Resting state electroencephalography microstates in autism spectrum disorder: a mini-review

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

Efficacy and safety of aripiprazole in the treatment of bipolar disorder: a systematic review

Abstract BACKGROUND: The current article is a systematic review concerning the efficacy and safety of aripiprazole in the treatment of bipolar disorder. METHODS: A systematic Medline and repositories search concerning the usefulness of aripiprazole in bipolar disorder was performed, with the combination of the words 'aripiprazole' and 'bipolar'. RESULTS: The search returned 184 articles and was last updated on 15 April 2009. An additional search included repositories of clinical trials and previous systematic reviews specifically in order to trace unpublished trials. There were seven placebo-controlled randomised controlled trials (RCTs), six with comparator studies and one with add-on studies. They assessed the usefulness of aripiprazole in acute mania, acute bipolar depression and during the maintenance phase in comparison to placebo, lithium or haloperidol. CONCLUSION: Aripiprazole appears effective for the treatment and prophylaxis against mania. The data on bipolar depression are so far negative, however there is a need for further study at lower dosages. The most frequent adverse effects are extrapyramidal signs and symptoms, especially akathisia, without any significant weight gain, hyperprolactinaemia or laboratory test changes

Basal ganglia dysfunction in OCD: subthalamic neuronal activity correlates with symptoms severity and predicts high-frequency stimulation efficacy

Functional and connectivity changes in corticostriatal systems have been reported in the brains of patients with obsessive–compulsive disorder (OCD); however, the relationship between basal ganglia activity and OCD severity has never been adequately established. We recently showed that deep brain stimulation of the subthalamic nucleus (STN), a central basal ganglia nucleus, improves OCD. Here, single-unit subthalamic neuronal activity was analysed in 12 OCD patients, in relation to the severity of obsessions and compulsions and response to STN stimulation, and compared with that obtained in 12 patients with Parkinson's disease (PD). STN neurons in OCD patients had lower discharge frequency than those in PD patients, with a similar proportion of burst-type activity (69 vs 67%). Oscillatory activity was present in 46 and 68% of neurons in OCD and PD patients, respectively, predominantly in the low-frequency band (1–8 Hz). In OCD patients, the bursty and oscillatory subthalamic neuronal activity was mainly located in the associative–limbic part. Both OCD severity and clinical improvement following STN stimulation were related to the STN neuronal activity. In patients with the most severe OCD, STN neurons exhibited bursts with shorter duration and interburst interval, but higher intraburst frequency, and more oscillations in the low-frequency bands. In patients with best clinical outcome with STN stimulation, STN neurons displayed higher mean discharge, burst and intraburst frequencies, and lower interburst interval. These findings are consistent with the hypothesis of a dysfunction in the associative–limbic subdivision of the basal ganglia circuitry in OCD's pathophysiology

Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning

Physical rehabilitation supported by the computer- assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts