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Yokoyama, H.

Publications and source records attributed to Yokoyama, H..

At least 19 recordsLinked to original sources

The mental conflict in risk-taking behavior: Decoding bias between optimism and pessimism

Humans and animals often face risky situations that require decision-making. Such decisions can be high-risk, high-return at some times, and low-risk, low-return at other times, depending on the balance between optimism and pessimism. However, how this optimism-pessimism bias is regulated across contexts remains unclear. Here, we introduced a computational model of decision-making in a risk-taking task based on the free-energy principle, together with a machine-learning framework that inversely estimates cognitive updating and optimism-pessimism bias from behavioral data. Applying this framework to monkey behavioral data, we found that a monkey quickly and accurately recognized the degree of risk, while frequently switching between optimism and pessimism during the task. In addition, we identified a characteristic control rule for optimism-pessimism bias that is distinct from reward-dependent regulation. Our framework provided a principled tool for understanding the latent cognitive processes underlying risky decision-making in animals and humans.

neuroscience↗

Causal Discovery of Synchronous Neural Oscillations based on Jacobian-informed VAR-LiNGAM

The primary objective of system neuroscience is to understand the functional mapping and its causation in the dynamics of the brain network. Some experimental and methodological studies suggest that functional modularity and its hierarchical information processing in the brain network are crucial to understanding the functional role of task-specific or state-specific information flow in the brain. However, because most of the established techniques for detecting effective network structures in the neuroscience research field are strongly based on the "Granger causality" perspective, existing causal discovery methods specified for brain network analysis cannot identify the causal hierarchy in the modular network in the brain due to spurious correlation issues and indistinguishability of causal direction under the Gaussianity of observational noise in a linear system. To address the issues, we developed a causal discovery method for synchronous neural dynamics, called the Jacobian-informed linear non-Gaussian acyclic model, "j-VAR-LiNGAM", by incorporating the information of the Jacobian matrix determined from a phase-coupled oscillator model estimated from observed neural data into the VAR-LiNGAM algorithms. The method was validated by showing that it could extract causal ordering in both synthetic data and empirical neural observed data. Moreover, by analyzing the observed neural oscillatory signals obtained from mice and humans, we confirmed that our method identified causally hierarchical structures in the brain, which aligned with the neurophysiological interpretations. These findings suggested that our proposed method can reveal the neural basis of hierarchical information processing in the brain network.

neuroscience↗

Distinct neural input strategies to motor units in the soleus and medial gastrocnemius during quiet standing

Standing posture control critically depends on the activation of the soleus (SOL) and medial gastrocnemius (MG), which serve distinct functional roles. Identifying underlying neural mechanisms has been challenging, as conventional invasive techniques sample only limited motor units (MUs). Recent advances in high-density surface electromyography (HDsEMG) have enabled analysis of MU activity and estimation of common synaptic inputs to spinal motoneurons. Therefore, we aimed to elucidate the common synaptic inputs underlying the distinct MU behaviors of SOL and MG during standing. We recorded HDsEMG from the SOL and MG, alongside electroencephalography, from 20 male participants during standing and isometric voluntary contractions. EMG signals were decomposed into individual MU activity, with common synaptic inputs estimated through intramuscular and corticomuscular coherence analyses (IMC and CMC). Compared to SOL, the MG exhibited significantly higher delta-, alpha-, and beta-band IMC during standing. In task comparisons, alpha-band IMC increased during standing specifically in the MG. Furthermore, although beta-band CMC decreased in both muscles while standing, IMC was preserved in the MG but markedly reduced in SOL. This dissociation suggests that the common neural drive to the MG during standing is likely derived from subcortical rather than cortical pathways. These results demonstrate that the SOL and MG are governed by distinct neural control strategies, which likely underlie their functional roles. Given the low CMC, the MG relies on strong common synaptic input from subcortical pathways (e.g., vestibulospinal and reticulospinal) to produce rapid corrective torque, whereas the SOL functions with lower neural synchrony to ensure steady ankle stiffness. Key pointsO_LIThe soleus and medial gastrocnemius play distinct roles in standing control, however, due to technical limitations, it has been difficult to identify the underlying neural mechanisms responsible for these differences. C_LIO_LIUsing high-density surface electromyography, we examined motor unit activity and neural inputs to these muscles during standing. C_LIO_LIThe medial gastrocnemius shows greater common synaptic input, potentially facilitating rapid ankle plantarflexion torque generation to correct postural sway. C_LIO_LIThe soleus exhibits lower motor unit synchrony, enabling stable and continuous ankle plantarflexion torque generation for body weight support. C_LIO_LIThis study demonstrates that the soleus and medial gastrocnemius are governed by distinct neural control strategies, which likely underlie their distinct functional roles. C_LI Abstract figure legend O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/698550v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@be53aorg.highwire.dtl.DTLVardef@f6793aorg.highwire.dtl.DTLVardef@190e8f9org.highwire.dtl.DTLVardef@af5e4b_HPS_FORMAT_FIGEXP M_FIG C_FIG Motor unit spike trains were decomposed from high-density surface electromyograms recorded from the medial gastrocnemius (MG; left; red) muscle and soleus (SOL; right; blue). To compare the neural input to the spinal motor neurons between them, we quantified the intramuscular coherence (IMC) of motor unit spike trains within the delta, alpha, and beta bands. MG exhibited greater IMC than SOL during standing, indicating stronger common synaptic input, likely mediated by vestibulospinal and reticulospinal pathways. In contrast, SOL showed lower IMC, suggesting a greater contribution of independent synaptic input. As a consequence, high motor-unit synchrony in the MG supports rapid, phasic torque generation for postural sway attenuation, whereas low synchrony in the SOL enables smooth, steady torque production for weight bearing during standing.

