bioRxiv Subject Collection: Neuroscience's Journal

Patch-leaving decisions and pupil-linked arousal systems
Deciding when to abandon a depleting resource in favour of potentially richer alternatives is fundamental to adaptive behaviour. Such patch-leaving decisions require balancing the expected advantage of leaving against both the cost of moving and the reward foregone in the current environment. Previous research suggests that activity of noradrenergic (NE) neurons in the locus coeruleus (LC) underpins patch-leaving. In the current study, we used pupil dilation as a time-resolved readout of subcortical neuromodulation during patch-leaving. We hypothesised that leave decisions will be preceded by a transient pupil dilation. Participants harvested from exponentially depleting patches (blueberry bushes) with three initial reward values, in two environments, which differed in the variability of initial reward values. Behavioural results show that, as predicted by the mathematically optimal solution, participants adjusted their decisions based on the instantaneous reward rate, but also displayed a bias to overharvest (stay longer in compared to the optimum), which was more pronounced in the high variability environment. Pupil size was overall larger in the high variability environment, associated with increased uncertainty. Importantly, we observed an increased transient pupil dilation in response to reward outcomes immediately preceding leave compared to stay decisions, as well as an increase in pupil dilation (and RT) across successive stay trials, leading up to leave decisions, presumably indicating an increased LC-NE activity associated with abandoning current options to explore alternatives.

British Version of the Iowa Test of Consonant Perception
The Iowa Test of Consonant Perception (ITCP) is a single-word closed-set speech-in-noise test with well-balanced phonetic features that provides a reliable testing option for real-world listening. Objectives: The current study aimed to establish a UK version of the test (B-ITCP) based on the British received pronunciation. Design: We conducted a validity test with 46 participants using the B-ITCP test, a sentence-in-noise test, and audiogram. Results: The B-ITCP demonstrated excellent test-retest reliability, cross-talker validity, and good convergent validity, consistent with the US results. Conclusions: These findings suggest that B-ITCP is a reliable measure of speech-in-noise perception, to facilitate comparative or combined studies in USA and UK. All materials (application and scripts) to run or construct the B-ITCP and ITCP are freely available online.

Astrocytic Regulation of Cocaine Locomotor Sensitization in EcoHIV Infected Mice
Cocaine use disorder (CUD) is highly comorbid with HIV infection and worsens HIV outcomes. Preclinical research on the outcomes of HIV infection may yield crucial information on neurobehavioral changes resulting from chronic drug exposure in people living with HIV (PLWH). Repeated exposure to cocaine alters behavioral responses to cocaine. This includes development of cocaine locomotor sensitization, or increased locomotor responses to the same doses of cocaine, which depends on nucleus accumbens (NAc) neural plasticity. NAc astrocytes are key regulators of neural activity and plasticity, and their function can be impaired by cocaine exposure and HIV infection, thus implicating them as potential regulators of HIV-induced changes in behavioral response to cocaine. To characterize the effects of HIV infection on cocaine locomotor sensitization, we employed the EcoHIV mouse model to assess changes in locomotor responses after repeated cocaine (10mg/kg) exposure and challenge. EcoHIV infection potentiated expression of cocaine sensitization. We also identified EcoHIV-induced increases in expression of the astrocytic nuclear marker Sox9 selectively in the NAc core. To investigate whether modulation of NAc astrocytes could reverse EcoHIV-induced deficits, we employed a chemogenetic approach. We found that chemogenetic activation of NAc astrocyte Gq signaling attenuated EcoHIV-enhanced cocaine sensitization. We propose that HIV infection contributes to cocaine behavioral sensitization and induces adaptations in NAc astrocytes, while promoting NAc astrocytic Gq-signaling can recover EcoHIV-induced behavioral changes. These findings identify potential cellular substrates of disordered cocaine-driven behavior in the context of HIV infection and point toward strategies to reduce cocaine-related behavior in PLWH.

