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Resting State Electroencephalogram Oscillatory Abnormalities in Schizophrenia and Psychotic Bipolar Patients and Their Relatives from the Bipolar and Schizophrenia Network on Intermediate Phenotypes Study
Olin Neuropsychiatry Research Center, Hartford Hospital (IOL campus), Hartford, ConnecticutDepartment of Psychiatry and Neurobiology, Yale University School of Medicine, New Haven, Connecticut
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.
Methods
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.
Results
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.
Conclusions
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.
). The neurobiological underpinning of these disorders might provide a clearer basis for understanding their similarities and differences. Evidence suggests that SZ and psychotic bipolar disorder (PBP) are strongly heritable (
). Intermediate phenotypes, sometimes referred to as endophenotypes, are heritable quantitative biological measures presumably related to disease risk rather than overt clinical disorders, presumed to have simpler genetic architecture and to be closer to the causative genes than the clinical illness (
). Intermediate phenotypes are believed to be modulated by the disease-related genes influencing biological risk and may be expressed in unaffected relatives of probands.
Neural oscillations represent one type of candidate intermediate phenotype for SZ and PBP. Such oscillations normally play a vital role in defining temporal communication between circuits that represent various brain functions, such as information processing, memory, attention, perception, and consciousness. Basic fundamental building blocks defining these oscillations are the neuronal firing within cohorts of neurons, which are described by the frequency characteristics of electroencephalogram (EEG) recordings during a resting state. Disturbances in the spectral behavior of the oscillation patterns in probands could indicate aberrant brain function via neural dysfunction (
) abnormalities are also often noted. The main purpose of this study is to examine electrophysiologic phenotypes across psychotic disorders to dissect the common and distinct aspects of the psychosis dimension, an important clinical feature common to both SZ and PBP, and to test the hypothesis that endophenotype characteristics are homogeneous within phenomenologically derived DSM-IV diagnoses, rather than differentiating them (
). We assessed resting state EEG spectral activity in SZ and PBP (major psychoses) but not in nonpsychotic bipolar disorder. In general, published resting state EEG studies are less common in both PBP (
) probands to determine whether risk for each disorder is genetically determined. Some limitations with these studies include the use of few scalp leads, small study samples, and uncertainty in identifying reliable intermediate phenotypes. To address the lack of spatial recording leads, Venables et al. (
) conducted a study with 28 channels in both eyes open and closed conditions and demonstrated that augmented gamma activity was expressed in SZ relatives (SZR) and nonpsychotic bipolar disorder relatives, whereas increased beta activity was specific to SZR. Similar findings were reported in SZ siblings (
) identified abnormal low-frequency theta-alpha as a specific SZ risk marker. No resting state EEG abnormalities were present in relatives in the study by Clementz et al. (
), which used only three central electrodes and may have lacked the ability to detect frontotemporal-parietal differences. Although the Clementz et al. (
) study clearly differentiated probands from nonpsychiatric control subjects, it did not distinguish between SZ and bipolar disorder probands. Because of the heterogeneity of these illnesses and the variable findings in establishing neural oscillations as potential intermediate phenotypes for SZ and bipolar disorder separately, we conducted a large-scale multisite study to test directly the hypotheses that resting state EEG frequency abnormalities are unique to SZ and PBP probands and to examine whether these abnormalities are expressed in their first-degree relatives, to confirm genetic liability for these disorders.
The objectives of this study were to 1) determine whether resting state EEG spectral profiles were unique to SZ and PBP or common to both illnesses, 2) examine whether abnormal spectral composition was present in SZR and PBPR to detect any genetic link to these disorders, 3) estimate heritability of frequency abnormalities present in probands and relatives, 4) compare SZ and PBP probands with relatives with DSM-IV cluster A (schizotypal, schizoid, paranoid) and cluster B personality disorders diagnoses regarded as formes frustes of the illnesses or psychosis spectrum personality disorders (PSPD) versus relatives with nonpsychotic DSM-IV Axis I disorders and relatives with neither Axis I nor cluster A or B diagnoses, and 5) correlate oscillatory activity in probands with symptom scores including the Schizophrenia-Bipolar Scale (SBS) (
We predicted that frontal slow wave (delta, theta, slow alpha) oscillations would be disrupted in both SZ and PBP probands confirming prior studies, with similar abnormalities likely manifesting in SZR and PBPR. From prior work, we expected augmented fast beta abnormalities in both SZ probands and SZR and gamma abnormality in both SZR and PBPR.
