Minimum spanning tree analysis of EEG resting-state functional networks in schizophrenia.

Schizophrenia is a serious and complex mental disease, known to be associated with various subtle structural and functional deviations in the brain. Recently, increased attention is given to the analysis of brain-wide, global mechanisms, strongly altering the communication of long-distance brain areas in schizophrenia. Data of 32 patients with schizophrenia and 28 matched healthy control subjects were analyzed. Two minutes long 64-channel EEG recordings were registered during resting, eyes closed condition. Average connectivity strength was estimated with Weighted Phase Lag Index (wPLI) in lower frequencies: delta and theta, and Amplitude Envelope Correlation with leakage correction (AEC-c) in higher frequencies: alpha, beta, lower gamma and higher gamma. To analyze functional network topology Minimum Spanning Tree (MST) algorithms were applied. Results show that patients have weaker functional connectivity in delta and alpha frequency bands. Concerning network differences, the result of lower diameter, higher leaf number, and also higher maximum degree and maximum betweenness centrality in patients suggest a star-like, and more random network topology in patients with schizophrenia. Our findings are in accordance with some previous findings based on resting-state EEG (and fMRI) data, suggesting that MST network structure in schizophrenia is biased towards a less optimal, more centralized organization.

Probing the biological consequences of a previously undescribed de novo mutation of ZMYND11 in a schizophrenia patient by CRISPR genome editing and induced pluripotent stem cell based in vitro disease-modeling.

BACKGROUND: Schizophrenia (SCZ) is a severe neuropsychiatric disorder of complex, poorly understood etiology, associated with both genetic and environmental factors. De novo mutations (DNMs) represent a new source of genetic variation in SCZ, however, in most cases their biological significance remains unclear. We sought to investigate molecular disease pathways connected to DNMs in SCZ by combining human induced pluripotent stem cell (hiPSC) based disease modeling and CRISPR-based genome editing. METHODS: We selected a SCZ case-parent trio with the case individual carrying a potentially disease causing 1495C > T nonsense DNM in the zinc finger MYND domain-containing protein 11 (ZMYND11), a gene implicated in biological processes relevant for SCZ. In the patient-derived hiPSC line the mutation was corrected using CRISPR, while monoallelic or biallelic frameshift mutations were introduced into a control hiPSC line. Isogenic cell lines were differentiated into hippocampal neuronal progenitor cells (NPCs) and functionally active dentate gyrus granule cells (DGGCs). Immunofluorescence microscopy and RNA sequencing were used to test for morphological and transcriptomic differences at NPC and DGCC stages. Functionality of neurons was investigated using calcium-imaging and multi-electrode array measurements. RESULTS: Morphology in the mutant hippocampal NPCs and neurons was preserved, however, we detected significant transcriptomic and functional alterations. RNA sequencing showed massive upregulation of neuronal differentiation genes, and downregulation of cell adhesion genes. Decreased reactivity to glutamate was demonstrated by calcium-imaging. CONCLUSIONS: Our findings lend support to the involvement of glutamatergic dysregulation in the pathogenesis of SCZ. This approach represents a powerful model system for precision psychiatry and pharmacological research.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Differential neurocognitive profiles in adult attention-deficit/hyperactivity disorder subtypes revealed by the Cambridge Neuropsychological Test Automated Battery.

Adult attention-deficit/hyperactivity disorder (aADHD) represents a heterogeneous entity incorporating different subgroups in terms of symptomatology, course, and neurocognition. Although neurocognitive dysfunction is generally associated with aADHD, its severity, association with self-reported symptoms, and differences between subtypes remain unclear. We investigated 61 outpatients (65.6% male, mean age 31.5 ± 9.5) diagnosed using DSM-5 criteria together with age-, sex-, and education-matched healthy controls (HC) (n = 58, 63.8% male, mean age 32.3 ± 9.6). Neurocognitive alterations were assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and compared between groups using the generalized linear model (GLM) method. Multivariate effects were tested by principal component analysis combined with multivariate pattern analysis. Self-reported symptom severity was tested for correlations with neurocognitive performance. GLM analyses revealed nominally significant differences between the aADHD and HC groups in several domains, however, only the Rapid Visual Information Processing measures survived correction, indicating impaired sustained attention and response inhibition in the aADHD group. Comparison of the predominantly inattentive and the hyperactive-impulsive/combined subtypes yielded nominally significant differences with higher levels of dysfunction in the inattentive group. In the stepwise discriminant analysis aADHD and HC groups were best separated with 2 factors representing sustained attention and reaction time. We found only weak correlations between symptom severity and CANTAB factors. aADHD patients are neuropsychologically heterogeneous and subtypes show different neurocognitive profiles. Differences between the aADHD and HC groups were driven primarily by the inattentive subtype. Sustained attention and its factor derivative showed the most significant alterations in aADHD patients.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this common causal network (CCN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis (Principal Component Analysis, PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CCN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CCN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes. This evidence further supports that treatment interventions converge on a CCN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Investigation of de novo mutations in a schizophrenia case-parent trio by induced pluripotent stem cell-based in vitro disease modeling: convergence of schizophrenia- and autism-related cellular phenotypes.