physiology↗

Numerical validation of 3D-VAR data assimilation for estimating network dynamics in multivariate EEGs

Data assimilation (DA) is widely used as a data-driven system identification method, enhancing our understanding of the generative mechanisms in the observational data based on the identified model and its parameters. Due to this advantage, DA has gradually begun to be applied in neuroscience studies. Recently, we introduced a DA-based method for reconstructing the internal state of the brain using the neural mass model and human scalp electroencephalography (EEG). This method allows us to estimate the balance of synaptic interactions between excitatory and inhibitory neurons (E/I balance) in the brain only from observed EEGs. Although we confirmed the neurophysiological validity of the proposed EEG-DA method in our recent works, this method cannot be applied to multivariate EEGs to parallelly infer the E/I balance changes and underlying network dynamics. In this study, to address this issue, we proposed an extended version of our DA method specified to multivariate observed EEGs. This method enables us to estimate sensor-level functional network and E/I balance changes of each EEG sensor in parallel. The method was validated by showing that it could parallelly estimate sensor-level network structures and E/I balance changes from synthetic multivariate EEGs. The results of this study indicate that it has the potential to quantify how the E/I balance changes functionally affect the brain network dynamics.

neuroscience↗

Excessive chondrogenesis in the blastema initiates during hypomorphic limb regeneration in Xenopus froglet, but not during patterned limb regeneration in Xenopus tadpoles and newts

Xenopus laevis tadpoles and newts regenerate a limb almost completely after amputation, with recapitulation of the patten formation of the limb. Metamorphosed Xenopus froglets form a cone-shaped regenerating blastema, similar to tadpoles and newts, but ultimately regenerate only a hypomorphic cartilaginous spike. Previous study suggested that excessive chondrogenesis, distinct from Xenopus tadpoles, may occur in the regenerating limb of a froglet and may prevent pattern formation during regeneration. However, it remains unclear whether excessive chondrogenesis actually occurs in froglet blastemas. If it does, when does it initiate and how does it progress in the blastemas? To answer these questions, we examined the extent of chondrogenesis in regenerating blastemas which have the common morphological shapes observed in newts (Pleurodeles waltl), Xenopus laevis tadpoles, and froglets. To evaluate excessive chondrogenesis, we developed a simplified procedure using immunofluorescence for cartilage markers (Sox9 or Col2a1) and quantitative image analysis. Our analysis revealed that signs of excessive chondrogenesis were detected not in newts and tadpoles but in froglets blastemas. During limb regeneration in froglets, the first sign of excessive chondrogenesis was detected in the cone-shaped blastema at the medium bud (MB) stage, and excessive chondrogenesis progressed to a more severe state as the blastema grew. These results indicate that excessive chondrogenesis initiates specifically in froglet blastemas at the MB stage at the latest and progresses in a definite spatio-temporal manner. Further elucidation of the mechanisms underlying froglet-specific excessive chondrogenesis in the blastema may lead to the recovery of patterned limb regeneration in froglets with adequate inhibition of chondrogenesis.

developmental biology↗

Validation of EEG data assimilation-based prefrontal excitation-inhibition balance estimation using TMS-EEG