Methods and Materials
Participant Recruitment
Probands were recruited from inpatient and outpatient units at the five centers (Supplement 1) comprising the collaborative Bipolar and Schizophrenia Network on Intermediate Phenotypes study (
Hippocampal and ventricular volumes in psychotic and nonpsychotic bipolar patients compared with schizophrenia patients and community control subjects: A pilot study.
), and having one or more eligible first-degree relatives participating in the study. There were 225 SZ probands and 234 PBP probands. Other groups comprised 201 SZR, 231 PBPR, and 200 healthy control (HC) subjects. Table 1 presents demographic information and characteristics for subjects. All subjects had the study explained to them, and written informed consent approved separately by institutional review boards of individual sites was obtained. Probands with schizoaffective disorder depressed and manic subtype diagnoses were classified as SZ and PBP, respectively (
). Relatives of probands with schizoaffective disorder depressed and manic subtypes were classified as SZR and PBPR, respectively. Relatives with lifetime Axis I psychoses who met the Structured Clinical Interview for DSM-IV criteria for SZ or PBP were assigned to the respective probands, but relatives with nonpsychotic Axis I disorders (e.g., major depression or anxiety disorder) were included in the SZR or PBPR category. Probands and relatives with psychotic psychiatric disorders were on stable doses of medication ≥4 weeks; nonpsychotic and unaffected relatives and HC subjects took no psychoactive medications (Table S1 in Supplement 1). The HC group included subjects not meeting DSM-IV criteria for any Axis I disorder.
Table 1Demographic Characteristics and Clinical Data for Subjects (N = 1091)
The EEG recordings were collected (Supplement 1) in electrically shielded booths with electrodes placed in accordance with the International 10–10 system using a 66-electrode cap with ground electrodes at the mid forehead and nose as references (Figure S1 in Supplement 1). The EEG data were preprocessed (Supplement 1) for generating artifact free epochs.
EEG Frequency Analysis
Frequency data for all subjects were estimated by applying spectral transformation (Supplement 1) to clean epochs. We included subjects with epochs ranging between 20 and 281 in the analysis.
Group Independent Component Analysis
Independent component analysis is a data-driven multivariate tool that employs higher order statistics for separating maximally independent sources from linear mixture, based on presumed EEG source independence, to provide better signal-to-noise ratio by identifying and eliminating unstructured noise sources from the data (
). Group independent component analysis (GICA) examines independent spatial-spectral components by treating spectral activity as a single effect across the entire group (probands, relatives, and HC subjects) data. Prior imaging studies have employed GICA (
) to identify spectral sources common to SZ probands, PBP probands, SZR, PBPR, and HC subjects. The data organization for the GICA procedure is detailed in Supplement 1 (Figure S2 in Supplement 1). The spectral data were decomposed into eight (>95% reliability) independent frequency components, estimated using Infomax independent component analysis in Infomax independent component analysis in EEGIFT (http://icatb.sourceforge.com). To facilitate the interpretability of GICA results, we also evaluated the spectral amplitude using the standard filtering by integrating the area under the spectral curve within various frequency bands and compared the data with data derived from the GICA.
Statistical Analysis
We examined group differences in scalp topographic weights using analyses of covariance (ANCOVA) (Supplement 1). The ANCOVA were carried out with four between-subjects factors: group, probands (SZ and PBP), relatives (SZR and PBPR), and HC subjects; sex, male/female; site, six levels; and race, six categories (Table 1). Age and number of epochs were included as covariates in all analyses. Post hoc t tests were conducted to assess pair-wise group comparisons. Similar tests were also conducted on the EEG spectral amplitude evaluated using the classic method. Additional post-hoc t tests were conducted to compare probands versus relatives with and without PSPD (cluster A and cluster B diagnoses) for both SZ and PBP. Pearson correlations were computed between the topographic weights of the frequency components that were abnormal in probands and psychopathology ratings PANSS positive, negative, and total and SBS totals to detect associations between spectral components and psychopathology. Supplementary analyses including relative risk estimation (
Spectral components from GICA were validated by visual inspection such that each component (Figure 1, Supplement 1) best describes the brain activity underlying eyes open resting state EEG. Omnibus ANCOVA testing carried out at all 64 leads showed a main effect of group in six of the eight components (Figure 2). There were significant site effects on the scalp weights, but no significant group × site interactions (Figure S3 in Supplement 1). Mean spatial weights and group comparisons (between probands, relatives, and HC subjects) for various oscillatory networks are shown in Figure 3, Figure 4.
