EEG microstates in schizophrenia: a candidate endophenotype

Objective: Electroencephalogram (EEG) microstates are on-going scalp potential configurations that remain stable for around 80 ms. Abnormal temporal dynamics of specific classes of microstates have been suggested as a potential endophenotype for schizophrenia. For an endophenotype, it is important that unaffected relatives of schizophrenia patients also show the abnormal dynamics. To the best of our knowledge, no study analyzed the resting dynamics of EEG microstate in relatives of schizophrenia patients. Methods: Here, we used high-density EEG to study the microstate dynamics in 5 minutes resting-state recordings of 38 unaffected siblings of schizophrenia patients, 89 schizophrenia patients, 69 healthy controls, 42 healthy students scoring either high or low in schizotypal traits, and 22 patients with first episodes of psychosis (FEP). We also tested the FEP patients two more times throughout one year to assess whether the microstates dynamics change with disease progression. Results: Schizophrenia patients showed increased presence of a microstate class labeled C and decreased presence of a microstate class labeled D compared to controls. Siblings showed similar patterns of microstate classes C and D as chronic patients (endophenotype concept). Surprisingly, siblings showed an increased presence of a microstate class labeled B compared to chronic patients. A similar result was also found in students scoring high in schizotypal traits compared to the ones scoring low. No difference was found between FEP and matched chronic patients. Moreover, the microstates dynamics remained stable throughout one year. Conclusions: Our findings suggest that the dynamics of microstate classes C and D meet most of the requirements for an endophenotype for schizophrenia. The novel finding of the increased presence of microstates class B in siblings, as well as students scoring high in schizotypal traits, may reflect a compensation mechanism opposing the vulnerability to develop schizophrenia.

EEG microstates and the schizophrenia spectrum: evidence for compensation mechanisms

Ophélie Gladys Favrod, Patricia Figueiredo, Michael Herzog, Christine Mohr, Maya Roinishvili

Introduction: Electroencephalogram (EEG) microstates are on-going scalp potential configurations that remain stable for around 80 ms (1). Four recurrent and dominant classes of microstates (labeled A-D) are observed in resting-state EEG, explaining around 65-84 % of the global variance of the data (2). Several studies have reported abnormalities in the dynamics of EEG microstates in schizophrenia patients (3). Similar abnormalities have also been observed in adolescents with 22q11.2 deletion syndrome, a population that has a 30% risk of developing psychosis (4). These results prompted researchers to suggest that the abnormal dynamics of EEG microstates is a potential endophenotype for schizophrenia. For endophenotypes, it is important that unaffected relatives also show the abnormal dynamics (5). To the best of our knowledge, no study analyzed the resting dynamics of these four EEG microstate classes in relatives of schizophrenia patients. Methods: We examined 5 minutes resting-state EEG data of 260 participants collected across experiments, and we estimated the dynamics of the four canonical EEG microstates using Cartool (6). In experiment 1, to investigate whether unaffected siblings of schizophrenia patients show EEG microstates abnormalities, we tested 38 unaffected siblings of schizophrenia patients, 89 schizophrenia patients, and 69 healthy controls. In experiment 2, to assess whether these abnormalities are also present in people with high schizotypal traits, we tested 42 healthy students scoring either high (n=22) or low (n=20) in schizotypal traits. In experiment 3, to investigate whether microstates abnormalities are already present at the beginning of the disorder, we tested 22 patients with first episodes of psychosis (FEP). We also tested the FEP patients two more times throughout one year to assess whether the microstates dynamics change with disease progression. For each group of participants, we identified four microstates classes and labeled them A-D according to their similarities to the previously reported microstate class topographies (2). For each subject, three per-class microstate parameters were computed: mean duration (in ms), time coverage (in %), and frequency of occurrence (occurrence). Results: In line with previous studies, schizophrenia patients showed increased presence of the microstate class C and decreased presence of the microstate class D compared to controls (Figure 1). Siblings showed similar patterns of microstates classes C and D as patients. Surprisingly, siblings showed increased presence of the microstate class B compared to patients and controls. A similar result was also found in students scoring high in schizotypal traits compared to the ones scoring low (Figure 2). No difference was found between FEP and matched chronic patients. Moreover, the microstates dynamics remained stable throughout one year. Conclusions: Our findings suggest that the dynamics of resting-state EEG microstates not only meet most of the requirements for an endophenotype for schizophrenia (5), particularly classes C and D, but they also reveal a potential compensation mechanism that unaffected siblings and healthy people with high schizotypal traits have that might prevent them to develop the disorder. We associate this compensation mechanism with the increased presence of microstate class B. Only little is known about microstate class B. It has been related to the resting-state visual network in fMRI (7), and in healthy participants it is the shortest and least frequent microstate from adolescence on (8, 9). This suggests that the temporal dynamics of microstate class B might be an early marker to discriminate people that are at risk to develop schizophrenia from people that might compensate for their vulnerability.

