SCHRES-06417; No of Pages 8 Schizophrenia Research xxx (2015) xxx–xxx

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Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia Michael R. Goldstein a,b, Michael J. Peterson a, Joseph L. Sanguinetti b, Giulio Tononi a, Fabio Ferrarelli a,⁎ a b

Department of Psychiatry, University of Wisconsin, Madison, WI, United States Department of Psychology, University of Arizona, Tucson, AZ, United States

a r t i c l e

i n f o

Article history: Received 21 March 2015 Received in revised form 10 June 2015 Accepted 12 June 2015 Available online xxxx Keywords: EEG Alpha Steady state visual evoked potential Occipital cortex Frontal cortex Topography

a b s t r a c t Deficits in both resting alpha-range (8–12 Hz) electroencephalogram (EEG) activity and steady state evoked potential (SSVEP) responses have been reported in schizophrenia. However, the topographic specificity of these effects, the relationship between resting EEG and SSVEP, as well as the impact of antipsychotic medication on these effects, have not been clearly delineated. The present study sought to address these questions with 256 channel high-density EEG recordings in a group of 13 schizophrenia patients, 13 healthy controls, and 10 nonschizophrenia patients with psychiatric diagnoses currently taking antipsychotic medication. At rest, the schizophrenia group demonstrated decreased alpha EEG power in frontal and occipital areas relative to healthy controls. With SSVEP stimulation centered in the alpha band (10 Hz), but not with stimulation above (15 Hz) or below (7 Hz) this range, the occipital deficit in alpha power was partially reverted. However, the frontal deficit persisted and contributed to a significantly reduced topographic relationship between occipital and frontal alpha activity for resting EEG and 10 Hz SSVEP alpha power in schizophrenia patients. No significant differences were observed between healthy and medicated controls or between medicated controls and schizophrenia. These findings suggest a potential intrinsic deficit in frontal eyes-closed EEG alpha oscillations in schizophrenia, whereby potent visual stimulation centered in that frequency range results in an increase in the occipital alpha power of these patients, which however does not extend to frontal regions. Future research to evaluate the cortical and subcortical mechanisms of these effects is warranted. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Alpha-band (~8–12 Hz) oscillations are a prominent feature of the human waking electroencephalogram (EEG). While the neural mechanisms contributing to alpha oscillations are complex and yet to be fully characterized, alpha rhythms have been widely studied and are associated with a variety of fundamental brain processes, including perception and attention (Klimesch et al., 2007; Palva and Palva, 2007; Klimesch, 2012). An increase in alpha activity spontaneously occurs when we close our eyes, and this increase is most prominently observed in the resting EEG over occipital regions, as consistently demonstrated experimentally from the seminal studies by Berger (1929, 1930) to more recent EEG–fMRI investigations (Feige et al., 2005). Several studies aimed at characterizing the neuronal circuitry underlying this phenomenon have demonstrated a dynamic interplay among cortical and subcortical areas, including the occipital cortex, the frontal cortex, and the thalamus (Guillery and Sherman, 2002; Sherman, 2005; Wang ⁎ Corresponding author at: Department of Psychiatry, University of Wisconsin– Madison, 6001 Research Park Blvd., Madison, WI 53719, United States. Tel.: +1 608 265 6220; fax: +1 608 263 0265. E-mail address: [email protected] (F. Ferrarelli).

et al., 2011; Vijayan and Kopell, 2012). The implication of subcortical regions, particularly the thalamus, in generating EEG alpha activity has been confirmed by studies using combined EEG–fMRI techniques (Zou et al., 2009; Sadaghiani et al., 2010; Liu et al., 2012; Scheeringa et al., 2012; Omata et al., 2013), whereas the involvement of the occipital and frontal cortices has been established by source modeling analysis of alpha-band brain oscillations recorded with EEG and MEG (Srinivasan et al., 2006). Deficits in resting state, spontaneous EEG alpha-band power in schizophrenia have been demonstrated by numerous studies and have been observed in patients with chronic schizophrenia, first episode psychosis, prodromal schizophrenia, as well as relatives of schizophrenia probands. These deficits have also been identified during remission, and higher alpha may predict treatment response (Boutros et al., 2008; Javitt et al., 2008; Luck et al., 2011). However, it is still debated whether schizophrenia patients have a peak of alpha activity shifted to a lower frequency (Knott et al., 2001; Harris et al., 2006), or rather whether alpha deficits reflect a reduced ability to generate oscillations in this frequency band (Danos et al., 2001; Mathiak et al., 2011). One paradigm that allows entraining brain oscillations at a given frequency is the steady-state visual evoked potential (SSVEP). The SSVEP is a frequency and phase-locked EEG response to a visual stimulus constantly

http://dx.doi.org/10.1016/j.schres.2015.06.012 0920-9964/© 2015 Elsevier B.V. All rights reserved.

