Drug Treated Schizophrenia, Schizoaffective and Bipolar Disorder Patients Evaluated by qEEG Absolute Spectral Power and Mean Frequency Analysis

Difference between Quantitative Electroencephalography, Loudness Dependence of Auditory Evoked Potential, and Mismatch Negativity between a Manic and a Depressive Episode in a Single Bipolar Patient with Mixed Features

This study compares the changes in Quantitative electroencephalography (QEEG), loudness dependence of auditory evoked potentials (LDAEP), and mismatch negativity (MMN) in the case of bipolar depression, mania, and euthymia in a single patient. the characteristic of QEEG in this patient with mixed depression was an increase in alpha; in mixed mania, there was little increase in alpha, and the decrease in delta, theta, and beta was noticeable. LDAEP increased more in the manic phase than in the depressive phase. In contrast, MMN decreased more in the manic than in the depressive phase. After remission of mania, QEEG, LDAEP, and MMN were re-measured. Compared with the manic phase, the decrease in delta, theta, and beta bands in the occipital, temporal, and parietal lobes improved significantly. The LDAEP decreased from LDAEP 1.67 to 0.97. However, in spite of the euthymic phase, MMN amplitude showed a further decrease, from -1.7 to -0.9. In conclusion, using QEEG, LDAEP, and MMN can help clinicians predict a patient's bipolar state and evaluate serotonin intensity and cognitive function, enabling customized treatment. However, there are still few consistent research results; therefore, there is a need to utilize a larger sample size.

Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG

Background

Accurate classification based on affordable objective neuroimaging biomarkers are important steps toward designing individualized treatment.

Methods

In this work, we investigated a deep learning classification model, multi-scale convolutional recurrent neural network (MCRNN), to explore psychiatric disorder-related biomarkers by leveraging the spatiotemporal information of resting-state EEG (rsEEG) using a multiple psychiatric disorder database containing 327 individuals diagnosed with schizophrenia, bipolar, major depressive disorders, and healthy controls. All subjects were mapped to a shared low-dimensional subspace for intuitively interpreting the inter-relationship and separation of psychiatric disorders.

Results

Psychiatric disorders were identified using rsEEG with high accuracy ranged from 78.6 to 91.3% in patient vs. controls two-class classification, and 68.2% in four-class classification. The control-to-schizophrenia trajectory interpretated by the model was consistent with the disease severity in clinical observation.

Conclusion

The MsRNN demonstrated a capability in extracting discriminative rsEEG biomarkers for psychiatric disorder classification, indicating its potential to facilitate our understanding of psychiatric disorders and monitoring interventions.

Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics

Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.

Abnormal Neural Oscillations in Schizophrenia Assessed by Spectral Power Ratio of MEG During Word Processing

This study investigated spectral power of neural oscillations associated with word processing in schizophrenia. Magnetoencephalography (MEG) data were acquired from 12 schizophrenia patients and 10 healthy controls during a visual word processing task. Two spectral power ratio (SPR) feature sets: the band power ratio (BPR) and the window power ratio (WPR) were extracted from MEG data in five frequency bands, four time windows of word processing, and at locations covering whole head. Cluster-based nonparametric permutation tests were employed to identify SPRs that show significant between-group difference. Machine learning based feature selection and classification techniques were then employed to select optimal combinations of the significant SPR features, and distinguish schizophrenia patients from healthy controls. We identified three BPR clusters and three WPR clusters that show significant oscillation power difference between groups. These include the theta/delta, alpha/delta and beta/delta BPRs during base-to-encode and encode time windows, and the beta band WPR from base to encode and from encode to post-encode windows. Based on two WPR and one BPR features combined, over 95% cross-validation classification accuracy was achieved using three different linear classifiers separately. These features may have potential as quantitative markers that discriminate schizophrenia patients and healthy controls; however, this needs further validation on larger samples.

COMT polymorphism modulates the resting-state EEG alpha oscillatory response to acute nicotine in male non-smokers

Performance improvements in cognitive tasks requiring executive functions are evident with nicotinic acetylcholine receptor (nAChR) agonists, and activation of the underlying neural circuitry supporting these cognitive effects is thought to involve dopamine neurotransmission. As individual difference in response to nicotine may be related to a functional polymorphism in the gene encoding catechol-O-methyltransferase (COMT), an enzyme that strongly influences cortical dopamine metabolism, this study examined the modulatory effects of the COMT Val158Met polymorphism on the neural response to acute nicotine as measured with resting-state electroencephalographic (EEG) oscillations. In a sample of 62 healthy non-smoking adult males, a single dose (6 mg) of nicotine gum administered in a randomized, double-blind, placebo-controlled design was shown to affect α oscillatory activity, increasing power of upper α oscillations in frontocentral regions of Met/Met homozygotes and in parietal/occipital regions of Val/Met heterozygotes. Peak α frequency was also found to be faster with nicotine (vs. placebo) treatment in Val/Met heterozygotes, who exhibited a slower α frequency compared to Val/Val homozygotes. The data tentatively suggest that interindividual differences in brain α oscillations and their response to nicotinic agonist treatment are influenced by genetic mechanisms involving COMT.

The influence of levetiracetam in cognitive performance in healthy individuals: neuropsychological, behavioral and electrophysiological approach

OBJECTIVE

The present study sought to analyze the influence of Levetiracetam (LEV) in cognitive performance by identifying the changes produced by LEV in reaction time, in neuropsychological assessment of attention and memory and in absolute theta power in frontal activity.

METHODS

Twelve healthy subjects (5 men and 7 women; mean age, 30.08 years, standard deviation, 4.71) were recruited for this study. The neuropsychological tests: Trail Making Test (A and B), Digit Span (direct and indirect numerical orders/working memory); Stroop test (inhibitory control of attention); Tower of London (planning and decision-making) and a quantitative electroencephalography were applied in 2 different days after and before the participants ingested the capsule of placebo or 500 mg LEV.

RESULTS

A two-way-ANOVA was implemented to observe the interaction between conditions (placebo or LEV 500 mg) and moments (pre- and post-ingestion of LEV or placebo). The data were analyzed by the SPSS statistical package (p<0.05). For the neuropsychological parameter, the Trail Making Test (A) was the only test that showed significant difference for condition in the task execution time (p=0.026). Regarding the reaction time in the behavioral parameter, an interaction between both factors (p=0.034) was identified through a two-way-ANOVA (condition versus moment). Electrophysiological measures showed a significant interaction for electrodes: F7, F3, and FZ.

CONCLUSIONS

The findings showed that LEV promotes an important cognitive enhancement in the executive functions.