bioRxiv Subject Collection: Neuroscience's Journal

Pharmacogenomic screening identifies and repurposes leucovorin and dyclonine as pro-oligodendrogenic compounds in brain repair
Oligodendrocytes are the myelin-forming cells of the central nervous system (CNS), with oligodendroglial pathologies leading to strong disabilities, from early preterm-birth brain injury (PBI) to adult multiple sclerosis (MS). No medication presenting convincing repair capacity in humans has been approved for these pathologies so far. Here, we present a pharmacogenomic approach leading to the identification of small bioactive molecules with a large pro-oligodendrogenic activity, selected through an expert curation scoring strategy (OligoScore) of their large impact on transcriptional programs controlling oligodendrogenesis and (re)myelination. We demonstrate the pro-oligodendrogenic activity of these compounds in vitro, using neural and oligodendrocyte progenitor cell (OPC) cultures, as well as ex vivo, using organotypic cerebellar explant cultures. Focusing on the two most promising molecules, i.e. leucovorin and dyclonine, we tested their therapeutic efficacy using a mouse model of neonatal chronic hypoxia, which faithfully mimics aspects of PBI. In this model, both compounds promoted proliferation and oligodendroglial fate acquisition from neural stem/progenitor cells, with leucovorin also promoting their differentiation. We extended these findings to an adult focal de/remyelination mouse MS model, in which both compounds improved lesion repair by promoting OPC differentiation while maintaining the pool of OPCs, and in parallel, by accelerating the transition from pro inflammatory to pro-regenerative microglial profiles and myelin debris clearance. This study paves the way for clinical trials aimed at repurposing these FDA-approved compounds to treat myelin pathologies such as PBI and MS.

Characterization of Postsynaptic Glutamate Transporter Functionality in the Zebrafish Retinal First Synapse Across Different Wavelengths
In the zebrafish retina, incident light undergoes wavelength-dependent processing encompassing mechanisms such as color opponency, contrast enhancement, and motion detection prior to neural transmission to the brain proper. In darkness, photoreceptors continuously release glutamate into the synaptic cleft, a process that diminishes in response to increased light intensity, thereby conveying visual signals to ON and OFF bipolar cells. Specifically, in zebrafish, the ON pathway signal transduction is mediated by metabotropic glutamate receptor 6b (mGluR6b) and Excitatory Amino Acid Transporters (EAATs). Here we demonstrate that knockout of eaat5b and eaat7 disrupts electroretinogram responses to short and long-wavelength stimuli while preserving middle-wavelength responses, suggesting wavelength-specific roles. We found differential expression of EAAT5b and EAAT7 in the outer plexiform layer, particularly in the strike zone, crucial for prey capture, supporting task specific involvement of these signaling pathways. In order to investigate this, we developed a virtual hunting assay using UV light stimuli. Such a behavioral assay targeting short and long wavelengths indicate that EAAT5b and EAAT7 influence UV-dependent prey detection and motion sensing differently. Our findings highlight the importance of EAAT5b and EAAT7 in modulating light integration dynamics in the zebrafish retina.

Seed-competent alpha-synuclein pathology in metachromatic leukodystrophy: the expanding spectrum of alpha-synucleinopathy in sphingolipidoses
Metachromatic leukodystrophy (MLD) is a rare - typically paediatric - sphingolipid storage disorder resulting from bi-allelic pathogenic variants in the ARSA gene, encoding the lysosomal arylsulphatase A (ASA). Heterozygous variants in ARSA are associated with risk of Lewy body diseases (LBD), a group of age-associated neurodegenerative disorders characterised by the accumulation of the protein alpha-synuclein; however, no study has yet determined whether alpha-synuclein with putative pathological features is observed in MLD brain tissue. We examined post-mortem brain tissue from MLD cases (N=5, age 2-33) compared to matched control cases using histological approaches and alpha-synuclein seeding amplification assay (SAA). Juvenile-onset MLD cases exhibited granular alpha-synuclein deposits in neurons of regions prone to neuronal pathology in MLD, and seed-competent conformers that generated atypical short, twisted fibrils on SAA. In contrast, infantile-onset MLD cases gave only variably positive reactions on SAA. In summary, this study suggests MLD cases manifest alpha-synuclein pathology reminiscent of that observed in LBD, even in juvenile populations, further expanding the spectrum of sphingolipid storage disorders associated with the aggregation of alpha-synuclein. These findings have important implications for understanding the disease process of both LBD and MLD, potentially highlighting novel pathways for therapeutic interventions in both conditions.

