Temporal Electroencephalography Traits Dissociating Tactile Information and Cross-Modal Congruence Effects.

To explore whether temporal electroencephalography (EEG) traits can dissociate the physical properties of touching objects and the congruence effects of cross-modal stimuli, we applied a machine learning approach to two major temporal domain EEG traits, event-related potential (ERP) and somatosensory evoked potential (SEP), for each anatomical brain region. During a task in which participants had to identify one of two material surfaces as a tactile stimulus, a photo image that matched ('congruent') or mismatched ('incongruent') the material they were touching was given as a visual stimulus. Electrical stimulation was applied to the median nerve of the right wrist to evoke SEP while the participants touched the material. The classification accuracies using ERP extracted in reference to the tactile/visual stimulus onsets were significantly higher than chance levels in several regions in both congruent and incongruent conditions, whereas SEP extracted in reference to the electrical stimulus onsets resulted in no significant classification accuracies. Further analysis based on current source signals estimated using EEG revealed brain regions showing significant accuracy across conditions, suggesting that tactile-based object recognition information is encoded in the temporal domain EEG trait and broader brain regions, including the premotor, parietal, and somatosensory areas.

Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals.

This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of a low-pass filter were changed. The participants attempted to grab a bottle by controlling a robot. They were asked to evaluate four indicators (stability, imitation, response time, and movement speed) and indicate their satisfaction at the end of each trial by completing a questionnaire. The electroencephalography signals of the participants were recorded while they controlled the robot and responded to the questionnaire. Two independent component clusters in the precuneus and postcentral gyrus were the most sensitive to subjective evaluations. For the moment that dominated satisfaction, we observed that brain activity exhibited significant differences in satisfaction not immediately after feeding an input but during the later stage. The other indicators exhibited independently significant patterns in event-related spectral perturbations. Comparing these indicators in a low-frequency band related to the satisfaction with imitation and movement speed, which had significant differences, revealed that imitation covered significant intervals in satisfaction. This implies that imitation was the most important contributing factor among the four indicators. Our results reveal that regardless of subjective satisfaction, objective performance evaluation might more fully reflect user satisfaction.

Generic Rotating-Frame-Based Approach to Chaos Generation in Nonlinear Micro- and Nanoelectromechanical System Resonators.

This Letter provides a low-power method for chaos generation that is generally applicable to nonlinear micro- and nanoelectromechanical systems (MNEMS) resonators. The approach taken is independent of the material, scale, design, and actuation of the device in question; it simply assumes a good quality factor and a Duffing type nonlinearity, features that are commonplace to MNEMS resonators. The approach models the rotating-frame dynamics to analytically constrain the parameter space required for chaos generation. By leveraging these common properties of MNEMS devices, a period-doubling route to chaos is generated using smaller forcing than typically reported in the literature.

Generation of diverse insect-like gait patterns using networks of coupled Rössler systems.

The generation of walking patterns is central to bio-inspired robotics and has been attained using methods encompassing diverse numerical as well as analog implementations. Here, we demonstrate the possibility of synthesizing viable gaits using a paradigmatic low-dimensional non-linear entity, namely, the Rössler system, as a dynamical unit. Through a minimalistic network wherein each instance is univocally associated with one leg, it is possible to readily reproduce the canonical gaits as well as generate new ones via changing the coupling scheme and the associated delays. Varying levels of irregularity can be introduced by rendering individual systems or the entire network chaotic. Moreover, through tailored mapping of the state variables to physical angles, adequate leg trajectories can be accessed directly from the coupled systems. The functionality of the resulting generator was confirmed in laboratory experiments by means of an instrumented six-legged ant-like robot. Owing to their simple form, the 18 coupled equations could be rapidly integrated on a bare-metal microcontroller, leading to the demonstration of real-time robot control navigating an arena using a brain-machine interface.

Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory.

Muscle functional MRI (mfMRI) is an imaging technique that assess muscles' activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of the method our group introduced in to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method's validity showing its potential in diagnostic and rehabilitation fields.

Warped phase coherence: An empirical synchronization measure combining phase and amplitude information.

