bioRxiv Subject Collection: Neuroscience's Journal

Navigating Memorability Landscapes: Hyperbolic Geometry Reveals Hierarchical Structures in Object Concept Memory
Why are some object concepts (e.g., birds, cars, vegetables, etc.) more memorable than others? Prior studies have suggested that features (e.g., color, animacy, etc.) and typicality (e.g., robin vs. penguin) of object images influences the likelihood of being remembered. However, a complete understanding of object memorability remains elusive. In this study, we examine whether the geometric relationship between object concepts explains differences in their memorability. Specifically, we hypothesize that image concepts will be geometrically arranged in hierarchical structures and that memorability will be explained by a concept's depth in these hierarchical trees. To test this hypothesis, we construct a Hyperbolic representation space of object concepts (N=1,854) from the THINGS database (Hebart et al., 2019), which consists of naturalistic images of concrete objects, and a space of 49 feature dimensions derived from data-driven models. Using ALBATROSS (Stier et al., In prep), a stochastic topological data analysis technique that detects underlying structures of data, we demonstrate that Hyperbolic geometry efficiently captures the hierarchical organization of object concepts above and beyond a traditional Euclidean geometry and that hierarchical organization is related to memorability. We find that concepts closer to the center of the representational space are more prototypical and also more memorable. Importantly, Hyperbolic distances are more predictive of memorability and prototypicality than Euclidean distances, suggesting that concept memorability and typicality are organized hierarchically. Taken together, our work presents a novel hierarchical representational structure of object concepts that explains memorability and typicality.

Transcriptome profiling of dopaminergic neurons derived from an ADHD induced pluripotent stem cell (iPSC) model
The most recent ADHD GWAS meta analysis highlighted the potential role of 76 genes enriched among genes expressed in early brain development and associated with midbrain dopaminergic neurons. However, the precise functional importance of the GWAS-identified single nucleotide polymorphisms (SNPs) remain unknown. In contrast to GWAS, transcriptome analysis directly investigates gene products by assessing the transcribed RNA. This allows one to gain functional insights into gene expression paving the way for a better understanding of the molecular risk mechanisms of conditions, such as ADHD. In this study, we performed transcriptome profiling of highly homogeneous dopamine neurons developed from induced pluripotent cells (iPSCs) that were derived from an individual with ADHD and a neurotypical comparison individual. Comparative gene expression analysis between the examined lines revealed that the top differentially expressed genes (DEGs) were predominantly associated with nervous system functions related to neuronal development and dopaminergic regulation. Notably, 29 of the DEGs overlapped with those identified by ADHD GWAS meta analysis. These genes are overrepresented in biological processes including developmental growth regulation, axonogenesis, and nervous system development. In addition, gene set analysis revealed significant enrichment for meta categories such as ion channel activity, synaptic function and assembly, neuronal development and cell differentiation. Further, we observed significantly reduced projections in the ADHD dopamine neurons at the mid differentiation stage (day 14 in vitro), providing preliminary support for the delayed neuronal maturation hypotheses of ADHD. This study underscores the potential of using iPSC derived cell type specific models that integrate genome and transcriptome analyses for biological discovery in ADHD.

Which perceptual categories do observers experience during multistable perception?
Multistable perceptual phenomena provide insights into the mind's dynamic states within a stable external environment and the neural underpinnings of these consciousness changes are often studied with binocular rivalry. Conventional methods to study binocular rivalry suffer from biases and assumptions that limit their ability to describe the continuous nature of this perceptual transitions and to discover what kind of percept was perceived across time. In this study, we propose a novel way to avoid those shortcomings by combining a continuous psychophysical method that estimates introspection during binocular rivalry with machine learning clustering and transition probability analysis. This combination of techniques reveals individual variability and complexity of perceptual experience in 28 normally sighted participants. Also, the analysis of transition probabilities between perceptual categories, i.e., exclusive and different kinds of mixed percepts, suggest that interocular perceptual competition, triggered by low-level stimuli, involves conflict between monocular and binocular neural processing sites rather than mutual inhibition of monocular sites.

