Changes in EEG spectral power on perception of neutral and emotional words in patients with schizophrenia, their relatives, and healthy subjects from the general population

Neural Circuitry of Salience and Reward Processing in Psychosis

The processing of salient and rewarding stimuli is integral to engaging our attention, stimulating anticipation for future events, and driving goal-directed behaviors. Widespread impairments in these processes are observed in psychosis, which may be associated with worse functional outcomes or mechanistically linked to the development of symptoms. Here, we summarize the current knowledge of behavioral and functional neuroimaging in salience, prediction error, and reward. Although each is a specific process, they are situated in multiple feedback and feedforward systems integral to decision making and cognition more generally. We argue that the origin of salience and reward processing dysfunctions may be centered in the subcortex during the earliest stages of psychosis, with cortical abnormalities being initially more spared but becoming more prominent in established psychotic illness/schizophrenia. The neural circuits underpinning salience and reward processing may provide targets for delaying or preventing progressive behavioral and neurobiological decline.

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data

Finding effective and objective biomarkers to inform the diagnosis of schizophrenia is of great importance yet remains challenging. Relatively little work has been conducted on multi-biological data for the diagnosis of schizophrenia. In this cross-sectional study, we extracted multiple features from three types of biological data, including gut microbiota data, blood data, and electroencephalogram data. Then, an integrated framework of machine learning consisting of five classifiers, three feature selection algorithms, and four cross validation methods was used to discriminate patients with schizophrenia from healthy controls. Our results show that the support vector machine classifier without feature selection using the input features of multi-biological data achieved the best performance, with an accuracy of 91.7% and an AUC of 96.5% (p < 0.05). These results indicate that multi-biological data showed better discriminative capacity for patients with schizophrenia than single biological data. The top 5% discriminative features selected from the optimal model include the gut microbiota features (Lactobacillus, Haemophilus, and Prevotella), the blood features (superoxide dismutase level, monocyte-lymphocyte ratio, and neutrophil count), and the electroencephalogram features (nodal local efficiency, nodal efficiency, and nodal shortest path length in the temporal and frontal-parietal brain areas). The proposed integrated framework may be helpful for understanding the pathophysiology of schizophrenia and developing biomarkers for schizophrenia using multi-biological data.

Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders

Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alpha power in psychosis indicate an abnormal arousal state. We investigated brain activity across the basic EEG frequencies and also dynamic functional connectivity of both intra and cross-frequency coupling that could reveal a neurophysiological biomarker linked to an aberrant modulating role of alpha frequency in adolescents with schizophrenia spectrum disorders (SSDs). A dynamic functional connectivity graph (DFCG) has been estimated using the imaginary part of phase lag value (iPLV) and correlation of the envelope (corrEnv). We analyzed DFCG profiles of electroencephalographic resting state (eyes closed) recordings of healthy controls (HC) (n = 39) and SSDs subjects (n = 45) in basic frequency bands {δ,θ,α₁,α₂,β₁,β₂,γ}. In our analysis, we incorporated both intra and cross-frequency coupling modes. Adopting our recent Dominant Coupling Mode (DοCM) model leads to the construction of an integrated DFCG (iDFCG) that encapsulates the functional strength and the DοCM of every pair of brain areas. We revealed significantly higher ratios of delta/alpha₁,₂ power spectrum in SSDs subjects versus HC. The probability distribution (PD) of amplitude driven DoCM mediated by alpha frequency differentiated SSDs from HC with absolute accuracy (100%). The network Flexibility Index (FI) was significantly lower for subjects with SSDs compared to the HC group. Our analysis supports the central role of alpha frequency alterations in the neurophysiological mechanisms of SSDs. Currents findings open up new diagnostic pathways to clinical detection of SSDs and support the design of rational neurofeedback training.