Figure 1Mean frequency component over epochs across all subjects (N = 1271) and associated scalp topography from group independent component analysis. The spectral components are randomly ordered in group independent component analysis, but the eight spectral components are reordered for ease from low frequency to high frequency (1.5–50 Hz). The spatial weights of each frequency component are dimensionless measures indicating the strength of the connection or association with that component. The spatial weights were transformed to Z scores for visualization.
Figure 2F maps and significance levels from the omnibus analysis of covariance test comparing spatial weights from group independent component analysis across three groups. The three groups comprised schizophrenia and psychotic bipolar disorder probands (n = 459), their relatives (n = 432), and healthy control subjects (n = 200). “X” indicates significant after Bonferroni correction for 64 leads (p = .05/64).
Figure 3Mean spatial weights in schizophrenia probands, their relatives, and healthy control subjects and significance levels from pair-wise post-hoc t test. The topographic weights are dimensionless quantity. “X” indicates significant after multiple comparison correction for five comparisons (p = .05/5). Activity and P maps are shown at all leads for continuity, but only leads significant in the analysis of covariance and significantly different in probands and relatives versus healthy control subjects are highlighted in relatives. HC, healthy control; SZ, schizophrenia; SZR, first-degree relative of schizophrenia proband.
Figure 4Mean spatial weights in psychotic bipolar disorder probands, their relatives, and healthy control subjects and significance levels from pair-wise post-hoc t test. The topographic weights are dimensionless quantity. “X” indicates significant after multiple comparison correction for five comparisons (p = .05/5). Activity and P maps are shown at all leads for continuity, but only leads significant in the analysis of covariance and significantly different in probands and relatives versus healthy control subjects are highlighted in relatives. HC, healthy control; PBP, psychotic bipolar disorder; PBPR, first-degree relative of psychotic bipolar disorder proband.
Several frontal and central sites showed a main effect of group (peak F2,1075 = 8.99, p < .0001 at lead FZ). Post hoc t tests revealed augmented activity in SZ and PBP probands versus HC subjects. Additionally, SZR exhibited delta abnormality at frontal leads F1, FZ, and F2 in the direction of probands versus HC subjects.
Delta (N6)
Main effect of group for delta component was significant at anterior and posterior leads (peak F2,1075 = 14.12, p < .0000008 at lead FCZ). Simple effects map showed both SZ and PBP probands exhibited increased activity at similar scalp locations versus HC subjects.
Theta (N1)
Main effect of group for all spatial weights excluding FP2 from theta component was significant with a peak F value at lead CP5 (F2,1075 = 26.7, p < 4.8e–12) following Bonferroni correction. Simple effect analyses showed both SZ and PBP probands had significantly increased theta activity (Figure 3, Figure 4) at most scalp locations compared with HC subjects. Theta activity also differed significantly between SZ and PBP probands (Figure S4 in Supplement 1) in frontal, temporal, and central regions with PBP probands showing reduced activity compared with SZ probands.
Slow Alpha (N3)
The slow alpha component displayed significant group effect across most scalp locations except a few frontal sites (with peak F2,1075 = 13.4, p < .000001 at lead P7). Post-hoc t tests showed both SZ and PBP probands had augmented activity at similar scalp sites compared with HC subjects.
Fast Alpha (N2)
Several frontal and central leads from the fast alpha component showed significant main effects of group (peak F2,1075 = 9.74, p < .00006 at lead FCZ). Follow-up contrast revealed both SZ and PBP probands had significantly increased activity at frontal and central channels compared with HC subjects. Also, PBPR exhibited increased activity at two central leads (C1 and CZ) compared with HC subjects. Abnormal fast alpha activity in PBPR was in the direction of PBP probands.
Fast Alpha (N5)
Spatial weights from the fast alpha component showed no significant group effects across any scalp region.
Slow Beta (N4)
Frontal and central leads from the slow beta component differed significantly between groups (peak F2,1075 = 16.58, p < .00000008 at lead CZ). Follow-up contrast revealed that both SZ and PBP probands exhibited increased activity at frontal and central leads. Increased activity was noted in SZR at subset of these leads compared with HC subjects. Similarly, PBPR showed increased activity at subset of lead locations. For both groups, the abnormality in relatives was in the same direction as probands.