2019

EEG signatures of schizophrenia: visual backward masking and microstates

Janir Nuno Ramos Antunes Da Cruz

Schizophrenia is a complex and devastating mental disorder that influences how one behaves, feels, and thinks. It affects a little less than 1 % of the world population and it is understood to be partly genetically mediated. Several genetic risk factors of schizophrenia have been identified. However, efforts to functionally characterize the genetic risk of schizophrenia have not been successful. To link the genetic risk to schizophrenia phenotype, schizophrenia research has turned to quantifiable stable trait markers with a clear genetic connection, so-called endo-phenotypes. Endophenotypes research in schizophrenia has heavily relied on the use of electroencephalogram (EEG) to identify endophenotypes and to better understand the neural mechanisms of neuropsychological candidate endophenotypes for schizophrenia. Here, I used high-density EEG to study two promising candidate endophenotypes of schizophrenia: visual backward masking (VBM) and EEG microstates. To cope with the heterogeneity of schizophrenia, I analyzed data from more than 300 participants (including schizophrenia patients, their unaffected siblings, healthy students scoring either high or low in schizotypal traits, patients with a first episode of psychosis, and healthy controls) performing a masking task or at rest. To deal with undesired signals that may affect the measurements and change the EEG signals of interest in a time-efficient manner and to minimize experts' subjectivity, I first proposed and validated an automatic pre-processing pipeline. This pipeline was subsequently used to pre-process all the analyzed EEG data. In a VBM study, performance of siblings was in between the ones of patients and controls (endophenotype concept). In patients, masking deficits were associated with decreased EEG amplitudes. In siblings, contrary to expectation, EEG amplitudes were even higher than in controls, which I interpret as a compensation signal. Based on EEG source localization, I propose that siblings over-activate a network of brain areas, including the right insula as a key player, to compensate for the deficits. In an EEG microstates study, schizophrenia patients, their siblings, and patients with a first episode of psychosis showed abnormal microstate dynamics compared to controls (endophenotype concept). Interestingly, siblings showed atypical dynamics of a particular microstate compared to patients, which I interpret also as a compensation signal. A similar result was also found in high schizotypes. My findings suggest that, even if there are genetic risks for developing schizophrenia, the brain is somehow capable of compensating for them. A better understanding of these compensation mechanisms and their commonalities might help to explain why some siblings develop schizophrenia, while others do not, which might open new avenues for characterization of schizophrenia and possible treatments of the disorder.

EPFL2019

Neural Compensation Mechanisms of Siblings of Schizophrenia Patients as Revealed by High-Density EEG

Ophélie Gladys Favrod, Patricia Figueiredo, Michael Herzog, Albulena Jashari-Shaqiri, Maya Roinishvili

Visual backward masking (VBM) deficits are candidate endophenotypes of schizophrenia indexing genetic liability of the disorder. In VBM, a target is followed by a mask that deteriorates target perception. Schizophrenia patients and, to a lesser extent, their unaffected relatives show strong and reproducible VBM deficits. In patients, VBM deficits are associated with strongly decreased amplitudes in the evoked-related potentials (ERPs). Here, to unveil the neural mechanisms of VBM in schizophrenia, circumventing illness-specific confounds, we investigated the electroencephalogram correlates of VBM in unaffected siblings of schizophrenia patients. We tested 110 schizophrenia patients, 60 siblings, and 83 healthy controls. As in previous studies, patients showed strong behavioral deficits and decreased ERP amplitudes compared to controls. Surprisingly, the ERP amplitudes of siblings were even higher than the ones of controls, while their performances were similar. ERP amplitudes in siblings were found to correlate with performance. These results suggest that VBM is deteriorated in patients and siblings. However, siblings, unlike patients, can partially compensate for the deficits by over-activating a network of brain regions.