Geometric eigenmode brain fingerprinting and its longitudinal associations with adolescent mental health and wellbeing
Background: Brain fingerprinting research posits that individual uniqueness can be identified by structural and functional features that may also be linked to mental health outcomes. Global structural features of the brain can be succinctly and directly captured from magnetic resonance imaging (MRI) via the eigenmodes of the cortical surface - known as geometric eigenmodes. This research investigates how the uniqueness of geometric eigenmodes changes across adolescence and their longitudinal relation to mental health and wellbeing. Methods: The current study utilised n=613 MRI, self-report and demographic datasets from N=116 community-recruited adolescents enrolled in the Longitudinal Adolescent Brain Study (LABS), between the ages of 12-17 years. MPRAGE scans at each participants visit were used to derive 225 left-hemisphere geometric eigenmodes. Eigenmodes were clustered into 14 eigengroups and developmental trajectories of their uniqueness and longitudinal associations with mental wellbeing and psychological distress were examined. Results: All eigengroups become significantly more unique longitudinally, and higher mode (shorter wavelength) eigengroups were more unique than lower mode groups in adolescence. Less uniqueness in eigengroup 6 was significantly associated with higher psychological distress and lower mental wellbeing at concurrent and future timepoints. Conclusion: Geometric eigengroup brain fingerprinting offers a novel way to examine neurodevelopment. This study provides evidence that eigengroups have distinct trajectories from adolescence to adulthood, consistent with other imaging studies demonstrating increasing uniqueness in this period. Importantly, they are associated with mental health state and thus may represent neurobiological markers for mental illness onset, building on previous LABS research demonstrating that the functional uniqueness of the cognitive control network predicts psychological distress four months later.

Bayes vs. Weber: how to break a law of psychophysics
A classic tenet of psychophysics due to Weber is that human perceptual judgments are more variable for larger magnitudes. The more recent Bayesian paradigm proposes that human perception results from an optimal statistical inference conducted on the basis of noisy internal signals. Both are supported by a wealth of empirical evidence. Do the two conflict, and if so, which best reproduces human behavior? In two preregistered experiments, we manipulate the prior distribution and the reward function in a numerosity-estimation task. When the large numerosities are more frequent, and when they are more rewarding, the Bayesian observer exhibits an 'anti-Weber' behavior, in which larger magnitudes results in less variable responses. Human subjects exhibit a similar pattern, thus breaking a long-standing law of psychophysics by showing the opposite behavior. This allows subjects to minimize the errors they make about the more frequent or the more rewarding magnitudes. Nevertheless, model fitting suggests that subjects' responses are best captured by a model that features a logarithmic encoding, a proposal of Fechner often regarded as accounting for Weber's law. We thus obtain an anti-Weber behavior together with a Fechner encoding. Our results suggest that Weber's law may be primarily due to the skewness of natural priors.

