Biology subjects

Iwane, F.

Publications and source records attributed to Iwane, F..

9 recordsLinked to original sources

bioRxiv2026

Constricted pupil states index neural replay during wakeful rest in humans

Neural replay has been linked to skill consolidation during wakeful rest, yet its physiological context in humans remains poorly understood. In animals, hippocampal ripples and associated replay occur more frequently within intrinsically oriented brain states associated with constricted pupils, suggesting that pupil dynamics may index neural replay events in humans. We combined magnetoencephalography-derived sequence replay with pupillometry in 25 participants learning a motor sequence across alternating intervals of practice and rest. Replay was indexed by smaller-pupil states, and replay timing clustered on the constricting phase of slow pupil fluctuations. More frequent replay accompanied larger cumulative offline gains during early learning, while smaller rest pupil was associated with better performance when practice resumed. Multivariate pupil features predicted replay-associated states across and within individuals while preceding pupil dynamics showed modest predictive ability to identify the precise onset of individual replay events. Here, we show that constricted-pupil states index neural replay during wakeful rest in humans, linking replay-associated physiology with early motor learning.

neuroscience↗

bioRxiv2026

Associations between striatal neurochemical changes and resting-state functional connectivity following transcranial temporal interference stimulation

Background Non-invasive modulation of deep brain structures remains a major challenge in human neuroscience and neurorehabilitation. Transcranial temporal interference stimulation (tTIS) has emerged as a promising approach for engaging subcortical regions, but its effects on striatal neurochemical markers and functional connectivity remain unclear. Objective To investigate whether 20-Hz tTIS designed to target the right striatum modulates GABA+ and glutamate-glutamine complex (Glx) levels and whether neurochemical changes are associated with changes in resting-state functional connectivity. Methods Thirty-four healthy right-handed participants were randomly assigned to the tTIS group (20-Hz beat frequency) or sham group (0-Hz frequency difference). Participants underwent proton magnetic resonance spectroscopy (1H-MRS) and resting-state fMRI before and after 30 min of stimulation. One participant in the tTIS group was excluded from the 1H-MRS analyses because of data corruption. Results The direct between-group difference in Glx change did not reach statistical significance, although the tTIS group showed a numerically greater reduction than the sham group (p = 0.066). Exploratory within-group analyses showed a significant decrease in striatal Glx in the tTIS group (p < 0.001), whereas the decrease in the sham group was not significant. No significant GABA+ changes were observed within or between groups. The association between striatal Glx change and functional connectivity change differed between groups in the sensorimotor cortex, right parahippocampal gyrus, and bilateral fusiform gyri. Conclusion These exploratory findings suggest that 20-Hz tTIS may be associated with striatal Glx modulation and group-dependent coupling between neurochemical and cortico-subcortical network changes.

neuroscience↗

bioRxiv2025

Expansion of Working Memory Capacity Supports Early Skill Learning

Motor skill learning depends on refining action sequences through practice, yet how learning unfolds moment-to-moment within trials remains unclear. Here, healthy participants learned generative sequential keypress skills at different speeds. Within trials, we identified reproducible high initial skill (HIS) segments, brief epochs of elevated performance followed by decline, that emerged only during learning of repeating sequences and were absent during practice of pseudo-randomized, non-repeating sequences. HIS keypress content scaled with execution speed and increased with practice. This expansion of HIS segment content continued after overall performance plateaued. Neural analyses showed increased theta-gamma phase-amplitude coupling ({theta}/{gamma} PAC) and beta-band bursts during HIS segments. Hippocampal {theta}/{gamma} PAC predicted HIS content, suggesting a mechanism for binding successive actions into progressively larger skill-memory representations during early learning. Together, these findings identify expansion of skill memory, rather than fatigue, as a key driver of early skill learning.

