bioRxiv Subject Collection: Neuroscience's Journal

Parameter Efficient Fine-tuning of Transformer-based Masked Autoencoder Enhances Resource Constrained Neuroimage Analysis
Recent innovations in artificial intelligence (AI) have increasingly focused on large-scale foundational models that are more general purpose in contrast to conventional models trained to perform specialized tasks. Transformer-based architectures have become the standard backbone in foundation models across data modalities (image, text, audio, video). There has been a keen interest in applying parameter-efficient fine-tuning (PEFT) methods to adapt these models to specialized downstream tasks in language and vision. These methods are particularly essential for medical image analysis where the limited availability of training data could lead to overfitting. In this work, we evaluated different types of PEFT methods on pre-trained vision transformers relative to typical training approaches, such as full fine-tuning and training from scratch. We used a transformer-based masked autoencoder (MAE) framework, to pretrain a vision encoder on T1-weighted (T1-w) brain MRIs. The pretrained vision transformers were then fine-tuned using different PEFT methods that reduced the trainable model parameters to as few as 0.04% of the original model size. Our study shows that: 1. PEFT methods were competitive with or outperformed the reference full fine-tuning approach and outperformed training from scratch, with only a fraction of the trainable parameters; 2. PEFT methods with a 32% reduction in model size boosted Alzheimer's disease (AD) classification by 3% relative to full fine-tuning and 11% relative to a 3D CNN, with only 258 training scans; and 3. PEFT methods performed well on diverse neuroimaging tasks including AD and Parkinson's disease (PD) classification, and "brain-age" prediction based on T1-w MRI datasets - a standard benchmark for deep learning models in neuroimaging; 4. smaller model sizes were competitive with larger models in test performance. Our results show the value of adapting foundation models to neuroimaging tasks efficiently and effectively in contrast to training stand-alone special purpose models.

Leveraging a Vision-Language Model with Natural Text Supervision for MRI Retrieval, Captioning, Classification, and Visual Question Answering
Large multimodal models are now extensively used worldwide, with the most powerful ones trained on massive, general-purpose datasets. Despite their rapid deployment, concerns persist regarding the quality and domain relevance of the training data, especially in radiology, medical research, and neuroscience. Additionally, healthcare data privacy is paramount when querying models trained on medical data, as is transparency regarding service hosting and data storage. So far, most deep learning algorithms in radiologic research are designed to perform a specific task (e.g., diagnostic classification) and cannot be prompted to perform multiple tasks using natural language. In this work, we introduce a framework based on vector retrieval and contrastive learning to efficiently learn visual brain MRI concepts via natural language supervision. We show how the method learns to identify factors that affect the brain in Alzheimers disease (AD) via joint embedding and natural language supervision. First, we pre-train separate text and image encoders using self-supervised learning, and jointly fine-tune these encoders to develop a shared embedding space. We train our model to perform multiple tasks, including MRI retrieval, MRI captioning, and MRI classification. We show its versatility by developing a retrieval and re-ranking mechanism along with a transformer decoder for visual question answering.

Clinical RelevanceBy learning a cross-modal embedding of radiologic features and text, our approach can learn to perform diagnostic and prognostic assessments in AD research as well as to assist practicing clinicians. Integrating medical imaging with clinical descriptions and text prompts, we aim to provide a general, versatile tool for detecting radiologic features described by text, offering a new approach to radiologic research.

Reactive Pericytes Lead to Microvascular Dysfunction and Cortical Neurodegeneration During Experimental Autoimmune Encephalomyelitis
The mechanisms underlying neurodegeneration in multiple sclerosis remain incompletely understood. In this study, we aimed to investigate the role of vascular dysfunction in cortical neurodegeneration using a chronic cranial window model of experimental autoimmune encephalomyelitis in mice. After the induction of experimental autoimmune encephalomyelitis with myelin oligodendrocyte glycoprotein peptides in C57BL/6J mice, we assessed cerebrovascular reactivity though a chronic cranial window using laser speckle contrast imaging and intrinsic optical signal imaging in awake animals. We observed a significant reduction in cortical cerebrovascular reactivity during peak inflammation in the EAE group, as detected by laser speckle contrast imaging after 5% hypercapnia (p=0.04) and optical signal imaging after whisker stimulation (p=0.008). Histological analysis revealed a diffuse increase in CD13+ pericyte coverage (p=0.001), accompanied by focal IgG deposition within the microvascular lumen (p=0.04) and increased amount of CD45+ leukocytes stalled in microvessels (p=0.03) in the cortex of experimental autoimmune encephalomyelitis mice. Microglial activation was also present in the cortex of experimental autoimmune encephalomyelitis mice (p=0.04) and was particularly evident around microvessels with IgG deposition. Subpial and intracortical foci exhibiting loss of NeuN reactivity (p=0.03) and axonal loss (p=0.007) were detected in experimental autoimmune encephalomyelitis, but not in control mice. Altogether, these results demonstrate that microvascular function and neurovascular unit elements are globally affected in the cortex during autoimmune neuroinflammation and is related to neurodegeneration.