Please cite this article as: Goldstein, M.R., et al., Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia, Schizophr. Res. (2015), http://dx.doi.org/10.1016/j.schres.2015.06.012

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M.R. Goldstein et al. / Schizophrenia Research xxx (2015) xxx–xxx

presented at a rapid rate (e.g., a light flicker), thereby measuring the visual system's ability to entrain the stimulus and its oscillatory characteristics (Vialatte et al., 2010). SSVEP deficits have been observed in patients with schizophrenia, most prominently in the alpha and beta frequency ranges (Krishnan et al., 2005; Brenner et al., 2009), and have been shown to implicate both the thalamus as well as frontal and occipital cortical regions (Butler et al., 2005; González-Hernández et al., 2014). In line with these findings, the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) has recently recommended the SSVEP as a promising electrophysiological paradigm to be used in clinical trials in schizophrenia research (Butler et al., 2012). Multiple key aspects of resting and event-related alpha activity in schizophrenia have not yet been fully explored. While prior studies have demonstrated effects in both occipital (Jin et al., 1995, 2000) and frontal (Rice et al., 1989; Wada et al., 1995) areas, these studies were performed with limited resolution EEG montages (19 channels or fewer). The recent availability of high-density EEG systems allows the characterization in greater detail of the topographic characteristics of alpha-range EEG deficits in schizophrenia. Furthermore, spontaneous and SSVEP recordings have not been concurrently evaluated to assess whether decreased alpha activity in schizophrenia reflects an intrinsic deficit to generate and entrain alpha-band oscillations, whether this intrinsic deficit is specific to the alpha range, and the extent to which a deficit can be reverted when adequately entrained. Finally, the effects of chronic exposure to antipsychotics on EEG alpha activity in nonschizophrenia patients, which could contribute to establishing the role of these medications as well as the specificity of alpha deficits in schizophrenia, are not well known. This study utilized high-density EEG recordings of spontaneous eyes-closed and multiple SSVEP (7 Hz, 10 Hz and 15 Hz) conditions in schizophrenia patients, healthy controls, and other psychiatric patients taking antipsychotic medications. It was hypothesized that schizophrenia patients would demonstrate decreased EEG alpha power in occipital and frontal regions at rest compared to both control groups, and that these deficits would be partially reverted by 10 Hz SSVEP in the occipital area where the alpha-specific sensory information is initially processed, but not in the frontal region where subsequent long-range propagation of this information is required. 2. Methods 2.1. Subjects Thirteen schizophrenia patients, 13 healthy controls, and 10 nonschizophrenia patients receiving antipsychotic medication were recruited (see Table 1 for demographic information). After providing informed consent, all subjects underwent a screening interview. The Structured Clinical Interview for DSM-IV-TR (First et al., 2002a) was administered by a psychiatrist (MJP) to assess psychiatric diagnoses of patients. Diagnoses for schizophrenia (SZ) patients were paranoid (N = 3), Table 1 Demographic and clinical data.

N Sex (m/f) Age Medication dose Years since onset PANSS—composite PANSS—positive PANSS—negative

HC

SZ

MC

p*

13 10/3 38.2 (11.2)

13 8/5 33.2 (10.7) 470.7 (345.5) 10.5 (6.9) 40.3 (4.3) 20.1 (3.4) 20.2 (1.9)

10 2/8 36.5 (8.8) 337.6 (225.2)

0.02 0.47 0.30

HC, healthy control; SZ, schizophrenia; MC, medicated (non-schizophrenia) control; PANSS, Positive and Negative Syndrome Scale. Values are displayed as mean (standard deviation). * p-value represents one-way ANOVA or independent samples t-test result, where applicable.

