Biology subjects

Nakai, T.

Publications and source records attributed to Nakai, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Cross-domain encoding models reveal shared and domain-specific neural representations across language and mathematics

Whether language and mathematics rely on shared or distinct neural representations remains an unresolved question in cognitive neuroscience. Here we combine latent features from a large language model (LLM) with vertex-wise encoding models to examine cross-domain generalization between language and mathematics. Thirty-two participants performed sentence comprehension and calculation tasks during fMRI, and encoding models were trained using features embedded in a common latent space. Cross-domain prediction identified cortical regions associated with partially shared representations, most prominently the left 55b, while control analyses suggested that these effects could not be fully explained by low-level visual processing or simple task-general factors. Task-specificity contrasts revealed stronger language-related prediction in the left anterior superior temporal and angular gyri and math-related prediction in the left precentral and intraparietal sulci. Model-weight analyses further showed that shared and domain-specific prediction patterns were reflected in distinct weight profiles across cortical regions. Connectivity analyses showed task-dependent functional coupling between cross-domain regions and language- or math-related networks. Together, these findings suggest that language and mathematics involve partially shared neural representations alongside domain-specific cortical organization, helping reconcile previous contrasting views on their neural basis.

neuroscience↗

bioRxiv2025

Second Language Proficiency Modulates Hierarchical Alignment between Brain Activity and Large Language Models

Second language (L2) comprehension is thought to proceed hierarchically, from basic word recognition to complex discourse-level understanding. However, the neural mechanisms underpinning this hierarchical progression remain poorly understood. Leveraging the hierarchical nature of linguistic representations in large language models (LLMs), we investigated how L2 proficiency modulates layer-wise brain-LLM alignment. Using functional magnetic resonance imaging (fMRI) data collected during discourse listening from 54 participants with varying levels of L2 proficiency, we constructed individualized encoding models to quantify how proficiency shapes the brain-LLM representational correspondence. In high-proficiency individuals, LLM-based models reliably predicted neural responses in a distributed network comprising temporal, frontal, and medial-parietal cortices, while for low-proficiency participants, prediction performance decreased. Notably, alignment in the superior temporal sulcus (STS) showed a robust correlation with L2 proficiency at mid-to-deep LLM layers, suggesting that the STS encodes higher-level linguistic abstractions essential for advanced language comprehension. These findings indicate that L2 proficiency modulates the alignment between LLMs and the brain in a graded, layer-dependent manner, providing insights into hierarchical neural language representations.

neuroscience↗

bioRxiv2025

Modulation of decodable semantic features of brain activity via selection attention

We frequently encounter linguistic information in multiple modalities, such as text and speech, simultaneously. In such cases, we understand the information by selectively attending to one of the modalities. Previous research has shown that selective attention to a specific stimulus modality modulates cortical activity patterns. However, it remains unclear which aspects of linguistic information are selectively modulated by attention and how such modulation influences the decodability of semantic content. To address this question, we constructed decoding models of latent semantic features from the functional magnetic resonance imaging data of six participants in both unimodal (either visual or auditory) and bimodal conditions (simultaneously visual and auditory, with participants attending to a single modality at a time). In unimodal conditions, we successfully decoded the semantic contents from the brain activity. Decodable features were consistent across modalities in both intra-modal and cross-modal decoding. For bimodal conditions, decoding accuracies for the attended stimuli were higher than for the ignored when training and test stimuli belonged to the same modality. Furthermore, decodable features were more consistent across modalities with attended than ignored stimuli in both intra-modal and cross-modal decoding. These results indicate common decodable semantic features regardless of the presentation modality and that selective attention enhances the semantic representations contributing to such decodability.

neuroscience↗

bioRxiv2025

Arithmetic learning is associated with developmental increases in similarity between brain activity and artificial neural networks

The ability to learn simple arithmetic is often thought to rely on the associative nature of human memory, where repeated exposure to arithmetic problems strengthens the relations between operands and outcomes. This view is reminiscent of learning in large language artificial neural networks (ANNs), where training involves statistical associations between symbolic inputs and outputs. Based on this parallel, we hypothesized that brain activity during arithmetic problem-solving should be increasingly similar to ANN-derived representations with learning and development. We used fMRI to investigate the relation between brain activity during single-digit addition problem-solving and ANN features in 104 participants across four age groups (8-, 11-, 14-year-olds, and adults). Vertex-wise encoding models were used to predict brain activity using latent features extracted from pre-trained ANNs. Results revealed that the ANN-based model better predicted arithmetic-related activity in older than younger participants in a specific region of the left precentral sulcus. Prediction accuracy was higher for smaller than larger problems, consistent with the idea that associations between problems may decrease in strength with problem size. Furthermore, ANN prediction patterns aligned with a model in which each addition problem was increasingly represented separately with age. On the one hand, these findings may support the idea that, with learning and development, some neural representations of arithmetic problems become discretely organized in memory and increasingly resemble ANN-like processing. On the other hand, the lack of relation between the ANN-based model and activity in other brain regions suggests some degree of dissimilarity between arithmetic processing in humans and ANNs, suggesting the existence of additional mechanisms for simple arithmetic learning in humans.