Sex-Specific Roles of Hypocretin Receptor Signaling in CRF Neurons on Alcohol Drinking, Anxiety, and BNST Neuronal Excitability
Alcohol use disorder (AUD) is characterized by compulsive alcohol consumption and negative emotional states during withdrawal, often perpetuating a cycle of addiction through arousal dysfunction. The hypocretin/orexin (Hcrt) neuropeptide system, a key regulator of arousal, has been implicated in these processes, particularly in its interactions with corticotropin-releasing factor (CRF) neurons within the bed nucleus of the stria terminalis (BNST). We investigated the role of Hcrt receptor signaling in CRF neurons in modulating alcohol intake, anxiety behaviors, and BNST excitability, with a focus on sex-specific differences. Using CRF-specific genetic deletion of HcrtR1 and/or HcrtR2 receptors in mice, we found that deletion of HcrtR1 significantly reduced alcohol intake, with sex-specific effects on BNST excitability. CRF-specific HcrtR2 deletion, while not affecting alcohol consumption, decreased baseline anxiety-like behaviors in males relative to females. Moreover, the double deletion of both Hcrt receptors from CRF neurons led to reduced alcohol drinking in males and dampened anxiety behaviors and BNST excitability in both sexes during protracted withdrawal. These findings suggest that Hcrt signaling in CRF neurons plays a critical role in the persistence of excessive alcohol consumption and the development of negative affective states, with distinct contributions from HcrtR1 and HcrtR2. The observed sex-specific differences underscore the need for tailored therapeutic approaches targeting the Hcrt system in the treatment of AUD.

Myosin XVA isoforms participate in the mechanotransduction-dependent remodeling of the actin cytoskeleton in auditory stereocilia
Auditory hair cells form precise and sensitive staircase-like actin protrusions known as stereocilia. These specialized microvilli detect deflections induced by sound through the activation of mechano-electrical transduction (MET) channels located at their tips. At rest, a small MET channel current results in a constant calcium influx, which regulates the morphology of the actin cytoskeleton in the shorter "transducing" stereocilia. However, the molecular mechanisms involved in this novel type of activity-driven plasticity in the stereocilium cytoskeleton are currently unknown. Here, we tested the contribution of myosin XVA (MYO15A) isoforms. We used electron microscopy to evaluate morphological changes in the cytoskeleton of auditory hair cell stereocilia after the pharmacological blockage of resting MET currents in cochlear explants from mice that lacked one or all isoforms of MYO15A. Hair cells lacking functional MYO15A isoforms did not exhibit MET-dependent remodeling in their stereocilia cytoskeleton. In contrast, hair cells that only lack the long isoform of MYO15A exhibited increased MET-dependent stereocilia remodeling, including remodeling in stereocilia from the tallest "non-transducing" row of the bundle. We conclude that MYO15A isoforms not only enable but also fine-tune the MET-dependent remodeling of the actin cytoskeleton in transducing stereocilia and contribute to the stability of the tallest row.

The spatial reference frame of history-driven distractor suppression and target enhancement
The world around us is inherently structured and often repetitive. Research has shown that we can implicitly learn to prioritize relevant objects and locations while filtering out distracting information, creating an integrated priority map for attention allocation. The current study examines whether these attentional biases are tied to environment-dependent (allocentric) or viewpoint-dependent (egocentric) coordinates. The search display consisted of six stimuli that were surrounded by a wheel and square frame. In two experiments, either a distractor or a target appeared more frequently in one location, leading to the suppression or enhancement of that location, respectively. Learning blocks were followed by test blocks, where the surrounding frame rotated, creating egocentric-matching and allocentric-matching locations. These experiments showed that both target and distractor learning relied on an egocentric reference frame only. In follow-up experiments, the likely target and distractor location rotated dynamically with the frame during learning. This revealed that participants can learn to enhance a likely target location in an object-centered, allocentric manner. We hypothesized that while space-based learning feeds into a priority map reliant on an egocentric reference frame, object-based learning allows for implicit prioritization of subparts of objects independent of their spatial orientation.

Public significance statementAttention is shaped by past experiences, guiding us to suppress locations likely to contain distractors while enhancing locations likely to contain relevant information. This study explores how these attentional biases behave when the search environment is viewed from different perspectives. Do these biases persist relative to our viewpoint, or do they remain stable within the environment? The findings reveal that implicitly suppressed and enhanced locations are tied to viewpoint-dependent coordinates. However, attentional biases can also be formed in an object-centered manner, where a likely target location within an object is prioritized, irrespective of the orientation of that object.