Abnormalities of Alpha Activity in Frontocentral Region of the Brain as a Biomarker to Diagnose Adolescents With Bipolar Disorder

Objectives. To investigate brain abnormalities in adolescents with new-onset bipolar disorder (BD) during acute hypomanic and depressive episodes using electroencephalogram (EEG) analysis and to derive a computer-based method for diagnosis of the disorder. Methods. EEG spectral power and entropy of 21 adolescents with BD (included 11 patients in the hypomanic episode and 10 patients in the depressive episode) and 18 healthy adolescents were compared. Moreover, using significant differences and K-nearest-neighbors (KNN) classifier, it was attempted to distinguish the BD adolescents from normal ones. Results. The BD adolescents had higher values of spectral power in all frequency bands, particularly in the frontocentral, mid-temporal, and right parietal regions. Also, spectral entropy had significantly increased in delta, alpha, and gamma frequency bands for BD. A high accuracy of 95.8% was achieved by all significant differences in the alpha band in discriminating adolescents with BD. The depressive state showed higher values of spectral power and entropy in low-frequency bands (delta and theta) compared to the hypomanic state. Conclusion. Based on BD symptoms, especially inattention, increased alpha power is a rational finding which is associated with thalamus dysfunction. Thus, it seems that EEG alpha oscillation is the main source of abnormality in BD. Furthermore, EEG slowing in the depressive episode is related to inhibition of electrical activity and reduced cognitive functions.

Acute Exposure to Pacific Ciguatoxin Reduces Electroencephalogram Activity and Disrupts Neurotransmitter Metabolic Pathways in Motor Cortex

Ciguatera fish poisoning (CFP) is a common human food poisoning caused by consumption of ciguatoxin (CTX)-contaminated fish affecting over 50,000 people worldwide each year. CTXs are classified depending on their origin from the Pacific (P-CTXs), Indian Ocean (I-CTXs), and Caribbean (C-CTXs). P-CTX-1 is the most toxic CTX known and the major source of CFP causing an array of neurological symptoms. Neurological symptoms in some CFP patients last for several months or years; however, the underlying electrophysiological properties of acute exposure to CTXs remain unknown. Here, we used CTX purified from ciguatera fish sourced in the Pacific Ocean (P-CTX-1). Delta and theta electroencephalography (EEG) activity was reduced remarkably in 2 h and returned to normal in 6 h after a single exposure. However, second exposure to P-CTX-1 induced not only a further reduction in EEG activities but also a 2-week delay in returning to baseline EEG values. Ciguatoxicity was detected in the brain hours after the first and second exposure by mouse neuroblastoma assay. The spontaneous firing rate of single motor cortex neuron was reduced significantly measured by single-unit recording with high spatial resolution. Expression profile study of neurotransmitters using targeted profiling approach based on liquid chromatography-tandem mass spectrometry revealed an imbalance between excitatory and inhibitory neurotransmitters in the motor cortex. Our study provides a possible link between the brain oscillations and neurotransmitter release after acute exposure to P-CTX-1. Identification of EEG signatures and major metabolic pathways affected by P-CTX-1 provides new insight into potential biomarker development and therapeutic interventions.

Review of evoked and event-related delta responses in the human brain

In the last decade, the brain's oscillatory responses have invaded the literature. The studies on delta (0.5-3.5Hz) oscillatory responses in humans upon application of cognitive paradigms showed that delta oscillations are related to cognitive processes, mainly in decision making and attentional processes. The present manuscript comprehensively reviews the studies on delta oscillatory responses upon cognitive stimulation in healthy subjects and in different pathologies, namely Alzheimer's disease, Mild Cognitive Impairment (MCI), bipolar disorder, schizophrenia and alcoholism. Further delta oscillatory response upon presentation of faces, facial expressions, and affective pictures are reviewed. The relationship between pre-stimulus delta activity and post-stimulus evoked and event-related responses and/or oscillations is discussed. Cross-frequency couplings of delta oscillations with higher frequency windows are also included in the review. The conclusion of this review includes several important remarks, including that delta oscillatory responses are involved in cognitive and emotional processes. A decrease of delta oscillatory responses could be a general electrophysiological marker for cognitive dysfunction (Alzheimer's disease, MCI, bipolar disorder, schizophrenia and alcoholism). The pre-stimulus activity (phase or amplitude changes in delta activity) has an effect on post-stimulus EEG responses.