neuroscience↗

bioRxiv2025

SpyRing-Mediated Cyclization of TEV Protease, Guided by AlphaFold, Improves Thermostability

Cyclization is a promising strategy to enhance protein stability, but its applicability is often limited by structural constraints such as the distance between terminal regions. Here, we report the rational design and characterization of a cyclized Tobacco Etch Virus protease (cTEVp) using the SpyRing system, which enables covalent cyclization through SpyTag/SpyCatcher-mediated isopeptide bond formation. We applied this approach to a widely used engineered TEVp variant (L56V, S135G, S219V, {Delta}238-242) and employed AlphaFold structure prediction to optimize linker length and domain positioning. Despite the ~40 [A] separation between the N- and C-termini of native TEVp, AlphaFold modeling suggested that the fused SpyTag and SpyCatcher domains can adopt a favorable configuration for intramolecular cyclization. The resulting cTEVp exhibited proteolytic activity comparable to the non-cyclized TEVp, indicating that structural constraint via SpyRing-mediated cyclization did not impair enzymatic function. Importantly, cTEVp displayed significantly improved thermostability relative to its non-cyclized counterpart, as demonstrated by higher retention of soluble enzyme and residual activity following heat treatment at 50{degrees}C. Our findings validate the effectiveness of SpyRing-mediated cyclization in improving TEVp stability and highlight the utility of computationally guided cyclization as a generalizable strategy for engineering thermally resilient proteins. This study establishes a framework that integrates structure prediction and rational protein engineering, contributing to the development of robust biocatalysts for synthetic biology and industrial biotechnology applications. Table of Contents (TOC) / Abstract Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/646106v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@4efc7eorg.highwire.dtl.DTLVardef@1a2eab8org.highwire.dtl.DTLVardef@e00b2forg.highwire.dtl.DTLVardef@fe78a8_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

Erythropoietin production in embryonic neural cells is controlled by hypoxia-inducible factors and histone deacetylases in an undifferentiated state

During mammalian development, production sites of the erythroid growth factor erythropoietin (EPO) shift from the neural tissues to the liver in embryos and to the adult kidneys. Embryonic neural EPO-producing (NEP) cells, a subpopulation of neuroepithelial and neural crest cells, express the Epo gene between embryonic day (E) 8.5 and E11.5 to promote primitive erythropoiesis in mice. While Epo gene expression in the liver and kidney is induced under hypoxic conditions through hypoxia-inducible transcription factor (HIF) 2, the Epo gene regulatory mechanisms in NEP cells remain to be elucidated. This study confirms the presence of cells coexpressing the genes encoding EPO and HIF2 in E9.5 neural tubes, where the hypoxic microenvironment activates HIF1. In human neural progenitors and mouse embryonic neural tissues, a HIF-activating compound upregulated EPO expression, and this induction was blocked by inhibiting HIFs. Additionally, a cell line of NEP cell derivatives that no longer expressed the Epo gene demonstrated that histone deacetylase inhibitors (HDACIs) reactivate EPO production while rejuvenating the cells. HDACIs also induced EPO gene expression in SK-N-BE(2)c human neuroblastoma cells and mouse primary neural crest cells. Thus, EPO production is controlled by epigenetic mechanisms and hypoxia signaling in an immature state of hypoxic NEP cells.

molecular biology↗

bioRxiv2024

Cortical representational geometry of diverse tasks reveals subject-specific and subject-invariant cognitive structures

The variability in brain function forms the basis for our uniqueness. Prior studies indicate smaller individual differences and larger inter-subject correlation (ISC) in sensorimotor areas than in the association cortex. These studies, deriving information from brain activity, leave individual differences in cognitive structures based on task similarity relations unexplored. This study quantitatively evaluates these differences by integrating ISC, representational similarity analysis, and vertex-wise encoding models using functional magnetic resonance imaging across 25 cognitive tasks. ISC based on cognitive structures enables subject identification with 100% accuracy using at least 14 tasks. ISC is larger in the fronto-parietal association and higher-order visual cortices, suggesting subject-invariant cognitive structures in these regions. Principal component analysis reveals different cognitive structure configurations within these regions. This study provides new evidence of individual variability and similarity in abstract cognitive structures.