Fast Beta (N7)
No significant group effect was detected in spatial weights from the fast beta component.
Both SZ and PBP probands showed abnormal low-frequency activity compared with HC subjects. Increased delta (N8) activity was common to SZ probands and SZR (Figure 5A). Theta (N1) activity significantly differed between HC subjects and both proband groups and between the two proband groups. Increased fast alpha (N2) activity was present in both PBP probands and PBPR (Figure 5B). Augmented slow beta (N4) activity at several frontal and central leads was present in SZ and PBP probands and their first-degree relatives (Figure 5C,D). Results from supplementary analyses including relative risk and heritability are detailed in Tables S2–S4 in Supplement 1. The ANCOVA and pair-wise group comparisons on the spectral amplitude in various bands from standard EEG analysis revealed comparable results with augmented low-frequency abnormalities in both proband groups (Figures S5–S7 in Supplement 1). Abnormality in relatives was similar to the aforementioned results except for minor differences in spatial locations where effects were noted.
Figure 5Mean spatial weights of clinically relevant leads significantly differing between healthy control subjects and both probands and relatives. (A) Increased delta (N8) activity in schizophrenia probands (n = 225) and their relatives (n = 201) compared with healthy control subjects (n = 200), (B) augmented fast alpha (N2) activity in psychotic bipolar disorder probands (n = 234) and their relatives (n = 231) compared with healthy control subjects (n = 200), (C) increased slow beta (N4) in schizophrenia probands and their relatives compared with healthy control subjects, and (D) increased slow beta (N4) in psychotic bipolar disorder probands and their relatives compared with healthy control subjects. All the displayed leads were significantly different between healthy control subjects and both probands and relatives after Bonferroni correction for pair-wise comparison (p = .05/5). Error bars represent SEM. HC, healthy control; PBP, psychotic bipolar disorder; PBPR, first-degree relative of psychotic bipolar disorder proband; SZ, schizophrenia; SZR, first-degree relative of schizophrenia proband.
Although only few SZR and PBPR met criteria for PSPD, comparison of SZR and PBPR with PSPD with probands and HC subjects (Figure S8A–D in Supplement 1) showed a monotonic increasing pattern in delta and fast alpha activity from HC subjects to other relatives to PSPD relatives to probands in SZ and PBP groups. Similar patterns were observed in slow beta activity in PBPR but not in SZR.
Clinical Correlation with Oscillatory Activity
Theta (N1) abnormality correlated positively with SBS scores at all 64 leads (peak r = .16, p < .0006 at lead F1) (Figure S9 in Supplement 1), such that high SBS scores were associated with increased theta/slow alpha abnormalities. No other frequency abnormality correlated with SBS or PANSS positive, negative, or total scores.
Discussion
The overall purpose of the multisite Bipolar and Schizophrenia Network on Intermediate Phenotypes study is to examine intermediate phenotypes in a large sample size cohort across the psychosis spectrum with generalizability. The main objectives of the current study were to examine whether similar resting state EEG oscillatory abnormalities were present in SZ and PBP probands, to determine if similar patterns characterized their first-degree relatives, and to evaluate their heritability. Six of the eight resting state EEG spectral components exhibited abnormalities in both SZ and PBP probands. Three abnormal spectral profiles were present in both probands and relatives; one was common to SZ and PBP, whereas each of the remaining two were disorder-specific. To our knowledge, this is the first large-scale multisite study with higher order spatial leads to report neural oscillatory abnormalities in SZ and PBP probands and their first-degree relatives using a novel, data-driven GICA approach with instantaneous resting state EEG spectral data.
Frequency Abnormalities Present in Both SZ and PBP Probands
Because SZ and PBP probands share psychotic features, we expected specific frequency abnormalities to characterize both disorders. Low-frequency activity including delta, theta, slow alpha, and slow beta were abnormal in both SZ and PBP probands. Low-frequency abnormality (delta and theta) may be associated with genetic variants of catechol-O-methyltransferase in SZ (
). Although spectral activity was not compared between schizoaffective, SZ, and PBP probands, no frequency components (results not shown here) differed between schizoaffective depressive type versus SZ and schizoaffective mania type versus PBP proband groups.
