bioRxiv Subject Collection: Neuroscience's Journal

Biologically informed cortical models predict optogenetic perturbations
Ideally a recurrent spiking neural network fitted to large electrophysiological data sets should illustrate the chain of cortical information transmission. In particular, successful network model reconstruction methods should enable a model to predict the response to optogenetic perturbations. We propose a method that extracts a recurrent spiking neural network from electrophysiological data sets and test it on unseen optogenetic interventions. Our method uses multiple objectives to reproduce various aspects of neuronal activity patterns as well as biology-based inductive biases like structured connectivity and separation of neurons into dominant excitatory and inhibitory cell types. We show that theses biological inductive biases are crucial to predict responses to optogenetic perturbations. Furthermore, once a model is fitted to experimental data, gradient evaluations in the extracted model network can, in principle, be used to design a minimal low-power optogenetic stimulus that causes a maximal effect by stimulating a limited subset of neurons. Our computational methods provide a systematic path towards translating observed spike data into a matching recurrent neural network that is able to predict the results of a variety of experimental interventions.

Rhythmic Sampling and Competition of Target and Distractor in a Motion Detection Task
It has been suggested that the visual system samples attended information rhythmically. Does rhythmic sampling also apply to distracting information? How do attended information and distracting information compete temporally for neural representations? We recorded electroencephalography from participants who detected instances of coherent motion in a random dot kinematogram (RDK; the target stimulus), overlayed on different categories (pleasant, neutral, and unpleasant) of affective images from the International Affective System (IAPS), serving as distractors. The moving dots were flickered at 4.29 Hz whereas the IAPS pictures were flickered at 6 Hz. The time course of spectral power at 4.29 Hz (dot response) was taken to index the temporal dynamics of target processing. The spatial pattern of the power at 6 Hz was similarly extracted and subjected to a MVPA decoding analysis to index the temporal dynamics of processing pleasant, neutral, or unpleasant distractor pictures. We found that (1) both target processing and distractor processing exhibited rhythmicity at ~1 Hz and (2) the phase difference between the two rhythmic time courses were related to task performance, i.e., relative phase closer to {pi} predicted a higher rate of coherent motion detection whereas relative phase closer to 0 predicted a lower rate of coherent motion detection. These results suggest that (1) in a target-distractor scenario, both attended and distracting information were sampled rhythmically and (2) the more target sampling and distractor sampling were separated in time within a sampling cycle, the less distraction effects were observed, both at the neural and the behavioral level.

Overestimation of sex differences in psychostimulant activity via comparisons of males and females from different behavioral groups.
Background: There are inconsistencies in the observation of sex differences in baseline activity and psychostimulant activity. To address this, we have developed the MISSING (Mapping Intrinsic Sex Similarities as an Integral quality of Normalized Groups) model. MISSING model proposes that sex similarities are observed when we compare similar behavioral groups of males and females, with sex differences occurring when we compare distinct groups of sexes, but this model has not been tested. Methods: To test this model, we identified within-sex groups of Sprague Dawley rats (male n = 22, female n = 23) by conducted normal mixtures clustering of baseline activity, cocaine activity (as distance traveled in cm over 90 min) and cocaine activity normalized-to-baseline activity (NBA) of all subjects. We employed 2-way ANOVA to determine the impact of within-sex heterogeneity on sex differences. We compared our cluster-based method to current median-split approaches.Results: Our new cluster-based method revealed three distinct clusters, each consisting of both males and females. We determined there were no sex differences in any of the variables when males and females from the same clusters were compared. The within-sex clusters for females were not defined by estrous phase. Median split analysis was ineffective in accurately identifying within-sex groups. Conclusions: Our results validate the MISSING model: there are no sex differences in psychostimulant activity except when we compare males and females from different behavioral groups. This has significant implications for how we proceed with research towards understanding the mechanism governing sex differences in psychostimulant activity.

