Effects of bihemispheric transcranial direct current stimulation on motor recovery in subacute stroke patients: a double-blind, randomized sham-controlled trial.

BACKGROUND: Bihemispheric transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) can simultaneously modulate bilateral corticospinal excitability and interhemispheric interaction. However, how tDCS affects subacute stroke recovery remains unclear. We investigated the effects of bihemispheric tDCS on motor recovery in subacute stroke patients. METHODS: We enrolled subacute inpatients who had first-ever ischemic stroke at subcortical regions and moderate-to-severe baseline Fugl-Meyer Assessment of Upper Extremity (FMA-UE) score 2-56. Participants between 14 and 28 days after stroke were double-blind, randomly assigned (1:1) to receive real (n = 13) or sham (n = 14) bihemispheric tDCS (with ipsilesional M1 anode and contralesional M1 cathode, 20 min, 2 mA) during task practice twice daily for 20 sessions in two weeks. Residual integrity of the ipsilesional corticospinal tract was stratified between groups. The primary efficacy outcome was the change in FMA-UE score from baseline (responder as an increase ≥ 10). The secondary measures included changes in the Action Research Arm Test (ARAT), FMA-Lower Extremity (FMA-LE) and explorative resting-state MRI functional connectivity (FC) of target regions after intervention and three months post-stroke. RESULTS: Twenty-seven participants completed the study without significant adverse effects. Nineteen patients (70%) had no recordable baseline motor-evoked potentials (MEP-negative) from the paretic forearm. Compared with the sham group, the real tDCS group showed enhanced improvement of FMA-UE after intervention (p < 0.01, effect size η2 = 0.211; responder rate: 77% vs. 36%, p = 0.031), which sustained three months post-stroke (p < 0.01), but not ARAT. Interestingly, in the MEP-negative subgroup analysis, the FMA-UE improvement remained but delayed. Additionally, the FMA-LE improvement after real tDCS was not significantly greater until three months post-stroke (p < 0.01). We found that the individual FMA-UE improvements after real tDCS were associated with bilateral intrahemispheric, rather than interhemispheric, FC strengths in the targeted cortices, while the improvements after sham tDCS were associated with predominantly ipsilesional FC changes after adjustment for age and sex (p < 0.01). CONCLUSIONS: Bihemispheric tDCS during task-oriented training may facilitate motor recovery in subacute stroke patients, even with compromised corticospinal tract integrity. Further studies are warranted for tDCS efficacy and network-specific neuromodulation. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov: (ID: NCT02731508).

Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification.

The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that enables users to communicate with external environments through their minds. However, current MI-BCI systems ask naïve subjects to perform unfamiliar MI tasks with simple textual instruction or a visual/auditory cue. The unclear instruction for MI execution not only results in large inter-subject variability in the measured EEG patterns but also causes the difficulty of grouping cross-subject data for big-data training. In this study, we designed an BCI training method in a virtual reality (VR) environment. Subjects wore a head-mounted device (HMD) and executed action observation (AO) concurrently with MI (i.e., AO + MI) in VR environments. EEG signals recorded in AO + MI task were used to train an initial model, and the initial model was continually improved by the provision of EEG data in the following BCI training sessions. We recruited five healthy subjects, and each subject was requested to participate in three kinds of tasks, including an AO + MI task, an MI task, and the task of MI with visual feedback (MI-FB) three times. This study adopted a transformer- based spatial-temporal network (TSTN) to decode the user's MI intentions. In contrast to other convolutional neural network (CNN) or recurrent neural network (RNN) approaches, the TSTN extracts spatial and temporal features, and applies attention mechanisms along spatial and temporal dimensions to perceive the global dependencies. The mean detection accuracies of TSTN were 0.63, 0.68, 0.75, and 0.77 in the MI, first MI-FB, second MI-FB, and third MI-FB sessions, respectively. This study demonstrated the AO + MI gave an easier way for subjects to conform their imagery actions, and the BCI performance was improved with the continual learning of the MI-FB training process.

The Full Informational Spectral Analysis for Auditory Steady-State Responses in Human Brain Using the Combination of Canonical Correlation Analysis and Holo-Hilbert Spectral Analysis.