neuroscience↗

bioRxiv2025

Brain-computer interface training fosters perceptual skills to detect errors

Accurate perception of visuo-motor errors is essential for perceptual and sensorimotor learning so that corrective actions are performed timely to maintain stability and goal-directed behavior. However, a key challenge remain: conventional, behavioral perceptual training -- typically based on response accuracy feedback-- is limited in improving sensitivity to small, subtle errors. While prior approaches have focused on modulating sensory regions to enhance perceptual learning, we propose an alternative approach: targeting a cognitive neural marker, namely the error positivity (Pe), a component of the error-related potential (ErrP) that primarily originates in the anterior cingulate cortex (ACC), a key region involved in decision-making. We hypothesize that the Pe, which reflects conscious awareness of errors, serves as a modifiable neural correlate of error perception. To address the performance bottlenecks seen in conventional, behavioral perceptual training, we show that real-time feedback on ErrP presence or absence during perceptual training over five longitudinal days enhances the Pe component and improves participants ability to detect small visuo-motor errors --an outcome not achieved through conventional, behavioral perceptual training. These findings offer new neurophysiological insights into error perception and learning, and establish ErrP-based BCI interventions as a promising tool for accelerating perceptual learning, particularly in contexts where subtle error detection is critical.

bioengineering↗

bioRxiv2024

Cingulate and striatal hubs are linked to early skill learning

Early skill learning develops in the context of activity changes in distributed cortico-subcortical regions. Here, we investigated network hubs--centers of information integration and transmission--within the brain network supporting early skill learning. We recorded magnetoencephalographic (MEG) brain activity in healthy human subjects who learned a moderately difficult sequence skill with their non-dominant left hand. We then computed network hub strength by summing top 10% functional connectivity over 86 parcellated brain regions (AAL3 atlas) and five brain oscillatory frequency bands (alpha, low-, high-beta, low- and high-gamma). Virtually all skill gains developed during rest intervals of early learning (micro-offline gains). MEG hub strength in the alpha band (8-13Hz) in bilateral anterior cingulate (ACC) and caudate and in the low-beta band (13-16Hz) in bilateral caudate and right putamen correlated with micro-offline gains. These regions linked strongly with the hippocampus, parahippocampal cortex, and lingual and fusiform gyri. Thus, alpha and low-beta brain oscillatory activity in cingulate and striatal regions appear to contribute as hubs of information integration and transmission during early skill learning. Significance StatementEarly learning of moderately difficult skill sequences develops over periods of rest interspersed with practice (micro-offline gains). We demonstrate here a link between alpha and low-beta oscillatory activity in a cingulate-hippocampo-striato network hubs and rest intervals of early learning.

neuroscience↗

bioRxiv2024

Sequence action representations contextualize during rapid skill learning

Activities of daily living rely on our ability to acquire new motor skills composed of precise action sequences. Here, we asked if the millisecond-level neural representation of an action performed at different contextual sequence locations within a skill differentiates or remains stable during early motor learning. We first optimized machine learning decoders predictive of sequence-embedded finger movements from magnetoencephalographic (MEG) activity. Using this approach, we found that the neural representation of the same action performed in different contextual sequence locations, progressively differentiated--primarily during rest intervals of early learning (offline)--correlating with skill gains. In contrast, representational differentiation during practice (online) did not reflect learning. The regions contributing to this representational differentiation evolved with learning, shifting from the contralateral pre- and post-central cortex during early learning (trials 1-11) to increased involvement of the superior and middle frontal cortex once skill performance plateaued (trials 12-36). Thus, the neural substrates supporting finger movements and their representational differentiation during early skill learning differ from those supporting stable performance during the subsequent skill plateau period. Representational contextualization extended to Day 2, exhibiting specificity for the practiced skill sequence. Altogether, our findings indicate that sequence action representations contextually differentiate during early skill learning, an issue relevant to brain-computer interface applications in neurorehabilitation.

neuroscience↗

bioRxiv2024

Motor sequence learning-induced corticospinal plasticity is biased towards sensorimotor mu rhythm peak phases

Motor cortical (M1) transcranial magnetic stimulation (TMS) interventions increase corticospinal output and improve motor learning when delivered during sensorimotor mu rhythm trough but not peak phases, suggesting that the mechanisms supporting motor learning may be most active during mu trough phases. Based on these findings, we predicted that motor sequence learning-related corticospinal plasticity would be most evident when measured during mu trough phases. Healthy adults were assigned to either a sequence or no-sequence group. Participants in the sequence group practiced the implicit serial reaction time task (SRTT), which contained an embedded, repeating 12-item sequence. Participants in the no-sequence group practiced a version of the SRTT that contained no sequence. We measured mu phase-independent and mu phase-dependent MEP amplitudes using EEG-informed single-pulse TMS before, immediately after, and 30 minutes after the SRTT in both groups. All participants performed a retention test one hour after SRTT acquisition. In both groups, mu phase-independent MEP amplitudes increased following SRTT acquisition, but the pattern of mu phase-dependent MEP amplitude changes after SRTT acquisition differed between groups. Relative to the no-sequence group, the sequence group showed greater peak-specific MEP amplitude increases 30 minutes after SRTT acquisition. Further, the magnitude of these peak-specific MEP amplitude increases was negatively associated with the magnitude of sequence-specific learning. Contrary to our original hypothesis, results revealed that motor sequence-specific learning elicits peak-specific corticospinal plasticity. Findings provide first direct evidence for the presence of a mu phase-dependent motor learning mechanism in the human brain. New and NoteworthyRecent work suggests that motor learnings neural mechanisms may be most active during specific sensorimotor mu rhythm phases. If so, motor sequence learning-induced corticospinal plasticity should be more evident during some mu phases than others. Our results show that motor sequence-specific learning elicits corticospinal plasticity that is most prominent during mu peak phases. Further, this peak-specific plasticity correlates with learning. Findings establish the presence of a mu phase-dependent motor learning mechanism in the human brain.

neuroscience↗

bioRxiv2022

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Error-related potentials (ErrP) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. Although there exists an agreement that ErrP signal awareness to errors, it remains poorly understood whether they encode further information. Here we report an experiment with sixteen participants during three recording sessions in which occasional visuomotor rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrP in single trials that provided real-time feedback to participants by changing the color of the cursor upon ErrP detection. The individual ErrP-BCI decoders exhibited good transfer across recording sessions and scalability over the varying magnitude of errors. Our results indicate that ErrPs encode not only the conscious perception of errors, but also their magnitude, in their amplitude and latency. Furthermore, a non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect rotations. The uncovered relationship is consistent with non-human primate studies, which found a similar relationship between the size of errors and simple spike activity of Purkinje cells, and we conjecture a cerebellar contribution to ErrP. Our experimental setup and findings open new avenues to probe and extend current theories of performance monitoring, which are based on response conflict tasks, by incorporating continuous human-interaction tasks as well as analysis of the ErrP complex as a whole rather than individual peaks.

neuroscience↗

bioRxiv2022

EEG signature of breaks in embodiment in VR

The brain mechanism of embodiment in a virtual body has grown a scientific interest recently, with a particular focus on providing optimal virtual reality (VR) experiences. Disruptions from an embodied state to a less- or non-embodied state, denominated Breaks in Embodiment (BiE), are however rarely studied despite their importance for designing interactions in VR. Here we use electroencephalography (EEG) to monitor the brains reaction to a BiE, and investigate how this reaction depends on previous embodiment conditions. The experimental protocol consisted of two sequential steps; an induction step where participants were either embodied or non-embodied in an avatar, and a monitoring step where, in some cases, participants saw the avatars hand move while their hand remained still. Our results show the occurrence of error-related potentials linked to observation of the BiE event in the monitoring step. Importantly, this EEG signature shows amplified potentials following the non-embodied condition, which is indicative of an accumulation of errors across steps. These results provide neurophysiological indications on how progressive disruptions impact the expectation of embodiment for a virtual body.

neuroscience↗