Deficiency of actin depolymerizing factors ADF/Cfl1 in microglia decreases motility and impairs memory
Microglia are highly motile cells that play a crucial role in the central nervous system in health and disease. Here we show that actin depolymerizing factors ADF and Cofilin1 (Cfl1) are key factors of microglia integrity and function. We found a profound morphological phenotype in absence of ADF and Cfl1 in microglia. In vivo two-photon imaging of microglia with ADF/Cfl1-KO revealed reduced microglial fine processes motility and impaired microglia migration towards a laser-induced lesion. We found increased accumulation of stabilized F-actin and altered microtubule dynamics in ADF/Cfl1-KO microglia, indicating that ADF/Cfl1 are necessary for microglial cytoskeleton dynamics. Interestingly, microglial ADF/Cfl1-deficiency decreased learning and memory, suggesting that impaired microglial cytoskeleton dynamics affect neuronal functions relevant for cognition. Our results reveal a fundamental role of ADF/Cfl1 in microglia function and underscore the importance of these innate immune cells for higher cognitive functions.

Astrocyte-derived PEA116 increases adult hippocampal neurogenesis and confers stress resilience
In the dentate gyrus of the hippocampus, the neurogenic niche regulates several steps of adult neurogenesis, from the proliferation to the integration of newly formed neurons in the hippocampal network. However, the role of astrocytes in the regulation of adult neural stem cell (aNSC) proliferation is still little described. Here, we found that blocking vesicular release from astrocytes decreased cell proliferation in the dentate gyrus, resulting in impaired adult neurogenesis. Inversely, astrocyte-conditioned medium increased cell proliferation in a vesicular release-dependent manner. We identified PEA116 as a peptide released by astrocytes, that is derived from the c-terminal portion of the PEA15 protein and increased cell proliferation. PEA116 increased ERK2 phosphorylation, decreased the expression of genes involved in aNSC quiescence, resulting in aNSC quiescence exit. The ensuing increase in hippocampal neurogenesis improved resilience to chronic stress. These findings highlight a novel peptide produced by astrocytes that regulates the early steps of adult neurogenesis, with an implication for mood disorders.

A spiking neural network model for proprioception of limb kinematics in insect locomotion
Proprioception plays a key role in all behaviours that involve the control of force, posture or movement. Computationally, many proprioceptive afferents share three common features: First, their strictly local encoding of stimulus magnitudes leads to range fractionation in sensory arrays. As a result, encoding of large joint angle ranges requires integration of convergent afferent information by first-order interneurons. Second, their phasic-tonic response properties lead to fractional encoding of the fundamental sensory magnitude and its derivatives (e.g., joint angle and angular velocity). Third, the distribution of disjunct sensory arrays across the body accounts for distributed encoding of complex movements, e.g., at multiple joints or by multiple limbs. The present study models the distributed encoding of limb kinematics, proposing a multi-layer spiking neural network for distributed computation of whole-body posture and movement. Spiking neuron models are biologically plausible because they link the sub-threshold state of neurons to the timing of spike events. The encoding properties of each network layer are evaluated with experimental data on whole-body kinematics of unrestrained walking and climbing stick insects, comprising concurrent joint angle time courses of 6x3 leg joints. The first part of the study models strictly local, phasic-tonic encoding of joint angle by proprioceptive hair field afferents by use of Adaptive Exponential Integrate-and-Fire neurons. Convergent afferent information is then integrated by two types of first-order interneurons, modelled as Leaky Integrate-and-Fire neurons, tuned to encode either joint position or velocity across the entire working range with high accuracy. As in known velocity-encoding antennal mechanosensory interneurons, spike rate increases linearly with angular velocity. Building on distributed position/velocity encoding, the second part of the study introduces second- and third-order interneurons. We demonstrate that simple combinations of two or three position/velocity inputs from disjunct arrays can encode high-order movement information about step cycle phases and converge to encode overall body posture.

Encoding of movement primitives and body posture through distributed proprioception in walking and climbing insects
Targeted reaching movements and spatial coordination of footfall patterns are prime examples of spatial coordination of limbs in insects. To explain this, both physiological and computational studies have suggested the use of movement primitives or the existence of an internal body representation, much like they are assumed to occur in vertebrates. Since insects lack a dedicated posture-sensing organ or vestibular system, it is hypothesized that they derive high-level postural information from low-level proprioceptive cues, integrated across their limbs. The present study tests the extent to which a multi-layer spiking neural network can extract high-level information about limb movement and whole-body posture from information provided by distributed local proprioceptors. In a preceding part of the study, we introduced the phasic-tonic encoding of joint angles by strictly local proprioceptive hair field afferents, as well as high-accuracy encoding of joint angles and angular velocities in first-order interneurons. Here, we extend this model by second-order interneurons that use coincidence detection from two or three leg-local inputs to encode movement primitives of a single leg. Using experimental data on whole-body kinematics of unrestrained walking and climbing stick insects, we show that these movement primitives can be used to signal particular step cycle phases, but also step cycle transitions such as leg lift-off. Additionally, third-order interneurons are introduced to indicate climbing behaviour, for example by encoding the body pitch angle from 6x3 local leg joints. All encoding properties are validated against annotated experimental data, allowing for relevance rating of particular leg types and/or leg joint actions for all measures encoded. Our results demonstrate that simple combinations of two or three position/velocity inputs from disjunct hair field arrays are sufficient to encode high-order movement information about step cycle phases. The resulting movement primitive encoding may converge to represent particular locomotor states and whole-body posture.

