Evidence of language-related left hypofrontality in Major Depression: An EEG Beta band study

Effect of acupuncture on brain activity in patients with decreasing ovarian reserve: a resting-state functional magnetic resonance imaging study

OBJECTIVE

To examine the variations in brain regions among individuals with decreasing ovarian reserve (DOR) compared to healthy controls using resting-state functional magnetic resonance imaging (rs-fMRI), and to assess the immediate effects of acupuncture stimulation on these brain regions in DOR patients.

METHODS

Twenty patients diagnosed with DOR (DOR group) and twenty healthy controls (HC group) who underwent rs-fMRI scans were included. The DOR group received acupuncture and underwent a subsequent rs-fMRI scan. Amplitude of low-frequency fluctuations (ALFF) analysis was utilized to identify disparities in brain regions between DOR and HC groups, and to evaluate the immediate effects of acupuncture on DOR patients' brain regions. Common brain regions were identified as seed points for functional connectivity (FC) analysis.

RESULTS

In this study, a total of 20 HCs and 20 patients with DOR were initially enrolled. However, due to incomplete personal information, three participants were removed from the HC group. Additionally, two DOR patients experienced symptoms such as physical discomfort and shortness of breath during the MRI scan, leading to their exclusion due to excessive head movement parameters. Consequently, 17 HCs and 16 DOR patients completed the entire study protocol. Comparative analysis revealed that DOR patients exhibited increased ALFF values in the left inferior temporal gyrus (ITG) and middle temporal gyrus (MTG), while ALFF values in the bilateral superior frontal gyrus (SFG), middle frontal gyrus (MFG), and left inferior frontal gyrus (IFG) were decreased compared to HCs. Following acupuncture intervention, ALFF values in the left SFG, MFG, and supplementary motor area (SMA) of DOR patients increased. Furthermore, functional connectivity (FC) analysis demonstrated increased connectivity of the left SFG with the bilateral calcarine sulcus and lingual gyrus post-acupuncture.

CONCLUSION

This study highlights abnormal neural activity in the SFG, MFG, IFG, and ACC in DOR patients compared to HCs. Acupuncture was found to regulate the activity of the SFG, bringing it closer to normal levels, and enhancing its functional connectivity with the bilateral calcarine sulcus and lingual gyrus.

TSF-MDD: A Deep Learning Approach for Electroencephalography-Based Diagnosis of Major Depressive Disorder with Temporal-Spatial-Frequency Feature Fusion

Major depressive disorder (MDD) is a prevalent mental illness characterized by persistent sadness, loss of interest in activities, and significant functional impairment. It poses severe risks to individuals' physical and psychological well-being. The development of automated diagnostic systems for MDD is essential to improve diagnostic accuracy and efficiency. Electroencephalography (EEG) has been extensively utilized in MDD diagnostic research. However, studies employing deep learning methods still face several challenges, such as difficulty in extracting effective information from EEG signals and risks of data leakage due to experimental designs. These issues result in limited generalization capabilities when models are tested on unseen individuals, thereby restricting their practical application. In this study, we propose a novel deep learning approach, termed TSF-MDD, which integrates temporal, spatial, and frequency-domain information. TSF-MDD first applies a data reconstruction scheme to obtain a four-dimensional temporal-spatial-frequency representation of EEG signals. These data are then processed by a model based on 3D-CNN and CapsNet, enabling comprehensive feature extraction across domains. Finally, a subject-independent data partitioning strategy is employed during training and testing to eliminate data leakage. The proposed approach achieves an accuracy of 92.1%, precision of 90.0%, recall of 94.9%, and F1-score of 92.4%, respectively, on the Mumtaz2016 public dataset. The results demonstrate that TSF-MDD exhibits excellent generalization performance.

Abnormal beta bursts of depression in the orbitofrontal cortex and its relationship with clinical symptoms

BACKGROUND

Recent researches have reported that frequency-specific patterns of neural activity contain not only rhythmically sustained oscillations but also transient-bursts of isolated events. The aim of this study was to investigated the correlation between beta burst and depression in order to explore depressive disease and the neurological underpinnings of disease-related symptoms.

METHODS

We collected resting-state MEG recordings from 30 depressive patients and a matched 40 healthy controls. A Hidden Markov Model (HMM) was applied on source-space time courses for 78 cortical regions of the AAL atlas and the temporal characteristics of beta burst from the matched HMM states were captured. Group differences were evaluated on these beta burst characteristics after permutation tests and, for the depressive group, associations between burst characteristics and clinical symptom severity were determined using Spearman correlation coefficients.

