Disrupted local beta band networks in schizophrenia revealed through graph analysis: A magnetoencephalography study

Predicting an EEG-Based hypnotic time estimation with non-linear kernels of support vector machine algorithm

Our ability to measure time is vital for daily life, technology use, and even mental health; however, separating pure time perception from other mental processes (like emotions) is a research challenge requiring precise tests to isolate and understand brain activity solely related to time estimation. To address this challenge, we designed an experiment utilizing hypnosis alongside electroencephalography (EEG) to assess differences in time estimation, namely underestimation and overestimation. Hypnotic induction is designed to reduce awareness and meta-awareness, facilitating a detachment from the immediate environment. This reduced information processing load minimizes the need for elaborate internal thought during hypnosis, further simplifying the cognitive landscape. To predict time perception based on brain activity during extended durations (5 min), we employed artificial intelligence techniques. Utilizing Support Vector Machines (SVMs) with both radial basis function (RBF) and polynomial kernels, we assessed their effectiveness in classifying time perception-related brain patterns. We evaluated various feature combinations and different algorithms to identify the most accurate configuration. Our analysis revealed an impressive 80.9% classification accuracy for time perception detection using the RBF kernel, demonstrating the potential of AI in decoding this complex cognitive function.

Multivariate sharp-wave ripples in schizophrenia during awake state

AIMS

Schizophrenia (SZ) is a brain disorder characterized by psychotic symptoms and cognitive dysfunction. Recently, irregularities in sharp-wave ripples (SPW-Rs) have been reported in SZ. As SPW-Rs play a critical role in memory, their irregularities can cause psychotic symptoms and cognitive dysfunction in patients with SZ. In this study, we investigated the SPW-Rs in human SZ.

METHODS

We measured whole-brain activity using magnetoencephalography (MEG) in patients with SZ (n = 20) and sex- and age-matched healthy participants (n = 20) during open-eye rest. We identified SPW-Rs and analyzed their occurrence and time-frequency traits. Furthermore, we developed a novel multivariate analysis method, termed "ripple-gedMEG" to extract the global features of SPW-Rs. We also examined the association between SPW-Rs and brain state transitions. The outcomes of these analyses were modeled to predict the positive and negative syndrome scale (PANSS) scores of SZ.

RESULTS

We found that SPW-Rs in the SZ (1) occurred more frequently, (2) the delay of the coupling phase (3) appeared in different brain areas, (4) consisted of a less organized spatiotemporal pattern, and (5) were less involved in brain state transitions. Finally, some of the neural features associated with the SPW-Rs were found to be PANSS-positive, a pathological indicator of SZ. These results suggest that widespread but disorganized SPW-Rs underlies the symptoms of SZ.

CONCLUSION

We identified irregularities in SPW-Rs in SZ and confirmed that their alternations were strongly associated with SZ neuropathology. These results suggest a new direction for human SZ research.

Functional network characteristics in adolescent psychotic mood disorder: associations with symptom severity and treatment effects

Adolescent psychotic mood disorder (MDP) is a specific phenotype that characterized by more severe symptoms and prognosis compared to nonpsychotic mood disorder (MDNP). But the underlying neural mechanisms remain unknown, and graph theory analysis can help to understand possible mechanisms of psychotic symptoms from the perspective of functional networks. A total of 177 adolescent patients with mood disorders were recruited, including 61 MDP and 116 MDNP. Functional networks were constructed, and topological properties were compared between the two groups at baseline and after treatment, and the association between properties changes and symptom improvement was explored. Compared to the MDNP group, the MDP group exhibited higher small-world properties (FDR q = 0.003) and normalized clustering coefficients (FDR q = 0.008) but demonstrated decreased nodal properties in the superior temporal gyrus (STG), Heschl's gyrus, and medial cingulate gyrus (all FDR q < 0.05). These properties were found to be correlated with the severity of psychotic symptoms. Topological properties also changed with improvement of psychotic symptoms after treatment, and changes in degree centrality of STG in the MDP was significantly positive correlated with improvement of psychotic symptoms (r = 0.377, P = 0.031). This study indicated that functional networks are more severely impaired in patients with psychotic symptoms. Topological properties, particularly those associated with the STG, hold promise as emerging metrics for assessing symptoms and treatment efficacy in patients with psychotic symptoms.