Investigating orientation adaptation following naturalistic film viewing
Humans display marked changes to their perceptual experience of a stimulus following prolonged or repeated exposure to a preceding stimulus. A well-studied example of such perceptual adaptation is the tilt-aftereffect. Here, prolonged exposure to one orientation leads to a shift in the perception of subsequent orientations. Such a capacity to adapt suggests the visual system is dynamically tuned to our current visual environment. However, it remains unclear to what extent adaptation occurs in response to systematic features in naturalistic scenes. We therefore investigated orientation adaptation in response to natural viewing of filtered live-action film stimuli. Within a session, participants freely viewed 45 minutes of a film which had been filtered to include increased contrast energy within a specified orientation band (0, 45, 90, or 135 degrees; i.e., the adaptor). To measure adaptation effects, the film was intermittently interrupted to have participants perform a simple orientation judgement task. Having participants complete behavioural trials throughout the testing session, including 45 minutes of total adaptation time, allowed investigation of the accumulation of response biases and changes in such biases over the course of the session. We found participants exhibited stronger adaptation effects in response to cardinal adaptors compared to obliques. However, overall adaptation effects were weaker than those observed under typical tilt-aftereffect paradigms. Further, within a single session, adaptation effects developed inconsistently. The current findings therefore demonstrate a resistance to adaptation in response to naturalistic viewing conditions, suggesting barriers to understanding perceptual adaptation as experienced in nature.

Delta opioid receptors engage multiple signaling cascades to differentially modulate prefrontal GABA release with input and target specificity
Opioids regulate circuits associated with motivation and reward across the brain. Of the opioid receptor types, delta opioid receptors (DORs) appear to have a unique role in regulating the activity of circuits related to reward without a liability for abuse. In neocortex, DORs are expressed primarily in interneurons, including parvalbumin- and somatostatin-expressing interneurons that inhibit somatic and dendritic compartments of excitatory pyramidal cells, respectively. But how DORs regulate transmission from these key interneuron classes is unclear. We found that DORs regulate inhibition from these interneuron classes using different G-protein signaling pathways that both converge on presynaptic calcium channels, but regulate distinct aspects of calcium channel function. This imposes different temporal filtering effects, via short-term plasticity, that depend on how calcium channels are regulated. Thus, DORs engage differential signaling cascades to regulate inhibition depending on the postsynaptic target compartment, with different effects on synaptic information transfer in somatic and dendritic domains.

Confidence and insight into working memories are shaped by attention and recent performance
Working memory (WM) is capacity-limited, and our ability to access information from WM is variable, but selective attention to working memory contents can improve performance. People are able to make introspective judgements regarding the quality of their memories, and these judgements are linked to objective memory performance. However, it remains unknown whether benefits of internally directed attention on memory performance occur alongside commensurate changes in introspective judgments. Across two experiments, we used retrospective cues (retro-cues) during working-memory maintenance to direct attention to items in memory. We then examined their consequence on introspective judgements. In the second experiment, we provided trial-wise feedback on performance. We found that selective attention improved confidence judgements and not just performance of the probed item. We were also able to judge participants' genuine insight into working-memory contents through the correlation between confidence judgements and memory quality. Neurophysiologically, alpha desynchronization correlated first with memory error and then confidence during retro-cueing, suggesting a sequential process of attentional enhancement of memory contents and introspective insight. Further, we showed that participants can use feedback on the accuracy of confidence judgements to update their beliefs across time, according to performance. Our results emphasize flexibility in working memory by showing we can selectively modulate our confidence about its contents based on internally directed attention or objective feedback.

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA
High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

Hierarchical Bayesian Augmented Hebbian Reweighting Model of Perceptual Learning
The Augmented Hebbian Reweighting Model (AHRM) has been effectively utilized to model the collective performance of observers in various perceptual learning studies. In this work, we have introduced a novel hierarchical Bayesian Augmented Hebbian Reweighting Model (HB-AHRM) to simultaneously model the learning curves of individual participants and the entire population within a single framework. We have compared its performance to that of a Bayesian Inference Procedure (BIP), which independently estimates the posterior distributions of model parameters for each individual subject without employing a hierarchical structure. To cope with the substantial computational demands, we developed an approach to approximate the likelihood function in the AHRM with feature engineering and linear regression, increasing the speed of the estimation procedure by 20,000 times. The HB-AHRM has enabled us to compute the joint posterior distribution of hyperparameters and parameters at the population, observer, and test levels, facilitating statistical inferences across these levels. While we have developed this methodology within the context of a single experiment, the HB-AHRM and the associated modeling techniques can be readily applied to analyze data from various perceptual learning experiments and provide predictions of human performance at both the population and individual levels. The likelihood approximation concept introduced in this study may have broader utility in fitting other stochastic models lacking analytic forms.