RESULTS

At a threshold of p=0.05corrected, burst characteristics revealed significant differences between depression patients and controls at the group level, including increased burst amplitude in frontal lobe, decreased burst duration in occipital regions, increased burst rate and decreased burst interval time in some brain regions. Furthermore, burst amplitude in the orbitofrontal cortex (OFC) was positively related to the severity of sleep disturbance and burst rate in the OFC was negatively related to the severity of anxiety in depression patients.

CONCLUSIONS

The findings highlight OFC may be a targeted area responsible for the anxiety and sleep disturbance symptom by abnormal beta burst in depressive patients and beta burst characteristics of OFC might serve as a neuro-marker for the depression.

Trial-by-Trial Variability of TMS-EEG in Healthy Controls and Patients With Depressive Disorder

Depressive disorder has been known to be associated with high variability in resting-state electroencephalography (EEG) signals. However, this phenomenon is often ignored in stimulus-related brain activities. This study proposed a new method to explore the EEG variability evoked by transcranial magnetic stimulation (TMS, TMS-EEG) in depressive disorder (DE) patients. The TMS-EEG data were collected from 34 DE patients and 36 healthy controls (HC). The maximum eigenvalue of the real binary correlation matrix, calculated between different trials using cross-correlation and surrogate methods, was extracted to assess trial-by-trial variability (TTV) of TMS-EEG. The new method was found to more sensitive and reliable than the standard deviation method. DE patients exhibited significantly smaller TTV in Gamma band and greater TTV in Delta band than HC. Furthermore, the HAMD-17 scores were negatively correlated with TTV values in Gamma band. This study represented the first investigation into the TTV in TMS-EEG data and revealed abnormal values in DE patients. Those findings enhance our understanding of TMS-EEG technology and provide valuable insights for studying the characteristics of DE.

Standardized weighted low-resolution electromagnetic tomography study of the amygdala activity in patients with comorbid major depressive disorder and anxiety symptoms

Major depressive disorder (MDD) often coexists with anxiety disorders or symptoms, as identified by previous functional magnetic resonance imaging (fMRI) studies. These studies have found abnormal amplitudes of low-frequency fluctuations (ALFF) in the amygdala, which serve as traits and state markers of MDD. This study used standardized weighted low-resolution electromagnetic tomography (swLORETA) technology to explore amygdala markers in patients with comorbid MDD and anxiety. Participants included patients with MDD comorbid with anxiety symptoms (MDD group) and healthy controls (HC group) who completed the Beck Depression Inventory-II (BDI-II) and the Beck Anxiety Inventory (BAI). EEG data collected under resting state, happiness recall, and depressive recall tasks were converted into current-source density (CSD) values using swLORETA to assess amygdala activation. The results indicated higher beta2, beta3, and high beta levels in both the left and right amygdalae during the resting state in the MDD group than in the HC group. Similarly, elevated levels of beta2, beta3, and high beta were observed in the left and right amygdalae of the MDD group during happiness and depressive recall tasks. These findings support the presence of hyperactivity in the amygdala under resting state and emotional tasks in patients with comorbid MDD and anxiety symptoms.

HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehensive consideration of depression's pathogenesis, leading to limited neuroscientific interpretability. To address these issues, we propose a hemisphere asymmetry network (HEMAsNet) inspired by the brain for depression recognition from EEG signals. HEMAsNet employs a combination of multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) blocks to extract temporal features from both hemispheres of the brain. Moreover, the model introduces a unique 'Callosum-like' block, inspired by the corpus callosum's pivotal role in facilitating inter-hemispheric information transfer within the brain. This block enhances information exchange between hemispheres, potentially improving depression recognition accuracy. To validate the performance of HEMAsNet, we first confirmed the asymmetric features of frontal lobe EEG in the MODMA dataset. Subsequently, our method achieved a depression recognition accuracy of 0.8067, indicating its effectiveness in increasing classification performance. Furthermore, we conducted a comprehensive investigation from spatial and frequency perspectives, demonstrating HEMAsNet's innovation in explaining model decisions. The advantages of HEMAsNet lie in its ability to achieve more accurate and interpretable recognition of depression through the simulation of physiological processes, integration of spatial information, and incorporation of the Callosum-like block.

