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The auditory sensory gating deficit has been considered a leading endophenotype in schizophrenia. However, the commonly used index of sensory gating, P50, has low heritability in families of people with schizophrenia, raising questions about its utility in genetic studies. We hypothesized that the sensory gating deficit may occur in a specific neuronal oscillatory frequency that reflects the underlying biological process of sensory gating. Frequency-specific sensory gating may be less complex than the P50 response, and therefore closer to direct genetic effects and thus a more valid endophenotype.
To compare gating of frequency-specific oscillatory response(s) with gating of P50; to compare their heritabilities.
We explored single-trial based oscillatory gating responses in people with schizophrenia, their relatives, and community controls.
People with schizophrenia (n=102), their first-degree relatives (n=74), and community controls (n=70).
Gating of frequency-specific oscillatory responses, gating of the P50 wave, and their heritability estimates.
Gating of the alpha-theta band responses significantly separated controls from people with schizophrenia (p<0.001) and their first-degree relatives (p=0.039 - 0.009). The heritability of alpha-theta band gating was estimated to be 0.49–0.83 and was at least 4-fold higher than the P50 heritability estimate.
Gating of the alpha-theta frequency oscillatory signal in the paired-click paradigm is more strongly associated with schizophrenia and has significantly higher heritability compared to the traditional P50 gating. This measure may be better suited for genetic studies of the gating deficit in schizophrenia.
Abnormal sensory gating may index the inability of people with schizophrenia to sufficiently filter unwanted sensory information, and is considered a leading endophenotype in schizophrenia 1;2. Sensory gating is efficiently probed using a simple paired-click auditory evoked potential paradigm: the gating response is reflected by a diminution of evoked potential response elicited by the second of a pair of identical auditory stimuli. Most previous sensory gating studies have focused on the averaged P50 wave in response to the auditory stimuli 3;4. P50 gating impairments have been observed in people with schizophrenia and their relatives in some (e.g., 5–8), but not all (e.g., 9–11) studies. More comprehensive review can be found elsewhere 4.
Although it is often considered an endophenotype for schizophrenia, the traditional P50 gating measure has a low heritability, which has been estimated at 0.10 in the families of people with schizophrenia 12. Low heritabilities compromise the utility of this phenotype for genetic studies, and indeed, even calls into question the validity of the phenotype given the high heritability of the schizophrenia phenotype (e.g., 13).
Given the limitation, we sought to refine the sensory gating paradigm by studying single trial oscillatory responses to an auditory stimulus, independent of any averaged waveforms. Schizophrenia is a disease associated with aberrant processing of sensory information. Sufficient data have shown that neuronal assemblies have the intrinsic capacity to oscillate at different frequencies in response to sensory input 14, which represent different stages of sensory information processing 14–16. These oscillations constitute rhythmic modulations in neuronal excitability that affect the likelihood of spike output in response to subsequent input 17;18. It is possible that a deficit in sensory gating can be more directly evaluated by examining the rhythmic modulatory process of sensory input rather than an averaged waveform, which may diminish the ability to examine the underlying oscillatory mechanism. We hypothesized that the sensory gating deficit in schizophrenia would be indexed by neural oscillations in a specific time-frequency measurable on single trials. This may represent a more elementary physiological process and thus be more sensitive to direct genetic effect underlying sensory gating, yielding higher heritability estimates compared to the averaged P50 wave. Using a discrete wavelet transform technique to identify single-trial oscillatory components contributing to sensory gating, we previously found that auditory responses are represented by a range of time-frequency specific oscillatory components, and that beta, and to a lesser extent alpha, frequency oscillations indexed the strength of P50 suppression in healthy controls 19. In this study, we sought to identify single trial scalp electrical oscillatory signals that are suppressed by repeated stimuli. Once such a signal was found, we proceeded to test whether it is a better endophenotype compared to P50, based on the following a priori endophenotype testing criteria: 1) its association with the schizophrenia phenotype; 2) its presence in non-schizophrenic family members [who are not on antipsychotic medications]; 3) whether it has significant heritability; and 4) if it is superior to the P50 gating measure, i.e., better differentiation between controls and people with schizophrenia, between controls and family members of people with schizophrenia, and higher heritability, when compared to P50 gating.
