Biology subjects

Yokoi, A.

Publications and source records attributed to Yokoi, A..

11 recordsLinked to original sources

bioRxiv2026

Encoding of bimanual movement directions across the human sensorimotor system

Bimanual coordination requires the control of each hand to account for movements of the other hand. To enable such coordination, it has been hypothesized that the brain has a representation of bimanual movements that supersedes a linear combination of the unimanual movements. To test for such a representation, we mapped the encoding of movement direction during unimanual and bimanual wrist movements in the human brain using functional magnetic resonance imaging (fMRI). For unimanual movements, we found that both the contralateral and ipsilateral movement directions were represented across most cortical motor regions. The representations were similar for mirror-symmetric movements across the hands, revealing an intrinsic (body-centric) code in premotor and parietal regions. For bimanual movements, the neural representations of movement directions could be explained by three components. First, the contralateral movement direction was represented as in the unimanual condition, whereas the representation of the ipsilateral movement direction disappeared. Second, in higher-order regions (rostral premotor and posterior parietal cortex) we found relatively elevated activity when the two movements were directed in unrelated directions. This activity was likely caused by the extra effort to deal with incongruent spatial targets across the hands. Third, and independent of this congruency effect, we found a non-linear interaction between contra- and ipsilateral movement that encoded the specific bimanual movement combination across sensorimotor, premotor, and parietal regions. This representation was suited to facilitate bimanual coordination and learning.

neuroscience↗

bioRxiv2025

Proteomic profiling of bacterial extracellular vesicles for exploring ovarian cancer biomarkers

Extracellular vesicles (EVs) are present in body fluids and act as disease biomarkers. Emerging evidence has proven that EVs are released not only from mammalian cells but also from bacteria. Ovarian cancer has a dismal prognosis because of difficulties in early detection. This study aimed to identify bacterial EV (BEV) proteins associated with ovarian cancer. Fifteen patients with ovarian cancer or benign tumors were recruited, and EVs were isolated from the ascites. BEVs were recovered from seven in vitro-cultured strains of bacteria present in the vaginal microbiota, and LC-MS/MS analysis was performed. The detected peptide data were annotated to both human and bacterial references, and the human data showed that the profiles of cancer EVs were distinct from those of patients with benign tumors. As analyzed by bacterial proteins, P15636_Protease1 was found as the BEV-associated protein highly expressed in patients with cancer. To distinguish patients with cancer, the area under the curve was 0.88 (95% CI, 0.64-1.00). In addition, homology analysis showed that P15636_Protease1 is a unique protein only detected in bacteria. In this study, ovarian cancer-specific bacterial proteins were identified on EVs, and BEVs in bodily fluids are promising in the discovery of disease biomarkers.

cancer biology↗

bioRxiv2025

Autonomic responses to proprioceptive and visual errors during single-trial reach adaptation

The autonomic nervous system (ANS) plays a crucial role in coordinating brain and bodily responses to cope with unexpected events. Extensive research in cognitive control has demonstrated that error-induced responses in peripheral autonomic measures, such as pupil diameter and heart rate, correlate with the quality of the subsequent behavioral adjustment. Here, we perform ANS measurements during a motor learning task to investigate error-induced ANS responses in implicit motor learning. In a series of experiments, we measured multimodal autonomic signals including pupil diameter, skin conductance, and heart rate while participants made a reaching movement under occasional proprioceptive or visual perturbations of different magnitudes. We characterized the phasic autonomic responses to the induced errors and evaluated their influence on motor learning, which was quantified by comparing the trials immediately before and after the error event (single-trial reach adaptation). Consistent with previous research, the results revealed the sequence of ANS responses induced by unexpected motor errors; the pupil dilation, increased skin-conductance, and heart rate deceleration. These responses also demonstrated a clear dose-dependent increase in responses to errors in both visual and proprioceptive modalities. Furthermore, a latent factor analysis using motor learning and multimodal autonomic response data allowed us to detect a statistical relationship between the latent autonomic states and the motor learning rate, suggesting the suppression of implicit motor adaptation by error-induced sympathetic activity. These results provide novel insights into how ANS-tagged internal states affect implicit motor learning. Significance statementThe autonomic nervous system (ANS) plays a crucial role in coordinating brain and bodily responses when coping with unexpected situations. Although extensively studied in the field of cognitive control, little is known about whether and how ANS is involved in implicit motor learning. Here, by monitoring multimodal ANS signals (pupil, skin conductance, and heart rate) during a motor adaptation task, we show that ANS measures show a size-dependent phasic response to proprioceptively/visually applied motor errors. We further show that the ANS, especially the sympathetic error response, mediates the suppression of error sensitivity in larger errors. These results provide novel insights into how ANS-tagged internal states, potentially relevant for statistical inference, affect implicit motor learning. Supplementary MaterialsOne Supplementary Material file is available.