The entrainment between weakly coupled nonlinear oscillators, as well as between complex signals such as those representing physiological activity, is frequently assessed in terms of whether a stable relationship is detectable between the instantaneous phases extracted from the measured or simulated time-series via the analytic signal. Here, we demonstrate that adding a possibly complex constant value to this normally null-mean signal has a non-trivial warping effect. Among other consequences, this introduces a level of sensitivity to the amplitude fluctuations and average relative phase. By means of simulations of Rössler systems and experiments on single-transistor oscillator networks, it is shown that the resulting coherence measure may have an empirical value in improving the inference of the structural couplings from the dynamics. When tentatively applied to the electroencephalogram recorded while performing imaginary and real movements, this straightforward modification of the phase locking value substantially improved the classification accuracy. Hence, its possible practical relevance in brain-computer and brain-machine interfaces deserves consideration.

Dissociable neural representations of wrist motor coordinate frames in human motor cortices.

There is a growing interest in how the brain transforms body part positioning in the extrinsic environment into an intrinsic coordinate frame during motor control. To explore the human brain areas representing intrinsic and extrinsic coordinate frames, this fMRI study examined neural representation of motor cortices while human participants performed isometric wrist flexions and extensions in different forearm postures, thereby applying the same wrist actions (representing the intrinsic coordinate frame) to different movement directions (representing the extrinsic coordinate frame). Using sparse logistic regression, critical voxels involving pattern information that specifically discriminates wrist action (flexion vs. extension) and movement direction (upward vs. downward) were identified within the primary motor and premotor cortices. Analyses of classifier weights further identified contributions of the primary motor cortex to the intrinsic coordinate frame and the ventral and dorsal premotor cortex and supplementary motor area proper to the extrinsic coordinate frame. These results are consistent with existing findings using non-human primates and demonstrate the distributed representations of independent coordinate frames in the human brain.

Suppression of the novel ER protein Maxer by mutant ataxin-1 in Bergman glia contributes to non-cell-autonomous toxicity.

Non-cell-autonomous effect of mutant proteins expressed in glia has been implicated in several neurodegenerative disorders, whereas molecules mediating the toxicity are currently not known. We identified a novel molecule named multiple alpha-helix protein located at ER (Maxer) downregulated by mutant ataxin-1 (Atx1) in Bergmann glia. Maxer is an endoplasmic reticulum (ER) membrane protein interacting with CDK5RAP3. Maxer anchors CDK5RAP3 to the ER and inhibits its function of Cyclin D1 transcription repression in the nucleus. The loss of Maxer eventually induces cell accumulation at G1 phase. It was also shown that mutant Atx1 represses Maxer and inhibits proliferation of Bergmann glia in vitro. Consistently, Bergmann glia are reduced in the cerebellum of mutant Atx1 knockin mice before onset. Glutamate-aspartate transporter reduction in Bergmann glia by mutant Atx1 and vulnerability of Purkinje cell to glutamate are both strengthened by Maxer knockdown in Bergmann glia, whereas Maxer overexpression rescues them. Collectively, these results suggest that the reduction of Maxer mediates functional deficiency of Bergmann glia, and might contribute to the non-cell-autonomous pathology of SCA1.

Proteome analysis of soluble nuclear proteins reveals that HMGB1/2 suppress genotoxic stress in polyglutamine diseases.

Nuclear dysfunction is a key feature of the pathology of polyglutamine (polyQ) diseases. It has been suggested that mutant polyQ proteins impair functions of nuclear factors by interacting with them directly in the nucleus. However, a systematic analysis of quantitative changes in soluble nuclear proteins in neurons expressing mutant polyQ proteins has not been performed. Here, we perform a proteome analysis of soluble nuclear proteins prepared from neurons expressing huntingtin (Htt) or ataxin-1 (AT1) protein, and show that mutant AT1 and Htt similarly reduce the concentration of soluble high mobility group B1/2 (HMGB1/2) proteins. Immunoprecipitation and pulldown assays indicate that HMGBs interact with mutant AT1 and Htt. Immunohistochemistry showed that these proteins were reduced in the nuclear region outside of inclusion bodies in affected neurons. Compensatory expression of HMGBs ameliorated polyQ-induced pathology in primary neurons and in Drosophila polyQ models. Furthermore, HMGBs repressed genotoxic stress signals induced by mutant Htt or transcriptional repression. Thus, HMGBs may be critical regulators of polyQ disease pathology and could be targets for therapy development.