The excitation and inhibition (E/I) balance of neural circuits is a crucial index reflecting neurophysiological homeostasis. Although several cutting-edge methods have been established to assess E/I balance in an intact brain, they have inherent limitations, such as difficulties in tracking changes in E/I balance over time. To tackle this issue, we have proposed neural-mass-model-based tracking of the brain states using a data assimilation (DA) scheme in our previous work. However, although we verified that sleep-dependent changes in E/I balance can be estimated from electroencephalography data, the neurophysiological validity of the method was not evaluated. Therefore, in the current study, we directly compared estimated E/I states based on the DA methods with the concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) based methods. The results showed that the E/I changes estimated by the DA-based method correlated significantly with E/I in the dorsolateral prefrontal cortex, as indexed by TMS-evoked EEG. These findings indicate that our proposed method can estimate neurophysiologically valid changes in E/I balance.

neuroscience↗

Modulation of cortico-muscular coupling associated with split-belt locomotor adaptation

Humans can adjust their walking patterns according to the demands of their internal and external environments, referred to as locomotor adaptation. Although significant functional coupling (i.e. cortico-muscular coherence [CMC]) has been shown between cortical and lower-limb muscle activity during steady-state walking, little is known about CMC in locomotor adaptation. Therefore, we investigated the adaptation-dependent modulation of the CMC between the sensorimotor region and the tibialis anterior muscle using a split-belt locomotor adaptation paradigm that can impose an asymmetric perturbation. We hypothesized that the CMC would temporarily decrease after exposure to the asymmetric perturbation and removal of the perturbation because of a mismatch between the predicted and actual sensory feedback. We also hypothesized that the CMC would increase as adaptation and de-adaptation to perturbation progressed because the motor system could become able to predict sensory feedback. Our findings revealed that the CMC temporarily decreased after exposure to and removal of the perturbation. Moreover, the CMC increased with adaptation and de-adaptation to perturbation. Although these results depend on the leg, frequency bands, and gait phases, they partially support our hypothesis. These findings suggest that flexible updating of cortico-muscular coupling in the motor system is a key mechanism underlying locomotor adaptation in humans. The results from our study on healthy young individuals contribute to the understanding of neuromuscular control of gait and provide valuable insight for optimising gait rehabilitation. Key pointsO_LILocomotor adaptation plays a crucial role in our daily activities and gait rehabilitation. C_LIO_LIAlthough knowledge regarding the brain and muscle activities associated with locomotor adaptation has been accumulated, little is known about the functional coupling of the brain and muscle activities. C_LIO_LIUsing high-density EEG and lower limb EMG, we demonstrated the modulation of cortico-muscular coherence between the sensorimotor region and the tibialis anterior muscle with adaptation and de-adaptation during a split-belt treadmill walking paradigm. C_LIO_LIOur findings suggest that flexible updating of cortico-muscular coupling in the motor system underlies locomotor adaptation in humans. C_LIO_LIUnderstanding the human brains control of muscles during split-belt locomotor adaptation will deepen our knowledge of neuromuscular control of gait and provide valuable insights for gait rehabilitation. C_LI

neuroscience↗

Accurate localization of cortical and subcortical sources of M/EEG signals by a convolutional neural network with a realistic head conductivity model: Validation with M/EEG simulation, evoked potentials, and invasive recordings

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established non-invasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Particularly challenging is the accurate localization of subcortical sources of M/EEG, which remains a subject of debate. To address this issue, we propose a four-layered convolutional neural network (4LCNN) designed to precisely locate both cortical and subcortical source activity underlying M/EEG signals. The 4LCNN was trained using a vast dataset generated by forward M/EEG simulations based on a realistic head volume conductor model. The 4LCNN implicitly learns the characteristics of M/EEG and their sources from the training data without need for explicitly formulating and fine-tuning optimal priors, a common challenge in conventional M/EEG source imaging techniques. We evaluated the efficacy of the 4LCNN model on a validation dataset comprising forward M/EEG simulations and two types of real experimental data from humans: 1) somatosensory evoked potentials recorded by EEG, and 2) simultaneous recordings from invasive electrodes implanted in the brain and MEG signals. Our results demonstrate that the 4LCNN provides robust and superior estimation accuracy compared to conventional M/EEG source imaging methods, aligning well with established neuroscience knowledge. Notably, the accuracy of the subcortical regions was as accurate as that of the cortical regions. The 4LCNN method, as a data-driven approach, enables accurate source localization of M/EEG signals, including in subcortical regions, suggesting future contributions to various research endeavors such as contributions to the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases and basic brain functions.