All participants were between the ages of 16 and 58, with no neurological conditions or current substance abuse or dependence. Patients with schizophrenia were diagnosed using the Structured Clinical Interview for DSM-IV (SCID) 20. Patients were recruited through our outpatient research programs and the Baltimore area mental health clinics. Four were on a first-generation antipsychotic, four not on antipsychotic, and the rest on second-generation antipsychotic agents. In addition, 18.6% were also on an SSRI and 9.8% on a benzodiazapine. Patients on benzodiazapine were asked to take the medication after testing on the day of testing. Clinical symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS). Global functions were measured by the Strauss-Carpenter Level of Function (LOF) scale, with higher score reflecting better functioning 21. All available first-degree relatives of the subjects with schizophrenia were recruited. Controls were recruited using an epidemiological sampling method that aimed to recover the population norm instead of a “super-clean” cohort. The control subjects were randomly selected from a list of subjects who matched schizophrenia probands on age (± 3 years), gender, ethnicity, and neighborhood (same zip code); the list was generated using the State of Maryland Motor Vehicle Administration registration. Controls had no family history of psychosis in three generations. Available first-degree relatives of the controls were also recruited. Controls and unaffected relatives of schizophrenia patients were screened using SCID and identical inclusion/exclusion criteria. They had no DSM-IV psychotic or bipolar disorders. Other Axis I diagnoses were allowed in the controls and unaffected relatives so that the group differences reflect difference in family history of schizophrenia alone and not on other psychiatric conditions. None of the subjects participated in our previous study 19. All subjects gave written informed consent in accordance with the University of Maryland Institutional Review Board guidelines.
The analysis included 246 subjects: 102 subjects with schizophrenia (93 probands and 9 of their relatives with schizophrenia), 74 non-schizophrenia first-degree relatives, and 70 community controls. Not included in the sample were 9 subjects who completed ERP recording but had excessive artifacts or had equipment problems during recording.
The above sample included 48 family units of subjects with schizophrenia consisting of 48 probands and 75 first-degree relatives (with or without schizophrenia). The 48 families included 30 of size 2 (2 subjects per family), 11 of size 3, 6 of size 4, and 1 of size 6. In total, this set included 134 sibling-sibling or parent-offspring pairs used for the heritability estimate. The community control samples included 20 small families (20 probands and 23 first-degree relatives), but formed only 23 informative relative pairs.
Evoked potentials were recorded and processed using the same procedures previously reported 22. Smokers were refrained from smoking for one hour prior to recording. Subjects sat in a semi-reclining chair in a sound chamber with eyes open and listened to 150 paired-click stimuli (1 ms duration, 72 dB, 500 ms interclick interval, 10 sec intertrial interval). EEG was sampled at 1 KHz (200 Hz low pass, 0.1 Hz high pass, a 60 Hz notch filter applied during recording) to yield 500 ms epochs including a 100 ms prestimulus window. Artifacts were removed from single trials with a rejection criterion of ±75 µV followed by visual inspection. The central channel (CZ) was used because it provides the most prominent P50 gating 23;24. The single trial records were baseline-corrected, 3 –100 Hz (24 octave slopes) bandpass filtered, and averaged to obtain the P50 waves. P50 response to the first stimulus (S1) was defined as the largest positive-going wave occurring 35–75 ms after the stimulus, measured from the trough of the preceding wave to the P50 peak. The S2 P50 was set to ±10 ms of the latency to S1 P50 23. P50 gating was the S2/S1 P50 ratio. P50 was scored by the consensus of two raters without diagnostic or demographic information.
The same artifact-free single trials for P50 measurement were used here. Unlike Fourier transform, wavelet transform allows detection of local variation in oscillations because it relies on wavelets of limited duration instead of unbound sine waves. Wavelet transform of single trial recording has the advantage of not being biased by trial-to-trial temporal variability, because it extracts both stationary and nonstationary energy, which should solve the P50 wave problem of different temporal variability between schizophrenia patients and controls 25;26. In the discrete wavelet transform (DWT) procedure each decomposition level, termed detail, is orthogonal to the other details 27. Since each detail has a unique frequency band, we can use DWT to separate EEG oscillatory signals into different frequency bands that are mathematically independent to the others. This property of the DWT is advantageous over the more commonly used continuous wavelet transform (CWT) in this context, because the latter yields continuous frequency bands, the separation of different frequency bands is somewhat arbitrary, and the neighboring bands are not necessarily independent to each other. The theory and methodology of using DWT to decompose evoked energy have been examined by simulation and tested in a large cohort of normal controls 19. We used an 8-level discrete biorthogonal wavelet 27;28, referred to as ‘bio5.5’ (Wavelet Toolbox, MathWorks, Inc. Natick, MA), to separate evoked energy into 8 details (D1 to D8), which represent 8 frequency bands. An example of the single trial DWT decomposition is presented in Figure 1. By simulation, we estimated the frequency band of each detail: D3 corresponded to very fast gamma frequency activities >85 Hz; D4: 40 – 85 Hz; D5: 20 – 40 Hz; D6: 12 – 20 Hz; D7: 5 – 12 Hz. D1 –D2 and D8 were not included because D1–D2 represented very high frequency noise and the frequency at D8 was too low to be resolved given the small time window between S1 and S2. The rationale to use this wavelet is described in more detail elsewhere 19.
