Dual-hemisphere tDCS facilitates greater improvements for healthy subjects' non-dominant hand compared to uni-hemisphere stimulation

Background  

Transcranial direct current stimulation (tDCS) is a non-invasive technique that has been found to modulate the excitability of neurons in the brain. The polarity of the current applied to the scalp determines the effects of tDCS on the underlying tissue: anodal tDCS increases excitability, whereas cathodal tDCS decreases excitability. Research has shown that applying anodal tDCS to the non-dominant motor cortex can improve motor performance for the non-dominant hand, presumably by means of changes in synaptic plasticity between neurons. Our previous studies also suggest that applying cathodal tDCS over the dominant motor cortex can improve performance for the non-dominant hand; this effect may result from modulating inhibitory projections (interhemispheric inhibition) between the motor cortices of the two hemispheres. We hypothesized that stimultaneously applying cathodal tDCS over the dominant motor cortex and anodal tDCS over the non-dominant motor cortex would have a greater effect on finger sequence performance for the non-dominant hand, compared to stimulating only the non-dominant motor cortex. Sixteen right-handed participants underwent three stimulation conditions: 1) dual-hemisphere – with anodal tDCS over the non-dominant motor cortex, and cathodal tDCS over the dominant motor cortex, 2) uni-hemisphere – with anodal tDCS over the non-dominant motor cortex, and 3) sham tDCS. Participants performed a finger-sequencing task with the non-dominant hand before and after each stimulation. The dependent variable was the percentage of change in performance, comparing pre- and post-tDCS scores.     

Transcranial direct current stimulation of the prefrontal cortex modulates working memory performance: combined behavioural and electrophysiological evidence

Background  

Transcranial direct current stimulation (tDCS) is a technique that can systematically modify behaviour by inducing changes in the underlying brain function. In order to better understand the neuromodulatory effect of tDCS, the present study examined the impact of tDCS on performance in a working memory (WM) task and its underlying neural activity. In two experimental sessions, participants performed a letter two-back WM task after sham and either anodal or cathodal tDCS over the left dorsolateral prefrontal cortex (DLPFC).     

Transfer effects on communication and collision avoidance behavior from playing a three-dimensional auditory game based on a virtual auditory display

Transfer effects on two everyday auditory skills from playing an auditory game using a virtual auditory display (VAD) were investigated using two groups of 20 normally sighted participants. Representative auditory skills of blindfolded participants were pre-tested using communication and collision avoidance tasks. They performed the tasks two weeks later. Participants in a training group played the VAD-based game on seven days (30 min/day) during the two weeks: control group participants did not. Playing the VAD-based game significantly increased face-contacts in the communication task and significantly improved participants’ collision avoidance. No significant differences were found between the groups’ subjective ratings of the rated tension levels during the communication task or between rated levels for the collision threat. Performance revealed transfer effects of playing the VAD-based game on communication behaviors in social interaction and on avoidance of approaching objects: VAD-based games are effective training tools for auditory skills used in daily life.     

Assessing the molecular genetics of attention networks

Background  

Current efforts to study the genetic underpinnings of higher brain functions have been lacking appropriate phenotypes to describe cognition. One of the problems is that many cognitive concepts for which there is a single word (e.g. attention) have been shown to be related to several anatomical networks. Recently, we have developed an Attention Network Test (ANT) that provides a separate measure for each of three anatomically defined attention networks.     

Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet

A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.     

Part-Aware Mask-Guided Attention for Thorax Disease Classification

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.     

Differences in dual-task performance and prefrontal cortex activation between younger and older adults

Background

The purpose of this study was to examine task-related changes in prefrontal cortex (PFC) activity during a dual-task in both healthy young and older adults and compare patterns of activation between the age groups. We also sought to determine whether brain activation during a dual-task relates to executive/attentional function and how measured factors associated with both of these functions vary between older and younger adults.