undifferentiated (N = 7), and residual (N = 3). Diagnoses for medicated control (MC) patients were unipolar depression (N = 4), bipolar depression (N = 4), and anxiety disorders (N = 2). All 13 SZ and 10 MC patients were receiving second-generation antipsychotic medication for a history of psychotic features confirmed during the structured interview. Schizophrenia patients were further evaluated with the Positive and Negative Syndrome Scale (PANSS). All subjects were between 18 and 55 years of age. Exclusion criteria for all subjects were identifiable neurologic disorders, substance use disorders within the last 6 months, or diagnosed sleep disorders. An additional exclusion criterion for healthy control subjects was personal psychiatric history or the presence of a first-degree relative with a psychiatric diagnosis, assessed with a non-patient version of the Structured Clinical Interview for DSM-IV-TR Axis I Disorders (First et al., 2002b). The study was approved by the University of Wisconsin–Madison Human Subjects Institutional Review Board. 2.2. Recording procedure Subjects were outfitted with a 256 channel high-density EEG net (Electrical Geodesics Inc., Eugene, OR). For each recording, subjects were seated in a reclining chair and were asked to relax and sit comfortably while upright with eyes closed, minimizing eye movement or muscle tension. Two minutes (120 s) of resting wakefulness were recorded, followed by 2 min each of several photic stimulation conditions to obtain steady state visual evoked potential (SSVEP) measures. For the SSVEP recordings, a diode photo stimulator (model PS33-PLUS, Grass Technologies, Warwick, RI) placed approximately 90 cm in front of the subject continuously emitted a sinusoidally modulated light stimulus for 120 s at theta (7 Hz), alpha (10 Hz), and beta (15 Hz) frequencies, counterbalanced across flickering frequency conditions. SSVEP sessions were pseudo-randomized with at least 5 min between sessions in order to avoid either order or carry-over effects. Luminance of the sinusoidal light flicker ranged from 300 cd/m2 at trough to 800 cd/m2 at peak. EEG signals were digitized and sampled at 500 Hz with a vertex reference. 2.3. EEG processing and analysis EEG recordings were band-pass filtered between 0.5 and 50 Hz. Channels located on the face and neck, which are most prone to muscle contamination, were initially removed to increase signal-to-noise ratio of subsequent analysis (Goncharova et al., 2003). EEG data were then segmented into 4-second epochs. Muscle and eye-movement artifacts were further removed for individual channels and/or epochs using semi-automatic procedures with amplitude-based threshold detection and visual inspection in EGI NetStation and MATLAB (Mathworks, Natick, MA). Removed channels were interpolated via spherical spline estimation. Combined spontaneous and SSVEP recordings were then subjected to Independent Component Analysis (ICA) using the EEGLAB plug-in (Delorme and Makeig, 2004) in MATLAB to identify and remove characteristic eye, muscle, and cardiac artifacts (Hulse et al., 2011). After ICA, individual session recordings were again visually inspected to remove residual artifacts and then re-referenced to the global average. The amount of data retained for analysis following all artifact removal procedures for the four recording conditions was comparable across the three groups (channels: 94.2 ± 4.8%, 94.0 ± 5.7%, and 93.2 ± 5.1% (mean ± standard deviation) for HC, SZ, and MC, respectively, p = .669; epochs: 57.4 ± 20.4%, 55.0 ± 16.0%, and 55.7 ± 18.0%, p = .836). Spectral power was then computed via Fast Fourier Transform (Welch's averaged modified periodogram, Hamming window; Supplementary Fig. 1), yielding a frequency resolution of 0.25 Hz. For correlations with clinical variables, alpha power was defined as the average 8–12 Hz power, and peak frequency for each participant was defined as the frequency corresponding to the highest amplitude point in the global (185 channels) average spectrum between 7 and

Please cite this article as: Goldstein, M.R., et al., Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia, Schizophr. Res. (2015), http://dx.doi.org/10.1016/j.schres.2015.06.012

M.R. Goldstein et al. / Schizophrenia Research xxx (2015) xxx–xxx

13 Hz. Thus, the higher of any 2 peaks within this range was used to define the peak frequency. Spectra were visually examined one at a time to confirm that each participant had at least one peak within the 7–13 Hz range. One prominent secondary peak was evident for one MC participant, though distinctly separate from the 7–13 Hz at 5.25 Hz (Supplementary Fig. 2).