BACKGROUND: De novo mutations (DNMs) have been implicated in the etiology of schizophrenia (SZ), a chronic debilitating psychiatric disorder characterized by hallucinations, delusions, cognitive dysfunction, and decreased community functioning. Several DNMs have been identified by examining SZ cases and their unaffected parents; however, in most cases, the biological significance of these mutations remains elusive. To overcome this limitation, we have developed an approach of using induced pluripotent stem cell (iPSC) lines from each member of a SZ case-parent trio, in order to investigate the effects of DNMs in cellular progenies of interest, particularly in dentate gyrus neuronal progenitors. METHODS: We identified a male SZ patient characterized by early disease onset and negative symptoms, who is a carrier of 3 non-synonymous DNMs in genes LRRC7, KHSRP, and KIR2DL1. iPSC lines were generated from his and his parents' peripheral blood mononuclear cells using Sendai virus-based reprogramming and differentiated into neuronal progenitor cells (NPCs) and hippocampal dentate gyrus granule cells. We used RNASeq to explore transcriptomic differences and calcium (Ca2+) imaging, cell proliferation, migration, oxidative stress, and mitochondrial assays to characterize the investigated NPC lines. RESULTS: NPCs derived from the SZ patient exhibited transcriptomic differences related to Wnt signaling, neuronal differentiation, axonal guidance and synaptic function, and decreased Ca2+ reactivity to glutamate. Moreover, we could observe increased cellular proliferation and alterations in mitochondrial quantity and morphology. CONCLUSIONS: The approach of reprograming case-parent trios represents an opportunity for investigating the molecular effects of disease-causing mutations and comparing these in cell lines with reduced variation in genetic background. Our results are indicative of a partial overlap between schizophrenia and autism-related phenotypes in the investigated family. LIMITATIONS: Our study investigated only one family; therefore, the generalizability of findings is limited. We could not derive iPSCs from two other siblings to test for possible genetic effects in the family that are not driven by DNMs. The transcriptomic and functional assays were limited to the NPC stage, although these variables should also be investigated at the mature neuronal stage.

Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls.

Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with schizophrenia and healthy controls. We applied multivariate pattern analysis of microstate features to create a specified feature set to represent microstate characteristics. Machine learning approaches using these features for classification of patients with schizophrenia were compared with prior EEG based machine learning studies. Our microstate segmentation in both patients with schizophrenia and healthy controls yielded topographies that were similar to the normative database established earlier by Koenig et al. Our machine learning model was based on large sample size, low number of features and state-of-art K-fold cross-validation technique. The multivariate analysis revealed three patterns of correlated features, which yielded an AUC of 0.84 for the group separation (accuracy: 82.7%, sensitivity/specificity: 83.5%/85.3%). Microstate segmentation of resting state EEG results in informative features to discriminate patients with schizophrenia from healthy individuals. Moreover, alteration in microstate measures may represent disturbed activity of networks in patients with schizophrenia.

Alterations in resting-state gamma activity in patients with schizophrenia: a high-density EEG study.

Alterations of EEG gamma activity in schizophrenia have been reported during sensory and cognitive tasks, but it remains unclear whether changes are present in resting state. Our aim was to examine whether changes occur in resting state, and to delineate those brain regions where gamma activity is altered. Furthermore, we wanted to identify the associations between changes in gamma activity and psychopathological characteristics. We studied gamma activity (30-48 Hz) in 60 patients with schizophrenia and 76 healthy controls. EEGs were acquired in resting state with closed eyes using a high-density, 256-channel EEG-system. The two groups were compared in absolute power measures in the gamma frequency range. Compared to controls, in patients with schizophrenia the absolute power was significantly elevated (false discovery rate corrected p < 0.05). The alterations clustered into fronto-central and posterior brain regions, and were positively associated with the severity of psychopathology, measured by the PANSS. Changes in gamma activity can lead to disturbed coordination of large-scale brain networks. Thus, the increased gamma activity in certain brain regions that we found may result in disturbances in temporal coordination of task-free/resting-state networks in schizophrenia. Positive association of increased gamma power with psychopathology suggests that altered gamma activity provides a contribution to symptom presentation.