Noninvasive localization of an epileptogenic zone is a fundamental step for presurgical evaluation of epileptic patients. Here, we applied long-term simultaneous functional near-infrared spectroscopy (fNIRS)/electroencephalogram (EEG) monitoring for focus diagnosis in patients with mesial temporal lobe epilepsy (MTLE). Six MTLE patients underwent long-term (8-16 h per day for 4 days) fNIRS/EEG monitoring for the occurrence of spontaneous seizures. Four spontaneous seizures were successfully recorded out of the six patients. To determine oxy-Hb amplitude, the period-average values of oxy-Hb across 20 s from the EEG- or clinically defined epileptic onset were calculated for both hemispheres from the simultaneously recorded fNIRS data. The average oxy-Hb values for the temporal lobe at the earlier EEG- or clinically defined epileptic onsets were greater for the epileptic side than for the contralateral side after EEG activity suppression, spike train, and clinical seizure in all four cases. The true laterality was determined based on the relief of seizures by selective amygdalo-hippocampectomy. Thus, oxy-Hb amplitude could be a reliable measure for determining the epileptic focus side. Long-term simultaneous fNIRS/EEG measurement serves as an effective tool for recording spontaneous seizures. Cerebral hemodynamic measurement by fNIRS would serve as a valuable supplementary noninvasive measurement method for presurgical evaluation of MTLE.

Epilepsy is a disease that attacks the nerves. To detect epilepsy, it is necessary to analyze the results of an EEG test. In this study, we compared the naive bayes, random tree forest and K-nearest neighbor (KNN) classification algorithms to detect epilepsy. The raw EEG data were pre-processed before doing feature extraction. Then, we have done the training in three algorithms: KNN Classification, naïve bayes classification and random tree forest. The last step was validation of the trained machine learning. Comparing those three classifiers, we calculated accuracy, sensitivity, specificity, and precision. The best trained classifier is KNN classifier (accuracy: 92.7%), rather than random tree forest (accuracy: 86.6%) and naïve bayes classifier (accuracy: 55.6%). Seen from precision performance, KNN Classification also gives the best precision (82.5%) rather than Naïve Bayes classification (25.3%) and random tree forest (68.2%). But, for the sensitivity, Naïve Bayes classification is the best with 80.3% sensitivity, compare to KNN 73.2% and random tree forest (42.2%). For specificity, KNN classification gives 96.7% specificity, then random tree forest 95.9% and Naïve bayes 50.4%. The training time of naïve bayes was 0.166030 sec, while training time of random tree forest was 2.4094sec and KNN was the slower in training that was 4.789 sec. Therefore, KNN Classification gives better performance than naïve bayes and random tree forest classification.

We aimed to find the differences in memory capabilities between pornography-addicted and nonaddicted juveniles. We enrolled 30 juveniles (12–16 y) consisting of 15 pornography addiction and 15 nonaddiction subjects. We used Rey Auditory Verbal Learning Test (RAVLT) to measure verbal memory, Rey–Osterrieth Complex Figure Test (ROCFT) for visual memory, along with Trail Making Test A and B (TMT-A and TMT-B) for attention. We found a significant reduction in the RAVLT A6 result of the addiction group (nonaddiction vs addiction: 13.47 ± 2.00 vs 11.67 ± 2.44, MD = −1.80, p=0.04), but not in ROCFT or attention tests. Analysis in sex subgroups yielded no sex-specific difference. We concluded that pornography addiction may be associated with impaired recent verbal memory in juveniles, regardless of sex and without association to attention.

BACKGROUND:When an action is being observed, it is matched to the observer’s internal representation of the action. The more similar, the more the action is perceived as natural. A factor influencing judgement of naturalness is the kinematic features of a movement. However, these features could be altered due to certain conditions that can modify movement such as Autism Spectrum Disorders. As a result, neurotypical observers may fail to interpret the action due to impaired naturalness.AIM:This work aims to investigate (1) whether neurotypical observers judge the autistic individuals’ movement as less natural, (2) which kinematic factors (jerk, acceleration, velocity and size) contribute to their perception and (3) whether cue reliance correlates with autistic traits.METHODS:Thirty neurotypical participants (20 – 33 years old; 15 males) completed autistic trait screening questionnaires (ADC, TAS-20, AQ). They completed a computer task showing 2D side-to-side arm movements recorded from neurotypical and autistic individuals. Finally, they rated the naturalness of the observed movements, and how certain they were with their answer.RESULTS:There was a significant difference between the participants’ perception of naturalness of the two movement groups. Jerk, acceleration and velocity contributed to shaping the participants’ perception with a jerk as the most significant factor. The correlation between the participants’ autistic trait and both their perception of naturalness as well as of each kinematic cue were not significant.CONCLUSION:Our neurotypical participants perceived the autistic movements as less natural. Their perceptions were influenced mainly by the jerk as well as acceleration and velocity of the autistic movements. Autistic traits in the participants did not correlate to their perception of movement naturalness nor to any of the kinematic factors.