Multivariate genetic determinants of EEG oscillations in schizophrenia and psychotic bipolar disorder from the BSNIP study.
ABSTRACT: Schizophrenia (SZ) and psychotic bipolar disorder (PBP) are disabling psychiatric illnesses with complex and unclear etiologies. Electroencephalogram (EEG) oscillatory abnormalities in SZ and PBP probands are heritable and expressed in their relatives, but the neurobiology and genetic factors mediating these abnormalities in the psychosis dimension of either disorder are less explored. We examined the polygenic architecture of eyes-open resting state EEG frequency activity (intrinsic frequency) from 64 channels in 105 SZ, 145 PBP probands and 56 healthy controls (HCs) from the multisite BSNIP (Bipolar-Schizophrenia Network on Intermediate Phenotypes) study. One million single-nucleotide polymorphisms (SNPs) were derived from DNA. We assessed eight data-driven EEG frequency activity derived from group-independent component analysis (ICA) in conjunction with a reduced subset of 10,422 SNPs through novel multivariate association using parallel ICA (para-ICA). Genes contributing to the association were examined collectively using pathway analysis tools. Para-ICA extracted five frequency and nine SNP components, of which theta and delta activities were significantly correlated with two different gene components, comprising genes participating extensively in brain development, neurogenesis and synaptogenesis. Delta and theta abnormality was present in both SZ and PBP, while theta differed between the two disorders. Theta abnormalities were also mediated by gene clusters involved in glutamic acid pathways, cadherin and synaptic contact-based cell adhesion processes. Our data suggest plausible multifactorial genetic networks, including novel and several previously identified (DISC1) candidate risk genes, mediating low frequency delta and theta abnormalities in psychoses. The gene clusters were enriched for biological properties affecting neural circuitry and involved in brain function and/or development.
Project description:Abnormal resting state electroencephalogram (EEG) oscillations are reported in schizophrenia (SZ) and bipolar disorder, illnesses with overlapping symptoms and genetic risk. However, less evidence exists on whether similar EEG spectral abnormalities are present in individuals with both disorders or whether these abnormalities are present in first-degree relatives, possibly representing genetic predisposition for these disorders.Investigators examined 64-channel resting state EEGs of 225 SZ probands and 201 first-degree relatives (SZR), 234 psychotic bipolar (PBP) probands and 231 first-degree relatives (PBPR), and 200 healthy control subjects. Eight independent resting state EEG spectral components and associated spatial weights were derived using group independent component analysis. Analysis of covariance was conducted on spatial weights to evaluate group differences. Relative risk estimates and familiality were evaluated on abnormal spectral profiles in probands and relatives.Both SZ and PBP probands exhibited increased delta, theta, and slow and fast alpha activity. Post-hoc pair-wise comparison revealed increased frontocentral slow beta activity in SZ and PBP probands as well as SZR and PBPR. Augmented frontal delta activity was exhibited by SZ probands and SZR, whereas PBP probands and PBPR showed augmented fast alpha activity.Both SZ and PBP probands demonstrated aberrant low-frequency activity. Slow beta activity was abnormal in SZ and PBP probands as well as SZR and PBPR perhaps indicating a common endophenotype for both disorders. Delta and fast alpha activity were unique endophenotypes for SZ and PBP probands, respectively. The EEG spectral activity exhibited moderate relative risk and heritability estimates, serving as intermediate phenotypes in future genetic studies for examining biological mechanisms underlying the pathogenesis of the two disorders.
Project description:The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.