Delta
Augmented delta activity (N6) was pronounced in frontal, left temporal, central, and parietal locations in both SZ and PBP. Similarly, increased delta activity (N8) confined to frontal scalp locations was seen in both probands. Increased delta activity might be associated with reduced glucose metabolism (
Abnormally increased theta (N1) activity across the entire scalp was present in both proband groups. The neurobiological basis for abnormal theta activity in psychosis is unclear. However, one possible interpretation is that theta oscillations are hippocampally generated (
) reported increased theta activity as a marker specific to SZ and not bipolar disorder, but their bipolar disorder subjects were nonpsychotic, further suggesting that augmented theta activity is a psychosis biomarker. Our finding of increased theta activity is in agreement with prior similar studies (
). Theta activity (N1) was prominently increased in SZ probands compared with PBP probands, perhaps reflecting greater psychotic symptom severity in SZ. Additionally, clinical correlation with the SBS scale was weakly significant for frontal theta (N1) activity, suggesting that the frontal region differentiated SZ and PBP probands. The current findings suggest that a continuous rating such as the SBS revealed differences between proband groups.
Slow Alpha and Fast Alpha
Increased slow (N3) and fast alpha (N2) oscillatory activity was common to SZ and PBP probands. Slow alpha activity was more significant from the anterior to posterior brain regions including temporal locations in SZ probands compared with PBP probands. Fast alpha (N2) abnormality was confined to several frontocentral scalp locations in both SZ and PBP probands. Although it is unclear what specific neural mechanisms contribute to alpha oscillations, prior evidence shows that alpha activity is functionally correlated with arousal (
). Augmented alpha activity reflects reduced cortical activity and decreased arousal, generally being more pronounced in SZ compared with bipolar disorder. Alpha rhythms are positively associated with default mode network activity (
). Increased alpha activity may indicate aberrant default mode network activity and deficient visual and dorsal attention in probands, which has been validated by functional MRI studies (
) of decreased alpha activity. Possible explanations might be between-study differences in methodology, sample distribution, processing parameters, diagnostic heterogeneity, illness chronicity, or clinical subtypes.
Slow Beta
Augmented frontocentral, high-frequency slow beta activity was common to SZ and PBP probands. Although the functional role of slow beta rhythms is unclear, they are generally associated with cognition (
We detected no aberrant fast beta activity in probands or relatives despite prior reports. The omnibus ANCOVA test failed to show group differences; post-hoc tests were not carried out in probands and relatives. However, if the analysis was restricted separately to SZ and PBP probands versus HC subjects, only SZ probands and not PBP probands showed increased fast beta activity. In one prior study (
) used averaged spectral data compared with the single trial analyses in our study.
The present study did not examine gamma activity. We were unable to extract a frontally active gamma component from GICA when the model order was varied from 2 to 20 likely owing to the diminished amplitude of high-frequency gamma activity covarying with dominant low-frequency oscillations. With increased model order, the estimated low-frequency components generally implicated in SZ and PBP disintegrated into weak subcomponents.
Frequency Abnormalities Present in Both Probands and Their Relatives
Augmented frontocentral slow beta activity was detected in SZR and PBPR versus HC subjects. As noted earlier, beta activity indexes neuronal excitability, suggesting altered cortical excitability in SZR and PBPR. Because abnormal beta activity was noted at the same scalp locations in both proband groups, beta activity may serve as a potential intermediate phenotype reflecting genetic predisposition to both SZ and PBP. Our finding is contrary to prior studies (
) that reported fast beta abnormality in SZR. The present study was unable to examine fast beta activity because of methodologic issues; omnibus ANCOVA failed to show group differences, and no post hoc tests were carried out, but increased fast beta activity was specific to SZ and not PBP probands. In general, beta activity is consistently identified as SZ intermediate phenotype indicating genetic liability for SZ.
Frequency Abnormalities Unique to SZR and PBPR
Abnormal delta activity was present in SZ probands and their relatives. Increased delta activity was localized frontally diffusely in both probands and relatives, indicating cortical hyperactivation. A prior study (
) failed to identify low-frequency delta activity as an intermediate phenotype marker; SZR exhibited decreased delta power or cortical hyperactivation. However, that study identified reduced delta band coherence as a heritable trait related to SZ risk. Another prior report (
) revealed low-frequency theta-alpha oscillation as an SZ intermediate phenotype. We did not observe theta-alpha abnormalities in SZR possibly owing to methodologic differences, but we detected subtle excess fast alpha activity in two central leads in PBPR, perhaps reflecting weak liability to PBP.