neuroscience↗

SART1 localizes to spindle poles forming a SART1 cap and promotes spindle pole assembly

The nuclear protein SART1 has been associated with pre-mRNA splicing but SART1 RNAi knockdown results also in defects in mitotic progression, centrosome biogenesis and chromosome cohesion. The mitotic roles of SART1 have not been characterized in detail and it remains unclear whether SART1 functions in mitosis directly or indirectly via pre-mRNA splicing. Here, we identify SART1 as a direct, mitosis-specific microtubule-associated protein. SART1 downregulation in human cells leads to spindle assembly defects with reduced microtubule dynamics, lack of end-on attachment, and checkpoint activation, while microtubule dynamics remain unaffected in interphase. SART1 uniquely localizes to the distal surface of mitotic centrosomes along the spindle axis, forming a previously not described structure we refer to as SART1 cap. Immunoprecipitation of SART1 consistently identifies centrosomal proteins as interaction partners. Immunostaining of these shows that SART1 downregulation does not affect centriole duplication and centrosome-accumulation of {gamma}-tubulin but reduces the accumulation of selective pericentriolar material (PCM) proteins like Ninein. Depletion of SART1 from frog egg extracts disrupts spindle pole assembly around sperm nuclei and DNA-coated beads. Spindles formed around DNA-coated beads do not contain centrosomes but still recruits PCM proteins for spindle pole assembly. We finally show that the N-terminus of SART1 is its microtubule-binding region and essential for spindle assembly. Our data unravel a unique localization of SART1 and a novel function to recruit selective PCM proteins for spindle pole assembly in centrosomal and acentrosomal spindle assembly.

cell biology↗

Neuromechanical Justification of Lower-Limb Functional Tests for a Return to Running: A Muscle Coordination Analysis

IntroductionThis study aimed to explore the shared muscle synergies between running and functional tests that are commonly used when considering the return to running (RTR) after sports-related injuries. We hypothesized that shared muscle synergies would differ among tasks, providing insights into prioritizing functional tests in the context of RTR. MethodsTen healthy male participants were recruited to perform running and 9 functional tasks and their 16 lower limb and trunk muscle activities were recorded using electromyography (EMG). Non-negative matrix factorization (NMF) was applied to the collected EMG data to explore shared muscle synergies between running and the functional tasks. We compared the percentages of shared synergies and temporal patterns between running and each functional test. ResultsAlthough all functional tests exhibited shared muscle synergies with running, the walk (75% [40%-100%]), single leg hops with 30% of maximum distance (SLH30) (60% [20%-100%]), and stepup (63% [0%-100%]) tasks displayed significantly higher percentages of shared synergies compared to other tests. However, significant differences in temporal patternss were observed between running and all functional tasks, indicating varying activation profiles of shared muscle synergies. ConclusionAlthough all functional tests shared muscle synergies with running, variations in the degree of shared synergies and temporal patterns imply that walking, SLH30, and step-up tests may be the most beneficial in predicting running behavior post-ACL injuries. However, functional tests cannot fully replicate running dynamics, suggesting the need for a careful interpretation when assessing readiness for RTR.

neuroscience↗

The shh limb enhancer is activated in patterned limb regeneration but not in hypomorphic limb regeneration in Xenopus laevis

Xenopus young tadpoles regenerate a limb with the anteroposterior (AP) pattern, but metamorphosed froglets regenerate a hypomorphic limb after amputation. The key gene for AP patterning, shh, is expressed in a regenerating limb of the tadpole but not in that of the froglet. Genomic DNA in the shh limb-specific enhancer, MFCS1 (ZRS), is hypermethylated in froglets but hypomethylated in tadpoles: shh expression may be controlled by epigenetic regulation of MFCS1. Is MFCS1 specifically activated for regenerating the AP-patterned limb? We generated transgenic Xenopus laevis lines that visualize the MFCS1 enhancer activity with a GFP reporter. The transgenic tadpoles showed GFP expression in hoxd13- and shh-expressing domains of developing and regenerating limbs, whereas the froglets showed no GFP expression in the regenerating limbs despite having hoxd13 expression. Genome sequence analysis and co-transfection assays using cultured cells revealed that Hoxd13 can activate Xenopus MFCS1. These results suggest that MFCS1 activation correlates with regeneration of AP-patterned limbs and that re-activation of epigenetically inactivated MFCS1 would be crucial to confer the ability to non-regenerative animals for regenerating a properly patterned limb.