Rare missense variants of the leukocyte common antigen related receptor (LAR) display reduced activity in transcellular adhesion and synapse formation
The leukocyte common antigen related receptor (LAR) is a member of the LAR receptor protein tyrosine phosphatase (RPTP) family of synaptic adhesion molecules that contribute to the proper alignment and specialization of synaptic connections in the mammalian brain. LAR-RPTP members have been genetically associated with neuropsychiatric disorders, but the molecular consequences of genetic perturbations of LAR remain unstudied. Using exome sequencing data from psychiatric patients and controls, we identify rare missense variants of LAR that render the extracellular domain (ECD) unstable and susceptible to proteolytic cleavage. Using recombinant and cellular systems, we describe three variants that cause disruption of the LAR:NGL-3 interaction, which results in loss of transcellular adhesion and synaptogenic effects. Furthermore, we show that overexpression of two of these variants elicit altered morphological phenotypes in an imaging-based morphological profiling assay compared to wild type LAR, suggesting that destabilization of the LAR ECD has broad effects on LAR function. In conclusion, our study identifies three rare, missense variants in LAR that could provide insights into LAR involvement with psychiatric pathobiology.

Cortical substrates of perceptual confusion between pitch and timbre
Pitch and timbre, two fundamental perceptual attributes of sound, are commonly regarded as distinct features, but can be confused when varied simultaneously. Here we combine human behavior and fMRI to demonstrate a neural substrate to explain the well-known perceptual confusion effects. We identify orderly mappings of both pitch and timbral brightness within auditory cortex and reveal two independent lines of evidence for cortical confusion between them. First, voxels' preferred pitch decreases systematically as brightness increases, and vice versa, consistent with predictions based on perceptual confusion. Second, pitch and brightness mapping share a common high-low-high gradient across auditory cortex, implying a shared trajectory of cortical activation for changes in each dimension. The results provide a cortical substrate at both local and global scales for an established auditory perceptual phenomenon that is thought to reflect efficient coding of ubiquitous features in natural sound statistics.

Rhythmic modulation of dorsal hippocampus across distinct behavioral timescales during spatial set-shifting
Previous work has shown frequency-specific modulation of dorsal hippocampus (dHPC) neural activity during simple behavioral tasks, suggesting shifts in neural population activity throughout different task phases and animal behaviors. Relatively little is known about task-relevant orchestrated shifts in theta, beta, and gamma rhythms across multiple behavioral timescales during a complex task that requires repeated adaptation of behavioral strategies based on changing reward contingencies. To address this gap in knowledge, we used a spatial set-shifting task to determine whether dHPC plays a specific role in strategy switching. The task requires rats to use two spatial strategies on an elevated plus maze: 1) alternating between East and West reward locations or 2) always going to the same reward location (e.g., only East or only West). Across specific timescales (session-based alignments, comparisons of trial types, within trial epochs), dHPC associated differentially with all three temporal categories. Across a session, we observed a decrease in theta and beta power before, and an increase in theta power after, the target strategy changed. Beta power was increased around the point at which rats learn the current rule. Comparing trial types, on trials before a rat learned the correct strategy, beta power increased. Within a single trial, after an incorrect (but not correct) choice, beta and gamma power increased while the rat returned to start a new trial. If gamma (but not beta) power was high during this return, the rat was more likely to make a correct choice on the next trial. On the other hand, low gamma power during the return was associated with incorrect trials. Rhythmic activity in dHPC, therefore, appears to track task demands, with the strength of each rhythmic frequency differentially associating with specific behaviors across three distinct timescales.

