genes implicated in Ca2+ homeostasis as being under circadian control. Notably, Herpud1 (Herp) exhibited robust circadian rhythmicity at both mRNA and protein levels, a rhythm disrupted in astrocytes lacking the circadian transcription factor, BMAL1. HERP regulated endoplasmic reticulum (ER) Ca2+ release by modulating the degradation of inositol 1,4,5-trisphosphate receptors (ITPRs). Intriguingly, ATP-stimulated ER Ca2+ release varied with the circadian cycle, being more pronounced at subjective night, likely owing to the rhythmic expression of ITPR2. Furthermore, this rhythmic ER Ca2+ response led to day/night variations in the phosphorylation of Cx43 (Ser368) and the gap junctional communication. Given the role of gap junction channel (GJC) in propagating Ca2+ signals, we suggest that this circadian regulation of ER Ca2+ responses could markedly affect astrocytic modulation of synaptic activity according to the time of day. Overall, our study enhances the understanding of how circadian clock influences astrocyte function in the CNS, shedding light on their potential role in daily variations of brain activity and health.

Direct modulation of TRPM8 ion channels by rapamycin and analog macrolide immunosuppressants
Rapamycin (sirolimus), a macrolide compound isolated from the bacterium Streptomyces hygroscopicus, is widely used as oral medication for the prevention of transplant rejection and the treatment of lymphangioleiomyomatosis. It is also incorporated in coronary stent coatings to prevent restenosis and in topical preparations for the treatment of skin disorders. Rapamycin's in vivo activities are generally ascribed to its binding to the protein FKBP12, leading to potent inhibition of the mechanistic target of rapamycin kinase (mTOR) by the FKBP12-rapamycin complex. The specific rapamycin-induced interaction between domains from mTOR and FKBP12 is also frequently employed in cell biological research, for rapid chemically-induced protein dimerization strategies. Here we show that rapamycin activates TRPM8, a cation channel expressed in sensory nerve endings that serves as the primary cold sensor in mammals. Using a combination of electrophysiology, Saturation Transfer Triple-Difference (STTD) NMR spectroscopy and molecular docking-based targeted mutagenesis, we demonstrate that rapamycin directly binds to TRPM8. We identify a rapamycin-binding site in the groove between voltage sensor-like domain and the pore domain, distinct from the interaction sites of cooling agents and known TRPM8 agonists menthol and icilin. Related macrolide immunosuppressants act as partial TRPM8 agonists, competing with rapamycin for the same binding site. These findings identify a novel molecular target for rapamycin and provide new insights into the mechanisms of TRPM8 activation, which may assist in the development of therapies targeting this ion channel. Moreover, our findings also indicate that caution is needed when using molecular approaches based on rapamycin-induced dimerization to study ion channel regulation.