Diagnostic utility of quantitative EEG in un-medicated schizophrenia

The aim of the current study was to evaluate the quantitative electroencephalography (QEEG) characteristics of patients with un-medicated schizophrenia (SPR) and to investigate the diagnostic utility of QEEG in assessing such patients during resting conditions. The subjects included 90 patients with schizophrenia and 90 normal controls. Spectral analysis was performed on the absolute power of all of the electrodes across five frequency bands following artifact removal. We conducted a repeated-measures ANOVA to examine group differences within the five frequency bands across several brain regions and receiver operator characteristic (ROC) analyses to examine the discrimination ability of each frequency band. Compared with controls, patients with schizophrenia showed increased delta and theta activity and decreased alpha 2 activity, particularly in the frontocentral area. There were no significant differences in the alpha 1 and beta activity. The ROC analysis performed on the delta frequency band generated the best result, with an overall classification accuracy of 62.2%. The results of this study confirmed the characteristics of the QEEG power in un-medicated schizophrenia patients compared with normal controls. These findings suggest that a resting EEG test can be a supportive tool for evaluating patients with schizophrenia.

Brain oscillations in bipolar disorder in search of new biomarkers

This report presents six cardinal results obtained with methods of oscillatory brain dynamics in euthymic and manic bipolar patients in comparison to healthy controls. Measurements include changes in oscillatory response activities in the theta, alpha, beta, and gamma frequency ranges. The analysis shows that spontaneous and response activities in the alpha range are highly reduced in euthymic and manic patients, respectively; conversely, beta responses are increased in euthymic and manic patients. Lithium use seems to be associated with further and significant increase in the beta frequency range in euthymic patients. Theta responses to auditory target stimulus during odd-ball paradigm appeared in two different frequency bands (4-6 and 6-8 Hz) in healthy participants. However, only fast theta responses were highly reduced under cognitive load in drug-free euthymic patients. The analysis of connectivity was performed by assessment of long-distance coherence function in the gamma frequency range. Both manic and euthymic patients presented significantly decreased fronto-temporal coherence function during visual odd-ball task, indicating a selective reduction in connectivity during cognitive processing. The present report also discusses that these six oscillatory parameters may serve as an ensemble of biomarkers for diagnostic purposes and tracking treatment response in bipolar disorder.

A short review of alpha activity in cognitive processes and in cognitive impairment

AIM OF THE REPORT: In the companion report (Başar, this volume), the physiological fundaments of alpha activity in integrative brain function are described. The present report is a review of the significant role of alpha activity in memory and cognitive processes in healthy subjects, and in cognitive impairment. The role of neurotransmitters is also described, briefly, in this context. TOWARDS AN UNDERSTANDING OF BRAIN ALPHA: Despite numerous experimental studies, it is indicated that the presented results are only appropriate to establish an ensemble of reasonings and suggestions for analyzing "alphas" in the whole brain. In turn, in the near future, these reasonings and suggestions may serve (or are indispensable to serve) as fundaments of more general and tenable hypotheses on the genesis and function of "alphas".

Brain's alpha activity is highly reduced in euthymic bipolar disorder patients

Brain's alpha activity and alpha responses belong to major electrical signals that are related to sensory/cognitive signal processing. The present study aims to analyze the spontaneous alpha activity and visual evoked alpha response in drug free euthymic bipolar patients. Eighteen DSM-IV euthymic bipolar patients (bipolar I n = 15, bipolar II n = 3) and 18 healthy controls were enrolled in the study. Patients needed to be euthymic at least for 4 weeks and psychotrop free for at least 2 weeks. Spontaneous EEG (4 min eyes closed, 4 min eyes open) and evoked alpha response upon application of simple visual stimuli were analyzed. EEG was recorded at 30 positions. The digital FFT-based power spectrum analysis was performed for spontaneous eyes closed and eyes open conditions and the response power spectrum was also analyzed for simple visual stimuli. In the analysis of spontaneous EEG, the ANOVA on alpha responses revealed significant results for groups (F(1,34) = 8.703; P < 0.007). Post-hoc comparisons showed that spontaneous EEG alpha power of healthy subjects was significantly higher than the spontaneous EEG alpha power of euthymic patients. Furthermore, visual evoked alpha power of healthy subjects was significantly higher than visual evoked alpha power of euthymic patients (F(1,34) = 4.981; P < 0.04). Decreased alpha activity in spontaneous EEG is an important pathological EEG finding in euthymic bipolar patients. Together with an evident decrease in evoked alpha responses, the findings may lead to a new pathway in search of biological correlates of cognitive impairment in bipolar disorder.