Ultrastructure of immature synaptic inputs in the lateral superior olive of the rodent brainstem
Neurons of the lateral superior olive (LSO), which compute intensity differences between the two ears, receive two primary inputs, an ipsilaterally arising excitatory input and a contralaterally arising inhibitory input, that are precisely matched for stimulus frequency. Circuit refinement to establish this precise match takes place within the first few postnatal weeks through elimination of single-fiber inputs and concomitant strengthening of the remaining inputs. However, little is known about the ultrastructure of these young synapses and about how changes in physical features of these synapses could contribute to refinement. To characterize pre-hearing postnatal development of somatic synapses in the LSO, we performed transmission electron microscopy and examined synapses in the rodent LSO from birth to hearing onset at postnatal day 13. Synaptic vesicles and mitochondria in putative synaptic boutons were surprisingly scarce at birth. During the second week, bouton enlargement was accompanied by an increase in the number of vesicles and mitochondria. The size of mitochondria also increased, pointing to changes in functional and metabolic needs of synapses. Our results reveal extensive remodeling at individual presynaptic terminals that could strengthen single-fiber inputs and contribute to the development of robust synaptic transmission.

Do transformers and CNNs learn different concepts of brain age?
"Predicted brain age" refers to a biomarker of structural brain health derived from machine learning analysis of T1- weighted brain magnetic resonance (MR) images. A range of machine learning methods have been used to predict brain age, with convolutional neural networks (CNNs) currently yielding state-of-the-art accuracies. Recent advances in deep learning have introduced transformers, which are conceptually distinct from CNNs, and appear to set new benchmarks in various domains of computer vision. However, transformers have not yet been applied to brain age prediction. Thus, we address two research questions: First, are transformers superior to CNNs in predicting brain age? Second, do conceptually different deep learning model architectures learn similar or different "concepts of brain age"? We adapted a Simple Vision Transformer (sViT) and a Shifted Window Transformer (SwinT) to predict brain age, and compared both models with a ResNet50 on 46,381 T1-weighted structural MR images from the UK Biobank. We found that SwinT and ResNet performed on par, while additional training samples will most likely give SwinT the edge in prediction accuracy. We identified that different model architectures may characterize different (sub-)sets of brain aging effects, representing diverging concepts of brain age. Thus, we systematically tested whether sViT, SwinT and ResNet focus on different concepts of brain age by examining variations in their predictions and clinical utility for indicating deviations in neurological and psychiatric disorders. Reassuringly, we did not find substantial differences in the structure of brain age predictions between model architectures. Based on our results, the choice of deep learning model architecture does not appear to have a confounding effect on brain age studies.

Overexpression of the Apoe gene in the frontal cortex of mice causes sex-dependent changes in learning, attention, and anxiety-like behavior
Apolipoprotein E (ApoE) is a protein that is important for lipid storage, transport, and metabolism. APOE gene variants are associated with Alzheimers disease (AD), as well as attentional function in healthy humans. Previous research has shown that Apoe transcription is increased following stimulation of the pathway between the locus coeruleus (LC) and frontal cortex (FC) in mice. This result suggests that Apoe may affect attentional function by virtue of its expression in circuits that control attention. Does Apoe causally regulate attention, or is its expression simply a byproduct of neuronal activity in the LC and FC? To answer this question, we synthetically induced Apoe transcription in the FC of male and female mice, and subsequently tested their ability to learn a touchscreen-based rodent version of the continuous performance test of sustained attention (the rCPT). We found that increased Apoe transcription impaired performance when attentional demand was increased in male mice, while in female mice, increased Apoe transcription significantly accelerated rCPT learning. We further found that this increase in Apoe transcription affected subsequent anxiety-like behavior and cellular activity in the FC in a sex-dependent manner. The results of this study provide insight into how Apoe causally regulates translationally relevant behaviors in rodent models.