Classification of pleasantness of wind by electroencephalography.

Thermal comfort of humans depends on the surrounding environment and affects their productivity. Several environmental factors, such as air temperature, relative humidity, wind or airflow, and radiation, have considerable influence on the thermal comfort or pleasantness; hence, these are generally controlled by electrical devices. Lately, the development of objective measurement methods for thermal comfort or pleasantness using physiological signals is receiving attention to realize a personalized comfortable environment through the automatic control of electrical devices. In this study, we focused on electroencephalography (EEG) and investigated whether EEG signals contain information related to the pleasantness of ambient airflow reproducing natural wind fluctuations using machine learning methods. In a hot and humid artificial climate chamber, we measured EEG signals while the participants were exposed to airflow at four different velocities. Based on the reported pleasantness levels, we performed within-participant classification from the source activity of the EEG and obtained a classification accuracy higher than the chance level using both linear and nonlinear support vector machine classifiers as well as an artificial neural network. The results of this study showed that EEG is useful in identifying people's transient pleasantness when exposed to wind.

Partial maximum correntropy regression for robust electrocorticography decoding.

The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably prone to the performance deterioration caused by the brain recording noises. To address this problem, this study aims to propose a new robust variant for PLSR. To this end, the maximum correntropy criterion (MCC) is utilized to propose a new robust implementation of PLSR, called Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point optimization method. The proposed PMCR is evaluated with a synthetic example and a public electrocorticography dataset under three performance indicators. For the synthetic example, PMCR realized better prediction results compared with the other existing methods. PMCR could also abstract valid information with a limited number of decomposition factors in a noisy regression scenario. For the electrocorticography dataset, PMCR achieved superior decoding performance in most cases, and also realized the minimal neurophysiological pattern deterioration with the interference of the noises. The experimental results demonstrate that, the proposed PMCR could outperform the existing methods in a noisy, inter-correlated, and high-dimensional decoding task. PMCR could alleviate the performance degradation caused by the adverse noises and ameliorate the electrocorticography decoding robustness for the brain-computer interface.

Classification of Parkinson's disease from smartphone recording data using time-frequency analysis and convolutional neural network.

BACKGROUND: Parkinson's disease (PD) is a long-term neurodegenerative disease of the central nervous system. The current diagnosis is dependent on clinical observation and the abilities and experience of a trained specialist. One of the symptoms that affects most patients is voice impairment. OBJECTIVE: Voice samples are non-invasive data that can be collected remotely for diagnosis and disease progression monitoring. In this study, we analyzed voice recording data from a smartphone as a possible medical self-diagnosis tool by using only one-second voice recording. The data from one of the largest mobile PD studies, the mPower study, was used. METHODS: A total of 29,798 ten-second voice recordings on smartphone from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by participants saying /aa/ for ten seconds into an iPhone microphone. A dataset comprising 385,143 short one-second audio samples was generated from the original ten-second voice recordings. The samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. RESULTS: Classification accuracies of the proposed method with LeNet-5, ResNet-50, and VGGNet-16 are 97.7 ± 0.1%, 98.6 ± 0.2%, and 99.3 ± 0.1%, respectively. CONCLUSIONS: We achieve a respectable classification performance using a generalized approach on a dataset with a large number of samples. The result emphasizes that an analysis based on one-second clip recorded on a smartphone could be a promising non-invasive and remotely available PD biomarker.

Correntropy-Based Logistic Regression With Automatic Relevance Determination for Robust Sparse Brain Activity Decoding.

OBJECTIVE: Recent studies have used sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's mental states and intentions, selecting the relevant features automatically in the model training process. However, existing sparse classification models will likely be prone to the performance degradation which is caused by the noise inherent in the brain recordings. To address this issue, we aim to propose a new robust and sparse classification algorithm in this study. METHODS: To this end, we introduce the correntropy learning framework into the automatic relevance determination based sparse classification model, proposing a new correntropy-based robust sparse logistic regression algorithm. To demonstrate the superior brain activity decoding performance of the proposed algorithm, we evaluate it on a synthetic dataset, an electroencephalogram (EEG) dataset, and a functional magnetic resonance imaging (fMRI) dataset. RESULTS: The extensive experimental results confirm that not only the proposed method can achieve higher classification accuracy in a noisy and high-dimensional classification task, but also it would select those more informative features for the decoding tasks. CONCLUSION: Integrating the correntropy learning approach with the automatic relevance determination technique will significantly improve the robustness with respect to the noise, leading to more adequate robust sparse brain decoding algorithm. SIGNIFICANCE: It provides a more powerful approach in the real-world brain activity decoding and the brain-computer interfaces.