Brain imaging studies of emotional well-being: a scoping review

This scoping review provides an overview of previous empirical studies that used brain imaging techniques to investigate the neural correlates of emotional well-being (EWB). We compiled evidence on this topic into one accessible and usable document as a foundation for future research into the relationship between EWB and the brain. PRISMA 2020 guidelines were followed. We located relevant articles by searching five electronic databases with 95 studies meeting our inclusion criteria. We explored EWB measures, brain imaging modalities, research designs, populations studied, and approaches that are currently in use to characterize and understand EWB across the literature. Of the key concepts related to EWB, the vast majority of studies investigated positive affect and life satisfaction, followed by sense of meaning, goal pursuit, and quality of life. The majority of studies used functional MRI, followed by EEG and event-related potential-based EEG to study the neural basis of EWB (predominantly experienced affect, affective perception, reward, and emotion regulation). It is notable that positive affect and life satisfaction have been studied significantly more often than the other three aspects of EWB (i.e., sense of meaning, goal pursuit, and quality of life). Our findings suggest that future studies should investigate EWB in more diverse samples, especially in children, individuals with clinical disorders, and individuals from various geographic locations. Future directions and theoretical implications are discussed, including the need for more longitudinal studies with ecologically valid measures that incorporate multi-level approaches allowing researchers to better investigate and evaluate the relationships among behavioral, environmental, and neural factors.

Systematic review registration

https://osf.io/t9cf6/.

Effects of Hyperdirect Pathway Theta Burst Transcranial Magnetic Stimulation on Inhibitory Control, Craving, and Smoking in Adults With Nicotine Dependence: A Double-Blind, Randomized Crossover Trial

BACKGROUND

Nicotine dependence is associated with dysregulated hyperdirect pathway (HDP)-mediated inhibitory control (IC). However, there are currently no evidence-based treatments that have been shown to target the HDP to improve IC and reduce cigarette cravings and smoking.

METHODS

Following a baseline nonstimulation control session, this study (N = 37; female: n = 17) used a double-blind, randomized crossover design to examine the behavioral and neural effects of intermittent theta burst stimulation (iTBS) and continuous TBS (cTBS) to the right inferior frontal gyrus (rIFG)-a key cortical node of the HDP. Associations between treatment effects were also explored.

RESULTS

At baseline, HDP IC task-state functional connectivity was positively associated with IC task performance, which confirmed the association between HDP circuit function and IC. Compared with iTBS, rIFG cTBS improved IC task performance. Compared with the baseline nonstimulation control session, both TBS conditions reduced cigarette craving and smoking; however, although craving and smoking were lower for cTBS, no differences were found between the two active conditions. In addition, although HDP IC task-state functional connectivity was greater following cTBS than iTBS, there was no significant difference between conditions. Finally, cTBS-induced improvement in IC task performance was associated with reduced craving, and cTBS-induced reduction in craving was associated with reduced smoking.

CONCLUSIONS

These findings warrant further investigation into the effects of rIFG cTBS for increasing IC and reducing craving and smoking among individuals with nicotine dependence. Future sham-controlled cTBS studies may help further elucidate the mechanisms by which rIFG cTBS mediates IC and smoking behavior.

Oscillatory network markers of subcallosal cingulate deep brain stimulation for depression

Identifying functional biomarkers related to treatment success can aid in expediting therapy optimization, as well as contribute to a better understanding of the neural mechanisms of the treatment-resistant depression (TRD) and subcallosal cingulate deep brain stimulation (SCC-DBS). Magnetoencephalography data were obtained from 16 individuals with SCC-DBS for TRD and 25 healthy subjects. The first objective of the study was to identify region-specific oscillatory modulations that both (i) discriminate individuals with TRD (with SCC-DBS OFF) from healthy controls, and (ii) discriminate TRD treatment responders from non-responders (with SCC-DBS ON). The second objective of this work was to further explore the effects of stimulation intensity and frequency on oscillatory activity in the identified brain regions of interest. Oscillatory power analyses led to the identification of brain regions that differentiated responders from non-responders based on modulations of increased alpha (8-12 Hz) and decreased gamma (32-116 Hz) power within nodes of the default mode, central executive, and somatomotor networks, Broca's area, and lingual gyrus. Within these nodes, it was also found that low stimulation frequency had stronger effects on oscillatory modulation than increased stimulation intensity. The identified functional network biomarkers implicate modulation of TRD-related activity in brain regions involved in emotional control/processing, motor control, and the interaction between speech, vision, and memory, which have all been implicated in depression. These electrophysiological biomarkers have the potential to be used as functional proxies for therapy optimization. Additional stimulation parameter analyses revealed that oscillatory modulations can be strengthened by increasing stimulation intensity or reducing frequency, which may represent potential avenues of direction in non-responders.