Combinatorial responsiveness of single chemosensory neurons to external stimulation of mouse explants revealed by DynamicNeuronTracker
Calcium fluorescence imaging enables us to investigate how individual neurons of live animals encode sensory input or drive specific behaviors. Extracting and interpreting large-scale neuronal activity from imaging data are crucial steps in harnessing this information. A significant challenge arises from uncorrectable tissue deformation, which disrupts the effectiveness of existing neuron segmentation methods. Here, we propose an open-source software, DynamicNeuronTracker (DyNT), which generates dynamic neuron masks for deforming and/or incompletely registered 3D calcium imaging data using patch-matching iterations. We demonstrate that DyNT accurately tracks densely populated neurons, whereas a widely used static segmentation method often produces erroneous masks. DyNT also includes automated statistical analyses for interpreting neuronal responses to multiple sequential stimuli. We applied DyNT to analyze the responses of pheromone-sensing neurons in mice to controlled stimulation. We found that four bile acids and four sulfated steroids activated 15 subpopulations of sensory neurons with distinct combinatorial response profiles, revealing a strong bias toward detecting sulfated estrogen and pregnanolone.

Elevated synaptic PKA activity and abnormal striatal dopamine signaling in Akap11 mutant mice, a genetic model of schizophrenia and bipolar disorder
Loss-of-function mutations in AKAP11 (a protein kinase A (PKA)-binding protein) greatly increase the risk of bipolar disorder and schizophrenia. We conducted multi-omic analyses of Akap11 mutant mouse brains and report the neurobiological functions of AKAP11 and the consequences of its absence. AKAP11 interacts with multiple proteins involved in signaling and proteostasis. In Akap11+/- and Akap11-/- synapses, PKA protein levels were markedly elevated, and many synaptic proteins were hyperphosphorylated at PKA substrate sites. Akap11 mutant brains showed extensive transcriptomic changes, prominently in synapse-related gene-sets and most profoundly in neurons of the striatum, a brain region implicated in motivation, cognition and psychiatric disorders. In vivo, real-time measurements of PKA activity in Akap11-/- revealed constitutively elevated kinase activity, which distorts the dynamic range of dopamine to PKA signaling in the striatum. Our work reveals the molecular basis of circuit dysfunction in a genetically valid model of psychotic disorder.

An integrative analysis of cell-specific transcriptomics and nuclear proteomics of sleep-deprived mouse cerebral cortex
Sleep regulation follows a homeostatic pattern. The mammalian cerebral cortex is the repository of homeostatic sleep drive and neurons and astrocytes of the cortex are principal responders of sleep need. The molecular mechanisms by which these two cell types respond to sleep loss are not yet clearly understood. By combining cell-type specific transcriptomics and nuclear proteomics we investigated how sleep loss affects the cellular composition and molecular profiles of these two cell types in a focused approach. The results indicate that sleep deprivation regulates gene expression and nuclear protein abundance in a cell-type-specific manner. Our integrated multi-omics analysis suggests that this distinction arises because neurons and astrocytes employ different gene regulatory strategies under accumulated sleep pressure. These findings provide a comprehensive view of the effects of sleep deprivation on gene regulation in neurons and astrocytes.

Transcriptomic and histological characterization of telocytes in the human dorsal root ganglion
Telocytes are interstitial cells with long processes that cover distances in tissues and likely coordinate interacts with other cell types. Though present in central and peripheral neuronal tissues, their role remains unclear. Dorsal root ganglia (DRG) house pseudounipolar afferent neurons responsible for signals such as temperature, proprioception and nociception. This study aimed to investigate the presence and function of telocytes in human DRG by investigating their transcriptional profile, location and ultrastructure. Sequencing data revealed CD34 and PDGFRA expressing cells comprise roughly 1.5-3% of DRG cells. Combined expression of CD34 and PDGFRA is a putative marker gene set for telocytes. Further analysis identified nine subclusters with enriched cluster-specific genes. KEGG and GO pathway analysis suggested vascular, immune and connective tissue associated putative telocyte subtypes. Over 3000 potential receptor-ligand interactions between sensory neurons and these CD34 and PDGFRA expressing putative telocytes were identified using a ligand-receptors interactome platform. Immunohisto-chemistry showed CD34+ telocytes in the endoneural space of DRGs, next to neuron-satellite complexes, in perivascular spaces and in the endoneural space between nerve fibre bundles, consistent with pathway analysis. Transmission electron microscopy (TEM) confirmed their location identifying characteristic elongated nucleus, long and thin telopods containing vesicles, surrounded by a basal lamina. This is the first study that provides gene expression analysis of telocytes in complex human tissue such as the DRG, highlighting functional differences based on tissue location with no significant ultrastructural variation.