To evaluate the temporal development of the oscillatory response, after the wavelet transform, each 500 ms detail was divided into four, 125-ms epochs (T0: -100 – 25 ms, T1: 26 – 150 ms, T2: 151 – 275 ms, and T3: 276 – 400 ms) (Figure 2). Energy within each epoch of each frequency band (detail) was measured by power spectrum density (PSD) using the nonparametric Welch method 29;30. A time-frequency component was the PSD of each epoch/detail. This process was repeated for S2. Sensory gating of each time-frequency component was calculated as the S2 PSD/S1 PSD ratio, and averaged across all trials for each participant. This method of measuring sensory gating is based entirely on computerized algorithms.
The dependent measures (S1 PSD, S2 PSD, S2/S1 PSD ratio) were compared between groups using a mixed model for unbalanced repeated measures ANOVA, which takes into account the correlations in phenotype among subjects from the same family (PROC MIXED in SAS, SAS, Inc) by including family as a random effect. Frequency band (D3 – D7) and epoch (T0 – T3) were within subject factors and diagnosis (patients, unaffected relatives, controls) was the between group factor. Significant effects were followed up by repeated measures ANOVA testing the diagnosis x epoch interaction for each frequency band, applying Bonferroni correction for comparisons of five frequencies (i.e., p<0.01). Post hoc tests of the significant models for effects of diagnosis and epoch were considered secondary analyses, and reported without p-value adjustment for multiple comparisons.
The heritability of each measure, which reflects the proportion of the variance attributed to additive genetic effects, was calculated using variance components analysis implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) software program 31. The total variance of a phenotype was partitioned into a genetic component due to additive polygenic effects and a random environmental component. We initially assessed the effects of age and sex on each phenotype, and when significant, adjusted for the effects of these variables in the heritability analyses. Statistical significance of the heritability was determined by comparing the log likelihoods between the polygenic model and the sporadic model, where the heritability was constrained at zero (e.g., see 12;32). We also tested whether the heritability of gating of an oscillatory component differed significantly from the heritability of P50 gating; this was tested by calculating the heritability of the sensory gating of an oscillatory component phenotype, after constraining the likelihoods on the heritability of the P50 gating phenotype.
Controls, patients, and unaffected relatives were not significantly different on age (mean ± s.e.- 40.4 ± 1.5, 39.1 ± 1.2, 43.6 ± 1.4, respectively, p= 0.10), but did significantly differ on sex ratio (male:female - 39:31, 74:28, 24:50, respectively, χ2=28.0, p<0.001), mainly due to a disproportionate number of female relatives. Smoker status differed among controls (21.4%), patients (54.9%), and unaffected relatives (12.5%) (χ2=39.68, p<0.001). Unaffected relatives did not significantly differ from controls on any Axis I psychiatric diagnosis or smoking status (all χ2 < 3.37, all p >0.18). Percentages of rejected trials were 21.8 ± 0.02%, 25.8 ± 0.02 %, and 21.3 ± 0.02%, respectively (F(2, 244) = 2.78, p=0.06), and had no significant correlation with any gating measures (data not shown). P50 ratios did not differ significantly between groups either before (mean ± s.e.: 0.56 ± 0.04, 0.62 ± 0.03, and 0.60 ± 0.04; F(2, 244) = 0.55, p = 0.58), or after (p=0.32) accounting for differences in sex. Patients who smoke (0.61 ± 0.05) did not significantly differ from patients who don’t smoke (0.62 ± 0.05, F(1, 101) = 0.06, p = 0.80). There were no significant group differences in S1 (4.04 ± 0.37, 3.91 ± 0.35, and 3.31 ± 0.32 uV; F(2, 244) = 1.11, p = 0.33) or S2 amplitudes (2.01 ± 0.20, 2.32 ± 0.28, and 2.01 ± 0.28; F(2, 244) = 0.47, p = 0.62).