Language Network Connectivity of Euthymic Bipolar Patients Is Altered at Rest and during a Verbal Fluency Task

Abnormalities of the Language Network (LN) have been found in different psychiatric conditions (e.g., schizophrenia and bipolar disorder), supporting the hypothesis that language plays a central role in a high-level integration/connectivity of second-level cognitive processes and the underlying cortical regions. This view implies a continuum of shared neural alterations along the psychotic disorder spectrum. In particular, bipolar disorder (BD) patients were recently documented to have an altered LN asymmetry during resting state. The extent to which the LN architecture is altered and stable also during a language task has yet to be investigated. To address this question, we analyzed fMRI data recorded during an open-eyes resting state session and a silent verbal fluency task in 16 euthymic BD patients and 16 matched healthy controls (HC). Functional connectivity in the LN of both groups was computed using spatial independent component analysis, and group comparisons were carried out to assess the network organization during both rest and active linguistic task conditions. The LN of BD patients involved left and right brain areas during both resting state and linguistic task. Compared to the left-lateralized network found in HC, the BD group was characterized by two anterior clusters (in left frontal and right temporo-insular regions) and the disengagement of the posterior language areas, especially during the verbal fluency task. Our findings support the hypothesis that reduced language lateralization may represent a biological marker across different psychotic disorders and that the altered language network connectivity found at rest in bipolar patients is stable and pervasive as it is also impaired during a verbal fluency task.

Theta-burst stimulation of TMS treatment for anxiety and depression: A FNIRS study

BACKGROUND

This study aimed to evaluate the intervention effect of intermittent Theta burst stimulation (iTBS) on anxiety and depression by using Functional Near-Infrared Spectroscopy technology for confirming the effect of iTBS on anxiety and depression and providing new parameter basis for the treatment and development of rTMS.

METHOD

37 patients with anxiety and depression were treated with rTMS intervention in iTBS mode, and the symptoms of depression and anxiety were assessed by Hospital Anxiety and Depression Scale at baseline and after 10 times of treatments. The brain activation was assessed by verbal fluency task. The scores of anxiety and depression were analyzed by paired sample t-test.

RESULTS

After 10 times of rTMS treatment in iTBS mode, the symptoms of anxiety and depression in patients were relieved. The anxiety scores before and after treatment were significantly different, and the post-test scores were significantly lower than the pre-test scores. Significant differences in depression scores were observed before and after treatment, and the post-test score was significantly lower than the pre-test score. In the brain functional connection, the connection of various brain regions was strengthened, and the strength of functional connection between all ROIs before the intervention was significantly lower than that after the intervention. Statistical significance was observed.

CONCLUSION

The intervention of iTBS model has a positive effect on improving symptoms and strengthening brain functional connection of patients with anxiety and depression. This performance supports the effectiveness of iTBS model in treating anxiety and depression.

Hierarchical fusion detection algorithm for sleep spindle detection

Background

Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “gold standard” of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity.

Methods

To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness.

Results

The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score.

Conclusion

A spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.

Altered gamma oscillations and beta-gamma coupling in drug-naive first-episode major depressive disorder: Association with sleep and cognitive disturbance

OBJECTIVE

Gamma oscillations contribute to the pathogenesis mechanisms of major depressive disorder (MDD) have been proposed, but gamma activity is not well characterized. This study is the first attempt to investigate the altered gamma oscillations in first-episode MDD, particularly the beta-gamma coupling, and to determine the potential symptomatic relationship with the identified gamma dysregulation.

METHODS

Resting electroencephalography was recorded for 43 drug-naive first-episode MDD and 57 healthy control (HC) subjects. Integrated analysis of relative spectral power, weighted phase lag index, and phase-amplitude coupling (PAC) were utilized to reveal the alterations of gamma activities. Pearson's correlation was implemented to identify the relationship between altered gamma activities and the clinical depressive symptoms, which were categorized into four factors: anxiety somatization, retardation, cognitive disturbance, and sleep disturbance.