Differential processing of intrinsic vs. extrinsic coordinates in wrist movement: connectivity and chronometry perspectives.

This study explores brain-network differences between the intrinsic and extrinsic motor coordinate frames. A connectivity model showing the coordinate frames difference was obtained using brain fMRI data of right wrist isometric flexions and extensions movements, performed in two forearm postures. The connectivity model was calculated by machine-learning-based neural representation and effective functional connectivity using psychophysiological interaction and dynamic causal modeling analyses. The model indicated the network difference wherein the inferior parietal lobule receives extrinsic information from the rostral lingual gyrus through the superior parietal lobule and transmits intrinsic information to the Handknob, whereas extrinsic information is transmitted to the Handknob directly from the rostral lingual gyrus. A behavioral experiment provided further evidence on the difference between motor coordinate frames showing onset timing delay of muscle activity of intrinsic coordinate-directed wrist movement compared to extrinsic one. These results suggest that, if the movement is externally directed, intrinsic coordinate system information is bypassed to reach the primary motor area.

Reconstruction of flexor and extensor muscle activities from electroencephalography cortical currents.

The ability to reconstruct muscle activity time series from electroencephalography (EEG) may lead to drastic improvements in brain-machine interfaces (BMIs) by providing a means for realistic continuous reproduction of dexterous movements in human beings. However, it is considered difficult to isolate signals related to individual muscle activities from EEG because EEG sensors record a mixture of signals originating from many cortical regions. Here, we challenge this assumption by reconstructing agonist and antagonist muscle activities (i.e. filtered electromyography (EMG) signals) from EEG cortical currents estimated using a hierarchical Bayesian EEG inverse method. Results of 5 volunteer subjects performing isometric right wrist flexion and extension tasks showed that individual muscle activity time series, as well as muscle activities at different force levels, were well reconstructed using EEG cortical currents and with significantly higher accuracy than when directly reconstructing from EEG sensor signals. Moreover, spatial distribution of weight values for reconstruction models revealed that highly contributing cortical sources to flexion and extension tasks were mutually exclusive, even though they were mapped onto the same cortical region. These results suggest that EEG sensor signals were reasonably isolated into cortical currents using the applied method and provide the first evidence that agonist and antagonist muscle activity time series can be reconstructed using EEG cortical currents.

Restricted Minimum Error Entropy Criterion for Robust Classification.

The minimum error entropy (MEE) criterion is a powerful approach for non-Gaussian signal processing and robust machine learning. However, the instantiation of MEE on robust classification is a rather vacancy in the literature. The original MEE purely focuses on minimizing Renyi's quadratic entropy of the prediction errors, which could exhibit inferior capability in noisy classification tasks. To this end, we analyze the optimal error distribution with adverse outliers and introduce a specific codebook for restriction, which optimizes the error distribution toward the optimal case. Half-quadratic-based optimization and convergence analysis of the proposed learning criterion, called restricted MEE (RMEE), are provided. The experimental results considering logistic regression and extreme learning machine on synthetic data and UCI datasets, respectively, are presented to demonstrate the superior robustness of RMEE. Furthermore, we evaluate RMEE on a noisy electroencephalogram dataset, so as to strengthen its practical impact.

An fMRI-study of leading and following using rhythmic tapping.

Leading and following is about synchronizing and joining actions in accordance with the differences that the leader and follower roles provide. The neural reactivity representing these roles was measured in an explorative fMRI-study, where two persons lead and followed each other in finger tapping using simple, individual, pre-learnt rhythms. All participants acted both as leader and follower. Neural reactivity for both lead and follow related to social awareness and adaptation distributed over the lateral STG, STS and TPJ. Reactivity for follow contrasted with lead mostly reflected sensorimotor and rhythmic processing in cerebellum IV, V, somatosensory cortex and SMA. During leading, as opposed to following, neural reactivity was observed in the insula and bilaterally in the superior temporal gyrus, pointing toward empathy, sharing of feelings, temporal coding and social engagement. Areas for continuous adaptation, in the posterior cerebellum and Rolandic operculum, were activated during both leading and following. This study indicated mutual adaptation of leader and follower during tapping and that the roles gave rise to largely similar neuronal reactivity. The differences between the roles indicated that leading was more socially focused and following had more motoric- and temporally related neural reactivity.