developmental biology↗

A data assimilation method to track time-varying changes in the excitation-inhibition balance using scalp EEG

Recent neuroscience studies have suggested that controlling the excitation and inhibition (E/I) balance is essential for maintaining normal brain function. However, while control of time-varying E/I balance is considered essential for perceptual and motor learning, an efficient method for estimating E/I balance changes has yet to be established. To tackle this issue, we propose a new method to estimate E/I balance changes by applying neural-mass model-based tracking of the brain state using the Ensemble Kalman Filter. In this method, the parameters of synaptic E/I gains in the model are estimated from observed electroencephalography (EEG) signals. Moreover, the index of E/I balance was defined by calculating the ratio between synaptic E/I gains based on estimated parameters. The method was validated by showing that it could estimate E/I balance changes from human EEG data at the sub-second scale, indicating that it has the potential to quantify how time-varying changes in E/I balance influence changes in perceptual and motor learning. Furthermore, this method could be used to develop an E/I balance-based neurofeedback training method for clinical use.

neuroscience↗

Firing behavior of single motor neurons of the tibialis anterior in human walking as non-invasively revealed by HDsEMG decomposition

Investigation of the firing behavior of spinal motor neurons (MNs) provides essential neuromuscular control information because MNs form the "final common pathway" in motor control. The MNs activated during human infants leg movements and rodent locomotion, mainly controlled by the spinal central pattern generator (CPG), show highly synchronous firing. In addition to spinal CPGs, the cerebral cortex is involved in neuromuscular control during walking in human adults. Thus, MN firing behavior during adult walking is expected to be similar to that of infants and rodents and has some unique features. Recent technical advances allow non-invasive investigation of MN firing by high-density surface electromyogram (HDsEMG) decomposition. Therefore, we investigated the MN firing behavior of the tibialis anterior muscle during walking by HDsEMG decomposition. We found motor unit recruitment modulation compared with steady isometric contractions, doublet firings, and gait phase-specific firings during walking. We also found high MN synchronization during walking over a wide range of frequencies, probably including cortical and spinal CPG-related components. The amount of MN synchronization was modulated between the gait phases and motor tasks. These results suggest that the central nervous system, including the spinal CPG and cerebral cortex, flexibly controls MN firing to generate appropriate muscle force during human walking. In addition to revealing the neural control mechanisms of walking, our data demonstrate the feasibility of non-invasive investigation of MNs during walking, which will open new frontiers for the study of neuromuscular function in medical and exercise sciences.

neuroscience↗

Flexible recruitments of fundamental muscle synergies in the trunk and lower limbs for highly variable movements and postures

The extent to which muscle synergies represent the neural control of human behavior remains unknown. Here, we tested whether certain sets of muscle synergies that are fundamentally necessary across behaviors exist. We measured the electromyographic activities of 26 muscles including bilateral trunk and lower limb muscles during 24 locomotion, dynamic and static stability tasks, and extracted the muscle synergies using non-negative matrix factorization. Our results showed that 13 muscle synergies that may have unique functional roles accounted for almost all 24 tasks by combinations of single and/or merging of synergies. Therefore, our results may support the notion of the low dimensionality in motor outputs, in which the central nervous system flexibly recruits fundamental muscle synergies to execute diverse human behaviors. Further studies using manipulations of the central nervous system and/or neural recording are required the neural representation with such fundamental components of muscle synergies.

neuroscience↗

Application of augmented topic model to predicting biomarkers and therapeutic targets using multiple human disease-omics datasets

Human diseases are characterized by multiple features such as their pathophysiological, molecular, and genetic changes. The rapid expansion of such multi-modal disease-omics space provides an opportunity to re-classify diverse human diseases and uncover their latent molecular similarities, which could be exploited to repurpose a therapeutic-target for one disease to another. Herein, we probe this underexplored space by soft-clustering 6,955 human diseases by multi-modal generative topic-modeling. Focusing on chronic kidney disease and myocardial infarction, two most life-threatening diseases, unveiled are their previously underrecognized molecular similarities to neoplasia and mental/neurological-disorders, and 69 repurposable therapeutic-targets for these diseases. Using an edit-distance based pathway-classifier, we also find molecular pathways by which these targets could elicit their clinical effects. Importantly, for the 17 targets, the evidence for their therapeutic usefulness is retrospectively found in the pre-clinical and clinical space, illustrating the effectiveness of the method, and suggesting its broader applications across diverse human diseases.