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.
The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We employed deep autoencoders in an anomaly detection framework, combined with a confounder removal step integrating training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of subject- and group-level brain normative-deviating patterns, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

Gradients of Recognition Molecules Shape Synaptic Specificity of a Visuomotor Transformation
Converting sensory information into motor commands is fundamental to most of our actions1,2. In Drosophila, visuomotor transformations are mediated by Visual Projection Neurons (VPNs)3,4. These neurons convert object location and motion into directional behaviors downstream through a synaptic gradient mechanism5. However, the molecular origins of such graded connectivity remain unknown. We addressed this question in a VPN cell type called LPLC26, which integrates looming motion and transforms it into an escape response through two parallel dorsoventral synaptic gradients at its inputs and outputs. We identified two corresponding dorsoventral expression gradients of cell recognition molecules within the LPLC2 population that regulate this synaptic connectivity. Dpr13 determines synaptic outputs of LPLC2 axons by interacting with its binding partner DIP-{varepsilon} expressed in the Giant Fiber, a neuron that mediates escape7. Similarly, beat-VI regulates synaptic inputs onto LPLC2 dendrites by interacting with Side-II expressed in upstream motion-detecting neurons. Behavioral, physiological, and molecular experiments demonstrate that these coordinated molecular gradients control differential synaptic connectivity, enabling the accurate transformation of visual features into motor commands. As within-neuronal-type continuous variation in gene expression is also observed in the mammalian brain8, graded expression of cell recognition molecules may represent a common mechanism underlying synaptic specificity.

Neural Representations of Extrasystoles: A Predictive Coding Perspective
The coordinated interplay between brain and body is essential for survival, with the heart-brain axis playing a fundamental role in perceptual, cognitive and motor processes. Central to this interaction, the heart-evoked potential (HEP) represents a neural correlate of the heart activity. Further, the HEP is thought to represent a neuronal prediction for each heartbeat, raising questions about its role in arrhythmia. Yet, previous studies have primarily focused on regular heart rhythms, with only a few investigations delving into arrhythmias and, notably, none into extrasystoles, a form of benign arrhythmias that disrupt the regular heart rhythm. Extrasystoles represent a unique possibility for investigating brain responses to unexpected pronounced alterations in heart rhythm. We took advantage of the largest dataset (103 subjects with 3000 extrasystoles), including both EEG and ECG recordings, and analysed the neural response to both types of extrasystoles (supraventricular and ventricular) using multiverse approaches and control analysis in time and source space. We found that the HEP was significantly reduced for ventricular extrasystoles with underlying sources in the left insular. For the postextrasystolic beat of both types of extrasystoles, we found a significantly earlier and increased HEP originating from sources in the left frontal orbital cortex and the anterior cingulate gyri. The reduced HEP response to the ventricular extrasystole might result from inactive interoceptive cardiac pathways. In contrast, increased HEP of the postextrasystolic beat along with the anatomical neural HEP generators may reflect an interoceptive prediction error arising from a naturally occurring discrepancy between the predicted and actual heart rhythm, with a main source in the anterior cingulate gyri, a primary location for prediction error processing.

Single-nucleus multi-omics identifies shared and distinct pathways in Pick's and Alzheimer's disease
The study of neurodegenerative diseases, particularly tauopathies like Pick's disease (PiD) and Alzheimer's disease (AD), offers insights into the underlying regulatory mechanisms. By investigating epigenomic variations in these conditions, we identified critical regulatory changes driving disease progression, revealing potential therapeutic targets. Our comparative analyses uncovered disease-enriched non-coding regions and genome-wide transcription factor (TF) binding differences, linking them to target genes. Notably, we identified a distal human-gained enhancer (HGE) associated with E3 ubiquitin ligase (UBE3A), highlighting disease-specific regulatory alterations. Additionally, fine-mapping of AD risk genes uncovered loci enriched in microglial enhancers and accessible in other cell types. Shared and distinct TF binding patterns were observed in neurons and glial cells across PiD and AD. We validated our findings using CRISPR to excise a predicted enhancer region in UBE3A and developed an interactive database (http://swaruplab.bio.uci.edu/scROAD) to visualize predicted single-cell TF occupancy and regulatory networks.

Modeling Cortical Versus Hippocampal Network Dysfunction in a Human Brain Assembloid Model of Epilepsy and Intellectual Disability
Neurodevelopmental disorders often impair multiple cognitive domains. For instance, a genetic epilepsy syndrome might cause seizures due to cortical hyperexcitability and present with memory impairments arising from hippocampal dysfunction. This study examines how a single disorder differentially affects distinct brain regions by using human patient iPSC-derived cortical- and hippocampal-ganglionic eminence assembloids to model Developmental and Epileptic Encephalopathy 13 (DEE-13), a condition arising from gain-of-function mutations in the SCN8A gene. While cortical assembloids showed network hyperexcitability akin to epileptogenic tissue, hippocampal assembloids did not, and instead displayed network dysregulation patterns similar to in vivo hippocampal recordings from epilepsy patients. Predictive computational modeling, immunohistochemistry, and single-nucleus RNA sequencing revealed changes in excitatory and inhibitory neuron organization that were specific to hippocampal assembloids. These findings highlight the unique impacts of a single pathogenic variant across brain regions and establish hippocampal assembloids as a platform for studying neurodevelopmental disorders.