Medication Effects
One challenge in interpreting our findings in probands is possible confounding effects of current medication treatment and illness chronicity, severity, and duration on spectral activity. Although there is prior evidence (
) for a lack of medication influence on resting state EEG spectra in probands, abnormalities detected in the present proband samples for increased delta, theta, and alpha activity, for example, may be due to medications, similar to effects of clozapine on resting state EEG as reported by Knott et al. (
). The current literature has conflicting reports on medication effects. In our study, probands were on several medications with varying dosages and durations, and it was not feasible to account for medication effects. The study did not collect detailed longitudinal medication histories for probands for complete assessment of possible historical medication influence on resting state EEG spectral measures. However, slow beta (N4), fast alpha (N2), and delta (N8) abnormalities were present in both relatives and probands, suggesting that such abnormalities are unlikely to be due to antipsychotic medications because the relatives were free of psychosis and not taking psychotropic medications.
Relative Risk and Heritability
Delta and slow beta activity manifested a moderate relative risk ratio suggesting familial characteristics associated with these measures and are candidates for intermediate phenotypes for SZ. Similarly, fast alpha and slow beta activity exhibited a low relative risk for PBP. Oscillatory abnormalities in probands and relatives were further validated by our findings of moderate heritability. Our estimates were lower compared with pedigree-based analyses, possibly secondary to less dense kinship structure in our sample (i.e., most probands were represented by only one relative); “familiality” may be a more accurate descriptor. Our results are consistent with prior studies (
) showing resting state EEG as a heritable trait across the frequency spectrum, suggesting a moderate amount of genetic influence in the variability of spectral activity.
Study Limitations
The current study has several limitations, including demographic data unbalanced in age and sex, which was accounted for in the analysis by regression at the cost of reduced statistical power. Second, we were unable to account for medication effects in probands, owing to the multiple medications taken by probands and unavailability of longitudinal medication history. We were unable to extract a gamma component to investigate its role. There were significant site effects that had to be accounted for in the model. However, these did not significantly interact with clinical diagnoses. Some advantages of this study included large sample sizes from multiple sites (generalizability) and high electrode density data covering the scalp.
Conclusions
The present findings reveal similar low-frequency abnormalities in SZ and PBP probands, consistent with prior reports. Delta abnormalities were present in both SZ probands and SZR, whereas PBP probands and PBPR revealed fast alpha abnormalities. Slow beta abnormality was present in both probands and their relatives. Future genetic investigations on the low-frequency abnormalities would help clarify whether the biological mechanisms underlying these abnormalities are similar or different in these disorders. The oscillatory abnormalities in probands may provide insight into the underlying neurophysiologic problems. The weaker, familial frequency abnormalities identified in relatives may serve as intermediate phenotypes reflecting genetic liability for these illnesses and ultimately help identify the genetic architecture. Familiality for these intermediate phenotypes was moderate but significant. Future studies need to investigate genetic underpinnings for spectral components and examine their functional implications by combining resting state EEG oscillations with functional magnetic resonance imaging resting state networks.
This work was supported by funding from the National Institute of Mental Health through linked Grant No. R01 MH077851 to Dr. Tamminga, Grant No. MH077945 to Dr. Pearlson, Grant No. MH078113 to Dr. Keshavan, Grant No. MH077862 to Dr. Sweeney, and Grant No. MH077852 to Dr. Thaker.
Presented at 67th Society of Biological Psychiatry Meeting, May 3–5, 2012, Philadelphia, Pennsylvania.
Dr. Sweeney has received support from Takeda, BMS, Eli Lilly, Roche, and Janssen. Dr. Keshavan has received support from Sunovion. Dr. Tamminga has received funding from Astellas, Eli Lilly, Intracellular Therapies, Lundbeck, and PureTech Ventures. All other authors report no biomedical financial interests or potential conflicts of interest.
Hippocampal and ventricular volumes in psychotic and nonpsychotic bipolar patients compared with schizophrenia patients and community control subjects: A pilot study.
In the past several years, there has been a great deal of interest in using oscillations of the electroencephalogram (EEG) to understand the neural substrates of neuropsychiatric disorders. These EEG oscillations represent the coordinated activity of large populations of neurons, and oscillations in different frequency bands of the EEG are generated by distinct neural circuitries. For example, oscillations in the gamma band of the EEG (30–100 Hz) can be produced by reciprocal interactions between pyramidal cells and fast-spiking inhibitory interneurons, whereas beta (13–30 Hz) oscillations can be produced by interactions between gap junction–connected bursting pyramidal cells (1).
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