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Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.The objective of this study is to develop a method for alleviating a novel pattern interference toward achieving a robust myoelectric pattern-recognition control system. To this end, a framework was presented for surface electromyogram (sEMG) pattern classification and novelty detection using hybrid neural networks, i.e., a convolutional neural network (CNN) and autoencoder networks. In the framework, the CNN was first used to extract spatio-temporal information conveyed in the sEMG data recorded via high-density (HD) 2-dimensional electrode arrays. Given the target motion patterns well-characterized by the CNN, autoencoder networks were applied to learn variable correlation in the spatio-temporal information, where samples from any novel pattern appeared to be significantly different from those from target patterns. Therefore, it was straightforward to discriminate and then reject the novel motion interferences identified as untargeted and unlearned patterns. The performance of the proposed method was evaluated with HD-sEMG data recorded by two 8 × 6 electrode arrays placed over the forearm extensors and flexors of 9 subjects performing seven target motion tasks and six novel motion tasks. The proposed method achieved high accuracies over 95% for identifying and rejecting novel motion tasks, and it outperformed conventional methods with statistical significance (p less then 0.05). The proposed method is demonstrated to be a promising solution for rejecting novel motion interferences, which are ubiquitous in myoelectric control. This study will enhance the robustness of the myoelectric control system against novelty interference.We have reported nanometer-scale three-dimensional studies of brain networks of schizophrenia cases and found that their neurites are thin and tortuous when compared to healthy controls. This suggests that connections between distal neurons are suppressed in microcircuits of schizophrenia cases. In this study, we applied these biological findings to the design of a schizophrenia-mimicking artificial neural network to simulate the observed connection alteration in the disorder. Neural networks that have a "schizophrenia connection layer" in place of a fully connected layer were subjected to image classification tasks using the MNIST and CIFAR-10 datasets. The results revealed that the schizophrenia connection layer is tolerant to overfitting and outperforms a fully connected layer. The outperformance was observed only for networks using band matrices as weight windows, indicating that the shape of the weight matrix is relevant to the network performance. A schizophrenia convolution layer was also tested using the VGG configuration, showing that 60% of the kernel weights of the last three convolution layers can be eliminated without loss of accuracy. The schizophrenia layers can be used instead of conventional layers without any change in the network configuration and training procedures; hence, neural networks can easily take advantage of these layers. The results of this study suggest that the connection alteration found in schizophrenia is not a burden to the brain, but has functional roles in brain performance.Alzheimer's disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer's Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

The preferable position of Deep Brain Stimulation (DBS) electrodes is proposed to be located in the dorsolateral subthalamic nucleus (STN) to improve general motor performance. The optimal DBS electrode localization for the post-operative improvement of balance and gait is unknown.

In this single-center, retrospective analyses, 66 Parkinson's disease (PD) patients (24 female, age 63 ± 7 years) were assessed pre- and post-operatively (8.45 ± 4.2 months after surgery) by using MDS-UPDRS, freezing of gait (FoG) score, Giladi's gait and falls questionnaire and Berg balance scale. The clinical outcome was related to the DBS electrode coordinates in x, y, z plane as revealed by image-based reconstruction (SureTune™). Binomial generalized linear mixed models with fixed-effect variables electrode asymmetry, parkinsonian subtype, medication, age class and clinical DBS induced changes were analyzed.

Subthalamic nucleus-deep brain stimulation improved all motor, balance and FoG scores in MED OFF condition, however there were heterogeneous results in MED ON condition. Etomoxir DBS electrode reconstructed coordinates impacted the responsiveness of axial symptoms. FoG and balance responders showed slightly more medially located STN electrode coordinates and less medio-lateral asymmetry of the electrode reconstructed coordinates across hemispheres compared to non-responders.

Deep brain stimulation electrode reconstructed coordinates, particularly electrode asymmetry on the medio-lateral axis affected the post-operative responsiveness of balance and FoG symptoms in PD patients.

Deep brain stimulation electrode reconstructed coordinates, particularly electrode asymmetry on the medio-lateral axis affected the post-operative responsiveness of balance and FoG symptoms in PD patients.Vocalisations play a central role in rodent communication, especially in reproduction related behaviours. In adult mice (Mus musculus) the emission of ultrasonic vocalisations (USVs) has been observed in courtship and mating behaviour, especially by males. These have been found to have distinctive individual signatures that influence female choice of mating partner. The most recent findings show that vocal communication also has a role in parental cooperation, in that female mice communicate with male partners in ultrasonic frequencies to induce paternal behaviour. Infant vocalisations form the other important part of reproductive vocal communication. Although born deaf, neonatal mice are capable of producing vocalisations since birth. As an altricial species, successful mother-infant communication is essential for survival, and these vocalisations are important modulators of maternal behaviour. Three main types of infant vocalisations have been identified and characterised. Most research has addressed pure Uledge on mouse reproductive vocal communication and (ii) discuss the most recent findings in order to provide a broad overview on this topic.Genetic background and age at first exposure have been identified as critical variables that contribute to individual vulnerability to drug addiction. Evidence shows that genetic factors may account for 40-70% of the variance in liability to addiction. Alcohol consumption by young people, especially in the form of binge-drinking, is becoming an alarming phenomenon predictive of future problems with drinking. Thus, the literature indicates the need to better understand the influence of age and genetic background on the development of alcohol dependence. To this aim, the inbred rat strains Lewis (LEW, addiction prone) and Fischer 344 (F344, addiction resistant) were used as a model of genetic vulnerability to addiction and compared with the outbred strain Sprague-Dawley (SD) in a two-bottle choice paradigm as a model of alcohol abuse. During a 9-week period, adolescent and adult male rats of the three strains were intermittently exposed to ethanol (20%) and water during three 24-h sessions/week. Adult and adoleset of alcohol use in the progression toward abuse and development of alcohol addiction.The analysis of kinematics, locomotion, and spatial tasks relies on the accurate detection of animal positions and pose. Pose and position can be assessed with video analysis programs, the "trackers." Most available trackers represent animals as single points in space (no pose information available) or use markers to build a skeletal representation of pose. Markers are either physical objects attached to the body (white balls, stickers, or paint) or they are defined in silico using recognizable body structures (e.g., joints, limbs, color patterns). Physical markers often cannot be used if the animals are small, lack prominent body structures on which the markers can be placed, or live in environments such as aquatic ones that might detach the marker. Here, we introduce a marker-free pose-estimator (LACE Limbless Animal traCkEr) that builds the pose of the animal de novo from its contour. LACE detects the contour of the animal and derives the body mid-line, building a pseudo-skeleton by defining vertices and edges.