Mixed effect ANOVA on the S2/S1 PSD ratios showed that there was a diagnosis by detail interaction (F(8, 4707) = 6.24, p < 0.001), a detail by epoch interaction (F(12, 4707) = 15.09, p < 0.001), and a main effect of detail (F(4, 4707) = 146.89, p < 0.001). Gating (S2/S1 < 1) occurred primarily at D6 (beta frequency) and D7 (alpha-theta frequency), while most of the D3–D5 (gamma frequencies) did not show gating but rather a tendency toward facilitated responses during S2 (S2/S1>1) (Figure 3).
For gamma frequencies, there was no statistically significant diagnosis effect or epoch by diagnosis interaction for D3, D4 (Figure 3). At D5, there were significant effects of diagnosis (F(2, 243)= 5.75, p=0.004). Post hoc tests showed that controls (p=0.03) and relatives (p=0.001) have elevated D5 ratio compared to the patients. However, this measure did not significantly separate controls from relatives (p=0.41).
At beta frequency (D6), there was a significant effect of epoch (F(3, 576)=31.41, p<0.001). Epoch T1 (t=8.43, df=245, p<0.001) and T2 (t=5.33, p<0.001), but not T3 (p=0.99) had significantly more gated responses compared to baseline T0, indicating that gating occurred at the beta frequency at the 26–275 ms window (Figure 3). However, there was no significant effect of diagnosis (p=0.80) or diagnosis by epoch interaction (p=0.52), suggesting that beta band gating is not a schizophrenia endophenotype.
At alpha-theta frequency (D7), there were significant effects of epoch (F(3, 480)=85.48, p<0.001), diagnosis (F(2, 243)=8.43, p<0.001), and their interaction (p<0.001). There was no group difference at T0 (p=0.68). There was a significant group difference at T1 (F(2, 245)=10.56, p<0.001). Patients (p<0.001, effect size in Cohen’s d=0.68) and their relatives (p=0.04, d=0.38) had significantly reduced gating compared to controls. There was also a significant group difference at T2 (F(2, 245)=15.78, p<0.001). Patients (p<0.001, d=0.84) and their relatives (p=0.009, d=0.51) had significantly reduced gating compared to controls. Finally, there was a significant group difference at T3 (F(2, 245)=4.84, p=0.009). Patients (p=0.002) but not their relatives (p=0.054) had significantly reduced gating compared to controls. Sex or smoking status was not a significant covariate in any of the analyses (all p ≥ 0.23). This suggested that gating of the alpha-theta band at the 25–275 ms window fulfilled the first two criteria for a schizophrenia endophenotype.
In the combined sample including both control and patient’s families, gating of the alpha-theta oscillations was significantly heritable at T1 (h2 ± s.e.: 0.68 ± 0.19, p=0.0004, n=157 pairs) and T2 (0.38 ± 0.20, p=0.03) (Figure 4). In patients’ families alone, the heritability was also significant at T1 (0.49 ± 0.24, p=0.02, n=134 pairs). The standard errors of the estimates were wide due to the modest sample size. Medication effects, such as effect of clozapine on sensory gating 33;34, if present, might bias the true heritability since they would affect only the subjects with schizophrenia. Removing patients on clozapine (n=18), the heritability at T1 was (0.50 ± 0.23, p=0.02). Removing patients taking any antipsychotic agents, the heritability at T1 actually increased (0.84 ± 0.40, p=0.028), although this estimate was based on only 37 relative pairs from 18 families. The heritability estimate at T1 in the community controls’ families was similar to that in the patients’ families (0.62 ± 0.39, p = 0.09, n=23 pairs), although the estimate did not differ significantly from zero possibly due to the small sample size.
In comparison, the heritability of P50 ratio ranged from 0 to 0.12 in the different diagnostic groups (Figure 4); with none achieving statistically significance. All of the significant alpha-theta PSD gating heritability estimates differed significantly from the point estimates obtained when constrained to those for the P50 gating phenotype (e,g., h2 = 0 – 0.12), suggesting that the genetic loading of D7 gating was significantly higher than that of the P50 gating.
