Review of Schizophrenia Research Using MEG

Rojas, Donald C.

doi:10.1007/978-3-030-00087-5_41

Cited by 4 publications

(5 citation statements)

References 151 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MEG has been already employed to study various psychiatric disorders, moving neurophysiological research closer to brain generators (Uhlhaas et al, 2017). The majority of MEG studies tested abnormal mMMN amplitudes in SCZ (for a review, see Rojas, 2019), but few experiments investigated reduced mMMN in BD (Shimano et al, 2014; Takei et al, 2010) and CHR (Shin et al, 2009), reporting results that were consistent with those in the EEG literature. Only one study investigated both SCZ and BD, finding that BD patients had an mMMN amplitude intermediate between SCZ and controls (Braeutigam, Dima, Frangou, & James, 2018).…”

Section: Introductionmentioning

confidence: 77%

Reduced magnetic mismatch negativity: a shared deficit in psychosis and related risk

et al. 2022

View full text Add to dashboard Cite

Background Abnormal auditory processing of deviant stimuli, as reflected by mismatch negativity (MMN), is often reported in schizophrenia (SCZ). At present, it is still under debate whether this dysfunctional response is specific to the full-blown SCZ diagnosis or rather a marker of psychosis in general. The present study tested MMN in patients with SCZ, bipolar disorder (BD), first episode of psychosis (FEP), and in people at clinical high risk for psychosis (CHR). Methods Source-based MEG activity evoked during a passive auditory oddball task was recorded from 135 patients grouped according to diagnosis (SCZ, BD, FEP, and CHR) and 135 healthy controls also divided into four subgroups, age- and gender-matched with diagnostic subgroups. The magnetic MMN (mMMN) was analyzed as event-related field (ERF), Theta power, and Theta inter-trial phase coherence (ITPC). Results The clinical group as a whole showed reduced mMMN ERF amplitude, Theta power, and Theta ITPC, without any statistically significant interaction between diagnosis and mMMN reductions. The mMMN subgroup contrasts showed lower ERF amplitude in all the diagnostic subgroups. In the analysis of Theta frequency, SCZ showed significant power and ITPC reductions, while only indications of diminished ITPC were observed in CHR, but no significant decreases characterized BD and FEP. Conclusions Significant mMMN alterations in people experiencing psychosis, also for diagnoses other than SCZ, suggest that this neurophysiological response may be a feature shared across psychotic disorders. Additionally, reduced Theta ITPC may be associated with risk for psychosis.

show abstract

Section: Introductionmentioning

confidence: 77%

Reduced magnetic mismatch negativity: a shared deficit in psychosis and related risk

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In a complex network, the distance between two nodes is defined as the number of edges in the shortest path connecting these two nodes. The average shortest path length L of a network is the average of the distances between all nodes [60], which is calculated by formula (6), where N represents the number of nodes in the network.…”

Section: E Establishment Of a Functional Network And Analysis Of Its ...mentioning

confidence: 99%

“…In addition, the diversity and complexity of its symptoms have led schizophrenia treatments to focus on symptom reduction [4]. However, identifying potential biomarkers of schizophrenia is of particular interest [1], [5], [6]. Magnetoencephalogram (MEG) signals not only have high temporal resolution but also high spatial resolution, which can be used to accurately locate the source of neural activity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Network Analysis of Magnetoencephalogram Signals in Schizophrenia Patients When Viewing Emotional Facial Stimuli

