Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data

OBJECTIVE

This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis.

METHODS

Twenty participants with apnea-hypopnea index (AHI) ≥ 15 and 18 participants with AHI <15 were recruited. Overnight polysomnography was conducted concurrently with 19-channel EEG. Preprocessed EEG data underwent computation of relative spectral power. A weighted network based on graph theory was generated; and indices of strength, path length, eigenvector centrality, and clustering coefficient were calculated. Microstate analysis was conducted to derive four topographic maps. Machine learning techniques were employed to assess EEG features capable of differentiating two groups.

RESULTS

Among 71 features that showed significant differences between the two groups, seven exhibited good classification performance, achieving 88.3 % accuracy, 92 % sensitivity, and 84 % specificity. These features were power at C4 theta, P3 theta, P4 theta, and F8 gamma during NREM1 sleep and at Pz gamma during REM sleep from power spectral analysis; eigenvector centrality at F7 gamma during REM sleep from network analysis; and duration of microstate 4 during NREM2 sleep from microstate analysis. These seven EEG features were significantly correlated with polysomnographic parameters reflecting the severity of OSA.

CONCLUSIONS

The application of machine learning techniques and various EEG analytical methods resulted in a model that showed good performance in classifying moderate to severe OSA and highlights the potential of EEG to serve as a biomarker of functional changes in OSA.

Comparative analysis of resting-state EEG-based multiscale entropy between schizophrenia and bipolar disorder

BACKGROUND

Studies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD).

METHODS

The eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed.

RESULTS

For patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41-70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13-40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores.

CONCLUSIONS

Patients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.

Neural oscillation in bipolar disorder: a systematic review of resting-state electroencephalography studies

Bipolar disorder (BD) is a severe psychiatric disease with high rates of misdiagnosis and underdiagnosis, resulting in a significant disease burden on both individuals and society. Abnormal neural oscillations have garnered significant attention as potential neurobiological markers of BD. However, untangling the mechanisms that subserve these baseline alternations requires measurement of their electrophysiological underpinnings. This systematic review investigates consistent abnormal resting-state EEG power of BD and conducted an initial exploration into how methodological approaches might impact the study outcomes. This review was conducted in Pubmed-Medline and Web-of-Science in March 2024 to summarize the oscillation changes in resting-state EEG (rsEEG) of BD. We focusing on rsEEG to report spectral power in different frequency bands. We identified 10 studies, in which neural oscillations was compared with healthy individuals (HCs). We found that BD patients had abnormal oscillations in delta, theta, beta, and gamma bands, predominantly characterized by increased power, indicating potential widespread neural dysfunction, involving multiple neural networks and cognitive processes. However, the outcomes regarding alpha oscillation in BD were more heterogeneous, which is thought to be potentially influenced by the disease severity and the diversity of samples. Furthermore, we conducted an initial exploration into how demographic and methodological elements might impact the study outcomes, underlining the importance of implementing standardized data collection methods. Key aspects we took into account included gender, age, medication usage, medical history, the method of frequency band segmentation, and situation of eye open/eye close during the recordings. Therefore, in the face of abnormal multiple oscillations in BD, we need to adopt a comprehensive research approach, consider the multidimensional attributes of the disease and the heterogeneity of samples, and pay attention to the standardized experimental design to improve the reliability and reproducibility of the research results.

A Virtual Reality Cognitive Stimulation Program as an Effective Tool Against Residual/Prodromal Depressive Symptoms in Bipolar Disorders

Background: Bipolar disorder (BD) is a severe psychiatric illness characterized by a chronic course and recurrent episodes, including residual depressive symptoms even during euthymic phases. These symptoms, although not meeting criteria for a depressive episode, are linked to relapse risk and impaired social functioning. This study aims to assess whether Virtual Reality Cognitive Remediation Training reduces depressive symptoms below the clinical threshold in individuals with BD. Methods: This post hoc analysis focuses on the secondary outcome (PHQ9) of a randomized-controlled trial. Participants were recruited from the Center of Liaison Psychiatry and Psychosomatics in Italy. The experimental group received Virtual Reality Cognitive Remediation, while the control group received standard treatment Results: Data from 39 individuals in the experimental group and 25 in the control group were analyzed. A greater reduction in PHQ-9 scores (>9) was observed in the experimental group (71.8% to 48.7%) compared to the control group. Significant improvements in total PHQ-9 scores and specific symptoms were noted in the experimental group compared to the control group. Conclusions: The study highlights the significant impact of virtual reality intervention on reducing depressive symptoms in bipolar disorder. This promising outcome underscores the potential preventive role of cognitive stimulation in relapse prevention. The intervention could offer valuable benefits for both treatment and prevention strategies in bipolar disorder.