Brain-wide mapping of oligodendrocyte organization and oligodendrogenesis across the murine lifespan
Insulating sheaths of myelin accelerate neuronal signaling in complex networks of the mammalian brain. In the CNS, myelin sheaths are exclusively produced by oligodendrocytes, which continue to be generated throughout life to change patterns of myelination. However, a brain-wide analysis of oligodendrocyte dynamics across the lifespan has not been performed. We developed a rapid, robust cellular mapping pipeline involving tissue clearing, lightsheet microscopy, atlas alignment, and automated segmentation to define the location of all oligodendrocytes in the mouse brain. This analysis demonstrated the remarkable consistency of oligodendrocyte patterns between hemispheres, individuals, and sexes, and established that oligodendrocyte maps estimate myelin coverage. We trained a vision transformer to identify newly generated oligodendrocytes from millions of mature cells, highlighting age- and region-specific differences in oligodendrogenesis, and revealing areas of enhanced oligodendrocyte resilience and regenerative capacity following demyelination, demonstrating the utility of this pipeline for uncovering brain-wide oligodendrocyte dynamics in health and disease.

Endophilin A1 facilitates organization of the GABAergic postsynaptic machinery to maintain excitation-inhibition balance
The assembly and operation of neural circuits in the brain rely on the coordination and balance of excitatory and inhibitory activities. Inhibitory synapses are key regulators of the functional balance of neural circuits. However, due to the diversity of inhibitory presynaptic neurons, the complex composition of postsynaptic receptor subunits and the lack of typical postsynaptic dense structure, there are relatively few studies on the regulatory mechanisms for inhibitory synaptic structure and function, and insufficient understanding of the cellular and molecular abnormalities of inhibitory synapses in neurological and neuropsychiatric disorders. Here, we report a crucial role for endophilin A1 in inhibitory synapses. We show that endophilin A1 directly interacts with the inhibitory postsynaptic scaffold protein gephyrin in excitatory neurons, and promotes organization of the inhibitory postsynaptic density and synaptic recruitment/stabilization of the {gamma}-aminobutyric acid type A receptors via its plasma membrane association and actin polymerization promoting activities. Loss of endophilin A1 by gene knockout in mouse hippocampal CA1 pyramidal cells weakens inhibitory synaptic transmission and causes imbalance in the excitatory/inhibitory function of neural circuits, leading to increased susceptibility to epilepsy. Our findings identify endophilin A1 as an iPSD component and provide new insights into the organization and stabilization of inhibitory postsynapses to maintain E/I balance as well as the pathogenesis of epilepsy.

Key words: endophilin A1, E/I balance, epilepsy, GABAAR, gephyrin, inhibitory synapse

A dopamine-dependent mechanism for reward-induced modification of fear memories
A positive mental state has been shown to modulate fear-related emotions associated with the recall of fear memories. These, and other observations suggest the presence of central brain mechanisms for affective states to interact. The neurotransmitter dopamine is important for both reward- and fear-related processes, but it is unclear whether dopamine contributes to such affective interactions. Here, we show that precisely timed reward-induced activation of dopamine neurons in mice potently modifies fear memories and enhances their extinction. This reward-based switch in fear states is associated with changes in dopamine release and dopamine-dependent regulation of fear encoding in the central amygdala (CeA). These data provide a central mechanism for reward-induced modification of fear states that have broad implications for treating generalized fear disorders.

Typical development of synaptic and neuronal properties can proceed without microglia
Brain-resident macrophages, microglia, have been proposed to play an active role in synaptic refinement and maturation, influencing plasticity and circuit-level connectivity. Here we show that a variety of neurodevelopmental processes previously attributed to microglia can proceed without them. Using a genetically modified mouse which lacks microglia (Csf1r{triangleup}FIRE/{triangleup}FIRE) we find that neuronal physiology, synapse number and synaptic maturation are largely normal in CA1 of the hippocampus and somatosensory cortex during development. Seizure susceptibility and hippocampal-prefrontal cortex coherence in awake behaving animals, processes disrupted in mice deficient in microglia-enriched genes, are also normal. Similarly, segregation of eye-specific inputs into the lateral geniculate nucleus proceeds normally in the absence of microglia. Furthermore, transcriptomic analysis did not uncover substantial perturbation due to neurons and astrocytes due to microglial absence. Thus, in the absence of microglia, the brain retains the capacity to execute developmental synaptic refinement, maturation and connectivity.