Auditory steady-state response (ASSR) is a translational biomarker for several neurological and psychiatric disorders, such as hearing loss, schizophrenia, bipolar disorder, autism, etc. The ASSR is sinusoidal electroencephalography (EEG)/magnetoencephalography (MEG) responses induced by periodically presented auditory stimuli. Traditional frequency analysis assumes ASSR is a stationary response, which can be analyzed using linear analysis approaches, such as Fourier analysis or Wavelet. However, recent studies have reported that the human steady-state responses are dynamic and can be modulated by the subject's attention, wakefulness state, mental load, and mental fatigue. The amplitude modulations on the measured oscillatory responses can result in the spectral broadening or frequency splitting on the Fourier spectrum, owing to the trigonometric product-to-sum formula. Accordingly, in this study, we analyzed the human ASSR by the combination of canonical correlation analysis (CCA) and Holo-Hilbert spectral analysis (HHSA). The CCA was used to extract ASSR-related signal features, and the HHSA was used to decompose the extracted ASSR responses into amplitude modulation (AM) components and frequency modulation (FM) components, in which the FM frequency represents the fast-changing intra-mode frequency and the AM frequency represents the slow-changing inter-mode frequency. In this paper, we aimed to study the AM and FM spectra of ASSR responses in a 37 Hz steady-state auditory stimulation. Twenty-five healthy subjects were recruited for this study, and each subject was requested to participate in two auditory stimulation sessions, including one right-ear and one left-ear monaural steady-state auditory stimulation. With the HHSA, both the 37 Hz (fundamental frequency) and the 74 Hz (first harmonic frequency) auditory responses were successfully extracted. Examining the AM spectra, the 37 Hz and the 74 Hz auditory responses were modulated by distinct AM spectra, each with at least three composite frequencies. In contrast to the results of traditional Fourier spectra, frequency splitting was seen at 37 Hz, and a spectral peak was obscured at 74 Hz in Fourier spectra. The proposed method effectively corrects the frequency splitting problem resulting from time-varying amplitude changes. Our results have validated the HHSA as a useful tool for steady-state response (SSR) studies so that the misleading or wrong interpretation caused by amplitude modulation in the traditional Fourier spectrum can be avoided.

Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants.

BACKGROUND: Support vector machines (SVMs) based on brain-wise functional connectivity (FC) have been widely adopted for single-subject prediction of patients with schizophrenia, but most of them had small sample size. This study aimed to evaluate the performance of SVMs based on a large single-site dataset and investigate the effects of demographic homogeneity and training sample size on classification accuracy. METHODS: The resting functional Magnetic Resonance Imaging (fMRI) dataset comprised 220 patients with schizophrenia and 220 healthy controls. Brain-wise FCs was calculated for each participant and linear SVMs were developed for automatic classification of patients and controls. First, we evaluated the SVMs based on all participants and homogeneous subsamples of men, women, younger (18-30 years), and older (31-50 years) participants by 10-fold nested cross-validation. Then, we hold out a fixed test set of 40 participants (20 patients and 20 controls) and evaluated the SVMs based on incremental training sample sizes (N = 40, 80, …, 400). RESULTS: We found that the SVMs based on all participants had accuracy of 85.05%. The SVMs based on male, female, young, and older participants yielded accuracy of 84.66, 81.56, 80.50, and 86.13%, respectively. Although the SVMs based on older subsamples had better performance than those based on all participants, they generalized poorly to younger participants (77.24%). For incremental training sizes, the classification accuracy increased stepwise from 72.6 to 83.3%, with >80% accuracy achieved with sample size >240. CONCLUSIONS: The findings indicate that SVMs based on a large dataset yield high classification accuracy and establish models using a large sample size with heterogeneous properties are recommended for single subject prediction of schizophrenia.

Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD.