Functional interaction of electrical coupling and H-current and its putative impact on inhibitory transmission
The flow of information within neural circuits depends on the communication between neurons, primarily taking place at chemical and electrical synapses. The coexistence of these two modalities of synaptic transmission and their dynamical interaction with voltage-gated membrane conductances enables a rich repertoire of complex functional operations. One such operation, coincidence detection, allows electrically coupled neurons to respond more strongly to simultaneous synaptic inputs than to temporally dispersed ones. Using the mesencephalic trigeminal (MesV) nucleus (a structure composed of large, somatically coupled neurons) as an experimental model, we first demonstrate that electrical coupling strength in the hyperpolarized voltage range is highly time-dependent due to the involvement of the IH current. We then show how this property influences the coincidence detection of hyperpolarizing signals. Specifically, simultaneous hyperpolarizing inputs induce larger membrane potential changes, resulting in stronger IH current activation. This, in turn, shortens the temporal window for coincidence detection. We propose that this phenomenon may be crucial for networks dynamics in circuits of electrically coupled neurons that receive inhibitory synaptic inputs and express the IH current. In particular, molecular layer interneurons (MLIs) of the cerebellar cortex provide an ideal model for studying coincidence detection of inhibitory synaptic inputs, and how this operation is shaped by the voltage-dependent conductances like the IH current, potentially impacting on motor coordination and learning.

Targeting the ClpP-αSynuclein Interaction with a Decoy Peptide to Mitigate Neuropathology in Parkinson's Disease Models
Parkinson's disease (PD), the most prevalent neurodegenerative movement disorder, is characterized by the progressive loss of dopaminergic (DA) neurons and the accumulation of -synuclein (Syn)-rich inclusions. Despite advances in understanding PD pathophysiology, disease-modifying therapies remain elusive, underscoring gaps in our knowledge of its underlying mechanisms. Mitochondria are key targets of Syn toxicity, and growing evidence suggests that Syn-mitochondrial interactions contribute to PD progression. Our recent findings identify mitochondrial protease ClpP as a crucial regulator of Syn pathology, with pathological Syn binding to and impairing ClpP function, thereby exacerbating mitochondrial impairment and neurodegeneration. To disrupt this deleterious interaction, we developed a decoy peptide, CS2, which directly binds to the non-amyloid-{beta} component (NAC) domain of Syn, preventing its association with ClpP. CS2 treatment effectively mitigated Syn toxicity in an Syn-stable neuronal cell line, primary cortical neurons inoculated with Syn pre-formed fibrils (PFFs), and DA neurons derived from PD patient-induced pluripotent stem cells (iPSCs). Notably, subcutaneous administration of CS2 in transgenic mThy1-hSNCA PD mice rescued cognitive and motor deficits while reducing Syn aggregation and neuropathology. These findings establish the ClpP-Syn interaction as a druggable target in PD and position CS2 as a promising therapeutic candidate for PD and other Syn-associated neurodegenerative disorders.

Dynamic regulation of NeuroD1 expression level by a novel viral construct during astrocyte-to-neuron reprogramming
Astrocyte-to-neuron reprogramming presents a viable approach for regenerative medicine. The reprogramming factor NeuroD1 has demonstrated capability of neuronal reprogramming with high efficiency both in culture and in the injured central nervous system. High level of NeuroD1 expression is required to break down the cellular identity barrier for a successful reprogramming, and yet persistence of this high level drives the reprogrammed neurons primarily to glutamatergic subtype. This is consistent with the critical role of NeuroD1 in determination of glutamatergic neuronal lineage during development. However, diversified neuronal subtypes are needed to establish appropriate neuronal connectivity in disease/injury conditions. We reason that continuously high level of NeuroD1 expression forces the reprogrammed neurons into glutamatergic subtype, and that reducing NeuroD1 level after reprogramming may allow generation of neurons with diversified subtypes. For this purpose, we engineered a novel viral expression vector by which NeuroD1 expression can be dynamically regulated during the reprogramming process. Specifically, the target site of a neuron-specific microRNA (miR-124) is incorporated in the expression system. Therefore, this novel construct would still achieve a high NeuroD1 expression level in astrocytes for reprogramming to occur and yet reduce its level in the reprogrammed neurons by suppression of endogenous miR-124. In this study, we demonstrated that this construct elicits a dynamic gene expression pattern with much reduced level of NeuroD1 at later stages of neuronal reprogramming. We also showed that this construct still retains relatively high reprogramming efficiency and can generate mature neurons with an enhanced GABAergic neuronal phenotype.