Project description:The complex molecular etiology of psychosis in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is not well defined, presumably due to their multifactorial genetic architecture. Neurobiological correlates of psychosis can be identified through genetic associations of intermediate phenotypes such as event-related potential (ERP) from auditory paired stimulus processing (APSP). Various ERP components of APSP are heritable and aberrant in SZ, PBP and their relatives, but their multivariate genetic factors are less explored.We investigated the multivariate polygenic association of ERP from 64-sensor auditory paired stimulus data in 149 SZ, 209 PBP probands, and 99 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Multivariate association of 64-channel APSP waveforms with a subset of 16 999 single nucleotide polymorphisms (SNPs) (reduced from 1 million SNP array) was examined using parallel independent component analysis (Para-ICA). Biological pathways associated with the genes were assessed using enrichment-based analysis tools.Para-ICA identified 2 ERP components, of which one was significantly correlated with a genetic network comprising multiple linearly coupled gene variants that explained ~4% of the ERP phenotype variance. Enrichment analysis revealed epidermal growth factor, endocannabinoid signaling, glutamatergic synapse and maltohexaose transport associated with P2 component of the N1-P2 ERP waveform. This ERP component also showed deficits in SZ and PBP.Aberrant P2 component in psychosis was associated with gene networks regulating several fundamental biologic functions, either general or specific to nervous system development. The pathways and processes underlying the gene clusters play a crucial role in brain function, plausibly implicated in psychosis.
Project description:<h4>Background</h4>We quantified frequency-specific, absolute, and fractional amplitude of low-frequency fluctuations (ALFF/fALFF) across the schizophrenia (SZ)-psychotic bipolar disorder (PBP) psychosis spectrum using resting functional magnetic resonance imaging data from the large BSNIP family study.<h4>Methods</h4>We assessed 242 healthy controls (HC), 547 probands (180 PBP, 220 SZ, and 147 schizoaffective disorder-SAD), and 410 of their first-degree relatives (134 PBPR, 150SZR, and 126 SADR). Following standard preprocessing in statistical parametric mapping (SPM8), we computed absolute and fractional power (ALFF/fALFF) in 2 low-frequency bands: slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz). We evaluated voxelwise post hoc differences across traditional Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnostic categories.<h4>Results</h4>Across ALFF/fALFF, in contrast to HC, BP/SAD showed hypoactivation in frontal/anterior brain regions in the slow-5 band and hypoactivation in posterior brain regions in the slow-4 band. SZ showed consistent hypoactivation in precuneus/cuneus and posterior cingulate across both bands and indices. Increased ALFF/fALFF was noted predominantly in deep subcortical and temporal structures across probands in both bands and indices. Across probands, spatial ALFF/fALFF differences in SAD resembled PBP more than SZ. None of these ALFF/fALFF differences were detected in relatives.<h4>Conclusions</h4>Results suggest ALFF/fALFF is a putative biomarker rather than a familial endophenotype. Overall sensitivity to discriminate proband brain alteration was stronger for fALFF than ALFF. Patterns of differences noted in SAD were more similar to those observed in PBP. Differential effects were noted across the 2 frequency bands, more prominently for BP/SAD compared with SZ, suggesting frequency-sensitive physiologic mechanisms for the former.
Project description:We evaluated whether abnormal frequency composition of the resting state electroencephalogram (EEG) in schizophrenia was associated with genetic liability for the disorder by studying first-degree biological relatives of schizophrenia patients. The study included a data-driven method for defining EEG frequency components and determined the specificity of resting state EEG frequency abnormalities by assessing schizophrenia patients, bipolar disorder patients, and relatives of both patient groups. Schizophrenia patients and their relatives, but not bipolar patients or their relatives, exhibited increased high-frequency activity (beta) providing evidence for disturbances in resting state brain activity being specific to genetic liability for schizophrenia. Schizophrenia patients exhibited augmented low-frequency EEG activity (delta, theta), while bipolar disorder patients and the 2 groups of relatives generally failed to manifest similar low-frequency EEG abnormalities. The Val(158)Met polymorphism for the catechol-O-methyl transferase (COMT) gene was most strongly associated with delta and theta activity in schizophrenia patients. Met homozygote schizophrenia patients exhibited augmented activity for the 2 low-frequency bands compared with control subjects. Excessive high-frequency EEG activity over frontal brain regions may serve as an endophenotype that reflects cortical expression of genetic vulnerability for schizophrenia. Low-frequency resting state EEG anomalies in schizophrenia may relate to disorder-specific pathophysiology in schizophrenia and the influence of the COMT gene on tonic dopamanergic function.