2.4. Statistics Dose of antipsychotic medication for patients was expressed as chlorpromazine equivalent (Andreasen et al., 2010) for comparison between patient groups, as well as for correlations with EEG variables. Between-group differences were analyzed via one-way ANOVAs and post-hoc unpaired t-tests with Bonferroni correction, where applicable. Importantly, in this study we did not have a hypothesis regarding which circumscribed regions would show the strongest effect, and therefore we could not select an a priori Region of Interest (ROI) required for an appropriate ANOVA to assess potential Region by Group interactions. By contrast, we employed a statistical nonparametric mapping (SnPM) with supra-threshold cluster technique (Nichols and Holmes, 2002). SnPM not only allows investigating the entire scalp without choosing a given ROI, but also accounts for the multiple comparison problems created by the numerous scalp electrodes utilized here, as we have demonstrated in a number of several recent publications from our group in schizophrenia and other clinical and non-clinical samples (Ferrarelli et al., 2007, 2010; Lustenberger and Huber, 2012; Buchmann et al., 2014). Correlational analyses were conducted with linear regression and contrasted with a Fisher z-transformation, where applicable. All statistical analyses were conducted in MATLAB.

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3.2. Spontaneous eyes-closed (EC) When evaluating global power spectra for spontaneous EC across the three groups, one-way ANOVA demonstrated an overall effect from 10.0 to 11.0 Hz (Fig. 1A). Post-hoc tests showed significantly lower power in SZ relative to HC within this frequency band. Given the possibility of a slowing in spontaneous EC alpha peak frequency reported in prior studies (Jin, et al., 2000; Fuggetta, et al., 2014), the average frequency of individual peaks was evaluated (Supplementary Fig. 2). However, no significant overall effect was observed via oneway ANOVA (F(2,33) = 1.06, p = 0.36). To evaluate the topographic characteristics of the group-wise differences in the alpha band, where we found differences in the global power spectra analysis across groups, power values across the alpha band (8– 12 Hz) were averaged and unpaired t-tests for each between-group contrast were performed, with correction for multiple comparisons using SnPM. The decrease in alpha power in SZ relative to HC was evident in frontal and occipital channel clusters (frontal: 20 channels, p = 0.04; occipital: 20 channels, p = 0.04) (Fig. 1B). No other groupwise differences were observed. 3.3. Steady-state visual evoked potential (SSVEP)

3. Results

To assess whether the frontal and occipital deficits in the resting alpha power in SZ could be reverted by visual stimulus entrainment, SSVEP stimulation within the alpha band (10 Hz) as well as outside of the alpha band (7 and 15 Hz) was evaluated. Topographic analysis of the 10 Hz SSVEP showed significantly decreased alpha power in SZ relative to HC in a frontal region (23 channels, p = 0.03); however, a significant difference in an occipital region was not observed (5 channels, p = 0.09; Fig. 2). No group-wise differences were observed for the other two SSVEP conditions with stimulation outside of the alpha band (p ≥ 0.12).

3.1. Demographic and clinical characteristics

3.4. Relationship between frontal and occipital alpha power

Table 1 displays relevant demographic and clinical information. Distribution of male versus female subjects was significantly different among the three groups (F(2,33) = 4.50, p = 0.02), specifically with greater proportion of females in medicated controls (MC) compared to the healthy control (HC) group (p = 0.02, Bonferroni correction). Age did not significantly differ among the three groups (F(2,33) = 0.77, p = 0.47). Dose of medication did not significantly differ between schizophrenia (SZ) and MC (t(21) = 1.06, p = 0.30).

We found that frontal and occipital regions showed the largest alpha band activity both at rest and during SSVEP across the three study groups. However, while both occipital and frontal alpha power was reduced in patients with schizophrenia at rest, during 10 Hz SSVEP only the frontal region showed decreased alpha activity in these patients. We hypothesized that this effect was driven by an increase in occipital power via 10 Hz SSVEP in schizophrenia patients that did not extend to the frontal region. To test this hypothesis, we correlated occipital

Fig. 1. Schizophrenia (SZ) subjects showed decreased spontaneous eyes-closed (EC) alpha-band power relative to healthy controls (HC), most prominently in frontal and occipital channel clusters. No significant differences were observed for comparisons with medicated non-schizophrenia controls (MC). [A] Global spectra (average of 185 channels). Shaded areas represent standard error of the mean. Post-hoc t-tests were limited to frequency bins with significant omnibus bin-by-bin ANOVA results. [B] Topography of average absolute alpha (8–12 Hz) activity. White dots denote significant channels following statistical nonparametric mapping (SnPM) to correct for multiple comparisons across the high density montage.