Abnormal hyperactivity of specific striatal ensembles encodes distinct dyskinetic behaviors revealed by high-resolution clustering
L-DOPA-induced dyskinesia (LID) is a debilitating complication of dopamine replacement therapy in Parkinson's disease and the most common hyperkinetic disorder of basal ganglia origin. Abnormal activity of striatal D1 and D2 spiny projection neurons (SPNs) is critical for LID, yet the link between SPN activity patterns and specific dyskinetic movements remains unknown. To explore this, we developed a novel method for clustering movements based on high-resolution motion sensors and video recordings. In a mouse model of LID, this method identified two main dyskinesia types and pathological rotations, all absent during normal behavior. Using single-cell resolution imaging, we found that specific sets of both D1 and D2-SPNs were abnormally active during these pathological movements. Under baseline conditions, the same SPN sets were active during behaviors sharing physical features with LID movements. These findings indicate that ensembles of behavior-encoding D1- and D2-SPNs form new combinations of hyperactive neurons mediating specific dyskinetic movements.

Feature-specific divisive normalization improves natural image encoding for depth perception
Vision science and visual neuroscience seek to understand how stimulus and sensor properties limit the precision with which behaviorally-relevant latent variables are encoded and decoded. In the primate visual system, binocular disparity-the canonical cue for stereo-depth perception-is initially encoded by a set of binocular receptive fields with a range of spatial frequency preferences. Here, with a stereo-image database having ground-truth disparity information at each pixel, we examine how response normalization and receptive field properties determine the fidelity with which binocular disparity is encoded in natural scenes. We quantify encoding fidelity by computing the Fisher information carried by the normalized receptive field responses. Several findings emerge from an analysis of the receptive field response statistics. First, broadband (or feature- unspecific) normalization yields Laplace-distributed receptive field responses, and narrowband (or feature-specific) normalization yields Gaussian-distributed receptive field responses. Second, the Fisher information in narrowband-normalized responses is larger than in broadband-normalized responses by a scale factor that grows with population size. Third, the most useful spatial frequency decreases with stimulus size and the range of spatial frequencies that is useful for encoding a given disparity decreases with disparity magnitude, consistent with neurophysiological findings. Fourth, the predicted patterns of psychophysical performance, and absolute detection threshold, match human performance with natural and artificial stimuli. The current computational efforts establish a new functional role for response normalization, and bring us closer to understanding the principles that should govern the design of neural systems that support perception in natural scenes.

Improved classification of alcohol intake groups in the Intermittent-Access Two-Bottle choice rat model using a latent class linear mixed model
Alcohol use disorder (AUD) is a major public health problem in which preclinical models allow the study of AUD development, comorbidities and possible new treatments. The intermittent access two-bottle choice (IA2BC) model is a validated preclinical model for studying alcohol intake patterns similar to those present in AUD in human clinical studies. Typically, the mean/median of overall alcohol intake or the last drinking sessions is used as a threshold to divide groups of animals into high or low alcohol consumers. However, it would be more statistically valuable to stratify the groups using the full consumption data from all drinking sessions. In this study, we aimed to evaluate the effectiveness of using the time series data of all drinking sessions to stratify the population into high or low alcohol consumption groups, using a latent class linear mixed model (LCLMM). We compared LCLMM to traditional classification methods: percentiles, k-means clustering, and hierarchical clustering, and used simulations to compare accuracy between methods. Our results demonstrated that LCLMM outperforms other approaches, achieving superior accuracy (0.94) in identifying consumption patterns. By considering the entire trajectory of alcohol intake, LCLMM provides a more robust and nuanced characterization of high and low alcohol consumers. We advocate for the adoption of longitudinal statistical models in substance use disorder research, both in human studies and preclinical investigations, as they hold promise for enhancing population stratification and refining treatment strategies.

