The sight of one's own body: Could qEEG help predict the treatment response in anorexia nervosa?

Aims of the study: The study aims to identify the differences in brain activity between participants with anorexia nervosa and healthy control using visual stimulus conditions combined with the quantitative dense-array EEG recording analysis method called Brain Activation Sequences (BAS). Materials and methods: 23 participants with anorexia nervosa and 21 healthy controls were presented with visual stimuli, including the subject's facial expressions and body images. The 128-channel EEG data were processed using BAS and displayed as activity in up to 66 brain regions. Subsequent cluster analysis was used to identify groups of participants exhibiting area-specific activation patterns. Results: Cluster analysis identified three distinct groups: one including all healthy controls (HC) and two consisting of all participants with anorexia (AN-I with 19 participants and AN-II with four participants). The AN-I and AN-II groups differed in their response to treatment. Comparisons of HC vs. AN confirmed the dominance of the right cerebral hemisphere in participants with anorexia nervosa in two of the three reported conditions. The facial expressions condition, specifically the facial reaction expressing disgust, indicates the existence of a social attentional bias toward faces, whereas emotions remained undetected in participants. High limbic activity, medial frontal gyrus involvement, low fusiform cortex activity, and milder visual cortex activity in healthy controls compared to participants indicate that the facial expression stimulus is perceived by healthy subjects primarily as an emotion, not as the face itself. In the body image condition, participants showed higher activity in the fusiform gyrus and right insula, indicating activation of the brain's "fear network." Conclusion: The study describes a specific pattern of brain activation in response to facial expression of disgust and body images that likely contributes to social-cognitive and behavioral impairments in anorexia. In addition, the substantial difference in the pattern of brain activation within the participants with AN and its association with treatment resistance deserves special attention because of its potential to develop a clinically useful prediction tool and identify potential targets for, for example, neuromodulatory treatments and/or individualized psychotherapy.

Idiopathic Hypersomnia-A Dynamic Simulation Model.

Aims of the study: Commonly used approach to illness assessment focuses on the patient's actual state supplemented by binary records of past events and conditions. This research project was designed to explain subjective experience in idiopathic hypersomnia (IH) patients influenced by their clinical symptoms and comorbidities. Material and Methods: Forty-three IH patients of both sexes (female 60.5%, male 39.5%) were assessed using a detailed structured examination. The interview covered neurologic, psychiatric, and internal medicine anamnesis, medication past and current, substance abuse, work impairment, detailed sleep-related data, specific sleep medication, and a full-length set of questionnaires including depression, quality of life, sleepiness, anxiety, fatigue, insomnia, and sleep inertia. The data were digitized and imported into statistical software (SPSS by IBM), and dynamic simulation software (Vensim by Ventana Systems Inc.) was used to build a causal loop diagram and stocks and flows diagram as a simulation structure. Results: The overall raw data and simulation-based patterns fit at 76.1%. The simulation results also identified the parameters that contribute the most to patients' subjective experience. These included sleep inertia, the refreshing potential of naps, the quality of nocturnal sleep, and the social aspects of the patient's life. Psychiatric disorders influence the overall pattern at a surprisingly low level. The influence of medication has been studied in detail. Although its contribution to the dynamics looks marginal at first sight, it significantly influences the contribution of other variables to the overall patient experience of the disease. Conclusion: Even the simplified dynamic structure designed by the research team reflects the real-life events in patients with IH at the acceptable level of 76.1% and suggests that a similar structure plays an important role in the course of the disease. Therapeutic focus on the parameters identified by the model should enhance the patients' subjective experience throughout illness duration and might even turn the progress from negative into positive. Further research is needed to understand the dynamics of idiopathic hypersomnia in greater detail to better understand the causes and design therapeutic approaches to improve patients' quality of life.

Brain activation sequences.

OBJECTIVE: Reported brain abnormalities in anatomy and function in psychiatric and neurological patients led to a project based on qualitative electroencephalography examination and analysis in an attempt to find specific brain derived pattern--or sequence of brain locations involved in processing various stimuli--both visual and auditory. METHODS: Specialized software called Brain Activation Sequences was built according to our team member specifications (M.S.). The software utilizes event related potentials recorded during cognitive/emotion processing in participants (healthy controls, neurological patients and psychiatric patients) to calculate the sequence of brain areas using nonlinear and linear algorithms. RESULTS: Results show significant differences in activation patterns between patients and healthy controls as well as significant similarities within the groups of patients and controls in both performed testing experiments.

Quantitative topographic differentiation of the neonatal EEG.

OBJECTIVE: To test the discriminatory topographic potential of a new method of the automatic EEG analysis in neonates. A quantitative description of the neonatal EEG can contribute to the objective assessment of the functional state of the brain, and may improve the precision of diagnosing cerebral dysfunctions manifested by 'disorganization', 'dysrhythmia' or 'dysmaturity'. METHODS: 21 healthy, full-term newborns were examined polygraphically during sleep (EEG-8 referential derivations, respiration, ECG, EOG, EMG). From each EEG record, two 5-min samples (one from the middle of quiet sleep, the other from the middle of active sleep) were subject to subsequent automatic analysis and were described by 13 variables: spectral features and features describing shape and variability of the signal. The data from individual infants were averaged and the number of variables was reduced by factor analysis. RESULTS: All factors identified by factor analysis were statistically significantly influenced by the location of derivation. A large number of statistically significant differences were also established when comparing the effects of individual derivations on each of the 13 measured variables. Both spectral features and features describing shape and variability of the signal are largely accountable for the topographic differentiation of the neonatal EEG. CONCLUSIONS: The presented method of the automatic EEG analysis is capable to assess the topographic characteristics of the neonatal EEG, and it is adequately sensitive and describes the neonatal electroencephalogram with sufficient precision. SIGNIFICANCE: The discriminatory capability of the used method represents a promise for their application in the clinical practice.

Comparison of quantitative EEG characteristics of quiet and active sleep in newborns.

BACKGROUND AND PURPOSE: The aim of the present study was to verify whether the proposed method of computer-supported EEG analysis is able to differentiate the EEG activity in quiet sleep (QS) from that in active sleep (AS) in newborns. A quantitative description of the neonatal EEG may contribute to a more exact evaluation of the functional state of the brain, as well as to a refinement of diagnostics of brain dysfunction manifesting itself frequently as 'dysrhythmia' or 'dysmaturity'. PATIENTS AND METHODS: Twenty-one healthy newborns (10 full-term and 11 pre-term) were examined polygraphically (EEG-eight channels, respiration, ECG, EOG and EMG) in the course of sleep. From each EEG record, two 5-min samples (one from QS and one from AS) were subject to an off-line computerized analysis. The obtained data were averaged with respect to the sleep state and to the conceptional age. The number of variables was reduced by means of factor analysis. RESULTS: All factors identified by factor analysis were highly significantly influenced by sleep states in both developmental periods. Likewise, a comparison of the measured variables between QS and AS revealed many statistically significant differences. The variables describing (a) the number and length of quasi-stationary segments, (b) voltage and (c) power in delta and theta bands contributed to the greatest degree to the differentiation of EEGs between both sleep states. CONCLUSION: The presented method of the computerized EEG analysis which has good discriminative potential is adequately sensitive and describes the neonatal EEG with convenient accuracy.