bioinformatics↗

Early Dynamics of Chronic Myeloid Leukemia on Nilotinib Predicts Deep Molecular Response

Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase1,2. ABL1-selective tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML3-7. The ultimate goal of CML treatment is likely to be TKI-free maintenance of deep molecular response (DMR), which is defined by quantitative measurement of BCR-ABL1 transcripts on the international scale (IS)8, and durable DMR is a prerequisite to reach this goal9. Thus, an algorithm to enable the early prediction of DMR achievement on TKI therapy is quite valuable. Here, we show that our mathematical framework based on a clinical trial dataset10 can accurately predict the response to frontline nilotinib. We found that our simple dynamical model can predict nilotinib response by using two common laboratory findings (observation values): IS11,12 and white blood cell (WBC) count. Furthermore, our proposed method identified patients who failed to achieve DMR with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs in clinical practice. Significance StatementChronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. The goal of this treatment is the sequential achievement of deep molecular response (DMR). Tyrosine kinase inhibitors (TKIs) are effective in the reduction because they inhibit CML cell proliferation. However, because of individual differences in the TKI efficacy, some patients are unable to achieve DMR, so that early prediction of DMR reachability is necessary for personalized medicine. By combining time series analysis and mathematical modeling, we developed a mathematical method that accurately predicts patients who do not achieve DMR in the early stages of TKI administration. Our prediction method gives a basis of effective personalized treatments for CML patients.

systems biology↗

Detection of the changes in dynamical structures insynchronous neural oscillations from a viewpoint of probabilistic inference

Recent neuroscience studies have suggested that cognitive functions and learning capacity are reflected in the time-evolving dynamics of brain networks. However, an efficient method to detect changes in dynamical brain structures using neural data has yet to be established. To address this issue, we developed a new model-based approach to detect change points in dynamical network structures by combining the model-based network estimation with a phase-coupled oscillator model and sequential Bayesian inference. By giving the model parameter as the prior distribution, applying Bayesian inference allows the extent of temporal changes in dynamic brain networks to be quantified by comparing the prior distribution with posterior distribution using information theoretical criteria. For this, we used the Kullback-Leibler divergence as an index of such changes. To validate our method, we applied it to numerical data and electroencephalography data. As a result, we confirmed that the Kullback-Leibler divergence only increased when changes in dynamical network structures occurred. Our proposed method successfully estimated both directed network couplings and change points of dynamical structures in the numerical and electroencephalography data. These results suggest that our proposed method can reveal the neural basis of dynamic brain networks.

neuroscience↗

Neural decoding of gait phase information during motor imagery and improvement of the decoding accuracy by concurrent action observation

Brain decoding of motor imagery (MI) is crucial for the control of neuroprosthesis, and it provides insights into the underlying neural mechanisms. Walking consists of stance and swing phases, which are associated with different biomechanical and neural control features. However, previous studies on the decoding of the MI of walking focused on the classification of more simple information (e.g., walk and rest). Here, we investigated the feasibility of electroencephalogram (EEG) decoding of the two gait phases during the MI of walking and whether the combined use of MI and action observation (AO) would improve decoding accuracy. We demonstrated that the stance and swing phases could be decoded from EEGs during AO or MI alone. Additionally, the combined use of MI and AO improved decoding accuracy. The decoding models indicated that the improved decoding accuracy following the combined use of MI and AO was facilitated by the additional information resulting from the concurrent cortical activations by multiple regions associated with MI and AO. This study is the first to show that decoding the stance versus swing phases during MI is feasible. The current findings provide fundamental knowledge for neuroprosthetic design and gait rehabilitation, and they expand our understanding of the neural activity underlying AO, MI, and AO+MI of walking. Significance StatementBrain decoding of detailed gait-related information during motor imagery (MI) is important for brain-computer interfaces (BCIs) for gait rehabilitation. However, previous knowledge on decoding the motor imagery of gait is limited to simple information (e.g., the classification of "walking" and "rest"). Here, we demonstrated the feasibility of EEG decoding of the two gait phases during MI. We also demonstrated that the combined use of MI and action observation (AO) improves decoding accuracy, which is facilitated by the concurrent and synergistic involvement of the cortical activations by multiple regions for MI and AO. These findings extend the current understanding of neural activity and the combined effects of AO and MI and provide a basis for developing effective techniques for walking rehabilitation.

neuroscience↗