neuroscience↗

bioRxiv2023

Decision uncertainty as a context for motor memory

The current view of perceptual decision-making suggests that once the decision is made, a single motor program associated with the decision is carried out, irrespective of the degree of uncertainty involved in the decision-making process. As opposed to this view, we show that different levels of decision uncertainty contextualize actions differently, allowing the brain to form different motor memories based on each context. The match between decision uncertainty during learning and retrieval is critical for successful motor memory retrieval. The same movement trajectory can be associated with different motor memories if each memory is linked to a different level of decision uncertainty. Encoding motor memories based on decision contexts may enhance the robustness of control during the varying neural activities induced by different cognitive states.

neuroscience↗

bioRxiv2022

The association of ubiquitin-associated protein 2-like and Ras-GTP-activating protein SH3 domain binding protein 1 mediated by small nucleolar RNA is essential for stress granule formation

Stress granules (SGs) are dynamic, non-membranous structures composed of non-translating mRNAs and various proteins and play critical roles in cell survival under stressed conditions. Extensive proteomics analyses have been performed to identify proteins in SGs; however, the molecular functions of these components in SG formation remain unclear. In this report, we show that ubiquitin-associated protein 2-like (UBAP2L) is a novel component of SGs. UBAP2L localized to SGs in response to various stresses, and its depletion significantly suppressed SG organization. Proteomics and RNA sequencing analyses found that UBAP2L formed a protein-RNA complex with Ras-GTP-activating protein SH3 domain binding protein 1 (G3BP1) and small nucleolar RNAs (snoRNAs). In vitro binding analysis demonstrated that snoRNAs were required for UBAP2L association with G3BP1. In addition, decreased expression of snoRNAs reduced the interaction between UBAP2L and G3BP1 and suppressed SG formation. Our results reveal a critical role of a novel SG component, the UBAP2L/snoRNA/G3BP1 protein-RNA complex, and provide new insights into the regulation of SG assembly.

cell biology↗

bioRxiv2021

Pupil-linked arousal modulates trial-by-trial motor adaptation in humans

Pupil diameter, under constant illumination, is known to reflect individuals internal states, such as surprise about observation and environmental uncertainty. Despite the growing use of pupillometry in cognitive learning studies as an additional measure for examining internal states, few studies have used pupillometry in human motor learning studies. Here we provide the first detailed characterization of pupil diameter changes in a short-term reach adaptation paradigm. We measured pupil changes in 91 human participants while they adapted to abrupt, gradual, or switching force field conditions. Sudden increases in movement error caused by the introduction/reversal of the force field resulted in strong phasic pupil dilation during movement accompanied by a transient increase in tonic pre-movement baseline pupil diameter in subsequent trials. In contrast, clear changes in pupil responses were absent when the force field was gradually introduced, indicating that error drove the changes in pupil responses. Nevertheless, we found an association between baseline pupil diameter and awareness of the gradually-introduced perturbation assessed post-experimentally. In all experiments, we also found a strong co-occurrence of larger baseline pupil diameter and slower reaction and movement time after each set break. Interestingly, error-induced pupil responses gradually became insensitive after experiencing multiple reversals. Collectively, these results suggest that tonic baseline pupil diameter reflects ones belief about environmental uncertainty, whereas phasic pupil dilation during movement reflects surprise about a sensory outcome (i.e., movement error), and both effects are modulated by novelty. Our results provide a new approach for non-verbally assessing participants internal states during motor learning.