FASER: A TOOL TO SIMULATE PSF DISTORTIONS IN STEDMICROSCOPY
We introduce Faser, a software package developed in Python as a plugin for the open-source napari platform, designed to simulate the excitation point spread functions (PSFs) of microscopes. Using a full-vectorial computational approach to simulate the electromagnetic fields within the focal region, it makes precise predictions and allows detailed analyses of excitation PSFs. Faser is intended as a pedagogical tool enabling users to explore the impact of various geometrical and optical parameters of practical importance on imaging performance. It supports the modeling of complex beam profiles, including donut and bottle-shaped beams, which are instrumental in advanced microscopy techniques such as Stimulated Emission Depletion (STED) microscopy. Through specific simulations and accessible illustrations, we showcase Faser's capabilities in replicating the distinctive properties of STED beams, making it a valuable resource for researchers and students in optical microscopy to explore and optimize high-resolution imaging techniques.

Heterogeneous plasticity of amygdala interneurons in associative learning and extinction
Neural circuits undergo experience-dependent plasticity to form long-lasting memories. Plasticity of excitatory projection neurons has been considered to be the primary neuronal substrate for the acquisition and storage of memories. However, a wide range of inhibitory interneurons controls the activity of projection neurons in a spatially and temporally precise manner, yet their contribution to memory acquisition, storage and expression remains poorly understood. Here, we employ a miniature microscope imaging approach to monitor the activity of large amygdala interneuron populations in freely moving mice during fear learning and extinction at the single cell level. We find that amygdala interneurons display mixed-selectivity and show complex plastic responses at both the ensemble and single neuron level across the acquisition, expression and extinction of aversive memories. In contrast to bidirectional single cell plasticity across distinct fear states, learning-induced changes at the population level occur transiently during conditioning and do not consolidate across days. Examining molecular interneuron subpopulations revealed that vasoactive intestinal peptide (VIP) expressing cells are predominantly activated by high fear states. In contrast, somatostatin (SST) interneurons display a preference for safety cues and thereby suppress excitatory neuron responsiveness. However, responses of individual neurons within the SST and VIP populations are non-uniform, indicating the presence of functional subtypes within classical molecularly-defined interneuron populations. Taken together, we identify complex neuronal plasticity within amygdala interneuron ensembles that goes beyond a passive processing function, suggesting a critical role of inhibitory microcircuit elements for memory selectivity and stability.

The Hurst exponent as a marker of inhibition in the developing brain
The maturation of inhibitory neurons is crucial for regulating plasticity in developing brains. Previous work using computational models has suggested that the Hurst exponent, the decay in power over frequency, reflects inhibition, but empirical data supporting this link is sparse. Here, we took a cross-species approach to validating the Hurst exponent of fMRI as a marker of inhibition, then characterized the development of the Hurst exponent in childhood. We found significant spatial correlations between the Hurst exponent and ex vivo parvalbumin mRNA expression in human children and adults, and between the Hurst exponent and parvalbumin-positive cell counts in mice. We identified a plateau in the mRNA expression by late childhood, aligning with the Hurst exponent plateau in both humans and rats. In sum, this work suggests that the Hurst exponent can be used to study the development of inhibition in vivo, and in the future, to understand individual differences in plasticity.