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers

BACKGROUND

Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation.

METHODS

We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories.

RESULTS

Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right.

LIMITATIONS

The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation.

CONCLUSIONS

DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.

Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia

Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.

Prenatal and perinatal risk factors for bipolar disorder: A systematic review and meta-analysis

BACKGROUND

Perinatal and prenatal risk factors may be implicated in the development of bipolar disorder, but literature lacks a comprehensive account of possible associations.

METHODS

We performed a systematic review and meta-analyses of observational studies detailing the association between prenatal and perinatal risk factors and bipolar disorder in adulthood by searching PubMed, Embase, Web of Science and Psycinfo for articles published in any language between January 1st, 1960 and September 20th, 2021. Meta-analyses were performed when risk factors were available in at least two studies.

FINDINGS

Twenty seven studies were included with 18 prenatal or perinatal factors reported across the literature. Peripartum asphyxia (k = 5, OR = 1.46 [1.02; 2.11]), maternal stress during pregnancy (k = 2, OR = 12.00 [3.30; 43.59]), obstetric complications (k = 6, OR = 1.41 [1.18; 1.69]), and birth weight less than 2500 g (k = 5, OR = 1.28 [1.04; 1.56]) were associated with an increased risk for bipolar disorder.

INTERPRETATION

Perinatal and prenatal risk factors are implicated in the pathogenesis of bipolar disorder, supporting a role of prenatal care in preventing the condition.

Prognostic associations of cortical gyrification in minimally medicated schizophrenia in an early intervention setting

The aberration in cortical gyrification seen in schizophrenia likely originates in the earliest phase of life, as gyrification begins in utero and reaches its peak in infancy. However, emerging observations have indicated a later reduction in gyrification, especially in early adulthood, may also occur in schizophrenia. At present, it is unclear whether the baseline and later gyrification reduction has any prognostic importance in schizophrenia. We address this question in a longitudinal design in patients minimally medicated at inception. About 108 minimally medicated (duration of medication = <14 days of antipsychotics) patients and 106 healthy controls underwent structural magnetic resonance imaging, with 34 patients being selectively re-scanned when clinically stable following antipsychotic treatment. The cortical surface from each structural image was reconstructed, and the local gyrification index and cortical thickness were computed for each vertex on the surface. We found minimally medicated schizophrenia patients during the first episode had a relatively higher gyrification in bilateral supramarginal, left superior temporal, and right posterior cingulate and paracentral regions. However, poor prognostic features were more likely in patients with lower baseline gyrification. Longitudinal reductions in left superior parietal and right precentral gyrification were associated with lower improvements in both positive and negative symptoms over time. The spatial pattern of longitudinal changes in gyrification was distinct from the changes in cortical thickness. These results indicated that schizophrenia is characterized by a relative hypergyrification in parieto-temporal and medial cortical areas at a group level at first presentation, but poor outcomes relate to lower-gyrification elsewhere both at the onset and during the early course. The early post-onset reduction of gyrification is rather limited in space and magnitude, but occurs unrelated to the progressive thinning, representing a distinct, prognostically important structural trajectory.

Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia

The analysis of resting-state fMRI signals usually focuses on the low-frequency range/band (0.01−0.1 Hz), which does not cover all aspects of brain activity. Studies have shown that distinct frequency bands can capture unique fluctuations in brain activity, with high-frequency signals (>0.1 Hz) providing valuable information for the diagnosis of schizophrenia. We hypothesized that it is meaningful to study the dynamic reconfiguration of schizophrenia through different frequencies. Therefore, this study used resting-state functional magnetic resonance (RS-fMRI) data from 42 schizophrenia and 40 normal controls to investigate dynamic network reconfiguration in multiple frequency bands (0.01−0.25 Hz, 0.01−0.027 Hz, 0.027−0.073 Hz, 0.073−0.198 Hz, 0.198−0.25 Hz). Based on the time-varying dynamic network constructed for each frequency band, we compared the dynamic reconfiguration of schizophrenia and normal controls by calculating the recruitment and integration. The experimental results showed that the differences between schizophrenia and normal controls are observed in the full frequency, which is more significant in slow3. In addition, as visual network, attention network, and default mode network differ a lot from each other, they can show a high degree of connectivity, which indicates that the functional network of schizophrenia is affected by the abnormal brain state in these areas. These shreds of evidence provide a new perspective and promote the current understanding of the characteristics of dynamic brain networks in schizophrenia.