Mitochondrially Transcribed dsRNA Mediates Manganese-induced Neuroinflammation
Manganese (Mn) is an essential trace element required for various biological functions, but excessive Mn levels are neurotoxic and lead to significant health concerns. The mechanisms underlying Mn-induced neurotoxicity remain poorly understood. Neuropathological studies of affected brain regions reveal astrogliosis, and neuronal loss, along with evidence of neuroinflammation. Here, we present a novel Mn-dependent mechanism linking mitochondrial dysfunction to neuroinflammation. We found that Mn disrupts mitochondrial transcriptome processing, resulting in the accumulation of complementary RNAs that form double-stranded RNA (dsRNA). This dsRNA is released to the cytoplasm, where it activates cytosolic sensor pathways, triggering type I interferon responses and inflammatory cytokine production. This mechanism is present in 100-day human cerebral organoids, where Mn-induced inflammatory responses are observed predominantly in mature astrocytes. Similar effects were observed in vivo in a mouse model carrying mutations in the SLC30A10 gene, which results in Mn accumulation. These findings highlight a previously unrecognized role for mitochondrial dsRNA in Mn-induced neuroinflammation and provide insights into the molecular basis of manganism. We propose that this mitochondrial dsRNA-induced inflammatory pathway has broad implications in for neurodegenerative diseases caused by environmental or genetic insults.

Specific neuroblast-derived signals control both cell migration and fate in the rostral migratory stream
Functional neuronal circuits require neuroblasts migrate to appropriate locations and then differentiate into neuronal subtypes. However, it remains unknown how neuroblasts in the subventricular zone (SVZ) are guided through the rostral migratory stream (RMS) to the olfactory bulb (OB). Here we define EphB2 as a neuroblast-derived cue that controls migration along the RMS and helps to determine cell fate. Within the RMS, EphB2 is expressed selectively in, kinase-active in, and required for the migration of neuroblasts. As neuroblasts enter the OB and differentiate, EphB kinase activity is down-regulated, and in the granule cell layer (GCL), EphB2 expression is down-regulated. Blocking EphB kinase activity or knocking down EphB2 results in defects in migration and premature cellular differentiation in the RMS. Unexpectedly, premature loss of EphB2 expression causes neuroblasts to stop migrating and differentiate into astrocyte-like cells. Thus, EphB2 kinase activity and expression are linked to migration and specification of neuroblast fate.

The forkhead transcription factor FKH-7/FOXP acts in chemosensory neurons to regulate developmental decision-making
Autism is a complex neurodevelopmental disorder with many associated genetic factors, including the forkhead transcription factor FOXP1. Although FOXP1's neuronal role is well-studied, the specific molecular consequences of different FOXP1 pathogenic variants in physiologically-relevant contexts are unknown. Here we ascribe the first function to Caenorhabditis elegans FKH-7/FOXP, which acts in two chemosensory neuron classes to promote the larval decision to enter the alternative, developmentally-arrested dauer life stage. We demonstrate that human FOXP1 can functionally substitute for C. elegans FKH-7 in these neurons and that engineering analogous FOXP1 hypomorphic missense mutations in the endogenous fkh-7 locus also impairs developmental decision-making. In a fkh-7/FOXP1 missense variant, single-cell transcriptomics identifies downregulated expression of autism-associated kcnl-2/KCNN2 calcium-activated potassium channel in a serotonergic sensory neuron. Our findings establish a novel framework linking two evolutionarily-conserved autism-associated genes for deeper characterization of variant-specific molecular pathology at single neuron resolution in the context of a developmental decision-making paradigm.