Sleep defined by arousal threshold reveals decreases in corticocortical functional correlations independently from the conventional sleep stages
Sleep research and sleep medicine have benefited from the use of polysomnography but have also suffered from an overreliance on the conventional, polysomnography-defined sleep stages. For example, reports of sleep-specific brain activity patterns have, with few exceptions, been constrained by assessing brain function as it relates to the conventional sleep stages. This limits the variety of sleep states and underlying activity patterns that one can discover. If undiscovered brain activity patterns exist during sleep, then removing the constraint of a stage-specific analysis may uncover them. The current study used all-night functional magnetic resonance imaging sleep data and defined sleep behaviorally with auditory arousal threshold (AAT) to begin to search for new brain states. It was hypothesized that, during sleep compared to wakefulness, corticocortical functional correlations would decrease. Functional correlation values calculated in a window immediately before the determination of AAT were entered into a linear mixed effects model, allowing multiple arousals across the night per subject into the analysis. The hypothesis was supported using both correlation matrices of brain networks and single seed-region analyses showing whole-brain maps. This represents a novel approach to studying the neuroanatomical correlates of sleep with high spatial resolution by defining sleep in a way that was independent from the conventional sleep stages. This work provides initial evidence to justify searching for sleep stages that are more neuroanatomically localized and unrelated to the conventional sleep stages.

Statement of SignificanceSleep is typically defined with a limited number of stages based on electro-encephalography (EEG). These EEG stages were established because they correlated with the original behavioral definitions of sleep, most notably, arousal threshold. This occurred before electro-oculography and electromyography were added to create polysomnographic (PSG) sleep stages. One might guess that when other techniques were invented to measure the brain during sleep, the first experiments would have been to perform correlations with arousal threshold. These experiments have never been performed, either with functional magnetic resonance imaging (MRI) or with any other modern technique. To begin to search for new sleep stages, the amount of communication between brain regions as measured by all-night functional MRI was correlated with arousal threshold. Communication between brain regions decreased as sleep depth, measured behaviorally, increased. This provides initial evidence to justify searching for sleep stages that are unrelated to the conventional sleep stages. Doing so will expand our understanding of sleep and its functions beyond the constraints imposed by PSG-defined stages.

A qMRI approach for mapping microscopic water populations and tissue relaxivity in the in vivo human brain.
Quantitative magnetic resonance imaging (qMRI) enables non-invasive mapping of brain tissue microstructure and is widely used for monitoring various physiological and pathological brain processes. Here, we introduce a qMRI approach for enriching the microstructural characterization of the sub-voxel environment. Inspired by pioneering magnetization transfer (MT) models, this approach employs MT saturation to differentiate between various water populations within each voxel. Our in vivo results align well with theoretical predictions and are reproducible using standard qMRI protocols. We present an array of new quantitative maps, highlighting different aspects of the tissue's water. Furthermore, by manipulating the effective water content and relaxation rate with MT, we approximate within the voxel the tissue relaxivity. This property reflects the dependency of R1 on the macromolecular tissue volume (MTV) and is associated with the lipid and macromolecular composition of the brain. Our approach also enables biophysically-informed modulation of the R1 contrast, resulting in a set of unique cortical profiles. Finally, we demonstrate the effectiveness of our technique in imaging the common pathology of white matter hyperintensities (WMH), revealing tissue degradation and molecular alterations.