Some previous studies pointed out that an abnormal ratio may not necessarily reflect a gating problem because response to S1 alone could account for the impaired (P50) ratio in people with schizophrenia 25;26;35. To examine whether this applies to the oscillatory measures, we analyzed the alpha-theta band responses to individual stimuli (S1 and S2). For response to S1, there were no significant group differences in T1, T2, or T3 epochs (p≥0.07). For response to S2, there was a significant group difference at T2 (p=0.04), but not at T1 or T3 (both p=0.07). Post hoc showed that subjects with schizophrenia had elevated S2 PSD compared to controls (p=0.04) at T2, suggesting that the gating dysfunction in people with schizophrenia was in part produced by an insufficient inhibition of the response to S2. However, controls and relatives were not significantly different in this time-frequency component (p=0.93), ruling out that S2 response alone was better than the PSD ratio for marking schizophrenia liability.
Finally, we explored individual responses in other frequency bands. Briefly, in none of the other time-frequency components were there measures that significantly and simultaneously differentiated patients and relatives from controls. A notable observation was that patients showed elevated gamma activities compared to controls and relatives (p=0.03 to 0.003 for D3 and D4 in response to S1 and S2, see Figure 2). Relatives and controls showed no significant differences. An exploration of potential medication effects failed to find conclusive evidence linking specific psychotropic medications to the elevated gamma responses in patients (data not shown).
Heritabilities of the individual alpha-theta band response to S1 and S2 were not significant in any epoch (all h2≤0.30, all p≥0.10). Heritabilities of the response in other time-frequency components (all h2≤0.40) were lower than that of the alpha-theta band gating. In summary, none of the gamma and beta range responses can simultaneously separate both patients and unaffected relatives from controls; and none of their heritability estimates was higher than that of the alpha-theta gating.
Gating of D7 (mean of T1 and T2) was significantly correlated with BPRS total score (Spearman’s rho=0.22, p=0.03) and psychosis (rho=0.31, p=0.003), thought disorder (rho=0.28, p=0.007), and hostility (rho=0.29, p=0.007) subscales, but not withdrawal, anxiety, or activation subscales (all rho<0.15, all p>0.13), suggesting that poor gating was weakly but significantly associated with more psychotic symptoms in subjects with schizophrenia. D7 gating was also significantly correlated with the LOF score (rho= - 0.30, p<0.001), suggesting that poor gating was correlated with poor overall function. In comparison, P50 gating was not significantly correlated with any BPRS (all p≥0.19) or LOF (p=0.90) scores.
This study applies modern signal processing methods to explore oscillatory signals that are suppressed during repeated stimuli. The results indicate that gating of auditory evoked oscillatory responses occurs primarily at the beta to theta frequencies when measured in single trials. Gating of the alpha-theta band marks the liability for schizophrenia and is heritable: its heritability is estimated to be at least 4-fold higher than that of the traditional P50 gating measure in the families of people with schizophrenia.
The heritability of the P50 gating measure was estimated at 0 – 0.12. Several twin studies of P50 gating in non-schizophrenia populations suggested that its heritability could be estimated to be as high as 0.40 – 0.68 by some genetic models 36–39. However, these twin-based estimates may be misleading for family studies, because they relied on models that weighted heavily on the familial correlations of the monozygotic twins. For instance, the familial correlations of P50 gating in dizygotic twins (50% genetic sharing) were 0 36 and 0.04 38 even though the heritability estimates were reported to be 0.44 and 0.68, respectively, in these studies. Genetic sharing in first-degree family members is 50%. Therefore, data from these twin studies could have actually predicted that the P50 gating measure is unlikely to have a high heritability in family samples, which is confirmed by the recent COGS sample (h2=0.10) 12, and now by our sample.
Sensory gating has been considered as one of the leading endophenotypes of schizophrenia. However, the use of P50 gating as the primary index for sensory gating has been questioned due to this measure’s low test-retest reliability 9;40;41, and is further discouraged recently by its low heritability in families of people with schizophrenia. Several alternative measures to P50 have been examined 25;26;42–49. For example, using frequency domain analyses, it was found that gating of lower frequency response (~ 1–20 Hz) of the averaged evoked potential provided better separation between patients and controls than P50 or gamma band gating 42–44. Another approach was to use evoked potentials occurring at the 100–200 ms post stimulus interval, namely the N1 and P2 components, which significantly separated patients from controls in some studies 41;43–45; but produced the opposite finding in a study of first-degree relatives 50. These prior efforts suggest that sensory gating can occur at a lower frequency and in a window after the P50 wave, even though a systematic evaluation of these alternative measures’ heritability in the families of people with schizophrenia have not been reported. Our finding of impaired single trial alpha-theta band gating at the 25–275 ms window may be viewed as consistent with these prior data. However, we have only compared the current wavelet approach with P50. It would be informative to compare it with other alternative processing approaches in the future.