Connectomic analysis of astrocyte-synapse interactions in the cerebral cortex
Astrocytes, a main type of glia cells in the cortex, provide metabolic support to neurons, and their possible function as a synaptic partner has given rise to the notion of "tripartite" synapses, suggesting a contribution to neuronal computations. For astrocytes to serve such purposes, the interactions with synapses in neuronal circuits require a level of specificity beyond overall synaptic support. A systematic mapping of the astrocyte-connectome relationship would enable the testing of these hypotheses - such analysis is however still lacking, in particular for circuits in the cerebral cortex. Here, utilizing previously published connectomic data of more than 200,000 synapses, we systematically analyzed the spatial relation between astrocytes and synapses in mouse somatosensory cortex. We developed a quantitative assessment of astrocyte-synapse proximity, finding that only 22.7% of synapses are contacted by astrocytic processes for more than 50% of their synaptic circumference. This non-ubiquitous astrocytic attachment would render astrocyte-synapse specificity plausible. Astrocytic coverage depended strongly on synapse types, with thalamocortical shaft synapses being the most covered by astrocytic processes. We furthermore observed a strong dependence of astrocytic synaptic coverage on synapse size, which was exclusive for excitatory spine synapses. We then investigated the possible relation of astrocytic synaptic coverage to neuronal activity and synaptic plasticity, finding ultrastructural evidence for substantially reduced astrocytic support at synapses consistent with long-term depression, but not for astrocytic coverage dependence on baseline neuronal presynaptic activity. Together, our data demonstrate a high level of specificity of astrocyte-synapse interactions for particular synaptic types. They indicate the potential relevance of astrocytic coverage for synapse stability, in particular for large synapses, suggesting a contribution to long-term maintenance of learned synaptic states. These methods will allow a systematic testing of hypotheses about glial-neuronal interaction in various brain regions, disease models and species including human.

Inferring Latent Behavioral Strategy from the Representational Geometry of Prefrontal Cortex Activity
Behavioral tasks can be solved employing various strategies. Sometimes, different strategies result in the same observable behavior, making them latent. In this study, we infer the latent behavioral strategy used by monkeys in a working memory updating task by comparing the representational geometry of two prefrontal regions, the lateral prefrontal cortex (LPFC) and the prearcuate cortex (PAC), with that of recurrent neural network (RNN) models trained to solve the task using different strategies. We found that neural activity patterns in both LPFC and PAC align with only one of the proposed strategies, suggesting that monkeys employ this latent strategy to perform the task. These findings open avenues for investigating the processes that lead to strategy learning and the decision-making mechanisms that determine which strategies are chosen when multiple options are available.

Characterization of neurite and soma organization in the brain and spinal cord with diffusion MRI
The central nervous system (CNS), comprised of both the brain and spinal cord, and is a complex network of white and gray matter responsible for sensory, motor, and cognitive functions. Advanced diffusion MRI (dMRI) techniques offer a promising mechanism to non-invasively characterize CNS architecture, however, most studies focus on the brain or spinal cord in isolation. Here, we implemented a clinically feasible dMRI protocol on a 3T scanner to simultaneously characterize neurite and soma microstructure of both the brain and spinal cord. The protocol enabled the use of Diffusion Tensor Imaging (DTI), Standard Model Imaging (SMI), and Soma and Neurite Density Imaging (SANDI), representing the first time SMI and SANDI have been evaluated in the cord, and in the cord and brain simultaneously. Our results demonstrate high image quality even at high diffusion weightings, reproducibility of SMI and SANDI derived metrics similar to those of DTI with few exceptions, and biologically feasible contrasts between and within white and gray matter regions. Reproducibility and contrasts were decreased in the cord compared to that of the brain, revealing challenges due to partial volume effects and image preprocessing. This study establishes a harmonized approach for brain and cord microstructural imaging, and the opportunity to study CNS pathologies and biomarkers of structural integrity across the neuroaxis.

Neuronal development shapes activity-dependent gene expression in a stimulus-specific manner
Neuronal activity-dependent gene expression is fundamental to a wide variety of brain functions. However, how development progress and stimulation modalities specifically affect neuron transcription is not fully understood. In this work, we first investigate the influence of development on neuronal firing and activity-driven transcription. We used an RNA sequencing approach over 7 days in vitro (DIV) or mature 21 DIV neurons, comparing neuronal depolarization with potassium chloride (KCl) versus Biccuculine application, a synaptic modality to induce neuronal activity. To further investigate how different activity patterns influence gene transcription in mature neurons, we compared global gene expression in neurons treated with three different and extensively used activation protocols: KCl, Bicuculine (Bic), and TTX withdrawal (TTXw). Our results demonstrate a strong influence of development on activity-dependent gene expression, and showed that different patterns of neuronal activity induce different transcriptional profiles and exhibit distinct temporal dynamics for the same genes. These findings offer novel insights into the complex relationship between neuronal activity and gene expression, shedding light on the context-dependent nature of activity-dependent transcriptional responses.