Chronic treatment with fluoxetine downregulates mitochondrial activity in parvalbumin interneurons of prefrontal cortex
Chronic treatment with fluoxetine, a widely prescribed selective serotonin reuptake inhibitor (SSRI), is known to promote neural plasticity through the activation of the neurotrophic receptor TrkB. Our previous studies have highlighted the role of TrkB in parvalbumin-positive interneurons (PV-INs) in mediating plasticity-related behaviors in the visual cortex and hippocampus. However, the impact of TrkB activity on gene expression and neuronal functions in the prefrontal cortex (PFC) remains unclear. This study aimed to investigate the effects of chronic fluoxetine on PV-INs in the PFC. Using Translating Ribosome Affinity Purification (TRAP), we found that fluoxetine treatment downregulated pathways involved in mitochondrial energy production, including NADH dehydrogenase and cytochrome c activity. Upregulated genes were associated with phosphatase activity, voltage-gated potassium channels, and amino acid transmembrane transport. Analysis of mitochondrial function specific in PV-INs revealed a reduction in mitochondrial membrane potential, while non-PV-INs in the PFC exhibited increased membrane potential. These findings suggest that fluoxetine selectively inactivates PV-INs in the PFC, leading to compensatory increases in mitochondrial activity in non-PV-INs. Immunohistochemical analyses further demonstrated reduced PV expression and weakened perineuronal nets in specific PFC regions. Our results underscore the differential impact of chronic fluoxetine on gene expression and mitochondrial function in PV-INs, suggesting region-specific disinhibition and enhanced plasticity in the PFC.

Generation of synthetic TSPO PET maps from structural MRI images
Background: Neuroinflammation, a pathophysiological process involved in numerous disorders, is typically imaged using [11C]PBR28 (or TSPO) PET. However, this technique is limited by high costs and ionizing radiation, restricting its widespread clinical use. MRI, a more accessible alternative, is commonly used for structural or functional imaging, but when used using traditional approaches has limited sensitivity to specific molecular processes. This study aims to develop a deep learning model to generate TSPO PET images from structural MRI data collected in human subjects. Methods: A total of 204 scans, from participants with knee osteoarthritis (n = 15 scanned once, 15 scanned twice, 14 scanned three times), back pain (n = 40 scanned twice, 3 scanned three times), and healthy controls (n=28, scanned once), underwent simultaneous 3T MRI and [11C]PBR28 TSPO PET scans. A 3D U-Net model was trained on 80% of these PET-MRI pairs and validated using 5-fold cross-validation. The model's accuracy in reconstructed PET from MRI only was assessed using various intensity and noise metrics. Results: The model achieved a low voxel-wise mean squared error (0.0033 {+/-} 0.0010) across all folds and a median contrast-to-noise ratio of 0.0640 {+/-} 0.2500 when comparing true to reconstructed PET images. The synthesized PET images accurately replicated the spatial patterns observed in the original PET data. Additionally, the reconstruction accuracy was maintained even after spatial normalization. Conclusion: This study demonstrates that deep learning can accurately synthesize TSPO PET images from conventional, T1-weighted MRI. This approach could enable low-cost, noninvasive neuroinflammation imaging, expanding the clinical applicability of this imaging method.

Molecular signatures of altered energy metabolism and circadian rhythm perturbations in a model of extra-nigral Synucleinopathy.
A pathological role of alpha-Synuclein (aSyn) aggregation in the central nervous system (CNS) is a recognized feature in Parkinson disease (PD) and related neurodegenerative conditions termed synucleinopathies. In order to characterize the cellular response in CNS to incipient and advanced aSyn pathology, we applied spatial transcriptomics on brain sections derived from a transgenic mouse model (M83+/+ line, Prnp-SNCA*A53T) in which aSyn aggregation was induced in a prion-like fashion through hindlimb intramuscular delivery of pre-formed fibrillar (PFF) murine aSyn. Our spatially-resolved transcriptomics (ST) data point to unique perturbations in brain energy metabolism during the progression of aSyn pathology, such that the early stage of aSyn aggregate pathology activates molecular pathways controlling metabolic flux through glycolysis, oxidative phosphorylation and fatty acid metabolism. In contrast, the ST data indicate a profound decline in mitochondrial metabolism in the brains of symptomatic animals with advanced aSyn pathology. The latter stage was also associated with drastic reduction in mRNA translation machinery, along with aberrant expression of molecular drivers involved in RNA splicing and inflammatory response. Intriguingly, our ST data also point to perturbed regulation of circadian rhythm, was corroborated by increased immunodetection of CREB-binding protein (a modulator of core clock machinery) in the brains of symptomatic animals, and transcriptional upregulation of CREBBP in 4 independent PD microarray datasets. Collectively, we anticipate that our findings offer novel opportunities in knowledge translation for mechanism-based drug discovery and biomarkers in neurodegenerative synucleinopathies.

