Functional clustering in EEG photic and auditory driving in schizophrenia

Directed brain interactions over time: A resting-state EEG comparison between schizophrenia and healthy individuals

Understanding the neurophysiological mechanisms of schizophrenia (SZ) is one of the challenges of neuroscience. Many anatomical and functional studies have pointed to problems in brain connectivity in SZ individuals. However, little is known about the relationships between specific brain regions and impairments in brain connectivity in SZ individuals. Herein we propose a new approach using time-varying graphs and the motif synchronization method to build dynamic brain functional networks (BFNs). Dynamic BFNs were constructed from resting-state electroencephalography (rs-EEG) of 14 schizophrenia (SZ) individuals and 14 healthy controls (HCs). BFNs were evaluated based on the percentage of synchronization importance between a pair of regions (considering external and internal interactions) over time. We found differences in the directed interaction between brain regions in SZ individuals compared to the control group. Our method revealed low bilaterally directed interactions between the temporal lobes in SZ individuals compared to HCs, indicating a potential link between altered brain connectivity and the characteristic symptoms of schizophrenia. From a clinical perspective, these results shed light on developing new therapeutic approaches targeting these specific neural interactions that are altered in individuals with SZ. This knowledge allows the application of better interventions focused on restoring or compensating for interrupted connectivity patterns.

Brain instability in dynamic functional connectivity in schizophrenia

Schizophrenia is a severe psychiatric disorder associated with altered connectivity of brain functional networks (BFNs). Researchers have observed a profound disruption in prefrontal-temporal interactions, damage to hub regions in brain networks and modified topological organization of BFNs in schizophrenia (SCZ) individuals. Assessment of BFNs with dynamic approaches allow the characterization of new functional structures, such as topological stability patterns and temporal connectivity, which are not accessible through static methods. In this perspective, the present study investigated the physiological processes of brain connectivity in SCZ. A resting-state EEG dataset of 14 SCZ individuals and 14 healthy controls (HC) was obtained at a sampling rate of 250 Hz. Dynamic BFNs were constructed using time-varying graphs combined with the motifs' synchronization method and the indexes were evaluated in different scales: global averages, by hemispheres, by regions, and by electrodes for both groups. The SCZ group exhibited lower temporal connectivity, lesser hub probability, and fewer number of edges in right and left temporal lobes over time, besides increased temporal connectivity in the central-parietal region. Neither differences for the full synchronization time of BFNs were observed, nor for intra- and inter-hemispheric connections between groups. These results indicate that SCZ BFNs exhibit a dynamic fluctuation pattern with abrupt increases in connectivity over time for the regions studied. This elucidates an attempted interaction of the temporal area with other regions (frontal, central-parietal, and occipital) that is not sufficient to maintain a connectivity pattern in schizophrenia individuals similar to that of healthy subjects. Our results suggest that changes in interaction of dynamic BFNs connections in SCZ can be better approached by dynamic analyses that enable a thorough glance at brain changes over time.

On the analysis of BIS stage epochs via fuzzy clustering

Among various types of clustering methods, partition-based methods such as k-means and FCM are widely used in the analysis of such data. However, when duration between stimuli is different, such methods are not able to provide satisfactory results because they find equal size clusters according to the fundamental running principle of these methods. In such cases, neighborhood-based clustering methods can give more satisfactory results because measurement series are separated from one another according to dramatic breaking points. In recent years, bispectral index (BIS) monitoring, which is used for monitoring the level of anesthesia, has been used in sleep studies. Sleep stages are classically scored according to the Rechtschaffen and Kales (R&K) scoring system. BIS has been shown to have a strong correlation with the R&K scoring system. In this study, fuzzy neighborhood/density-based spatial clustering of applications with noise (FN-DBSCAN) that combines speed of the DBSCAN algorithm and robustness of the NRFJP algorithm is applied to BIS measurement series. As a result of experiments, we can conclude that, by using BIS data, the FN-DBSCAN method estimates sleep stages better than the fuzzy c-means method.

Clinical symptoms and alpha band resting-state functional connectivity imaging in patients with schizophrenia: implications for novel approaches to treatment

BACKGROUND

Schizophrenia (SZ) is associated with functional decoupling between cortical regions, but we do not know whether and where this occurs in low-frequency electromagnetic oscillations. The goal of this study was to use magnetoencephalography (MEG) to identify brain regions that exhibit abnormal resting-state connectivity in the alpha frequency range in patients with schizophrenia and investigate associations between functional connectivity and clinical symptoms in stable outpatient participants.

METHODS

Thirty patients with SZ and 15 healthy comparison participants were scanned in resting-state MEG (eyes closed). Functional connectivity MEG source data were reconstructed globally in the alpha range, quantified by the mean imaginary coherence between a voxel and the rest of the brain.

RESULTS

In patients, decreased connectivity was observed in left prefrontal cortex (PFC) and right superior temporal cortex, whereas increased connectivity was observed in left extrastriate cortex and the right inferior PFC. Functional connectivity of left inferior parietal cortex was negatively related to positive symptoms. Low left PFC connectivity was associated with negative symptoms. Functional connectivity of midline PFC was negatively correlated with depressed symptoms. Functional connectivity of right PFC was associated with other (cognitive) symptoms.

CONCLUSIONS

This study demonstrates direct functional disconnection in SZ between specific cortical fields within low-frequency resting-state oscillations. Impaired alpha coupling in frontal, parietal, and temporal regions is associated with clinical symptoms in these stable outpatients. Our findings indicate that this level of functional disconnection between cortical regions is an important treatment target in SZ.

Automatic interpretation of hyperventilation-induced electroencephalogram constructed in the way of qualified electroencephalographer's visual inspection

Quantitative analysis and detection of electroencephalogram (EEG) recordings during evoked activities is essential for clinical diagnosis on neurological disorders. However, the process of interpreting EEG is time consuming for electroencephalographers (EEGers). In this study, an automatic EEG interpretation system constructed in the way of qualified EEGer's visual inspection was proposed. The system was applied to interpret hyperventilation-induced EEG automatically. The final results of automatic interpretation were compared with EEGer's visual inspection, and showed high consistence.

DIFFERENCES IN EEG BETWEEN GIFTED AND AVERAGE STUDENTS: NEURAL COMPLEXITY AND FUNCTIONAL CLUSTER ANALYSIS

The main aim of the present study was to assess the differences in EEG between gifted and average students. Another aim of the present study was to investigate which brain areas are related to a Rey-Osterrieth complex figure (ROCF) memorizing using a functional cluster (FC) analysis and how the complexity of cortical activities changes in both gifted and average students. The EEG was recorded from 16 electrodes in both 18 right-handed healthy gifted and age-matched average students before and during ROCF memorizing. FC was estimated to characterize the joint interactions among many brain regions and neural complexity. The study assessed the visuo-spatial memory abilities through examining EEG profiles using the measure of FC, and planning and executive function using recall score. The gifted students made a significantly high score compared to the average students during ROCF memorizing. Both groups showed very different FC patterns. ROCF memorizing is related to the visual mental process, thus simultaneous neuronal activities appears on the right central, temporal, occipital, and bilateral prefrontal regions. One of the notable characteristics of gifted students' FC map is the dominance of the right hemisphere compared with that of average students, and it is accordance with the characteristics of gifted brain.