Results

Thirty-five healthy volunteers (20 young and 15 elderly) participated in this study. Near-infrared spectroscopy (NIRS) was employed to measure PFC activation during a single-task (performing calculations or stepping) and dual-task (performing both single-tasks at once). Cognitive function was assessed in the older patients with the Trail-making test part B (TMT-B). Major outcomes were task performance, brain activation during task (oxygenated haemoglobin: Oxy-Hb) measured by NIRS, and TMT-B score. Mixed ANOVAs were used to compare task factors and age groups in task performance. Mixed ANOVAs also compared task factors, age group and time factors in task-induced changes in measured Oxy-Hb. Among the older participants, correlations between the TMT-B score and Oxy-Hb values measured in each single-task and in the dual-task were examined using a Pearson correlation coefficient. Oxy-Hb values were significantly increased in both the calculation task and the dual-task within patients in both age groups. However, the Oxy-Hb values associated with there were higher in the older group during the post-task period for the dual-task. Also, there were significant negative correlations between both task-performance accuracy and Oxy-Hb values during the dual-task and participant TMT-B scores.

Conclusions

Older adults demonstrated age-specific PFC activation in response to dual-task challenge. There was also a significant negative correlation between PFC activation during dual-task and executive/attentional function. These findings suggest that the high cognitive load induced by dual-task activity generates increased PFC activity in older adults. However, this relationship appeared to be strongest in participants with better baseline attention and executive functions.     

Assessing the heritability of attentional networks

Background  

Current efforts to study the genetics of higher functions have been lacking appropriate phenotypes to describe cognition. One of the problems is that many cognitive concepts for which there is a single word (e.g. attention) have been shown to be related to several anatomical networks. Recently we have developed an Attention Network Test (ANT) that provides a separate measure for each of three anatomically defined attention networks. In this small scale study, we ran 26 pairs of MZ and DZ twins in an effort to determine if any of these networks show sufficient evidence of heritability to warrant further exploration of their genetic basis.     

Incidental categorization of spectrally complex non-invariant auditory stimuli in a computer game task

This study examined perceptual learning of spectrally complex nonspeech auditory categories in an interactive multi-modal training paradigm. Participants played a computer game in which they navigated through a three-dimensional space while responding to animated characters encountered along the way. Characters' appearances in the game correlated with distinctive sound category distributions, exemplars of which repeated each time the characters were encountered. As the game progressed, the speed and difficulty of required tasks increased and characters became harder to identify visually, so quick identification of approaching characters by sound patterns was, although never required or encouraged, of gradually increasing benefit. After 30 min of play, participants performed a categorization task, matching sounds to characters. Despite not being informed of audio-visual correlations, participants exhibited reliable learning of these patterns at posttest. Categorization accuracy was related to several measures of game performance and category learning was sensitive to category distribution differences modeling acoustic structures of speech categories. Category knowledge resulting from the game was qualitatively different from that gained from an explicit unsupervised categorization task involving the same stimuli. Results are discussed with respect to information sources and mechanisms involved in acquiring complex, context-dependent auditory categories, including phonetic categories, and to multi-modal statistical learning.     

Perceptual learning of spectrally degraded speech and environmental sounds

Adaptation to the acoustic world following cochlear implantation does not typically include formal training or extensive audiological rehabilitation. Can cochlear implant (CI) users benefit from formal training, and if so, what type of training is best? This study used a pre-/posttest design to evaluate the efficacy of training and generalization of perceptual learning in normal hearing subjects listening to CI simulations (eight-channel sinewave vocoder). Five groups of subjects were trained on words (simple/complex), sentences (meaningful/anomalous), or environmental sounds, and then were tested using an open-set identification task. Subjects were trained on only one set of materials but were tested on all stimuli. All groups showed significant improvement due to training, which successfully generalized to some, but not all stimulus materials. For easier tasks, all types of training generalized equally well. For more difficult tasks, training specificity was observed. Training on speech did not generalize to the recognition of environmental sounds; however, explicit training on environmental sounds successfully generalized to speech. These data demonstrate that the perceptual learning of degraded speech is highly context dependent and the type of training and the specific stimulus materials that a subject experiences during perceptual learning has a substantial impact on generalization to new materials.     

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance.     