Alteration of cortical functional networks in mood disorders with resting-state electroencephalography

Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal; however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs. Our findings might suggest more altered cortical functional network in patients with BD than in those with MDD.

Effect of neuromorphic transcutaneous electrical nerve stimulation (nTENS) of cortical functional networks on tactile perceptions: An event-related electroencephalogram study

BACKGROUND

Transcutaneous electrical nerve stimulation (TENS) is generally applied for tactile feedback in the field of prosthetics. The distinct mechanisms of evoked tactile perception between stimulus patterns in conventional TENS (cTENS) and neuromorphic TENS (nTENS) are relatively unknown. This is the first study to investigate the neurobiological effect of nTENS for cortical functional mechanism in evoked tactile perception.

METHODS

Twenty-one healthy participants were recruited in this study. Electroencephalogram (EEG) was recorded while the participants underwent a tactile discrimination task. One cTENS pattern (square pattern) and two nTENS patterns (electromyography and single motor unit patterns) were applied to evoke tactile perception in four fingers, including the right and left index and little fingers. EEG was preprocessed and somatosensory-evoked potentials (SEPs) were determined. Then, source-level functional networks based on graph theory were evaluated, including clustering coefficient, path length, global efficiency, and local efficiency in six frequency bands.

RESULTS

Behavioral results suggested that the single motor units (SMU) pattern of nTENS was the most natural tactile perception. SEPs results revealed that SMU pattern exhibited significant shorter latency in P1 and N1 components than the other patterns, while nTENS patterns have significantly longer latency in P3 component than cTENS pattern. Cortical functional networks showed that the SMU pattern had the lowest short path and highest efficiency in beta and gamma bands.

CONCLUSION

This study highlighted that distinct TENS patterns could affect brain activities. The new characteristics in tactile manifestation of nTENS would provide insights for the application of tactile perception restoration.

Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography

Schizophrenia has a complex etiology and symptomatology that is difficult to untangle. After decades of research, important advancements toward a central biomarker are still lacking. One of the missing pieces is a better understanding of how non-linear neural dynamics are altered in this patient population. In this study, the resting-state neuromagnetic signals of schizophrenia patients and healthy controls were analyzed in the framework of criticality. When biological systems like the brain are in a state of criticality, they are thought to be functioning at maximum efficiency (e.g., optimal communication and storage of information) and with maximum adaptability to incoming information. Here, we assessed the self-similarity and multifractality of resting-state brain signals recorded with magnetoencephalography in patients with schizophrenia patients and in matched controls. Schizophrenia patients had similar, although attenuated, patterns of self-similarity and multifractality values. Statistical tests showed that patients had higher values of self-similarity than controls in fronto-temporal regions, indicative of more regularity and memory in the signal. In contrast, patients had less multifractality than controls in the parietal and occipital regions, indicative of less diverse singularities and reduced variability in the signal. In addition, supervised machine-learning, based on logistic regression, successfully discriminated the two groups using measures of self-similarity and multifractality as features. Our results provide new insights into the baseline cognitive functioning of schizophrenia patients by identifying key alterations of criticality properties in their resting-state brain data.

Pareidolia in Schizophrenia and Bipolar Disorder

While there are many studies on pareidolia in healthy individuals and patients with schizophrenia, to our knowledge, there are no prior studies on pareidolia in patients with bipolar disorder. Accordingly, in this study, we, for the first time, measured pareidolia in patients with bipolar disorder (N = 50), and compared that to patients with schizophrenia (N = 50) and healthy controls (N = 50). We have used (a) the scene test, which consists of 10 blurred images of natural scenes that was previously found to produce illusory face responses and (b) the noise test which had 32 black and white images consisting of visual noise and 8 images depicting human faces; participants indicated whether a face was present on these images and to point to the location where they saw the face. Illusory responses were defined as answers when observers falsely identified objects that were not on the images in the scene task (maximum illusory score: 10), and the number of noise images in which they reported the presence of a face (maximum illusory score: 32). Further, we also calculated the total pareidolia score for each task (the sum number of images with illusory responses in the scene and noise tests). The responses were scored by two independent raters with an excellent congruence (kappa > 0.9). Our results show that schizophrenia patients scored higher on pareidolia measures than both healthy controls and patients with bipolar disorder. Our findings are agreement with prior findings on more impaired cognitive processes in schizophrenia than in bipolar patients.