Steady-state visual evoked potential (SSVEP) has been used to implement brain-computer interface (BCI) due to its advantages of high information transfer rate (ITR) and high accuracy. In recent years, owing to the developments of head-mounted device (HMD), the HMD has become a popular device to implement SSVEP-based BCI. However, an HMD with fixed frame rate only can flash at its subharmonic frequencies which limits the available number of stimulation frequencies for SSVEP-based BCI. In order to increase the number of available commands for SSVEP-based BCI, we proposed a phase-approaching (PA) method to generate visual stimulation sequences at user-specified frequency on an HMD. The flickering sequence generated by our PA method (PAS sequence) tries to approximate user-specified stimulation frequency by means of minimizing the difference of accumulated phases between our PAS sequence and the ideal wave of user-specified frequency. The generated sequence of PA method determines the brightness state for each frame to approach the accumulated phase of the ideal wave. The SSVEPs evoked from stimulators, driven by PAS sequences, were analyzed using canonical correlation analysis (CCA) to identify user's gazed target. In this study, a six-command SSVEP-based BCI was designed to operate a flying drone. The ITR and detection accuracy are 36.84 bits/min and 93.30%, respectively.

Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation.

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

ß-Oscillations Reflect Recovery of the Paretic Upper Limb in Subacute Stroke.

Background. Recovery of upper limb function post-stroke can be partly predicted by initial motor function, but the mechanisms underpinning these improvements have yet to be determined. Here, we sought to identify neural correlates of post-stroke recovery using longitudinal magnetoencephalography (MEG) assessments in subacute stroke survivors. Methods. First-ever, subcortical ischemic stroke survivors with unilateral mild to moderate hand paresis were evaluated at 3, 5, and 12 weeks after stroke using a finger-lifting task in the MEG. Cortical activity patterns in the ß-band (16-30 Hz) were compared with matched healthy controls. Results. All stroke survivors (n=22; 17 males) had improvements in action research arm test (ARAT) and Fugl-Meyer upper extremity (FM-UE) scores between 3 and 12 weeks. At 3 weeks post-stroke the peak amplitudes of the movement-related ipsilesional ß-band event-related desynchronization (ß-ERD) and synchronization (ß-ERS) in primary motor cortex (M1) were significantly lower than the healthy controls (p<0.001) and were correlated with both the FM-UE and ARAT scores (r=0.51-0.69, p<0.017). The decreased ß-ERS peak amplitudes were observed both in paretic and non-paretic hand movement particularly at 3 weeks post-stroke, suggesting a generalized disinhibition status. The peak amplitudes of ipsilesional ß-ERS at week 3 post-stroke correlated with the FM-UE score at 12 weeks (r=0.54, p=0.03) but no longer significant when controlling for the FM-UE score at 3 weeks post-stroke.Conclusions. Although early ß-band activity does not independently predict outcome at 3 months after stroke, it mirrors functional changes, giving a potential insight into the mechanisms underpinning recovery of motor function in subacute stroke.

Steady-state auditory evoked fields reflect long-term effects of repetitive transcranial magnetic stimulation in tinnitus.

OBJECTIVES: Evidence of plastic changes in tinnitus has been demonstrated in functional brain imaging. Although repetitive transcranial magnetic stimulation (rTMS) has been shown to decrease steady-state auditory evoked fields (SSAEFs) in tinnitus, the long-term consequence remained unknown. In addition, association between plastic changes as reflected by hemispheric asymmetry and tinnitus handicap inventory (THI) before and after rTMS have not been addressed. METHODS: Twelve tinnitus patients received rTMS and 12 received sham stimulation. Another 12 healthy participants served as the normal hearing controls. Patients responded to the THI before the 1st session and at one month after the final session of rTMS/sham stimulation. Changes in brain activity were assessed by measuring SSAEFs. RESULTS: SSAEFs remained decreased one month after rTMS compared to before treatment, along with a significant reduction in THI score. There was no significant effect between the index of hemispheric asymmetry and THI score. CONCLUSIONS: The current study objectively demonstrated the long-term effects of rTMS on tinnitus using SSAEFs. A longitudinal study to develop an index using SSAEFs to assess the subjective severity of tinnitus is warranted. SIGNIFICANCE: This study suggests the possible use of SSAEFs to assess the long-term effects of rTMS on tinnitus.

Analyses of EEG Oscillatory Activities during Slow and Fast Repetitive Movements using Holo-Hilbert Spectral Analysis.