Cerebellar and Subcortical Contributions to Working Memory Manipulation
Working memory enables us to temporarily store and manipulate information, a crucial function for problem-solving. However, most working memory models emphasize cortical interactions ignoring contributions from subcortical and cerebellar regions. Given the dense connectivity between the cerebellum, subcortex, and cortex, we hypothesize that these regions provide unique contributions during working memory manipulation. We tested this hypothesis using functional Magnetic Resonance Imaging (fMRI) to measure blood oxygen-level dependent (BOLD) activity during a mental rotation task, where participants judged whether rotated pairs of three-dimensional stimuli were identical. Our results revealed a distributed network spanning the cortex, subcortex, and cerebellum that differentiates rotated from non-rotated stimuli and correct from incorrect responses. BOLD recruitment in these regions increased with larger angles of rotation. We observed delayed responses in premotor, subcortical, and cerebellar regions during incorrect trials. These findings suggest that cerebellar and subcortical regions support working memory manipulation, highlighting a broader mechanism by which distributed brain regions interact to coordinate higher cognitive functions.

Bayesian Integration in Sense of Agency: Understanding Self-attribution and Individual Differences
This study developed a Bayesian integration model to explore individual differences in the sense of agency, particularly when multiple sensory cues influence action perception. Behavioral results showed that individuals consistently integrate sensory cues in agency judgments, though cue weighting varies significantly across individuals. The estimated parameters of our model successfully captured these differences, reflecting individual sensitivities and criteria for agency. Higher sensitivity to sensory inputs was associated with lower variance in the likelihood distribution under the assumption of control. Our model provides critical insights into the mechanisms of the sense of agency and its variability, highlighting the model's potential for understanding both typical and disordered agency experiences.

Short-term monocular deprivation in adult humans: a meta-analysis and new perspectives
Starting from the early 2010s [1, 2], several studies have shown that a short period of monocular deprivation in adult volunteers transiently shifts ocular dominance in favor of the deprived eye. We compiled a meta-analysis of 73 such studies that measured the effects of monocular deprivation and related manipulations, using a diverse set of techniques [1-73]. The ocular dominance shift elicited by monocular deprivation was comparable across studies where deprivation was achieved with an opaque or translucent patch and irrespectively of whether the dominant or non-dominant eye was deprived. Effects were larger and longer lasting after longer periods of deprivation. Qualitatively similar effects were produced by monocular manipulations that did not reduce the strength of the stimulus: filtering or distorting the image in one eye, suppressing it from awareness or merely making it task irrelevant. We discuss the available evidence in the light of current models and a new perspective inspired by predictive coding.

Saturation kinetics and specificity of transporters for L-arginine and asymmetric dimethylarginine (ADMA) at the blood-brain and blood-CSF barriers.
Nitric oxide synthases (NOS) synthesize nitric oxide (NO) from L-arginine in endothelial and neuronal cells. Asymmetric dimethylarginine (ADMA) is a homologue of arginine and an endogenous inhibitor of NOS. As NO is a critical signalling molecule and influences physiological pathways in health and disease, the transfer of arginine and ADMA across the blood-CNS barriers is of interest. Our research group have previously demonstrated the presence of saturable transporters for [3H]-L-arginine and [3H]-ADMA at the blood-brain and blood-CSF barriers using in vitro and in situ methods. In this study, we determine the identity and kinetic characteristics of these transporters by means of the in situ brain/choroid plexus perfusion technique in anaesthetised mice. Results indicated that [3H]-arginine and [3H]-ADMA could be transported across blood-brain and blood-CSF barriers by the cationic amino acid transporter, system-y+. In contrast to the results obtained with arginine where transport was predominately by a single transport system (system-y+), ADMA delivery to the CNS was more complex and involved multiple transport systems (system y+, B0,+, y+L and b0,+) suggesting its concentration is tightly regulated. System y+ and system y+L transporters could be involved in the CNS to blood efflux of ADMA that we have previously observed. The half-saturation constant (Km) and maximal influx rate of the saturable component (Vmax) for [3H]-ADMA transport into the frontal cortex was 29.07{+/-}7.19 mM and 0.307{+/-}0.017 nmol.min-1.g-1, respectively, and into the CSF was 30.59{+/-}25.41 M and 2.07{+/-}0.38 nmol.min-1.g-1, respectively. This information could help explain the arginine paradox providing evidence that ADMA interacts with transporters that can remove ADMA from cells. These removal mechanisms could be stimulated by excess arginine in the plasma resulting in increased NO production. It remains to be seen if arginine supplementation could be used to increase NO production and improve hypoperfusion observed in disease states such as Alzheimers and stroke.