neuroscience↗

bioRxiv2023

PKCdelta splicing variant (PKCdeltaIII) induces neurite outgrowth in PC12 cells in the absence of NGF

Protein kinase C (PKC){delta} is a serine/threonine kinase involved in many cellular processes in response to diverse stimuli, including cell proliferation, differentiation, apoptosis, tumor inhibition, and cell migration. PKC{delta} consists of the N terminal regulatory domain and the C terminal catalytic domain, which are linked with a hinge region. An alternative splicing variant of rat PKC{delta} (also known as PKC{delta}III) has been reported, and this PKC{delta} splicing variant (SV) has only the regulatory domain without the catalytic domain. However, its function remains unclear. In this study, we found that PKC{delta} SV induced neurite outgrowth in the absence of nerve growth factor (NGF) in PC12 cells. Immunoblot analysis revealed that ERK was phosphorylated by NGF treatment, but not by PKC{delta} SV induction, indicating that PKC{delta} SV-mediated neurite outgrowth was different from NGF-mediated one. In addition, we showed that PKC{delta} full length and SV increased their mRNA expression after NGF treatment and that PKC{delta} SV was more susceptible to its proteasomal degradation. Our findings provided insights into the role of PKC{delta} SV in neurite outgrowth.

neuroscience↗

bioRxiv2023

A novel plasmid-based experimental system in Saccharomyces cerevisiae that enables the introduction of 10 different plasmids into cells

The budding yeast Saccharomyces cerevisiae is commonly used as an expression platform to produce valuable compounds. Yeast-based genetics research can uniquely utilize use auxotrophy in transformant selection: auxotrophic complementation by an auxotrophic marker gene on exogenous DNA (such as plasmids). However, the number of auxotrophic nutrients required restricts the number of plasmids maintained by the cells. We therefore developed novel {Delta}10 strains that are auxotrophic for 10 different nutrients, and new plasmids with two multicloning sites and auxotrophic markers for use in {Delta}10 strains. We confirmed that {Delta}10 strains could maintain 10 types of these plasmids. Because each plasmid can express two different genes, this {Delta}10 strain-based expression system has the potential to co-express a maximum of 20 different proteins.

bioengineering↗

bioRxiv2022

Artificial neural network modelling of the neural population code underlying mathematical operations

Mathematical operations have long been regarded as a sparse, symbolic process in neuroimaging studies. In contrast, advances in artificial neural networks (ANN) have enabled extracting distributed representations of mathematical operations. Recent neuroimaging studies have compared distributed representations of the visual, auditory and language domains in ANNs and biological neural networks (BNNs). However, such a relationship has not yet been examined in mathematics. Here we used the fMRI data of a series of mathematical problems with nine different combinations of operators to construct voxel-wise encoding models using both sparse operator and latent ANN features. Representational similarity analysis demonstrated shared representations between ANN and BNN, an effect particularly evident in the intraparietal sulcus. Feature-brain similarity analysis served to reconstruct a sparse representation of mathematical operations based on distributed ANN features. Such reconstruction was more efficient when using features from deeper ANN layers. Moreover, latent ANN features allowed the decoding of novel operators not used during model training from brain activity. The current study provides novel insights into the neural code underlying mathematical thought.

neuroscience↗

bioRxiv2022

Quantitative modeling demonstrates format-invariant representations of mathematical problems in the brain

Mathematical problems can be described in either symbolic form or natural language. Previous studies have reported that activation overlaps exist for these two types of mathematical problems, but it is unclear whether they are based on similar brain representations. Furthermore, quantitative modeling of mathematical problem solving has yet to be attempted. In the present study, subjects underwent 3 h of functional magnetic resonance experiments involving math word and math expression problems, and a read word condition without any calculations was used as a control. To evaluate the brain representations of mathematical problems quantitatively, we constructed voxel-wise encoding models. Both intra- and cross-format encoding modeling significantly predicted brain activity predominantly in the left intraparietal sulcus (IPS), even after subtraction of the control condition. Representational similarity analysis and principal component analysis revealed that mathematical problems with different formats had similar cortical organization in the IPS. These findings support the idea that mathematical problems are represented in the brain in a format-invariant manner.

neuroscience↗

bioRxiv2021

Preserved representations and decodability of diverse cognitive functions across the cortex, cerebellum, and subcortex