Neural oscillatory activities existing in multiple fre-quency bands usually represent different levels of neurophysiolog-ical meanings, from micro-scale to macro-scale organizations. In this study, we adopted Holo-Hilbert spectral analysis (HHSA) to study the amplitude-modulated (AM) and frequency-modulated (FM) components in sensorimotor Mu rhythm, induced by slow- and fast-rate repetitive movements. The HHSA-based approach is a two-layer empirical mode decomposition (EMD) architecture, which firstly decomposes the EEG signal into a series of frequency-modulated intrinsic mode functions (IMF) and then decomposes each frequency-modulated IMF into a set of amplitude-modulated IMFs. With the HHSA, the FM and AM components were incor-porated with their instantaneous power to achieve full-informa-tional spectral analysis. We observed that the instantaneous power induced by slow-rate movements was significantly higher than that induced by fast-rate movements (p < 0.01, Wilcoxon signed rank test). The alpha-band AM frequencies induced by slow-rate movements were higher than those induced by fast-rate move-ments, while no statistical difference was found in beta-band AM frequencies. In addition, to study the functional coupling between the primary sensorimotor area and other brain regions, spectral coherence was applied and statistical difference was found in frontal area in slow-rate versus fast-rate movements. The discrep-ancy between slow- and fast-rate movements might be owing to the change of motor functional modes from default mode network (DMN) to automatic timing with the increase of movement rates. The use of HHSA for oscillatory activity analysis can be an effi-cient tool to provide informative interaction among different fre-quency bands.

Characterization of the role of endogenous nitric oxide in myogenic vascular oscillations during cooling-evoked hemodynamic perturbations of rats.

Rapid immersion of a rat's limbs into 4 °C water, a model of cold stress, can elicit hemodynamic perturbations (CEHP). We previously reported that CEHP is highly relevant to sympathetic activation and nitric oxide production. This study identifies the role of nitric oxide in CEHP. Conscious rats were pretreated with the nitric oxide synthase inhibitor L-NAME (NG-nitro-l-arginine methyl ester) alone or following the removal of sympathetic influences using hexamethonium or guanethidine. Rats were then subjected to a 10 min cold-stress trial. Hemodynamic indices were telemetrically monitored throughout the experiment. The analyses included measurements of systolic blood pressure; heart rate; dicrotic notch; short-term cardiovascular oscillations and coherence between blood pressure variability and heart rate variability in regions of very low frequency (0.02-0.2 Hz), low frequency (0.2-0.6 Hz), and high frequency (0.6-3.0 Hz). We observed different profiles of hemodynamic reaction between hexamethonium and guanethidine superimposed on L-NAME, suggesting an essential role for a functional adrenal medulla release of epinephrine under cold stress. These results indicate that endogenous nitric oxide plays an important role in the inhibition of sympathetic activation and cardiovascular oscillations in CEHP.

Study of Repetitive Movements Induced Oscillatory Activities in Healthy Subjects and Chronic Stroke Patients.

Repetitive movements at a constant rate require the integration of internal time counting and motor neural networks. Previous studies have proved that humans can follow short durations automatically (automatic timing) but require more cognitive efforts to track or estimate long durations. In this study, we studied sensorimotor oscillatory activities in healthy subjects and chronic stroke patients when subjects were performing repetitive finger movements. We found the movement-modulated changes in alpha and beta oscillatory activities were decreased with the increase of movement rates in finger lifting of healthy subjects and the non-paretic hands in stroke patients, whereas no difference was found in the paretic-hand movements at different movement rates in stroke patients. The significant difference in oscillatory activities between movements of non-paretic hands and paretic hands could imply the requirement of higher cognitive efforts to perform fast repetitive movements in paretic hands. The sensorimotor oscillatory response in fast repetitive movements could be a possible indicator to probe the recovery of motor function in stroke patients.

Quantifying Spasticity With Limited Swinging Cycles Using Pendulum Test Based on Phase Amplitude Coupling.