China Autism Brain Imaging Consortium: Charting Brain Growth in Chinese Children with Autism
Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition characterized by atypical brain growth. While advances in neuroimaging and openly sharing large-sample datasets such as the Autism Brain Imaging Data Exchange (ABIDE) have improved understanding of ASD, most studies focus on adolescents and adults, with early brain development-critical for diagnosis and intervention-remaining underexplored. Existing research predominantly involves Western samples, offering limited insight and generalizability into non-Caucasian populations. We introduce the China Autism Brain Imaging Consortium (CABIC) (https://php.bdnilab.com/resources/), a grassroots effort by researchers across the country to aggregate previously collected multi-site structural MRI datasets and phenotypic information from 1,451 autistic children and 1,119 typically developing children, covering an age range from early childhood to school age (1.0 - 12.92 years). Here, we present this resource and depict brain growth charts to push forward a more comprehensive understanding of the brain development in Chinese autism children. We constructed brain growth charts that reveal a developmental shift in autistic children, transitioning from early overgrowth to delayed maturation. Regional analyses identified distinct atypical trajectories across specific brain regions. Individual deviation scores quantified inter-subject variability, characterizing the heterogeneity of brain development in ASD. Comparative analyses between CABIC and ABIDE highlighted differences potentially attributable to ethnicity and culture, advancing our understanding of cross-population neurodevelopmental diversity. CABIC MRI datasets will be shared publicly to foster investigation of the potential neural mechanisms underlying ASD in non-Western populations and support efforts toward precision medicine for autistic individuals across diverse backgrounds.

Cholesterol taste avoidance in Drosophila melanogaster
The question as to whether animals taste cholesterol taste is not resolved. This study investigates whether the fruit fly, Drosophila melanogaster, is capable of detecting cholesterol through their gustatory system. We found that flies are indifferent to low levels of cholesterol and avoid higher levels. The avoidance is mediated by gustatory receptor neurons (GRNs), demonstrating that flies can taste cholesterol. The cholesterol responsive GRNs comprise a subset that also respond to bitter substances. Cholesterol detection depends on five ionotropic receptor (IR) family members, and disrupting any of these genes impairs the flies' ability to avoid cholesterol. Ectopic expressions of these IRs in GRNs reveals two classes of cholesterol receptors, each with three shared IRs and one unique subunit. Additionally, expressing cholesterol receptors in sugar-responsive GRNs confers attraction to cholesterol. This study reveals that flies can taste cholesterol, and that the detection depends on IRs in GRNs.

Rationally Designed PKD1 Activator Protects Against Neurodegeneration in Pre-clinical Models of Parkinson's Disease
Oxidative stress leads to degeneration in Parkinson's disease (PD). The key signal transduction and regulatory networks that are involved during this degenerative process in PD are currently being investigated for novel neuro-protective strategies. We recently discovered that the activation of Protein Kinase D1 (PKD1) acts as a novel compensatory mechanism in PD models and positive modulation of PKD1 can be a therapeutic strategy. Therefore, the purpose of the present study was to take a translational approach by developing a PKD1 activator and characterizing the protective function in pre-clinical models of PD. Positive genetic modulation of PKD1 by overexpression of constitutively active PKD1 protected against MPP+ induced dopaminergic neurotoxicity. Pharmacological activation by Rosiglitazone protected, whereas inhibition by kb NB 142-70 exacerbated against MPP+ and 6-OHDA toxicity in cell culture PD models. Importantly, peptides were rationally designed and screened for their ability to activate PKD1 using our screening methods. Peptide AK-P4 was identified to activate PKD1 specifically and protect against MPP+ and 6-OHDA in both N27 cells and primary mesencephalic neurons. Further AK-P4 tagged with TAT sequence (AK-P4T) delivered using intra-venous injections activated PKD1 in mice. The neuro-protective effects of AK-P4T were tested using the sub-chronic MPTP mice model. Co-treatment with AK-P4T significantly restored the neurotransmitter levels and the behavioral and locomotory activities of the MPTP mouse model of PD. Collectively, our results demonstrate that rationally designed PKD1 activator peptide AK-P4T positively modulated PKD1 and protected against neurodegeneration in the pre-clinical models of PD. Our results suggest that positive modulation of the PKD1 using AK-P4T shows promise as a potential therapeutic agent against PD.