Which part of the brain contributes to our complex cognitive processes? Studies have revealed contributions of the cerebellum and subcortex to higher-order cognitive functions; however it is unclear whether such functional representations are preserved across the cortex, cerebellum, and subcortex. In this study, we used functional magnetic resonance imaging data with 103 cognitive tasks and constructed three voxel-wise encoding and decoding models independently using cortical, cerebellar, and subcortical voxels. Representational similarity analysis revealed that the structure of task representations is preserved across the three brain parts. Principal component analysis visualized distinct organizations of abstract cognitive functions in each part of the cerebellum and subcortex. More than 90% of the cognitive tasks were decodable from the cerebellum and subcortical activities, even for the novel tasks not included in model training. Furthermore, we discovered that the cerebellum and subcortex have sufficient information to reconstruct activity in the cerebral cortex.

neuroscience↗

bioRxiv2021

Brain networks are decoupled from external stimuli during internal cognition

Our cognition can be directed to external stimuli or to internal information. While there are many different forms of internal cognition (mind-wandering, recall, imagery etc.), their essential feature is independence from the immediate sensory input. This is thought to be reflected in the decoupling of brain networks from the external stimuli, but a quantitative investigation of this remains outstanding. Here we present a conceptual and analysis framework that links stimulus responses to connectivity between brain networks. This allows us to quantify the coupling of brain networks to the external stimuli. We tested this framework by presenting subjects with an audiovisual stimulus and instructing them to either attend to the stimulus (external task) or engage in mental imagery, recall or arithmetic (internal tasks) while measuring the evoked brain activity using functional MRI. We found that stimulus responses were generally attenuated for the internal tasks, though they increased in a subset of tasks and brain networks. However, using our new measure of coupling, we showed that brain networks became increasingly decoupled from the stimulus, even in the subset of tasks and brain networks in which stimulus responses increased. These results quantitatively demonstrate that during internal cognition brain networks are decoupled from external stimuli, opening the door for a fundamental and quantitative understanding of internal cognition.

neuroscience↗

bioRxiv2020

Biased re-orientation in the chemotaxis of peritrichous bacteria

Many kinds of peritrichous bacteria that repeat runs and tumbles by using multiple flagella exhibit chemotaxis by sensing a difference in the concentration of the attractant or repellent between two adjacent time points. If a cell senses that the concentration of an attractant has increased, their flagellar motors decrease the switching frequency from counterclockwise to clockwise direction of rotation, which causes a longer run in swimming up the concentration gradient than swimming down. We investigated the turn angle in tumbles of peritrichous bacteria swimming across the concentration gradient of a chemoattractant because the change in the switching frequency in the rotational direction may affect the way tumbles. We tracked several hundreds of runs and tumbles of single Salmonella typhimurium cells in the concentration gradient of L-serine, and found that the turn angle depends on the concentration gradient that the cell senses just before the tumble. The turn angle is biased toward a smaller value when the cells swim up the concentration gradient, whereas the distribution of the angle is almost uniform (random direction) when the cells swim down the gradient. The effect of the observed bias in the turn angle on the degree of chemotaxis was investigated by random walk simulation. In the concentration field where attractants diffuse concentrically from the point source, we found that this angular distribution clearly affects the reduction of the mean square displacement of the cell that has started at the attractant source, that is, the bias in the turn angle distribution contributes to chemotaxis in peritrichous bacteria. Statement of SignificanceWe found another aspect in the chemotactic behavior of peritrichous bacteria. Chemotactic behaviors in peritrichous bacteria were predicted to be observed at the turn angles during tumbles motion as well as at the duration of runs; smaller changes in the swimming direction of cells swimming up the attractants gradient can be observed. This behavior is appropriate because the nature of bacterial chemotaxis changes the switching rate of the rotational direction of the flagellar motors according to the environment. Cells swimming upward reduce the turn angle by switching fewer flagellar motors to loosen flagellar filaments from the bundle during tumbles. We have shown that this prediction is correct.

biophysics↗

bioRxiv2020

Convergence of modality invariance and attention selectivity in the cortical semantic circuit