Parameters derived from the goniometer measures in the Pendulum test are insufficient in describing the function of abnormal muscle activity in the spasticity. To explore a quantitative evaluation of muscle activation-movement interaction, we propose a novel index based on phase amplitude coupling (PAC) analysis with the consideration of the relations between movement and surface electromyography (SEMG) activity among 22 hemiplegic stroke patients. To take off trend and noise, we use the empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) of the angular velocity due to its superior decomposing ability in nonlinear oscillations. Shannon entropy based on angular velocity (phase)-envelope of EMG (amplitude) distribution was calculated to demonstrate characteristics of the coupling between SEMG activity and joint movement. We also compare our results with those from traditional methods such as the normalized relaxation index derived from the Pendulum test and the mean root mean square (RMS) of the SEMG signals in the study. Our results show effective discrimination ability between spastic and nonaffected limbs using our method . This study indicates the feasibility of using the novel indices based on the PAC in evaluation the spasticity among the hemiplegic stroke patients with less than three swinging cycles.

Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups.

This paper studies the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps, were measured in eight young subjects, eight elders and seven ALS patients. Each subject was requested to participate in a calibration study and an application study. The calibration study was designed to find the amplitude-frequency characteristics of SSVEPs recorded from Oz and Fpz positions, while the application study was designed to test the feasibility of using frontal SSVEP to control a two-command SSVEP-based BCI. The SSVEP amplitude was detected by an epoch-average process which enables artifact-contaminated epochs can be removed. The seven ALS patients were severely impaired, and four patients, who were incapable of completing our BCI task, were excluded from calculation of BCI performance. The averaged accuracies, command transfer intervals and information transfer rates in operating frontal SSVEP-based BCI were 96.1%, 3.43 s/command, and 14.42 bits/min in young subjects; 91.8%, 6.22 s/command, and 6.16 bits/min in elders; 81.2%, 12.14 s/command, and 1.51 bits/min in ALS patients, respectively. The frontal SSVEP could be an alternative choice to design SSVEP-based BCI.

Novel application of a Wii remote to measure spasticity with the pendulum test: Proof of concept.

BACKGROUND: The pendulum test is a standard clinical test for quantifying the severity of spasticity. In the test, an electrogoniometer is typically used to measure the knee angular motion. The device is costly and difficult to set up such that the pendulum test is normally time consuming. OBJECTIVE: The goal of this study is to determine whether a Nintendo Wii remote can replace the electrogroniometer for reliable assessment of the angular motion of the knee in the pendulum test. METHODS: The pendulum test was performed in three control participants and 13 hemiplegic stroke patients using both a Wii remote and an electrogoniometer. The correlation coefficient and the Bland-Altman difference plot were used to compare the results obtained from the two devices. The Wilcoxon signed-rank test was used to compare the difference between hemiplegia-affected and nonaffected sides in the hemiplegic stroke patients. RESULTS: There was a fair to strong correlation between measurements from the Wii remote and the electrogoniometer (0.513

Role of Efferent Sympathoadrenal Effects in Cooling-Induced Hemodynamic Perturbations in Rats: An Investigation by Spectrum Analysis.

Cold stress may produce hemodynamic perturbations but the underlying mechanisms are still not clear. Spectral analysis was used in this study to explore that sympathoadrenal activation could be involved in mechanisms of hemodynamic perturbations to cooling. Conscious rats after treatment with a control vehicle (saline) compared with withdrawal of sympathetic influences by ganglion blocker hexamethonium (HEX) or chemical sympathectomy guanethidine (GUA) were challenged by stressful cooling as acute immersing all four extremities in ice water (4 ± 2°C) for 10 min. Plasma nitric oxide (NO) and the appearance of Dichroitic notch (DN) were measured in comparison between treatment groups throughout the experimental course. Hemodynamic indices were telemetrically monitored, and variability of blood pressure and heart rate (BPV; HRV) were assessed over a range of frequencies: very-low frequency (VLF: 0.02-0.2 Hz), low frequency (LF: 0.2-0.6 Hz), high frequency (HF: 0.6-3 Hz), normalized (n)LF, nHF, ratio LF/HF of HRV (LF/HF(HRV)), and total power (TP: ≤3 Hz). Results showed that the concomitant reciprocal changes of spectral powers existed between frequencies of BPV and HRV to the stressful cooling (i.e. VLF(BPV) versus VLF(HRV), LF(BPV) versus LF(HRV), and nLF(BPV) versus nLF(HRV)) which contribute to the underlying mechanisms of sympathetic efferent influences and myogenic cardiovascular responsiveness. Furthermore, compared with the control vehicle in the stressful cooling, HEX restrained the increase of the pressor, tachycardia and VLF(BPV), except that VLF(HRV) was reduced. GUA abolished pressor, however, restrained the increase of the tachycardia, VLF(BPV) and LF(BPV). In addition, GUA reversed the downward tendency of nLF(BPV) into an upward tendency and attenuated both nLF(HRV) and LF/HF(HRV). DN was virtually undetectable after HEX management but was apparently noticeable after GUA management. Finally, the increase of plasma NO after cooling was diminished after HEX or GUA management. Taken together, these results substantiate that the spectral changes during stressful cooling are highly relevant to the efferent sympathetic rhythmicity and subsequent NO production.

Spectral analysis of cooling induced hemodynamic perturbations indicates involvement of sympathetic activation and nitric oxide production in rats.

AIMS: Oscillations in arterial pressure and heart period in response to cold stress are poorly understood. We used a telemetric device with spectral and cross-spectral analyses to assess variabilities in the heart rate (HRV) and blood pressure (BPV) of conscious rats receiving a cooling stimulus (CS) (rapid immersion of palms and soles into 4°C water) at ambient thermoneutral conditions (TC), in a cold room (CC), and when under pentobarbital anesthesia (UA). MAIN METHODS: Power spectra of very low, low, and high frequencies (VLF: 0.02 to 0.2Hz, LF: 0.2 to 0.6Hz, and HF: 0.6 to 3.0Hz), dicrotic notch (Dn) and plasma nitric oxide (NO) levels were measured for statistical comparisons. KEY FINDINGS: When compared to resting rats (PreCS), CS evoked in rats the following hemodynamic perturbations: 1) pressor and tachycardia; 2) increases in VLFBPV, LFBPV, HFBPV, and TPBPV but decreases in VLFHRV, LFHRV, and TPHRV; 3) a positive correlation between LFBPV and VLFBPV but an inverse correlation between VLFHRV and VLFBPV and LFHRV and LFBPV; 4) high coherence value at frequency region of LF between BPV and HRV; and 5) increase of NO production and disappearance of Dn. Additionally, CS of CC and UA rats compared with TC rats evoked different patterns of hemodynamic perturbations; CC rats were activated but UA rats were inactivated. SIGNIFICANCE: Our findings indicate that changes in VLFBPV are related to the myogenic vascular responsiveness to CS. Power spectra changes to CS are highly relevant to sympathetic activation and NO production.

Extractions of steady-state auditory evoked fields in normal subjects and tinnitus patients using complementary ensemble empirical mode decomposition.

BACKGROUND: Auditory steady-state response (ASSR) induced by repetitive auditory stimulus is commonly used for audiometric testing. ASSR can be measured using electro-encephalography (EEG) and magnetoencephalography (MEG), referred to as steady-state auditory evoked potential (SSAEP) and steady-state auditory evoked field (SSAEF), respectively. However, the signal level of SSAEP and SSAEF are weak so that signal processing technique is required to increase its signal-to-noise ratio. In this study, a complementary ensemble empirical mode decomposition (CEEMD)-based approach is proposed in MEG study and the extraction of SSAEF has been demonstrated in normal subjects and tinnitus patients. METHODS: The CEEMD utilizes noise assisted data analysis (NADA) approach by adding positive and negative noise to decompose MEG signals into complementary intrinsic mode functions (IMF). Ten subjects (five normal and five tinnitus patients) were studied. The auditory stimulus was designed as 1 kHz carrier frequency with 37 Hz modulation frequency. Two channels in the vicinities of right and left temporal areas were chosen as channel-of-interests (COI) and decomposed into IMFs. The spatial distribution of each IMF was correlated with a pair of left- and right-hemisphere spatial templates, designed from each subject's N100m responses in pure-tone auditory stimulation. IMFs with spatial distributions highly correlated with spatial templates were identified using K-means and those SSAEF-related IMFs were used to reconstruct noise-suppressed SSAEFs. RESULTS: The current strengths estimated from CEEMD processed SSAEF showed neural activities greater or comparable to those processed by conventional filtering method. Both the normal and tinnitus groups showed the phenomenon of right-hemisphere dominance. The mean current strengths of auditory-induced neural activities in tinnitus group were larger than the normal group. CONCLUSIONS: The present study proposes an effective method for SSAEF extraction. The enhanced SSAEF in tinnitus group echoes the decreased inhibition in tinnitus's central auditory structures as reported in previous studies.

