Effects of Non-invasive Neurostimulation on Autism Spectrum Disorder: A Systematic Review

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by major impairments in social communication, stereotyped and ritualistic behaviors and deficits in sensory reactivity. Recently, noninvasive brain stimulation (NIBS) methods, namely transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS), have been examined as possible new therapeutic options for modifying the pathological neuroplasticity involved in neuropsychiatric disorders including ASD. Therefore, we conducted a systematic review on the therapeutic uses of tDCS and repetitive TMS (rTMS) in ASD patients. A systematic search was performed on Scopus, Web of Science, PubMed, Cochrane and Embase. Original articles reporting the use of tDCS or rTMS to treat ASD were screened and studied by two researchers independently based on PRISMA guidelines. We found 32 eligible studies including 8 tDCS reports, 23 rTMS reports and one report with both tDCS and rTMS. These studies comprised 6 case-reports, 9 non-controlled trials and 17 controlled trials which assessed NIBS effects on the three cognitive, behavioral and biological dimensions in ASD. Existing evidence demonstrates that NIBS methods could be helpful for treating some dimensions of ASD such as repetitive behavior, sociability or some aspects of executive and cognitive functions. However, such evidence should be regarded with care because of the quality of original researches and serious publication bias as well as the heterogeneity of data. Further randomized, double-blind, sham-controlled trials with appropriate follow-up periods should be designed to assess the efficacy of NIBS methods for ASD treatment.

Neuro-cognitive Ramifications of Fasting and Feeding in Obese and Non-obese Cases

Preliminary studies have claimed that short term fasting would negatively affect school performance and cognition. In contrast some other studies have reported not important decline in cognition and executive function as a result of fasting. Also limited attention was generally devoted to dietetic regimens, nutritional status and body weight. Yet neuroscience and neuro-cognitive aspects of acute hunger on the electroencephalogram and differences between obese and non-obese cases is not well understood. Hence, we decided to design and perform a case study in a more controlled situation similar to reality. Therefore, we performed several examinations including subjective tests (for eating status) and objective tests (cognitive tests such as Stroop effect and Sternberg search and electroencephalogram measures such as steady-state visual evoked potential and auditory steady-state responses) for an obese and a non-obese academic case before and after a simple breakfast. The results showed that the breakfast effects on the neuro-cognitive functions depend on either obesity status, nutritional status of the case or the type of cognitive task (visual or auditory). This paper would open a new insight to answer some important questions about the neuro-cognitive implications of fasting and feeding in obese and non-obese human cases.

Subject-based discriminative sparse representation model for detection of concealed information

BACKGROUND AND OBJECTIVES

The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each subject is considered independent of the others. The main goal of this study is to adapt the discriminative sparse models to be applicable for subject-based concealed information test.

METHODS

In order to provide sufficient discriminability between guilty and innocent subjects, we introduced a novel discriminative sparse representation model and its appropriate learning methods. For evaluation of the method forty-four subjects participated in a mock crime scenario and their EEG data were recorded. As the model input, in this study the recurrence plot features were extracted from single trial data of different stimuli. Then the extracted feature vectors were reduced using statistical dependency method. The reduced feature vector went through the proposed subject-based sparse model in which the discrimination power of sparse code and reconstruction error were applied simultaneously.

RESULTS

Experimental results showed that the proposed approach achieved better performance than other competing discriminative sparse models. The classification accuracy, sensitivity and specificity of the presented sparsity-based method were about 93%, 91% and 95% respectively.

CONCLUSIONS

Using the EEG data of a single subject in response to different stimuli types and with the aid of the proposed discriminative sparse representation model, one can distinguish guilty subjects from innocent ones. Indeed, this property eliminates the necessity of several subject EEG data in model learning and decision making for a specific subject.

EEG classification of adolescents with type I and type II of bipolar disorder

Bipolar disorder (BD) is a severe psychiatric disorder and has two common types: type I and type II. Early diagnosis of the subtypes is very challenging particularly in adolescence. In this study, 38 adolescents are participated including 18 patients with BD I and 20 patients with BD II. The electroencephalogram signal is recorded by 19 electrodes in open eyes at resting state. After preprocessing, the state of the art methods from various domains are implemented to provide a good feature set for classifying the two groups. In order to improve the classification accuracy, four different feature selection methods named mutual information maximization (MIM), conditional mutual information maximization (CMIM), fast correlation based filter (FCBF), and double input symmetrical relevance (DISR) are applied to select the most informative features. Multilayer perceptron (MLP) neural network with a hidden layer containing five neurons is used for classification with and without applying the feature selection methods. The accuracy of 82.68, 86.33, 89.67, 84.61, and 91.83 % were observed using entire extracted features and selected features using MIM, CMIM, FCBF, and DISR methods by MLP, respectively. Therefore, the proposed method can be used in clinical setting for more validation.