带介质分布式Bragg反射镜结构高性能共振腔发光二极管的研究

采用等离子体增强化学气相沉积高低频交替生长法生长了SiO₂/Si₃N₄透明介质分布式Bragg反射镜(DDBR), 提出了对DDBR采用干、湿法并用的腐蚀方法. 采用传输矩阵法理论分析了DDBR, 得出了为满足出光增益要求的反射率和DDBR结构. 使用光致发光(PL)谱仪测量分析了DDBR反射谱和光致发光谱, 获得了使光致发光谱辐射增强的DDBR结构, 在整个光致发光谱380–780 nm波段, 整体辐射增强1.058倍, 在谐振波长处辐射增强1.5倍, 半峰全宽值由23 nm变窄为10.5 nm, 获得了很好的光谱纯度. 利用最优DDBR结构制成了高性能共振腔发光二极管器件, 与普通结构相比, 实现了低开启电压1.78 V; 在20 mA注入电流下, 轴向光强提高了20%, 光功率和光效分别提高了27.7%和26.8%, 光功率衰减缓慢; 在0–100 mA注入电流下, 没有明显的下降趋势, 表现出了良好的温度稳定性. 关键词： 发光二极管 共振腔 介质分布式布拉格反射镜 辐射增强     

Effects of brain polarization on reaction times and pinch force in chronic stroke

Background  

Previous studies showed that anodal transcranial DC stimulation (tDCS) applied to the primary motor cortex of the affected hemisphere (M1_{affected hemisphere}) after subcortical stroke transiently improves performance of complex tasks that mimic activities of daily living (ADL). It is not known if relatively simpler motor tasks are similarly affected. Here we tested the effects of tDCS on pinch force (PF) and simple reaction time (RT) tasks in patients with chronic stroke in a double-blind cross-over Sham-controlled experimental design.     

Gene expression changes following extinction testing in a heroin behavioral incubation model

Background

By enabling individuals to self-regulate their brainwave activity in the field of optimal performance in healthy individuals, neurofeedback has been found to improve cognitive and artistic performance. Here we assessed whether two distinct EEG neurofeedback protocols could develop surgical skill, given the important role this skill plays in medicine.

Results

National Health Service trainee ophthalmic microsurgeons (N = 20) were randomly assigned to either Sensory Motor Rhythm-Theta (SMR) or Alpha-Theta (AT) groups, a randomized subset of which were also part of a wait-list 'no-treatment' control group (N = 8). Neurofeedback groups received eight 30-minute sessions of EEG training. Pre-post assessment included a skills lab surgical procedure with timed measures and expert ratings from video-recordings by consultant surgeons, together with state/trait anxiety self-reports. SMR training demonstrated advantages absent in the control group, with improvements in surgical skill according to 1) the expert ratings: overall technique (d = 0.6, p < 0.03) and suture task (d = 0.9, p < 0.02) (judges' intraclass correlation coefficient = 0.85); and 2) with overall time on task (d = 0.5, p = 0.02), while everyday anxiety (trait) decreased (d = 0.5, p < 0.02). Importantly the decrease in surgical task time was strongly associated with SMR EEG training changes (p < 0.01), especially with continued reduction of theta (4–7 Hz) power. AT training produced marginal improvements in technique and overall performance time, which were accompanied by a standard error indicative of large individual differences. Notwithstanding, successful within session elevation of the theta-alpha ratio correlated positively with improvements in overall technique (r = 0.64, p = 0.047).

Conclusion

SMR-Theta neurofeedback training provided significant improvement in surgical technique whilst considerably reducing time on task by 26%. There was also evidence that AT training marginally reduced total surgery time, despite suboptimal training efficacies. Overall, the data set provides encouraging evidence of optimised learning of a complex medical specialty via neurofeedback training.     

The scaling behavior of hand motions reveals self-organization during an executive function task

Recent approaches to cognition explain cognitive phenomena in terms of interaction-dominant dynamics. In the current experiment, we extend this approach to executive function, a construct used to describe flexible, goal-oriented behavior. Participants were asked to perform a widely used executive function task, card sorting, under two conditions. In one condition, participants were given a rule with which to sort the cards. In the other condition, participants had to induce the rule from experimenter feedback. The motion of each participant’s hand was tracked during the sorting task. Detrended fluctuation analysis was performed on the inter-point time series using a windowing strategy to capture changes over each trial. For participants in the induction condition, the Hurst exponent sharply increased and then decreased. The Hurst exponents for the explicit condition did not show this pattern. Our results suggest that executive function may be understood in terms of changes in stability that arise from interaction-dominant dynamics.     

Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery

In this article, we consider a version of the challenging problem of learning from datasets whose size is too limited to allow generalisation beyond the training set. To address the challenge, we propose to use a transfer learning approach whereby the model is first trained on a synthetic dataset replicating features of the original objects. In this study, the objects were smartphone photographs of near-complete Roman terra sigillata pottery vessels from the collection of the Museum of London. Taking the replicated features from published profile drawings of pottery forms allowed the integration of expert knowledge into the process through our synthetic data generator. After this first initial training the model was fine-tuned with data from photographs of real vessels. We show, through exhaustive experiments across several popular deep learning architectures, different test priors, and considering the impact of the photograph viewpoint and excessive damage to the vessels, that the proposed hybrid approach enables the creation of classifiers with appropriate generalisation performance. This performance is significantly better than that of classifiers trained exclusively on the original data, which shows the promise of the approach to alleviate the fundamental issue of learning from small datasets.     

Transcranial direct current stimulation for the treatment of tinnitus: a review of clinical trials and mechanisms of action

Background

Tinnitus is the perception of sound in the absence of any external acoustic stimulation. Transcranial direct current stimulation (tDCS) has shown promising though heterogeneous therapeutic outcomes for tinnitus. The present study aims to review the recent advances in applications of tDCS for tinnitus treatment. In addition, the clinical efficacy and main mechanisms of action of tDCS on suppressing tinnitus are discussed.

Methods

The study was performed in accordance with the PRISMA guidelines. The databases of the PubMed (1980–2018), Embase (1980–2018), PsycINFO (1850–2018), CINAHL, Web of Science, BIOSIS Previews (1990–2018), Cambridge Scientific Abstracts (1990–2018), and google scholar (1980–2018) using the set search terms. The date of the most recent search was 20 May, 2018. The randomized controlled trials that have assessed at least one therapeutic outcome measured before and after tDCS intervention were included in the final analysis.

Results

Different tDCS protocols were used for tinnitus ranging single to repeated sessions (up to 10) consisting of daily single session of 15 to 20-min and current intensities ranging 1–2 mA. Dorsolateral prefrontal cortex (DLPFC) and auditory cortex are the main targets of stimulation. Both single and repeated sessions showed moderate to significant treatment effects on tinnitus symptoms. In addition to improvements in tinnitus symptoms, the tDCS interventions particularly bifrontal DLPFC showed beneficial outcomes on depression and anxiety comorbid with tinnitus. Heterogeneities in the type of tinnitus, tDCS devices, protocols, and site of stimulation made the systematic reviews of the literature difficult. However, the current evidence shows that tDCS can be developed as an adjunct or complementary treatment for intractable tinnitus. TDCS may be a safe and cost-effective treatment for tinnitus in the short-term application.

Conclusions

The current literature shows moderate to significant therapeutic efficacy of tDCS on tinnitus symptoms. Further randomized placebo-controlled double-blind trials with large sample sizes are needed to reach a definitive conclusion on the efficacy of tDCS for tinnitus. Future studies should further focus on developing efficient disease- and patient-specific protocols.

     

Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering

Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have been developed to improve the original k-means clustering method such as k-means ++ and kernel k-means. K-means is a linear clustering method; that is, it divides the objects into linearly separable groups, while kernel k-means is a non-linear technique. Kernel k-means projects the elements to a higher dimensional feature space using a kernel function, and then groups them. Different kernel functions may not perform similarly in clustering of a data set and, in turn, choosing the right kernel for an application could be challenging. In our previous work, we introduced a weighted majority voting method for clustering based on normalized mutual information (NMI). NMI is a supervised method where the true labels for a training set are required to calculate NMI. In this study, we extend our previous work of aggregating the clustering results to develop an unsupervised weighting function where a training set is not available. The proposed weighting function here is based on Silhouette index, as an unsupervised criterion. As a result, a training set is not required to calculate Silhouette index. This makes our new method more sensible in terms of clustering concept.     

Bifrontal transcranial direct current stimulation slows reaction time in a working memory task

Background  

Weak transcortical direct current stimulation (tDCS) applied to the cortex can shift the membrane potential of superficial neurons thereby modulating cortical excitability and activity. Here we test the possibility of modifying ongoing activity associated with working memory by tDCS. The concept of working memory applies to a system that is capable of transiently storing and manipulating information, as an integral part of the human memory system. We applied anodal and cathodal transcranial direct current (tDCS) stimulation (260 μA) bilaterally at fronto-cortical electrode sites on the scalp over 15 min repeatedly (15 sec-on/15 sec-off) as well as sham-tDCS while subjects performed a modified Sternberg task.