A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses.

Visually-induced near-infrared spectroscopy (NIRS) response was utilized to design a brain computer interface (BCI) system. Four circular checkerboards driven by distinct flickering sequences were displayed on a LCD screen as visual stimuli to induce subjects' NIRS responses. Each flickering sequence was a concatenated sequence of alternative flickering segments and resting segments. The flickering segment was designed with fixed duration of 3s whereas the resting segment was chosen randomly within 15-20s to create the mutual independencies among different flickering sequences. Six subjects were recruited in this study and subjects were requested to gaze at the four visual stimuli one-after-one in a random order. Since visual responses in human brain are time-locked to the onsets of visual stimuli and the flicker sequences of distinct visual stimuli were designed mutually independent, the NIRS responses induced by user's gazed targets can be discerned from non-gazed targets by applying a simple averaging process. The accuracies for the six subjects were higher than 90% after 10 or more epochs being averaged.

Potential Therapeutic Role of Hispidulin in Gastric Cancer through Induction of Apoptosis via NAG-1 Signaling.

Gastric cancer is one of the most common malignant cancers due to poor prognoses and high mortality rates worldwide. However, an effective chemotherapeutic drug without side effects remains lacking. Saussurea involucrata (SI) Kar. et Kir., also known as snow lotus, grows in mountainous rocky habitats at 2600 m elevation in the Tian Shan and A'er Tai regions of China. The ethyl acetate extract of SI had been shown to inhibit proliferation and induce apoptosis in various tumor cells. In this study, we demonstrated that Hispidulin, active ingredients in SI, inhibits the growth of AGS gastric cancer cells. After Hispidulin treatment, NAG-1 remained highly expressed, whereas COX-2 expression was downregulated. Flow cytometric analysis indicated that Hispidulin induces G1/S phase arrest and apoptosis in time- and concentration-dependent manners. G1/S arrest correlated with upregulated p21/WAF1 and p16 and downregulated cyclin D1 and cyclin E, independent of p53 pathway. In addition, Hispidulin can elevate Egr-1 expression and ERK1/2 activity, whereas ERK1/2 inhibitor markedly attenuated NAG-1 mediated apoptosis. Taken together, Hispidulin can efficiently activate ERK1/2 signaling followed by NAG-1 constitutive expression and trigger cell cycle arrest as well as apoptosis in cancer cell. It can be a potential compound for combination therapy of gastric cancer in the future.

Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms.

BACKGROUND: The multi-mode modulation is a key feature of sleep EEG. And the short-term fractal property reflects the sympathovagal modulation of heart rate variability (HRV). The properties of EEG and HRV strongly correlated with sleep status and are interesting in clinic diagnosis. NEW METHOD: 19 healthy female subjects were included for over-night standard polysomnographic study. Hilbert Huang transform (HHT) was used to characterize the temporal features of slow- and fast-wave oscillations decomposed from sleep EEG at different stages. Masking signals were used for solving the mode-mixing problem in HHT. On the other hand, detrended fluctuation analysis (DFA) was used to assess short-term property of HRV denoted as DFA α1, which reflects the temporal activity of autonomic nerve system (ANS). Thus, the dynamic interaction between sleep EEG and HRV can be examined through the relationship between the features of sleep EEG and DFA α1 of HRV. RESULTS: The frequency feature of sleep EEG serves as a good indicator for the depth of sleep during non-rapid eye movement (NREM) sleep, and amplitude feature of fast-wave oscillation is a good index for distinguishing rapid eye movement (REM) from NREM sleep. COMPARISON WITH EXISTING METHOD: The relationship between DFA α1 of HRV and the mean amplitude of fast-wave oscillation of sleep EEG affirmed with Pearson correlation coefficient is more significant than the correlation verified by the traditional spectral analysis. CONCLUSION: The dynamic properties of sleep EEG and HRV derived by EMD and DFA represent important features for cortex and ANS activities during sleep.