RESULTS

Compared with HC subjects, MDD patients showed not only significantly decreased gamma powers in the left temporal and the bilateral occipital regions but also weakened gamma connectivity between the left hemisphere and the right frontal region. Furthermore, attenuated beta-gamma PAC of MDD patients was observed in the left temporal regions. Importantly, the suppression of left occipital mid- and high gamma oscillations were negatively correlated with sleep disturbance, while the deficits in left temporal beta-mid-gamma PAC and beta-high gamma PAC showed negative correlations with cognitive disturbance.

LIMITATIONS

Important limitations are the small sample size and the possible inclusion of bipolar depression in the MDD group.

CONCLUSIONS

Our findings provide the first evidence that in first-episode MDD, aberrant gamma powers and beta-gamma coupling are associated with sleep and cognitive impairments, respectively, deepening our understanding of the physiological mechanisms underlying sleep and cognitive symptoms in first-episode MDD. Altered gamma oscillations emerge as promising biomarkers for diagnosing MDD.

Altered language network lateralization in euthymic bipolar patients: a pilot study

Bipolar patients (BD) in the euthymic phase show almost no symptoms, nevertheless possibility of relapse is still present. We expected to find a psychobiological trace of their vulnerability by analyzing a specific network-the Language Network (LN)-connecting many high-level processes and brain regions measured at rest. According to Crow's hypothesis on the key role of language in the origin of psychoses, we expected an altered asymmetry of the LN in euthymic BDs. Eighteen euthymic BD patients (10 females; age = 54.50 ± 11.38 years) and 16 healthy controls (HC) (8 females; age = 51.16 ± 11.44 years) underwent a functional magnetic resonance imaging scan at rest. The LN was extracted through independent component analysis. Then, LN time series was used to compute the fractional amplitude of the low-frequency fluctuation (fALFF) index, which was then correlated with clinical scales. Compared with HC, euthymic patients showed an altered LN with greater activation of Broca's area right homologous and anterior insula together with reduced activation of left middle temporal gyrus. The normalized fALFF analysis on BD patients' LN time series revealed that the Slow-5 fALFF band was positively correlated with residual mania symptoms but negatively associated with depression scores. In line with Crow's hypothesis postulating an altered language hemispheric asymmetry in psychoses, we revealed, in euthymic BD patients, a right shift involving both the temporal and frontal linguistic hubs. The fALFF applied to LN allowed us to highlight a number of significant correlations of this measure with residual mania and depression psychiatric symptoms.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

EEG Frontal Asymmetry in Dysthymia, Major Depressive Disorder and Euthymic Bipolar Disorder

In the last few decades, the incidence of mood disorders skyrocketed worldwide and has brought an increasing human and economic burden. Depending on the main symptoms and their evolution across time, they can be classified in several clinical subgroups. A few psychobiological indices have been extensively investigated as promising markers of mood disorders. Among these, frontal asymmetry measured at rest with quantitative EEG has represented the main available marker in recent years. Only a few studies so far attempted to distinguish the features and differences among diagnostic types of mood disorders by using this index. The present study measured frontal EEG asymmetry during a 5-min resting state in three samples of patients with bipolar disorder in a Euthymic phase (EBD, n = 17), major depressive disorder (MDD, n = 25) and persistent depressive disorder (PDD, n = 21), once termed dysthymia. We aimed to test the hypothesis that MDD and PDD lack the typical leftward asymmetry exhibited by normal as well as EBD patients, and that PDD shows greater clinical and neurophysiological impairments than MDD. Clinical scales revealed no symptoms in EBD, and significant larger anxiety and depression scores in PDD than in MDD patients. Relative beta (i.e., beta/alpha ratio) EEG asymmetry was measured from lateral frontal sites and results revealed the typical greater left than right frontal beta activity in EBD, as well as a lack of asymmetry in both MDD and PDD. The last two groups also had lower bilateral frontal beta activity in comparison with the EBD group. Results concerning group differences were interpreted by taking into account both the clinical and the neurophysiological domains.