Drosophila PQBP1 regulates learning acquisition at projection neurons in aversive olfactory conditioning.

Polyglutamine tract-binding protein-1 (PQBP1) is involved in the transcription-splicing coupling, and its mutations cause a group of human mental retardation syndromes. We generated a fly model in which the Drosophila homolog of PQBP1 (dPQBP1) is repressed by insertion of piggyBac. In classical odor conditioning, learning acquisition was significantly impaired in homozygous piggyBac-inserted flies, whereas the following memory retention was completely normal. Mushroom bodies (MBs) and antennal lobes were morphologically normal in dPQBP1-mutant flies. Projection neurons (PNs) were not reduced in number and their fiber connections were not changed, whereas gene expressions including NMDA receptor subunit 1 (NR1) were decreased in PNs. Targeted double-stranded RNA-mediated silencing of dPQBP1 in PNs, but not in MBs, similarly disrupted learning acquisition. NR1 overexpression in PNs rescued the learning disturbance of dPQBP1 mutants. HDAC (histone deacetylase) inhibitors, SAHA (suberoylanilide hydroxamic acid) and PBA (phenylbutyrate), that upregulated NR1 partially rescued the learning disturbance. Collectively, these findings identify dPQBP1 as a novel gene regulating learning acquisition at PNs.

Knock-down of PQBP1 impairs anxiety-related cognition in mouse.

PQBP1 (polyglutamine tract-binding protein 1) is a causative gene for a relatively frequent X-linked syndromic and non-syndromic mental retardation (MR). To analyze behavioral abnormalities of these patients from molecular basis, we developed a knock-down (KD) mouse model. The KD mice possess a transgene expressing 498 bp double-strand RNA that is endogenously cleaved to siRNA suppressing PQBP1 efficiently. After confirming that PQBP1 is selectively suppressed to nearly 50% of the control mice, we performed behavioral analyses of PQBP1-KD mice. The KD mice possessed normal ability in ordinary memory tests including water-maze test, whereas they showed abnormal anxiety-related behavior in light/dark exploration test and open-field test and showed obvious declines of anxiety-related cognition in the repetitive elevated plus maze or novel object recognition test. Correspondingly, we found c-fos upregulation and histone H3 acetylation after behavior tests were declined in neurons of amygdala, prefrontal cortex and hippocampus. Furthermore, we found that 4-phenylbutyric acid, an HDAC inhibitor, efficiently improved expression of these genes and rescued the abnormal phenotypes in adult PQBP1-KD mice. These results suggested that PQBP1 dysfunction in regulating gene expression might underlie the abnormal behavior and cognition of PQBP1-KD mice and that the recovery of expression of such PQBP1 target genes might improve the symptoms in adult patients.

Transcriptional repression induces a slowly progressive atypical neuronal death associated with changes of YAP isoforms and p73.

Transcriptional disturbance is implicated in the pathology of polyglutamine diseases, including Huntington's disease (HD). However, it is unknown whether transcriptional repression leads to neuronal death or what forms that death might take. We found transcriptional repression-induced atypical death (TRIAD) of neurons to be distinct from apoptosis, necrosis, or autophagy. The progression of TRIAD was extremely slow in comparison with other types of cell death. Gene expression profiling revealed the reduction of full-length yes-associated protein (YAP), a p73 cofactor to promote apoptosis, as specific to TRIAD. Furthermore, novel neuron-specific YAP isoforms (YAPDeltaCs) were sustained during TRIAD to suppress neuronal death in a dominant-negative fashion. YAPDeltaCs and activated p73 were colocalized in the striatal neurons of HD patients and mutant huntingtin (htt) transgenic mice. YAPDeltaCs also markedly attenuated Htt-induced neuronal death in primary neuron and Drosophila melanogaster models. Collectively, transcriptional repression induces a novel prototype of neuronal death associated with the changes of YAP isoforms and p73, which might be relevant to the HD pathology.