Aberrant thalamocortical connectivity and shifts between the resting state and task state in patients with schizophrenia

Prominent pathological hypotheses for schizophrenia include auditory processing deficits and dysconnectivity within cerebral networks. However, most neuroimaging studies have focused on impairments in either resting-state or task-related functional connectivity in patients with schizophrenia. The aims of our study were to examine (1) blood oxygen level-dependent (BOLD) signals during auditory steady-state response (ASSR) tasks, (2) functional connectivity during the resting-state and ASSR tasks and (3) state shifts between the resting-state and ASSR tasks in patients with schizophrenia. To reduce the functional consequences of scanner noise, we employed resting-state and sparse sampling auditory fMRI paradigms in 25 schizophrenia patients and 25 healthy controls. Auditory stimuli were binaural click trains at frequencies of 20, 30, 40 and 80 Hz. Based on the detected ASSR-evoked BOLD signals, we examined the functional connectivity between the thalamus and bilateral auditory cortex during both the resting state and ASSR task state, as well as their alterations. The schizophrenia group exhibited significantly diminished BOLD signals in the bilateral auditory cortex and thalamus during the 80 Hz ASSR task (corrected p < 0.05). We observed a significant inverse relationship between the resting state and ASSR task state in altered functional connectivity within the thalamo-auditory network in schizophrenia patients. Specifically, our findings demonstrated stronger functional connectivity in the resting state (p < 0.004) and reduced functional connectivity during the ASSR task (p = 0.048), which was mediated by abnormal state shifts, within the schizophrenia group. These results highlight the presence of abnormal thalamocortical connectivity associated with deficits in the shift between resting and task states in patients with schizophrenia.

Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.

Abnormal phase entrainment of low- and high-gamma-band auditory steady-state responses in schizophrenia

Introduction

Gamma-band oscillatory deficits have attracted considerable attention as promising biomarkers of schizophrenia (SZ). Notably, a reduced auditory steady-state response (ASSR) in the low gamma band (40 Hz) is widely recognized as a robust finding among SZ patients. However, a comprehensive investigation into the potential utility of the high-gamma-band ASSR in detecting altered neural oscillations in SZ has not yet been conducted.

Methods

The present study aimed to assess the ASSR using magnetoencephalography (MEG) data obtained during steady-state stimuli at frequencies of 20, 30, 40, and 80 Hz from 23 SZ patients and 21 healthy controls (HCs). To evaluate the ASSR, we examined the evoked power and phase-locking factor (PLF) in the time-frequency domain for both the primary and secondary auditory cortices. Furthermore, we calculated the phase-locking angle (PLA) to examine oscillatory phase lead or delay in SZ patients. Taking advantage of the high spatial resolution of MEG, we also focused on the hemispheric laterality of low- and high-gamma-band ASSR deficits in SZ.

Results

We found abnormal phase delay in the 40 Hz ASSR within the bilateral auditory cortex of SZ patients. Regarding the 80 Hz ASSR, our investigation identified an aberrant phase lead in the left secondary auditory cortex in SZ, accompanied by reduced evoked power in both auditory cortices.

Discussion

Given that abnormal phase lead on 80 Hz ASSR exhibited the highest discriminative power between HC and SZ, we propose that the examination of PLA in the 80 Hz ASSR holds significant promise as a robust candidate for identifying neurophysiological endophenotypes associated with SZ. Furthermore, the left-hemisphere phase lead observed in the deficits of 80 Hz PLA aligns with numerous prior studies, which have consistently proposed that SZ is characterized by left-lateralized brain dysfunctions.

Schizophrenia MEG Network Analysis Based on Kernel Granger Causality

Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (p=0.001). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (p=0.0018). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (p=0.012); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.