Please cite this article as: Goldstein, M.R., et al., Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia, Schizophr. Res. (2015), http://dx.doi.org/10.1016/j.schres.2015.06.012

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Fig. 2. Significantly decreased alpha-band (8–12 Hz) activity in schizophrenia (SZ) relative to healthy controls (HC) was observed specifically in frontal channels during 10 Hz stimulation, suggesting a potential deficit in frontal integration of visual stimulation at the predominant resting oscillatory frequency. Topography is shown for each of the 7, 10, and 15 Hz steady state visual evoked potential (SSVEP) conditions. White dots denote significant channels following SnPM.

and frontal power within each study group for resting state and 10 Hz SSVEP conditions. Since both HC and MC showed a significant level of correlation for alpha power in both conditions, their data were combined for illustrative purposes (Fig. 3), although correlation values for these groups are reported separately in the related figure legend. We found that both SZ and the combined control groups demonstrated a strong correlation between frontal and occipital power for spontaneous EC (r = 0.92, p b 0.001 for both groups) (Fig. 3A). By contrast, when we assessed how this correlation changed during 10 Hz SSVEP, which was calculated by subtracting the frontal from the occipital mean power for resting state EC and SSVEP conditions, a significant difference was observed between SZ (r = 0.50, p = 0.08) and control groups (r =

0.85, p b 0.001, Fisher z = 1.86, p = 0.03) (Fig. 3B). This decrease in normalized frontal-occipital power across EC and 10 Hz SSVEP corresponded to a 48% reduction in the amount of variance accounted for in these relationships (R2 = 0.73 vs. 0.25). 3.5. Correlations with clinical variables Previous studies have suggested that both the spectral power in the alpha band and the slowing of the alpha frequency peak are clinically and functionally relevant parameters in schizophrenia (Jin et al., 2000; Gaspar et al., 2011; Abeles and Gomez-Ramirez, 2014; Fuggetta et al., 2014). Thus, correlational analyses focusing on alpha power and

Fig. 3. Correlation of normalized topographic alpha (8–12 Hz) power between EC and 10 Hz SSVEP conditions is decreased in schizophrenia relative to controls (Fisher z = 1.86, p = 0.03). [A] Relationship between frontal and occipital power values derived from the significant topographic comparison clusters. [B] Correlation of normalized topographic power (computed as the absolute difference between frontal and occipital power) between EC and 10 Hz SSVEP conditions. Given statistically similar patterns for HC and MC groups (HC for 3A: r = 0.91, p b 0.001; MC for 3A: r = 0.96, p b 0.001; HC for 3B: r = 0.90, p b 0.001; MC for 3B: r = 0.64, p = 0.04), and to limit the number of comparisons, all control subjects were combined into one group. A linear least-squares fit was applied to each scatterplot for visualization.

Please cite this article as: Goldstein, M.R., et al., Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia, Schizophr. Res. (2015), http://dx.doi.org/10.1016/j.schres.2015.06.012

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individual peak frequency for the two conditions of interest, spontaneous EC and 10 Hz SSVEP, with clinical variables were conducted for the SZ group. We found that a slower peak frequency during spontaneous EC was significantly correlated with longer duration of illness in patients with schizophrenia (r = −0.59, p = 0.03) (Fig. 4). By contrast, neither peak frequency nor spectral power was correlated with other clinical variables, including medication dose and PANSS scores (p = 0.18–0.99 across the 7 additional correlations). 4. Discussion These findings demonstrate deficits in EEG alpha-band activity in schizophrenia patients relative to healthy controls, significant in both occipital and frontal areas during resting eyes-closed, while specific to only frontal channels during 10 Hz SSVEP stimulation. These resting state and 10 Hz SSVEP alpha power reductions were not observed in non-schizophrenia patients taking antipsychotics, suggesting that the effects are independent of medication status. Additionally, the correlation between frontal and occipital alpha power, which was present in all three groups during the resting state condition, was markedly decreased in patients with schizophrenia during the 10 Hz SSVEP condition, suggesting a specific deficit in the ability of frontal areas to resonate at this frequency. Finally, peak alpha frequency, but not alpha power, was proportionately related to schizophrenia illness chronicity. 4.1. Alterations in EEG alpha band oscillations Resting eyes-closed alpha power is typically reduced over frontal and occipital electrodes in schizophrenia (Boutros et al., 2008), a result replicated here. However, the functional significance of these alpha power abnormalities as they relate to the cognitive and perceptual issues in schizophrenia is not clear. Alpha amplitude has been taken as an index of cortical arousal or activation such that increased amplitude with eyes closed is indicative of decreased brain activation (Cole and Ray, 1985; Schimke et al., 1990; Pfurtscheller, 1992; Barry et al., 2007). Some researchers have taken alpha deficits in schizophrenia as indicators of global brain hyperarousal (Shagass et al., 1982), although the reversal of occipital alpha with SSVEP reported here does not entirely fit with this interpretation. When the eyes are opened alpha oscillators in the brain are thought to become out of phase, causing a “desynchronization” in the EEG that is reflected by a decrease in alpha power. Desynchronization could be due to communication of thalamo-cortical and cortical interactions (see below) associated with information processing (Başar and Schürmann, 1999; Klimesch, 1999). Additionally, it has recently been argued that alpha activity is associated