Project description:Individuals with schizophrenia (SZ) or bipolar disorder with psychosis (BPP) may share neurophysiological abnormalities as measured in auditory paired-stimuli paradigms with electroencephalography (EEG). Such investigations have been limited, however, by quantifying only event-related potential peaks and/or broad frequency bands at limited scalp locations without considering possible mediating factors (e.g., baseline differences). Results from 64-sensor EEG collected in 180 age- and gender-matched participants reveal (i) accentuated prestimulus gamma oscillations and (ii) reduced P2 amplitudes and theta/alpha oscillations to S1 among participants with both SZ and BPP. Conversely, (iii) N1s in those with SZ to S1 were reduced compared to healthy volunteers and those with BPP, whereas (iv) beta range oscillations 200-300 ms following S2 were accentuated in those with BPP but not those with SZ. Results reveal a pattern of both unique and shared neurophysiological phenotypes occurring within major psychotic diagnoses.
Project description:The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.
Project description:BACKGROUND:With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically. METHODS:Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses. RESULTS:SWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function. CONCLUSIONS:EEG features derived by SVM are consistent with literature reports of gamma's role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.
Project description:AIMS/HYPOTHESIS: In diabetic children and adolescents, a history of severe hypoglycaemia (SH) has been associated with increased slow EEG activity and reduced cognition, possibly due to harmful effects of SH on the developing brain. In a group of type 1 diabetic patients with early exposure to SH, who had EEG abnormalities and reduced cognition in childhood, we have recently demonstrated that the reduced cognition may persist into adulthood. We have now assessed whether the reduced cognition was accompanied by lasting EEG abnormalities. METHODS: In 1992-1993, we studied EEG and cognition in 28 diabetic children and 28 matched controls. 16 years later, we re-investigated the same participants, with 96% participation rate. Diabetic participants were classified as with (n = 9) or without (n = 18) early SH, defined as episodes with convulsions or loss of consciousness by 10 years of age. For each EEG band (delta, theta, alpha and beta) and cerebral region (frontocentral, temporal, and parietooccipital), we calculated relative amplitudes and amplitude asymmetry. We also calculated occipital alpha mean frequency, alpha peak frequency at maximum amplitude, alpha peak width, and theta regional mean frequencies. We examined whether these EEG measures, relative to age- and sex-matched controls, differed between diabetic participants with and without early SH. RESULTS: We found no association of early SH with any of the EEG measures. CONCLUSIONS/INTERPRETATION: Childhood SH was not associated with EEG abnormalities in young type 1 diabetic adults. Our findings suggest that the reduced adulthood cognition associated with childhood exposure to SH is not accompanied by lasting EEG abnormalities.
Project description:This study investigated the use of resting-state electroencephalography (EEG) data to help differentiate posttraumatic stress disorder (PTSD) symptom factors. The sample, 147 combat-exposed OIF/OEF (Operation Iraqi Freedom/Operation Enduring Freedom) Veterans and service members, was a polytrauma population with variable PTSD and mild traumatic brain injury (mTBI) diagnoses. Participants completed the PTSD Checklist (PCL) and resting-state EEG was assessed for 10 minutes, with eyes closed. Regional averages of absolute power in alpha, beta, delta, and theta frequency bands were computed to estimate a single EEG common factor per band. An oblique 4 common-factor model was then fit to the 17 PCL items that included a residual EEG factor as an exogenous predictor with the group mean effect of mTBI on the EEG factor removed. Separate comparative model testing sequences for the alpha, beta, delta, and theta EEG factor frequency bands were conducted. An inverse relationship of delta and theta frequency bands on avoidance and numbing symptom factors (but not re-experiencing and hyperarousal) was found. Results provide evidence for possible neurobiological basis for the 4 PTSD symptom factors.