neuroscience↗

bioRxiv2018

Parcellation of motor sequence representations in the human neocortex

While previous studies have revealed an extended network of cortical regions associated with motor sequence production, the specific role of each of these areas is still elusive. To address this issue, we designed a novel behavioural paradigm that allowed us to experimentally manipulate the structure of motor sequences representations in individual participants. We then conducted fMRI while participants executed 8 trained sequences to examine how this structure is reflected in the associated activity patterns. Both model-based and model-free approaches revealed a clear distinction between primary and non-primary motor cortices in their representational contents, with M1 specifically representing individual finger movements, and premotor and parietal cortices showing a mixture of chunk, sequence and finger transition representations. Using model-free representational parcellation, we could divide these non-primary motor cortices into separate clusters, each with a unique representation along the stimulus-to-action gradient. These results provide new insights into how human neocortex organizes movement sequences.

neuroscience↗

bioRxiv2018

The effects of habits on motor skill learning

Skill learning involves the formation of stable motor patterns. In musical and athletic training, however, these stable motor habits can also impede the attainment of higher levels of performance. We developed an experimental paradigm to induce a specific motor pattern in the context of a discrete sequence production task and to investigate how these habits affect performance over a 3-week training period. Participants initially practiced small segments of 2 to 3 finger movements ("chunks") and then learned longer sequences composed of these chunks. This initial training induced a persistent temporal pattern during execution, with shorter inter-press-intervals within a chunk and longer ones at chunk boundaries. This pattern remained stable during the subsequent 10 days of training, in which participants were asked to produce the sequence as fast as possible from memory. The habit was also preserved when the sequences were directly displayed, removing the need for memory recall. We were able to induce chunking patterns that were either beneficial or detrimental to performance by taking into consideration the biomechanical constraints of the sequences. While we observed an overall reduction in the detrimental effect of the disadvantageous chunking instructions with training, our results show that the degree to which these detrimental chunk structures were maintained, was predictive of lower levels of final performance. In sum, we were able to induce beneficial and detrimental motor habits in a motor sequence production task and show that these initial instructions influenced performance outcomes over a prolonged period of time. Significance StatementA habit is defined as an automatized action that resists modification once sufficiently established. Preventing bad habits, while reinforcing good habits, is a key objective when teaching new motor skills. While habit formation is an integral part of motor skill acquisition, previous research has focused on habit formation in terms of action selection. In this paper, we examine habit formation in terms of motor skill execution, after the action has been selected. We were able to induce beneficial or detrimental motor habits in the production of motor sequences. Habits were stable over a prolonged training period. Our results demonstrate how cognitive instruction can lead to persistent motor habits and we explore how these habits are potentially modified with training.

neuroscience↗

bioRxiv2018

Stability of representational geometry across a wide range of fMRI activity levels

Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Subjects were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3-2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.

neuroscience↗

bioRxiv2017

Does human primary motor cortex represent sequences of finger movements?

Human primary motor cortex (M1) is an essential structure for the production of dexterous hand movements. While distinct sub-populations of neurons are activated during single finger movements, it remains unknown whether M1 also represents sequences of multiple finger movements. Using novel multivariate fMRI analysis techniques, we show here that even after 5 days of intense practice there was little or no evidence for a true sequence representation in M1. Rather, the activity patterns for sequences in M1 could be explained by linear combination of patterns associated with the constituent individual finger movements, with the strongest weight on the finger making the first response of the sequence. These results suggest that M1 only represents single finger movements, but receives increased input at the start of a sequence. In contrast, the reliable differences between different sequences in premotor and parietal areas could not be explained by a strong weighting of the first finger, supporting the view that these regions exhibit a true representation of sequences.

neuroscience↗

bioRxiv2017

Pattern Component Modeling: A Flexible Approach For Understanding The Representational Structure Of Brain Activity Patterns

Representational models specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling (PCM), a practical Bayesian approach for evaluating such models. Similar to encoding models, PCM evaluates the ability of models to predict novel brain activity patterns. In contrast to encoding models, however, the activity of individual voxels across conditions (activity profiles) are not directly fitted. Rather, PCM integrates over all possible activity profiles and computes the marginal likelihood of the data under the activity profile distribution specified by the representational model. By using an analytical expression for the marginal likelihood, PCM allows the fitting of flexible representational models, in which the relative strength and form of the encoded features can be estimated from the data. We discuss here a number of different forms with which such flexible representational models can be specified, and how models of different complexity can be compared. We then provide a number of practical examples from our recent work in motor control, ranging from fixed models to more complex non-linear models of brain representations. The code for the fitting and cross-validation of representational models is provided in a open-source Matlab toolbox.

neuroscience↗