Biology subjects

Imaizumi, Y.

Publications and source records attributed to Imaizumi, Y..

10 recordsLinked to original sources

bioRxiv2025

Super-resolution microscopy of alpha-synuclein aggregates in brain samples indicates a subset of cells have disrupted protein homeostasis

Nanoscopic aggregates of alpha-synuclein have been observed in Parkinsons disease (PD). However, the processes that occur in-vivo leading to the formation of these small aggregates are not well understood. We used ultra-sensitive single-molecule methods including SIMOA and super-resolution microscopy to quantify and characterise alpha-synuclein aggregates harvested from human brain samples alongside the Line 61 mouse model using different tissue processing methods. While aggregate numbers did not differ between PD and control samples, larger aggregates were detected in PD brain samples. Moreover, different sub-populations of aggregates were obtained by different extraction methods, with diffusible and membrane-bound aggregates producing a more pronounced difference between disease and control samples. Our data suggests that alpha-synuclein aggregates slowly in the brain, leading to formation of larger aggregates in a sub-set of cells.

neuroscience↗

bioRxiv2025

Fast-spiking neurons in monkey orbitofrontal cortex underlie economic value computation

Inhibitory interneurons are fundamental constituents of cortical circuits that process information to shape economic behaviors. However, the role of inhibitory interneurons in this process in the core cortical reward region, the orbitofrontal cortex (OFC), remains elusive. Here, we show that presumed parvalbumin-containing GABAergic interneurons (fast-spiking neurons, FSNs) differ from regular-spiking pyramidal neurons (RSNs) in computing economic value. While monkeys perceived a visual lottery for probability and magnitude of rewards, identified FSNs constituted 12% of OFC neurons and showed high-frequency firing rates and wide dynamic ranges, which are both key intrinsic cellular characteristics regulating cortical computation. We found that FSNs showed stronger neural modulation compared to RSNs in signaling probability and magnitude information. Unambiguously, both neural populations signaled the expected values, but FSNs processed the reward information more strongly under the influence of dynamic range. Thus, stronger FSN signals may appear with wider dynamic range for expected value computation.

neuroscience↗

bioRxiv2024

Formation of brain-wide neural geometry during visual item recognition in monkeys

Neural dynamics assumes to reflect computations that relay and transform information in the brain. Previous studies have identified the neural population dynamics in many individual brain regions as a trajectory geometry, preserving a manifest of computational motifs. However, whether these populations share particular geometric patterns across brain-wide neural populations remains unclear. Here, by mapping neural dynamics widely across temporal/frontal/limbic regions in the cortical and subcortical structures of monkeys, we show that 10 neural populations, including 2,500 neurons, propagate visual item information in a stochastic manner. We found that visual inputs predominantly evoked rotational dynamics in the higher-order visual area, TE and its downstream striatum tail, while curvy/straight dynamics appeared frequently downstream in the orbitofrontal/hippocampal network. These geometric changes were not deterministic but rather stochastic according to their respective emergence rates. Our meta-analysis results indicated that visual information propagates as a heterogeneous mixture of stochastic neural population signals in the brain.

neuroscience↗

bioRxiv2022

Mechanistic insights into intramembrane proteolysis by E. coli site-2 protease homolog RseP

Site-2 proteases are a conserved family of intramembrane proteases that cleave transmembrane substrates to regulate signal transduction and maintain proteostasis. Here, we elucidated crystal structures of inhibitor-bound forms of bacterial site-2 proteases including E. coli RseP. Our observations are consistent with a rearrangement of the RseP domains surrounding the active center to expose the substrate-binding site where a conserved electrostatic linkage between the transmembrane and membrane-associated domains mediates the conformational changes, suggesting that RseP has a gating mechanism to regulate substrate entry. Mutational analysis also supports that the substrate transmembrane helix is unwound by strand addition to the intramembrane {beta} sheet and is clamped at the active center for efficient cleavage. Furthermore, this substrate accommodation mechanism appears to be common across distinct intramembrane proteases.

biochemistry↗

bioRxiv2022

Comparison of neural population dynamics in the regression subspace between continuous and categorical task parameters

Neural population dynamics, presumably fundamental computational units in the brain, provide a key framework for understanding information processing in the sensory, cognitive, and motor functions. However, neural population dynamics is not explicitly related to the conventional analytic framework for single-neuron activity, i.e., representational models that analyze neuronal modulations associated with cognitive and motor parameters. In this study, we applied a recently developed state-space analysis to incorporate the representational models into the dynamic model in combination with these parameters. We compared neural population dynamics between continuous and categorical task parameters during two visual recognition tasks, using the datasets originally designed for a single-neuron approach. We successfully extracted neural population dynamics in the regression subspace, which represent modulation dynamics for both continuous and categorical task parameters with reasonable temporal characteristics. Furthermore, we combined the classical optimal-stimulus analysis paradigm for the single-neuron approach (i.e., stimulus identified as maximum neural responses) into the dynamic model, and found that the most prominent modulation dynamics at the lower dimension were derived from these optimal responses. Thus, our approach provides a unified framework for incorporating knowledge acquired with the single-neuron approach into the dynamic model as a standard procedure for describing neural modulation dynamics in the brain.

neuroscience↗

bioRxiv2021

A neuronal prospect theory model in the brain reward circuitry

Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, reflects prospect theory remains unknown. Here, we show with theoretical accuracy equivalent to that of human neuroimaging studies that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards, parameterized as a multiplicative combination of a utility and probability weighting functions in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reliably reconstructed risk preferences and subjective probability perceptions revealed by the animals choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.

neuroscience↗

bioRxiv2021

Dynamic prospect theory - two core economic decision theories coexist in the gambling behavior of monkeys