While we have used sensory gating to described both P50 and alpha-theta band gating, there is a lack of direct evidence to support or refute whether P50 gating and the alpha-theta gating are measuring the same underlying information processing. There was a lack of substantial correlation between the two gating measures (Pearson’s r=0.01–0.13 in different epochs). On the other hand, P50 is a time-locked response, while the alpha-theta response includes time-locked and nonstationary responses, therefore there is likely some overlap in their underlying mechanism but additional studies are clearly needed to understand the convergent and divergent mechanisms of the two gating phenomena. The new measure of gating is based on the decomposition of evoked energy into its oscillatory components in different frequency bands. This frequency-specific oscillatory gating measure is thought to be more elementary than the traditional P50 measure that is based on the averaged signal across all frequencies. The high heritability and impairment in unaffected relatives suggest that this new oscillatory gating measure indexes a biological process associated with the genetic liability for schizophrenia.
The finding that suppression at the beta and alpha-theta frequencies is the primary event during sensory gating is supported by another recent sensory gating study using single trial independent component analysis (ICA) based approach, which showed that it was the beta, alpha, and theta activities that contributed to the N1 suppression 51. So how might a failure in suppressing low frequency oscillations be related to schizophrenia pathology and its liability? The data from this study demonstrated a genetic effect but did not address the physiological origin of the problem, therefore we should emphasize that the following discussion is speculative. Elevated on-going low frequency activity has been shown to delay behavioral response in humans 52, and weakens the synchronization of inter-neuronal spiking in animal recordings 15. The sensory gating problem has long been theorized as related to the inability of people with schizophrenia to filter out unwanted sensory information, leading to psychotic symptoms1;2;53;54. The identification of failed low frequency gating in schizophrenia may suggest that a dysfunction in suppressing alpha-theta activities in response to repeated stimulus might lead to impaired neuronal synchronization in response to subsequent sensory information. There was a modest but significant correlation between alpha-theta gating and psychotic symptoms/level of function, suggesting that this deficit maybe related to clinical functions. However, additional human and animal studies are needed to test and expand this hypothesis. We should emphasize that it was the gating of the alpha-theta response, not the actual responses that had higher heritability, implicating that the alpha-theta inhibition is perhaps indexing a more elementary or distinct biological process separated from the process of individual responses (but see limitation discussed below).
Low frequency activities are related to reduced alertness. A question is whether psychotropic medications contributed to the alpha-theta gating abnormality by their sedating effects in the patients. However, the finding of alpha-theta gating deficit in unaffected relatives is inconsistent with a direct medication or chronic disease effect.
The traditional approach of using P50 to index sensory gating is also problematic due to its measurement procedures: although the scoring is semi-automated it still requires some subjective decisions to select the P50 peak within a window where there may be more than one peak or the selection of trough, which may be affected by the descending slope of the previous wave. This may add further noise to the data. In comparison, the DWT based single trial method, while computationally intense, does not require rater intervention, thus removing potential subjective biases. However, single trial analysis also has its own inherent limitations, since it includes the background noise. The sensory gating measure happens to partially circumvent the problem because the ratio measure removes the noise that is equally present in responses to S1 and S2. However, this limitation would be present when responses to individual stimuli are analyzed and may partially contribute to their lower ability to differentiate groups and their lower heritability estimates.
In conclusion, this study supports the hypothesis that the gating deficit represents an elementary neuronal dysfunction in people with schizophrenia 55;56. Our data demonstrate that deficit in gating of evoked responses remains a critical biomarker for the liability of schizophrenia, and is highly heritable. However, frequency-based analytic methods are needed to facilitate the use of this endophenotype in genetic studies. This finding is especially timely and relevant given the fact that a large amount of sensory gating data has already been collected across many laboratories. If our finding can be replicated by other laboratories, this or similar methods maybe used to reanalyze existing data. The neural oscillatory approach may also provide a new framework for studying the neurobiological pathway of sensory gating, and for testing novel compounds that can reverse specific oscillatory dysfunctions.
Support was received from grants MH49826, MH79172, MH77852, MH70644, MH68282, MH67014, MH68580 from the National Institute of Mental Health; The Neurophysiology Core of the University of Maryland General Clinical Research Center General Clinical Research Center grant M01-RR16500; and the VA Capitol Health Care Network (VISN 5) Mental Illness Research, Education, and Clinical Center (MIRECC).