Bai

Yao

Yan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Schizophrenia is a serious mental disorder. Network analysis of magnetoencephalogram signals may help to identify potential biomarkers of schizophrenia. The goal of this investigation was to identify potential biomarkers in the magnetoencephalogram signals of patients with schizophrenia, global brain connectivity measures was used for emotion recognition in discriminating the patients from controls. First, we employed a mutual information method to explore the topological characteristics of the brain network in patients with schizophrenia among different frequency bands in response to four different stimulus conditions. Second, multidimensional cross-recurrence quantification analysis was performed to investigate the differences in dynamic coupling among different frequencies of brain magnetic waves in patients with schizophrenia in response to four different stimulus conditions, as the major novel contribution of our study. We found that the differences in topological features of the brain network appear in different frequency bands under different stimulus conditions. The differences are evident in the alpha 1 (8-10 Hz) and beta (13-30 Hz) frequency bands in response to negative stimuli, in the alpha 1 (8-10 Hz) frequency band in response to positive stimuli, and in the theta (4-8 Hz) and alpha 1 (8-10 Hz) frequency bands in response to neutral and gray-cross stimuli. In addition, differences in dynamic coupling among pairs of frequency bands were the most prominent in response to positive stimuli. The characteristics identified by our methods may be potential markers of schizophrenia present in magnetoencephalogram data, which can facilitate the clinical identification of schizophrenia patients. Our method provides a comprehensive perspective of brain networks in patients with schizophrenia and has practical applications for the clinical diagnosis of this disease.

show abstract

“…In this regard, researchers in the field of computational neuroscience, considering the concept of time-dependent disorders and dynamical diseases, have utilized chaotic approaches to study patterns of neural activity in schizophrenia ( 8 , 9 ). Electroencephalogram (EEG)/Magnetoencephalogram (MEG) studies have used nonlinear and chaotic methods to estimate the complexity patterns of brain electrical activity and to investigate the brain dynamics in schizophrenia due to (i) the nonlinear properties of dynamical neural systems, (ii) dynamical nature of schizophrenia symptoms and their severity, reflecting a disturbance in underlying nonlinear processes as state switches in the cortical system, and (iii) deficits in stability, self-organization and hierarchical processes of brain system in schizophrenia ( 10 - 15 ).…”

mentioning

confidence: 99%

Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study

Khaleghi

Mohammadi

Shahi

et al. 2023

IJPS

View full text Add to dashboard Cite

Objective: Schizophrenia is a complex neurodevelopmental illness that is associated with different deficits in the cerebral cortex and neural networks, resulting in irregularity of brain waves. Various neuropathological hypotheses have been proposed for this irregularity that we intend to examine in this computational study. Method: We used a mathematical model of a neuronal population based on cellular automata to examine two hypotheses about the neuropathology of schizophrenia: first, reducing neuronal stimulation thresholds to increase neuronal excitability; and second, increasing the percentage of excitatory neurons and decreasing the percentage of inhibitory neurons to increase the excitation to inhibition ratio in the neuronal population. Then, we compare the complexity of the output signals produced by the model in both cases with real healthy resting-state electroencephalogram (EEG) signals using the Lempel-Ziv complexity measure and see if these changes alter (increase or decrease) the complexity of the neuronal population dynamics. Results: By lowering the neuronal stimulation threshold (i.e., the first hypothesis), no significant change in the pattern and amplitude of the network complexity was observed, and the model complexity was very similar to the complexity of real EEG signals (P > 0.05). However, increasing the excitation to inhibition ratio (i.e., the second hypothesis) led to significant changes in the complexity pattern of the designed network (P < 0.05). More interestingly, in this case, the complexity of the output signals of the model increased significantly compared to real healthy EEGs (P = 0.002) and the model output of the unchanged condition (P = 0.028) and the first hypothesis (P = 0.001). Conclusion: Our computational model suggests that imbalances in the excitation to inhibition ratio in the neural network are probably the source of abnormal neuronal firing patterns and thus the cause of increased complexity of brain electrical activity in schizophrenia.

show abstract

Review of Schizophrenia Research Using MEG

Cited by 4 publications

References 151 publications

Reduced magnetic mismatch negativity: a shared deficit in psychosis and related risk

Reduced magnetic mismatch negativity: a shared deficit in psychosis and related risk

Network Analysis of Magnetoencephalogram Signals in Schizophrenia Patients When Viewing Emotional Facial Stimuli

Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study

Contact Info

Product

Resources

About