Electroencephalographic Microstates in Schizophrenia and Bipolar Disorder

Schizophrenia (SCH) and bipolar disorder (BD) are characterized by many types of symptoms, damaged cognitive function, and abnormal brain connections. The microstates are considered to be the cornerstones of the mental states shown in EEG data. In our study, we investigated the use of microstates as biomarkers to distinguish patients with bipolar disorder from those with schizophrenia by analyzing EEG data measured in an eyes-closed resting state. The purpose of this article is to provide an electron directional physiological explanation for the observed brain dysfunction of schizophrenia and bipolar disorder patients. Methods: We used microstate resting EEG data to explore group differences in the duration, coverage, occurrence, and transition probability of 4 microstate maps among 20 SCH patients, 26 BD patients, and 35 healthy controls (HCs). Results: Microstate analysis revealed 4 microstates (A-D) in global clustering across SCH patients, BD patients, and HCs. The samples were chosen to be matched. We found the greater presence of microstate B in BD patients, and the less presence of microstate class A and B, the greater presence of microstate class C, and less presence of D in SCH patients. Besides, a greater frequent switching between microstates A and B and between microstates B and A in BD patients than in SCH patients and HCs and less frequent switching between microstates C and D and between microstates D and C in BD patients compared with SCH patients. Conclusion: We found abnormal features of microstate A, B in BD patients and abnormal features of microstate A, B, C, and D in SCH patients. These features may indicate the potential abnormalities of SCH patients and BD patients in distributing neural resources and influencing opportune transitions between different states of activity.

Ketamine Alters Functional Gamma and Theta Resting-State Connectivity in Healthy Humans: Implications for Schizophrenia Treatment Targeting the Glutamate System

Disturbed functional connectivity is assumed to cause neurocognitive deficits in patients suffering from schizophrenia. A Glutamate N-methyl-D-aspartate receptor (NMDAR) dysfunction has been suggested as a possible mechanism underlying altered connectivity in schizophrenia, especially in the gamma- and theta-frequency range. The present study aimed to investigate the effects of the NMDAR-antagonist ketamine on resting-state power, functional connectivity, and schizophrenia-like psychopathological changes in healthy volunteers. In a placebo-controlled crossover design, 25 healthy subjects were recorded using resting-state 64-channel-electroencephalography (EEG) (eyes closed). The imaginary coherence-based Multivariate Interaction Measure (MIM) was used to measure gamma and theta connectivity across 80 cortical regions. The network-based statistic was applied to identify involved networks under ketamine. Psychopathology was assessed with the Positive and Negative Syndrome Scale (PANSS) and the 5-Dimensional Altered States of Consciousness Rating Scale (5D-ASC). Ketamine caused an increase in all PANSS (p < 0.001) as well as 5D-ASC scores (p < 0.01). Significant increases in resting-state gamma and theta power were observed under ketamine compared to placebo (p < 0.05). The source-space analysis revealed two distinct networks with an increased mean functional gamma- or theta-band connectivity during the ketamine session. The gamma-network consisted of midline regions, the cuneus, the precuneus, and the bilateral posterior cingulate cortices, while the theta-band network involved the Heschl gyrus, midline regions, the insula, and the middle cingulate cortex. The current source density (CSD) within the gamma-band correlated negatively with the PANSS negative symptom score, and the activity within the gamma-band network correlated negatively with the subjective changed meaning of percepts subscale of the 5D-ASC. These results are in line with resting-state patterns seen in people who have schizophrenia and argue for a crucial role of the glutamate system in mediating dysfunctional gamma- and theta-band-connectivity in schizophrenia. Resting-state networks could serve as biomarkers for the response to glutamatergic drugs or drug development efforts within the glutamate system.