On the Role of Bilateral Brain Hypofunction and Abnormal Lateralization of Cortical Information Flow as Neural Underpinnings of Conventional Metaphor Processing Impairment in Schizophrenia: An fMRI and EEG Study

Figurative language processing (e.g. metaphors) is commonly impaired in schizophrenia. In the present study, we investigated the neural activity and propagation of information within neural circuits related to the figurative speech, as a neural substrate of impaired conventional metaphor processing in schizophrenia. The study included 30 schizophrenia outpatients and 30 healthy controls, all of whom were assessed with a functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG) punchline-based metaphor comprehension task including literal (neutral), figurative (metaphorical) and nonsense (absurd) endings. The blood oxygenation level-dependent signal was recorded with 3T MRI scanner and direction and strength of cortical information flow in the time course of task processing was estimated with a 64-channel EEG input for directed transfer function. The presented results revealed that the behavioral manifestation of impaired figurative language in schizophrenia is related to the hypofunction in the bilateral fronto-temporo-parietal brain regions (fMRI) and various differences in effective connectivity in the fronto-temporo-parietal circuit (EEG). Schizophrenia outpatients showed an abnormal pattern of connectivity during metaphor processing which was related to bilateral (but more pronounced at the left hemisphere) hypoactivation of the brain. Moreover, we found reversed lateralization patterns, i.e. a rightward-shifted pattern during metaphor processing in schizophrenia compared to the control group. In conclusion, the presented findings revealed that the impairment of the conventional metaphor processing in schizophrenia is related to the bilateral brain hypofunction, which supports the evidence on reversed lateralization of the language neural network and the existence of compensatory recruitment of alternative neural circuits in schizophrenia.

Electroencephalogram patterns in patients comorbid with major depressive disorder and anxiety symptoms: Proposing a hypothesis based on hypercortical arousal and not frontal or parietal alpha asymmetry

BACKGROUND

Major depressive disorder (MDD) is often comorbid with anxiety disorders or symptoms. Brain hyperactivity, frontal alpha asymmetry (FAA), and parietal alpha asymmetry (PAA) have been considered as trait markers in patients with MDD. This study investigated the electroencephalogram (EEG) patterns among patients with MDD comorbid with anxiety symptoms.

METHODS

One hundred and thirty-five patients with MDD comorbid with anxiety (MDD group) and 135 healthy controls (HC group) were analyzed. The Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) were completed, and 19 EEG channels were measured during the resting state, depressive recall and recovery tasks, and happiness recall and recovery tasks. FAA and PAA were computed by log (F4 alpha)-log (F3 alpha) and log (P4 alpha)-log (P3 alpha).

RESULTS

The FAA and PAA indices between the two groups showed no significant differences; however, compared with the HC group, the MDD group had lower total delta and theta values, and higher total beta, low beta, and high beta values in the resting state. The total beta value positively correlated with the BDI-II and BAI scores in the MDD group.

LIMITATIONS

Most patients had anxious MDD and taking prescriptions, antidepressants or benzodiazepine may affect EEG patterns.

CONCLUSION

Compared with HCs, patients with MDD comorbid with anxiety had a higher beta activity in the entire brain region, supporting the role of brain hyperactivity, instead of FAA or PAA, as a trait marker in these patients. A neurofeedback protocol could be developed in future based on the brain hyperactivity findings.

Comparison of frontal alpha asymmetry among schizophrenia patients, major depressive disorder patients, and healthy controls

BACKGROUND

Electroencephalography (EEG) frontal alpha asymmetry (FAA) has been observed in several psychiatric disorders. Dominance in left or right frontal alpha activity remains inconsistent in patients with major depressive disorder (MDD), patients with schizophrenia, and healthy controls. This study compared FAA among patients with MDD and schizophrenia, and healthy controls.

METHODS

We recruited 20 patients with MDD, 18 patients with schizophrenia, and 16 healthy individuals. The EEG alpha frequency ranged from 8 Hz to 12 Hz. FAA was expressed as the difference between absolute power values of right and left hemisphere electrodes in the alpha frequency range (common-log-transformed frontal right- and left-hemisphere electrodes: F4-F3, F8-F7, FP2-FP1, AF4-AF3, F6-F5, and F2-F1). Hamilton depression and anxiety rating scales were evaluated in patients with MDD. Positive and negative syndrome scales were evaluated in patients with schizophrenia.

RESULTS

Patients with schizophrenia showed significantly lower left FAA than healthy controls (F4-F3, schizophrenia vs. healthy controls: - 0.10 ± 0.04 vs. -0.05 ± 0.05). There were no significant differences in FAA between patients with schizophrenia and MDD as well as between patients with MDD and healthy controls.

CONCLUSIONS

The present study suggests that FAA indicates a relatively lower activation of left frontal electrodes in schizophrenia. The left-lateralized FAA could be a neuropathological attribute in patients with schizophrenia, but a lack of sample size and information such as medication and duration of illness might obscure the interpretation and generalization of our findings. Thus, further studies to verify the findings would be warranted.