The human linguistic system is characterized by modality invariance and attention selectivity. Previous studies have examined these properties independently and reported perisylvian region involvement for both; however, their relationship and the linguistic information they harbor remain unknown. Participants were assessed by functional MRI, while spoken narratives (auditory) and written texts (visual) were presented, either separately or simultaneously. Participants were asked to attend to one stimulus when both were presented. We extracted phonemic and semantic features from these auditory and visual modalities, to train multiple, voxel-wise encoding models. Cross-modal examinations of the trained models revealed that perisylvian regions were associated with modality-invariant semantic representations. Attentional modulation was quantified by examining the modeling performance for attended and unattended conditions. We have determined that perisylvian regions exhibited attention selectivity. Both modality invariance and attention selectivity are both prominent in models that use semantic but not phonemic features. Modality invariance was significantly correlated with attention selectivity in some brain regions; however, we also identified cortical regions associated with only modality invariance or only attention selectivity. Thus, paying selective attention to a specific sensory input modality may regulate the semantic information that is partly processed in brain networks that are shared across modalities.

neuroscience↗

bioRxiv2019

Data-driven models reveal the organization of diverse cognitive functions in the brain

Our daily life is realized by the complex orchestrations of diverse brain functions including perception, decision, and action. One of the central issues in cognitive neuroscience is to reveal the complete representations underlying such diverse functions. Recent studies have revealed representations of natural perceptual experiences using encoding models1-5. However, there has been little attempt to build a quantitative model describing the cortical organization of multiple active, cognitive processes. Here, we measured brain activity using functional MRI while subjects performed over 100 cognitive tasks, and examined cortical representations with two voxel-wise encoding models6. A sparse task-type encoding model revealed a hierarchical organization of cognitive tasks, their representation in cognitive space, and their mapping onto the cortex. A cognitive factor encoding model utilizing continuous intermediate features by using metadata-based inferences7 predicted brain activation patterns for more than 80 % of the cerebral cortex and decoded more than 95 % of tasks, even under novel task conditions. This study demonstrates the usability of quantitative models of natural cognitive processes and provides a framework for the comprehensive cortical organization of human cognition.

neuroscience↗

bioRxiv2019

A language-familiarity effect on the recognition of computer-transformed vocal emotional cues

People are more accurate in voice identification and emotion recognition in their native language than in other languages, a phenomenon known as the language familiarity effect (LFE). Previous work on cross-cultural inferences of emotional prosody has left it difficult to determine whether these native-language advantages arise from a true enhancement of the auditory capacity to extract socially relevant cues in familiar speech signals or, more simply, from cultural differences in how these emotions are expressed. In order to rule out such production differences, this work employed algorithmic voice transformations to create pairs of stimuli in the French and Japanese language which differed by exactly the same amount of prosodic expression. Even though the cues were strictly identical in both languages, they were better recognized when participants processed them in their native language. This advantage persisted in three types of stimulus degradation (jabberwocky, shuffled and reversed sentences). These results provide univocal evidence that production differences are not the sole drivers of LFEs in cross-cultural emotion perception, and suggest that it is the listeners lack of familiarity with the individual speech sounds of the other language, and not e.g. with their syntax or semantics, which impairs their processing of higher-level emotional cues.

neuroscience↗

bioRxiv2018

Representation of music genres based on the spectro-temporal modulation responses of the human brain

Music genre is an essential category for understanding human musical preferences and is provided based on the abstract categorization upon complex auditory stimuli. Previous neuroimaging studies have reported the involvement of the superior temporal gyrus (STG) in response to general music-related features. However, it remains largely unclear how abstract categories of music genre are represented in the brain and what acoustic features are more suited for explaining such representations. Here we examined comprehensive cortical representations and functional organization of music genres using 540 music clips. We applied a voxel-wise modeling approach to music-evoked brain activity measured using functional magnetic resonance imaging (fMRI). We observed distinct cortical organizations for different music genres in the bilateral STG, which revealed the representational relationship between various music genres, e.g., classical and hip-hop music showed opposite representations. Representations of music genres were largely explained by spectro-temporal modulation, which was modeled by a biologically plausible spectro-temporal modulation-transfer function (MTF) model. Our results elucidate the quantitative representation of music genres in the human cortex and indicate the possibility of modeling our abstract categorization of complex auditory stimuli based on the brain activity.\n\nSignificance statementMusic genre is an essential category for understanding human preferences of music. However, it is largely unknown how abstract categories of music genre are represented in the brain. Here, we examined comprehensive cortical representations of music genres by building voxel-wise models of fMRI data collected while human subjects listened to 540 music clips. We found distinct cortical organizations for various music genres in the bilateral STG. Such genre-specific cortical organization was explained by the biologically plausible MTF model. The current study elucidates the quantitative representation of music genres in the human cortex for the first time and indicates the possibility of modeling our abstract categorization of complex auditory stimuli based on the brain activity.

neuroscience↗