Identification of SLC45A4 as a pain gene encoding a neuronal polyamine transporter.
Polyamines are regulatory metabolites with key roles in transcription, translation, cell signalling and autophagy1. They are implicated in multiple neurological disorders including stroke, epilepsy and neurodegeneration and can regulate neuronal excitability through interactions with ion channels2. Polyamines have been linked to pain showing altered levels in human persistent pain states and modulation of pain behaviour in animal models3. However, the systems governing polyamine transport within the nervous system remain unclear. In undertaking a Genome Wide Association Study (GWAS) of chronic pain intensity in the UK-Biobank we found significant association with variants mapping to the SLC45A4 gene locus. In the mouse nervous system SLC45A4 expression is enriched in all sensory neuron sub-types within the dorsal root ganglion including nociceptors. Cell-based assays show that SLC45A4 is a selective plasma membrane polyamine transporter, whilst the cryo-EM structure reveals a novel regulatory domain and basis for polyamine recognition. Mice lacking SLC45A4 show normal mechanosensitivity but reduced sensitivity to noxious heat and algogen induced tonic pain that is associated with reduced excitability of peptidergic nociceptors. Our findings thus establish a role for neuronal polyamine transport in pain perception and identify a new target for therapeutic intervention in pain treatment.

Acoustic parameter combinations underlying mapping of auditory pseudoword sounds to multiple domains of meaning: a machine learning approach
Sound symbolism, referring to the resemblance between the sound structure of words and their meaning, is commonly studied using auditory pseudowords. Companion studies across seven meaning domains demonstrated systematic relationships, varying by domain, between the perceptual ratings, phonetic features, and acoustic parameters of a set of 537 pseudowords (Lacey et al. 2024a, 2024b). Here we employed a k-nearest-neighbor (KNN) machine-learning algorithm to compare 4094 combinations of twelve acoustic parameters (3 spectro-temporal and 9 characterizing vocal quality) and identify the parameter combination that best predicted perceptual ratings in each domain. Using multiple regression, we then examined the relative contributions of the parameters comprising the best performing acoustic model for each domain. Finally, we used the KNN approach to generate sound-symbolic ratings, in the shape domain, for 160 real words and compared these predicted ratings with corresponding perceptual ratings. We found that sound-symbolic mappings rely on domain-specific combinations and weights of acoustic parameters. One spectro-temporal parameter, the fast Fourier transform, and one vocal parameter, the fraction of unvoiced frames, were both present in the best performing model for each meaning domain studied, indicating the general importance of these two parameters for sound-symbolic judgments. The predicted and perceptual ratings of the real words were strongly correlated, indicating the value of this approach to measure the degree of sound-symbolic mapping in natural languages, unconfounded by semantic bias. Our findings support the proposed relevance of sound symbolism to language.

Current Reporting Practices in Human Neuroscience Research
Concerns for the replicability, reliability, and generalizability of human neuroimaging research have led to intense debates over sample size and open science practices, with more recent attention on the contributions of sampling and recruitment practices. Key to understanding the state of neuroscience research is an assessment of reporting practices that influence replicability, reliability, and generalizability. In this structured review, we evaluated reporting practice across three domains: (1) demographic (e.g., reporting participant race-ethnicity, age, any measure of socioeconomic position), (2) methodological (e.g., reporting recruitment methods, inclusion and exclusion criteria, why participants were excluded from analyses), and (3) open science and generalizability (e.g., analyses were preregistered, target population was stated). Included were 919 published MRI and fMRI studies from 2019 in nine top-ranked journals (N = 3,856 records screened). Reporting across domains was infrequent, with participant racial or ethnic identity (14.8%), reasons for missing imaging data (31.2%), and identification of a target population (19.4%) being particularly low/underreported. Reporting likelihood varied by study characteristics (e.g., participant age group) and was correlated across domains. The median sample size of studies was 55 participants. Study sample size, reporting frequency, was positively associated with two-year citation counts, providing some evidence that the complete reporting of demographic characteristics, methodological decisions, and open science and generalizability practices may not be as valued as study sample size. Recommendations for structural interventions at the journal level are proposed.