Phonological decoding ability is associated with fiber density of the left arcuate fasciculus longitudinally across reading development
Numerous studies have linked reading ability to white matter microstructure using diffusion tensor imaging, but recent large studies have failed to show consistency. Fiber-specific diffusion-weighted magnetic resonance imaging (dMRI) models offer enhanced precision to measure specific features of white matter structure and may be more sensitive to individual differences in reading skills. However, fiber-specific models have not yet been applied to examine associations between reading ability and white matter microstructure over the course of reading acquisition. In this accelerated longitudinal study, we applied constrained spherical deconvolution (CSD) and fiber-specific modelling to characterize developmental changes in fiber density of key white matter tracts of the reading network bilaterally, and investigated associations between tract-wise fiber density and children's phonological decoding abilities. Fiber density was measured from ages 2-13 years, and decoding ability (pseudoword reading) was assessed at ages 6 years and older. Higher decoding ability was associated with greater fiber density in the left arcuate fasciculus, and effects remained consistent over time. Follow-up analysis revealed that asymmetry changes in the arcuate fasciculus were moderated by decoding ability: good decoders showed leftward asymmetry from early childhood onward, while poorer decoders shifted toward leftward asymmetry over time. These results suggest that densely organized fibers in the left arcuate fasciculus serve as a foundation for the development of reading skills from the pre-reading stage through fluent reading. Ongoing developmental changes in fiber density and microstructural asymmetry throughout childhood may reflect a reciprocal relationship in which reading experience continues to refine and strengthen these pathways.

Adipocyte metabolic state regulates glial phagocytic function
Obesity and type 2 diabetes are well-established risk factors for neurodegenerative disorders, yet the underlying mechanisms remain poorly understood. The adipocyte-brain axis is crucial for brain function, as adipocytes secrete signaling molecules, including lipids and adipokines, that impinge on neural circuits to regulate feeding and energy expenditure. Disruptions in the adipocyte-brain axis are associated with neurodegenerative conditions, but the causal links are not fully understood. Neural debris accumulates with age and injury, and glial phagocytic function is crucial for clearing this debris and maintaining a healthy brain microenvironment. Using adult Drosophila, we investigate how adipocyte metabolism influences glial phagocytic activity in the brain. We demonstrate that a prolonged obesogenic diet increases adipocyte fatty acid oxidation and ketogenesis. Genetic manipulations that mimic obesogenic diet-induced changes in adipocyte lipid and mitochondrial metabolism unexpectedly reduce the expression of the phagocytic receptor Draper in Drosophila microglia-like cells in the brain. We identify Apolpp, the Drosophila equivalent of human apolipoprotein B (ApoB), as a critical adipocyte-derived signal that regulates glial phagocytosis. Additionally, we show that Lipoprotein Receptor 1 (LpR1), the LDL receptor on phagocytic glia, is required for glial capacity to clear injury-induced neuronal debris. Our findings establish that adipocyte-brain lipoprotein signaling regulates glial phagocytic function, revealing a novel pathway that links adipocyte metabolic disorders with neurodegeneration.

Hyperexcitability precedes CA3 hippocampal neurodegeneration in a dox-regulatable TDP-43 mouse model of ALS-FTD.
Neuronal hyperexcitability is a hallmark of Amyotrophic lateral sclerosis (ALS) but its relationship with the TDP-43 aggregates that comprise the predominant pathology in over 90% of ALS cases remains unclear. Emerging evidence in tissue and slice culture models indicate that TDP-43 pathology induces neuronal hyperexcitability suggesting it may be responsible for the excitotoxicity long believed to be a major driver of ALS neuron death. Here, we characterized hyperexcitability and neurodegeneration in the hippocampus of doxycycline-regulatable rNLS8 mice (NEFH-tTA x tetO-hTDP-43deltaNLS), followed by treatment with AAV encoded DREADDs and anti-seizure medications to measure the effect on behavioral function and neurodegeneration. We found that approximately half of the CA3 neurons in the dorsal hippocampus are lost between 4 and 6 weeks after TDP-43deltaNLS induction. Neurodegeneration was preceded by selective hyperexcitability in the mossy fiber-CA3 circuit, leading us to hypothesize that glutamate excitotoxicity may be a significant contributor to neurodegeneration in this model. Interestingly, hippocampal injection of AAV encoded inhibitory DREADDs (hM4Di) and daily activation with CNO ligand rescued anxiety deficits on elevated zero maze (EZM) but did not reduce neurodegeneration. Therapeutic doses of the anti-seizure medications, valproic acid and levetiracetam, did not improve behavior or prevent neurodegeneration. These results highlight the complexity of TDP-43 -induced alterations to neuronal excitability and suggest that whereas targeting hyperexcitability can meliorate some behavioral deficits, it may not be sufficient to halt or slow neurodegeneration in TDP-43-related proteinopathies.