Lipidomic and Proteomic Insights from Extracellular Vesicles in Postmortem Dorsolateral Prefrontal Cortex Reveal Substance Use Disorder-Induced Brain Changes
Substance use disorder (SUD) significantly increases the risk of neurotoxicity, inflammation, oxidative stress, and impaired neuroplasticity. The activation of inflammatory pathways by substances may lead to glial activation and chronic neuroinflammation, potentially mediated by the release of extracellular particles (EPs), such as extracellular condensates (ECs) and extracellular vesicles (EVs). These particles, which reflect the physiological, pathophysiological, and metabolic states of their cells of origin, might carry molecular signatures indicative of SUD. In particular, our study investigated neuroinflammatory signatures in SUD by isolating EVs from the dorsolateral prefrontal cortex (dlPFC) Brodmanns area 9 (BA9) in postmortem subjects. We isolated BA9-derived EVs from postmortem brain tissues of eight individuals (controls: n=4, SUD: n=4). The EVs were analyzed for physical properties (concentration, size, zeta potential, morphology) and subjected to integrative multi-omics analysis to profile the lipidomic and proteomic characteristics. We assessed the interactions and bioactivity of EVs by evaluating their uptake by glial cells. We further assessed the effects of EVs on complement mRNA expression in glial cells as well as their effects on microglial migration. No significant differences in EV concentration, size, zeta potential, or surface markers were observed between SUD and control groups. However, lipidomic analysis revealed significant enrichment of glycerophosphoinositol bisphosphate (PIP2) in SUD EVs. Proteomic analysis indicates downregulation of SERPINB12, ACYP2, CAMK1D, DSC1, and FLNB, and upregulation of C4A, C3, and ALB in SUD EVs. Gene ontology and protein-protein interactome analyses highlight functions such as cell motility, focal adhesion, and acute phase response signaling that is associated with the identified proteins. Both control and SUD EVs increased C3 and C4 mRNA expression in microglia, but only SUD EVs upregulated these genes in astrocytes. SUD EVs also significantly enhanced microglial migration in a wound healing assay.This study successfully isolated EVs from postmortem brains and used a multi-omics approach to identify EV-associated lipids and proteins in SUD. Elevated C3 and C4 in SUD EVs and the distinct effects of EVs on glial cells suggest a crucial role in acute phase response signaling and neuroinflammation.

Spatial transcriptomic data reveals pure cell types via the mosaic hypothesis
Neurons display remarkable diversity in their anatomical, molecular, and physiological properties. Although observed stereotypy in subsets of neurons is a pillar of neuroscience, clustering in high-dimensional feature spaces, such as those defined by single cell RNA-seq data, is often inconclusive and cells seemingly occupy continuous, rather than discrete, regions. In the retina, a layered structure, neurons of the same discrete type avoid spatial proximity with each other. While this principle, which is independent of clustering in feature space, has been a gold standard for retinal cell types, its applicability to the cortex has been only sparsely explored. Here, we provide evidence for such a mosaic hypothesis by developing a statistical point process analysis framework for spatial transcriptomic data. We demonstrate spatial avoidance across many excitatory and inhibitory neuronal types. Spatial avoidance disappears when cell types are merged, potentially offering a gold standard metric for evaluating the purity of putative cell types.

Correlation and Matching Representations of Binocular Disparity across the Human Visual Cortex
Seeing three-dimensional objects requires multiple stages of representational transformation, beginning in the primary visual cortex (V1). Here, neurons compute binocular disparity from the left and right retinal inputs through a mechanism similar to local cross-correlation. However, correlation-based representation is ambiguous because it is sensitive to disparities in both similar and dissimilar features between the eyes. Along the visual pathways, the representation transforms to a cross-matching basis, eliminating responses to falsely matched disparities. We investigated this transformation across the human visual areas using functional magnetic resonance imaging (fMRI) and computational modeling. By fitting a linear weighted sum of cross-correlation and cross-matching model representations to the brains representational structure of disparity, we found that areas V1-V3 exhibited stronger cross-correlation components, V3A/B, V7, and hV4 were slightly inclined towards cross-matching, and hMT+ was strongly engaged in cross-matching. To explore the underlying mechanism, we identified a deep neural network optimized for estimating disparity in natural scenes that matched human depth judgment in the random-dot stereograms used in the fMRI experiments. Despite not being constrained to match fMRI data, the network units responses progressed from cross-correlation to cross-matching across layers. Activation maximization analysis on the network suggests that the transformation incorporates three phases, each emphasizing different aspects of binocular similarity and dissimilarity for depth extraction. Our findings suggest a systematic distribution of both components throughout the visual cortex, with cross-matching playing a greater role in areas anterior to V3, and that the transformation exploits responses to false matches rather than discarding them.