Mouse Brain Extractor: Brain segmentation of mouse MRI using global positional encoding and SwinUNETR
In spite of the great progress that has been made towards automating brain extraction in human magnetic resonance imaging (MRI), challenges remain in the automation of this task for mouse models of brain disorders. Researchers often resort to editing brain segmentation results manually when automated methods fail to produce accurate delineations. However, manual corrections can be labor-intensive and introduce interrater variability. This motivated our development of a new deep-learning-based method for brain segmentation of mouse MRI, which we call Mouse Brain Extractor. We adapted the existing SwinUNETR architecture (Hatamizadeh et al., 2021) with the goal of making it more robust to scale variance. Our approach is to supply the network model with supplementary spatial information in the form of absolute positional encoding. We use a new scheme for positional encoding, which we call Global Positional Encoding (GPE). GPE is based on a shared coordinate frame that is relative to the entire input image. This differs from the positional encoding used in SwinUNETR, which solely employs relative pairwise image patch positions. GPE also differs from the conventional absolute positional encoding approach, which encodes position relative to a subimage rather than the entire image. We trained and tested our method on a heterogeneous dataset of N=223 mouse MRI, for which we generated a corresponding set of manually-edited brain masks. These data were acquired previously in other studies using several different scanners and imaging protocols and included in vivo and ex vivo images of mice with heterogeneous brain structure due to different genotypes, strains, diseases, ages, and sexes. We evaluated our method's results against those of seven existing rodent brain extraction methods and two state-of-the art deep-learning approaches, nnU-Net (Isensee et al., 2018) and SwinUNETR. Overall, our proposed method achieved average Dice scores on the order of 0.98 and average HD95 measures on the order of 100m when compared to the manually-labeled brain masks. In statistical analyses, our method significantly outperformed the conventional approaches and performed as well as or significantly better than the nnU-Net and SwinUNETR methods. These results suggest that Global Positional Encoding provides additional contextual information that enables our Mouse Brain Extractor to perform competitively on datasets containing multiple resolutions.

Pupil self-regulation modulates markers of cortical excitability and cortical arousal
The brain's arousal state (i.e., central arousal) is regulated by multiple neuromodulatory nuclei in the brainstem and significantly influences high-level cognitive processes. By exploiting the mechanistic connection between the locus coeruleus (LC), a key regulator of central arousal, and pupil dynamics, we recently demonstrated that participants can gain volitional control over arousal-regulating centers including the LC using a pupil-based biofeedback approach. Here, we test whether pupil-based biofeedback modulates electrophysiological markers of cortical excitability, cortical arousal, and phasic LC activity. Combining pupil-based biofeedback with single-pulse TMS, EEG recordings, and an auditory oddball task revealed three main results: pupil self-regulation significantly modulates (i) cortical excitability, (ii) the EEG spectral slope, a marker of cortical arousal, and (iii) the P300 response to target tones, an event-related potential suggested to be tightly linked to phasic LC activity. Interestingly, pupil self-regulation strength was linearly linked to the modulation of the spectral slope, suggesting a common physiological mechanism. Here, we have shown that pupil-based biofeedback modulates fundamental aspects of brain function. Whether this method could further be used to modulate these aspects in case of disturbances associated with neurological and psychiatric disorders needs to be investigated in future studies.

Stimulus repetition induces a two-stage learning process in primary visual cortex
Repeated stimulus exposure alters the brain's response to the stimulus. We investigated the underlying neural mechanisms by recording functional MRI data from human observers passively viewing 120 presentations of two Gabor patches (each Gabor repeating 60 times). We evaluated support for two prominent models of stimulus repetition, the fatigue model and the sharpening model. Our results uncovered a two-stage learning process in the primary visual cortex. In Stage 1, univariate BOLD activation in V1 decreased over the first twelve repetitions of the stimuli, replicating the well-known effect of repetition suppression. Applying MVPA decoding along with a moving window approach, we found that (1) the decoding accuracy between the two Gabors decreased from above-chance level (~60% to ~70%) at the beginning of the stage to chance level at the end of the stage (~50%). This result, together with the accompanying weight map analysis, suggested that the learning dynamics in Stage 1 were consistent with the predictions of the fatigue model. In Stage 2, univariate BOLD activation for the remaining 48 repetitions of the two stimuli exhibited significant fluctuations but no systematic trend. The MVPA decoding accuracy between the two Gabor patches was at chance level initially and became progressively higher as stimulus repetition continued, rising above and staying above chance level starting at the ~35th repetition. Thus, results from the second stage supported the notion that sustained and prolonged stimulus repetition prompts sharpened representations. Additional analyses addressed (1) whether the neural patterns within each learning stage remained stable and (2) whether new neural patterns were evoked in Stage 2 relative to Stage 1.