Research in behavioral economics and reinforcement learning has given rise to two influential theories describing human economic choice under uncertainty. The first, prospect theory, assumes that decision-makers use static mathematical functions, utility and probability weighting, to calculate the values of alternatives. The second, reinforcement learning theory, posits that dynamic mathematical functions update the values of alternatives based on experience through reward prediction error (RPE). To date, these theories have been examined in isolation without reference to one another. Therefore, it remains unclear whether RPE affects a decision-makers utility and/or probability weighting functions, or whether these functions are indeed static as in prospect theory. Here, we propose a dynamic prospect theory model that combines prospect theory and RPE, and test this combined model using choice data on gambling behavior of captive macaques. We found that under standard prospect theory, monkeys, like humans, had a concave utility function. Unlike humans, monkeys exhibited a concave, rather than inverse-S shaped, probability weighting function. Our dynamic prospect theory model revealed that probability distortions, not the utility of rewards, solely and systematically varied with RPE: after a positive RPE, the estimated probability weighting functions became more concave, suggesting more optimistic belief about receiving rewards and over-weighted subjective probabilities at all probability levels. Thus, the probability perceptions in laboratory monkeys are not static even after extensive training, and are governed by a dynamic function well captured by the algorithmic feature of reinforcement learning. This novel evidence supports combining these two major theories to capture choice behavior under uncertainty. Significance statementWe propose and test a new decision theory under uncertainty by combining pre-existing two influential theories in the neuroeconomics: prospect theory from economics and prediction error theory from reinforcement learning. Collecting a large dataset (over 60,000 gambling decisions) from laboratory monkeys enables us to test the hybrid model of these two core decision theories reliably. Our results showed over-weighted subjective probabilities at all probability levels after lucky win, indicating that positive prediction error systematically bias decision-makers more optimistically about receiving rewards. This trial-by-trial prediction-error dynamics in probability perception provides outperformed performance of the model compared to the standard static prospect theory. Thus, both static and dynamic elements coexist in monkeys risky decision-making, an evidence contradicting the assumption of prospect theory.

animal behavior and cognition↗

bioRxiv2021

Compartmentalized three-dimensional human neuromuscular tissue models fabricated on a well-plate-format microdevice

Engineered three-dimensional models of neuromuscular tissues are promising for use in mimicking their disorder states in vitro. Although several models have been developed, it is still challenging to mimic the physically separated structures of motor neurons (MNs) and skeletal muscle (SkM) fibers in the motor units in vivo. In this study, we aimed to develop microdevices for precisely compartmentalized coculturing of MNs and engineered SkM tissues. The developed microdevices, which fit a well of 24 well plates, had a chamber for MNs and chamber for SkM tissues. The two chambers were connected by microtunnels for axons, permissive to axons but not to cell bodies. Human iPSC (hiPSC)-derived MN spheroids in one chamber elongated their axons into microtunnels, which reached the tissue-engineered human SkM in the SkM chamber, and formed functional neuromuscular junctions with the muscle fibers. The cocultured SkM tissues with MNs on the device contracted spontaneously in response to spontaneous firing of MNs. The addition of a neurotransmitter, glutamate, into the MN chamber induced contraction of the cocultured SkM tissues. Selective addition of tetrodotoxin or vecuronium bromide into either chamber induced SkM tissue relaxation, which could be explained by the inhibitory mechanisms. We also demonstrated the application of chemical or mechanical stimuli to the middle of the axons of cocultured tissues on the device. Thus, compartmentalized neuromuscular tissue models fabricated on the device could be used for phenotypic screening to evaluate the cellular type specific efficacy of drug candidates and would be a useful tool in fundamental research and drug development for neuromuscular disorders.

bioengineering↗

bioRxiv2020

Neural population dynamics underlying expected value computation

Computation of expected values, i.e., probability times magnitude, seems to be a dynamic integrative process performed in the brain for efficient economic behavior. However, neural dynamics underlying this computation remain largely unknown. We examined (1) whether four core reward-related regions detect and integrate the probability and magnitude cued by numerical symbols and (2) whether these regions have distinct dynamics in the integrative process. Extractions of mechanistic structure of neural population signal demonstrated that expected-value signals simultaneously arose in central part of orbitofrontal cortex (cOFC, area 13m) and ventral striatum (VS). These expected-value signals were incredibly stable in contrast to weak and/or fluctuated signals in dorsal striatum and medial OFC. Notably, temporal dynamics of these stable expected-value signals were unambiguously distinct: sharp and gradual signal evolutions in cOFC and VS, respectively. These intimate dynamics suggest that cOFC and VS compute the expected-values with unique time constants, as distinct, partially overlapping processes.

neuroscience↗

bioRxiv2019

Role of the imprinted allele of the p57Kip2 gene in mouse neocortical development

Imprinted genes are expressed from only one allele in a parent of origin-specific manner. The cyclin-dependent kinase inhibitor p57Kip2 (p57) is encoded by an imprinted gene, with the paternal allele being silenced. The possible expression and function of the paternal allele of p57 have remained little studied, however. We now show that the paternal allele of the p57 gene is expressed at a low level in the developing mouse neocortex. Surprisingly, the central nervous system-specific conditional deletion of the paternal allele (pat cKO) at the p57 locus resulted in a marked reduction in brain size. Furthermore, pat cKO gradually reduced the number of neural stem-progenitor cells (NPCs) during neocortical development, and thus reduced the number of upper-layer neurons, which were derived from late-stage NPCs. Our results thus show that the paternal allele of the p57 locus plays a key role in maintenance of NPCs during neocortical development.

developmental biology↗