Significant StatementHumans perceive the visual world in 3D by exploiting binocular disparity. To achieve this, the brain transforms neural representation from the cross-correlation of signals from both eyes into a cross-matching representation, filtering out responses to disparities from falsely matched features. The location and mechanism of this transformation in the human brain are unclear. Using fMRI, we demonstrated that both representations were systematically distributed across the visual cortex, with cross-matching exerting a stronger effect in cortical areas anterior to V3. A neural network optimized for disparity estimation in natural scenes replicated human depth judgment in various stereograms and exhibited a similar transformation. The transformation from correlation to matching representation may be driven by performance optimization for depth extraction in natural environments.

Type I TARPs regulate Kv7.2 potassium channels andsusceptibility to seizures
The M-current is a low-threshold potassium current that modulates neuronal excitability and suppresses repetitive firing. However, the mechanisms regulating M-channel function remain unclear. We identified type I Transmembrane AMPA receptor Regulatory Proteins (TARPs) as M-channel Kv7.2 subunit interactors in cortical neurons, with their interaction increasing upon neuronal depolarization. Co-expression of TARPs with Kv7.2 increased channel surface expression and Kv7.2-mediated currents, while disrupting TARP-{gamma}2 expression in neurons perturbed dendritic Kv7.2 nano-clusters and decreased M-currents. Knock-in mice with an intellectual disability-associated TARP-{gamma}2 variant showed reduced hippocampal M-currents and increased seizure susceptibility, indicating that disrupting TARP-{gamma}2 regulation of Kv7.2-M-channels is epileptogenic. These findings show that TARP-{gamma}2, a synaptic protein crucial for excitatory transmission, also controls intrinsic excitability via M-channels. This discovery provides a link between synaptic transmission and neuronal excitability, with implications for disease, as the interplay between synaptic and intrinsic plasticity is pivotal to how the brain adapts to varying input signals.

HighlightsO_LIType I TARPs bind to Kv7.2-M-channels and enhance Kv7.2-mediated currents.
C_LIO_LITARP-{gamma}2 governs the neuronal nano-organization and function of Kv7.2 channels.
C_LIO_LIIntellectual disability-associated TARP-{gamma}2 variant impairs M-currents and facilitates seizures.
C_LIO_LIType I TARPs can serve as molecular integrators of synaptic and intrinsic excitability.
C_LI

The inattentional rhythm in audition
The detection of temporally unpredictable visual targets depends on the preceding phase of alpha oscillations (~7-12 Hz). In audition, however, such an effect seemed to be absent. Due to the transient nature of its input, the auditory system might be particularly vulnerable to information loss that occurs if relevant information coincides with the low excitability phase of the oscillation. We therefore hypothesised that effects of oscillatory phase in audition will be restored if auditory events are made task-irrelevant and information loss can be tolerated. To this end, we collected electroencephalography (EEG) data from 29 human participants (21F) while they detected pure tones at one sound frequency and ignored others. Confirming our hypothesis, we found that the neural response to task-irrelevant but not to task-relevant tones depends on the pre-stimulus phase of neural oscillations. Alpha oscillations modulated early stages of stimulus processing, whereas theta oscillations (~3-7 Hz) affected later components, possibly related to distractor inhibition. We also found evidence that alpha oscillations alternate between sound frequencies during divided attention. Together, our results suggest that the efficacy of auditory oscillations depends on the context they operate in, and demonstrate how they can be employed in a system that heavily relies on information unfolding over time.

Significance StatementThe phase of neural oscillations shapes visual processing, but such an effect seemed absent in the auditory system when confronted with temporally unpredictable events. We here provide evidence that oscillatory mechanisms in audition critically depend on the degree of possible information loss during the oscillations low excitability phase, possibly reflecting a mechanism to cope with the rapid sensory dynamics that audition is normally exposed to. We reach this conclusion by demonstrating that the processing of task-irrelevant but not task-relevant tones depends on the pre-stimulus phase of neural oscillations during selective attention. During divided attention, cycles of alpha oscillations seemed to alternate between possible acoustic targets similar to what was observed in vision, suggesting an attentional process that generalises across modalities.