BrainScale: Enabling Scalable Online Learning in Spiking Neural Networks
Whole-brain simulation stands as one of the most ambitious endeavors of our time, yet it remains constrained by significant technical challenges. A critical obstacle in this pursuit is the absence of a scalable online learning framework capable of supporting the efficient training of complex, diverse, and large-scale spiking neural networks (SNNs). To address this limitation, we introduce BrainScale, a framework specifically designed to enable scalable online learning in SNNs. BrainScale achieves three key advancements for scalability. (1) Model diversity: BrainScale accommodates the complex dynamics of brain function by supporting a wide spectrum of SNNs through a streamlined abstraction of synaptic interactions. (2) Efficient scaling: Leveraging SNN intrinsic characteristics, BrainScale achieves an online learning algorithm with linear memory complexity. (3) User-friendly programming: BrainScale provides a programming environment that automates the derivation and execution of online learning computations for any user-defined models. Our comprehensive evaluations demonstrate BrainScale's efficiency and robustness, showing a hundred-fold improvement in memory utilization and several-fold acceleration in training speed while maintaining performance on long-term dependency tasks and neuromorphic datasets. These results suggest that BrainScale represents a crucial step towards brain-scale SNN training and whole-brain simulation.

Hebbian induction adds AMPA-labile signaling units to CA3-CA1 synapses in the developing hippocampus
In the 2nd postnatal week hippocampus, Hebbian-induced long-term potentiation (LTP) of AMPA receptor-mediated transmission in CA3-CA1 synapses is not a genuine potentiation. Instead, it is a de-depression (unsilencing) and temporary stabilization of postsynaptically AMPA-labile synapses silenced by a prior test pulse (0.03 - 0.2 Hz) stimulation. In addition to such an LTP, Hebbian induction at these synapses also results in a labile potentiation that becomes depotentiated by test pulse stimulation, thus appearing as an Hebbian-induced short-term potentiation (STP). Although the induction of this labile potentiation was blocked in the combined presence of N-methyl-D-aspartate (NMDA) and metabotropic glutamate (mGlu) receptor antagonists, the depotentiation was not affected by these drugs. The labile potentiation was not associated with a change in paired-pulse ratio and was, after a depotentiation, fully re-established by a 20 min interruption of test pulse stimulation. These properties are shared with the silencing of previously non-stimulated (naive) AMPA-labile synapses by such test pulse stimulation. However, the depotentiation following an Hebbian induction is not a re-silencing of naive AMPA labile synapses since there is no correlation between the magnitudes of depotentiation and preceding silencing of naive synapses. The present results suggest that Hebbian induction at these neonatal CA3-CA1 synapses, in addition to unsilencing and temporary stabilization of AMPA-labile transmission, creates a labile potentiation based on the insertion/activation of an additional AMPA-labile signaling unit to a pre-existing synapse.

Neural Maturation Provides the Stability of Representation and the Solution for Understanding Complex Concepts
The mechanisms of the human mind remain a major mystery for science. Progress in these efforts has come from psychology, behavioral science, physiology, and biology. Especially in the past 40 years, advances in molecular biology have expanded significantly. Despite these numerous expansions in understanding the mechanisms of higher brain functions, there are still significant gaps in integrating each research result. In particular, the gaps between gene function and the overall phenotypes related to the mind have not been elucidated at all. Recent technological progress in realizing human-like brain functions provided by artificial intelligence has progressed day by day. However, major parts of the current astonishing progress in AI do not necessarily require an understanding of modern molecular biology. Here, we examine the significance of neural maturation in neural network-based information processing models. In both supervised and reinforcement learning paradigms, immature transmission could learn properties similar to normal high-fidelity conditions before reaching catastrophe points. However, continuous long-term learning induced a loss of learning results. Then, I looked for genes with physiological significance for neuronal transmission fidelity. I found that the candidate gene KCNH7 is expressed at higher levels during the development of the mouse brain. In the simulated neural model with the KCNH7 channel property, the excitation threshold increased, providing a linear response property. These properties enhanced the stability of representation recall and enabled the understanding of complex concepts in unsupervised learning models. These results demonstrate the significance of neural maturation in achieving higher recognition abilities in adults.