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with the timing and inhibition of cortical processing, especially in relation to attention and perceptual processing (Klimesch et al., 2007; Mathewson et al., 2011). For example, alpha could play an active role in inhibiting distracting or irrelevant visual information either via topdown attentional control (Foxe and Snyder, 2011) or in the task irrelevant visual networks (Klimesch et al., 2007). Thus the deficits observed here in schizophrenia could be due to an overall decrease in cortical activation or to timing-inhibition issues, alternative interpretations that cannot be distinguished in the current experiment. Given that the alpha rhythm likely indexes both activation and inhibition-timing processes simultaneously (Bazanova and Vernon, 2014), future research will need to parse these apart in relation to alpha deficits in schizophrenia. The lack of correlations between EEG alpha power and schizophrenia symptoms in the present study could be related to the relatively small sample size investigated, or the relatively mild level of symptoms experienced by patients, who were chronic, stable outpatients. Indeed, while alpha-range EEG activity has been previously correlated with schizophrenia symptomatology, findings have been inconsistent across studies (Sponheim et al., 2000). In addition to decreased alpha band power, it is important to note that some studies have also found increases in power across lower frequency bands (Boutros et al., 2008). For example, a recent study with a large sample of unmedicated schizophrenia patients demonstrated elevated delta (1–4 Hz) and theta (4–8 Hz) power concurrent with decreased high alpha (10–12 Hz) activity (Kim et al., 2015). While the current study replicated alpha-range deficits particularly in a frontocentral region, significant differences were not found for lower frequencies. These results may be due to lack of statistical power, however, given the pattern of elevated low-frequency activity evident in Fig. 1. Future studies would benefit from continuing to explore both alpha and other frequency bands to further delineate diagnosis-specific and more generalized neuropsychiatric EEG deficits (Schulman et al., 2011; Başar et al., 2012). 4.2. Alpha peak frequency considerations Possibly related to reported alterations in frequency bands below the alpha range, prior research has reported a slowing of the EEG alpha frequency peak in schizophrenia at rest (Clementz et al., 1994; Cañive et al., 1996; Harris et al., 2006; Garakh et al., 2011), in response to SSVEP stimulation (Jin et al., 2000), as well as during a working memory task (Haenschel et al., 2009). Building on these findings and the high intra-individual reliability of alpha peak frequency (Salinsky et al., 1991), transcranial magnetic stimulation (TMS) studies have utilized

Fig. 4. While there is not a significant relationship between power in the SSVEP stimulation condition of interest with schizophrenia chronicity, slower frequency of spontaneous EC alphaband peak was associated with longer illness duration (r = −0.59, p = 0.03). Normalized power for 10 Hz SSVEP reflects the difference between frontal and occipital power for that condition that was significantly different between SZ and controls (see Fig. 3). Peak frequency values were derived from global power spectra (average of 185 channels).

Please cite this article as: Goldstein, M.R., et al., Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia, Schizophr. Res. (2015), http://dx.doi.org/10.1016/j.schres.2015.06.012

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individual alpha peak frequency (α-TMS) to increase alpha-range oscillations as a treatment modality (Jin et al., 2006, 2012). Notably, improvement in symptoms correlated with alpha EEG normalization after treatment with α-TMS. Following this prior research, the current study also explored relationships of clinical variables with alpha peak frequency. While peak frequency correlated with illness chronicity, a significant group-wise difference in peak frequency was not observed. This observation may be due in part to similar levels of positive and negative symptoms, which have been shown to have differential effects on peak frequency (Garakh et al., 2011). Nevertheless, neither positive or negative symptom severity, nor the combined profile correlated with peak alpha frequency. Furthermore, while considering the limitations described above, the correlation of illness chronicity with alpha peak frequency but not overall power in the alpha range may suggest that an intrinsic deficit in the ability to generate alpha-band oscillation, as reflected by reduced alpha power, is present at illness onset, whereas the peak frequency within the alpha range is affected by the course of illness in schizophrenia patients. 4.3. Potential pathophysiological mechanisms SSVEP is advantageous over resting-state methods in that deficits in resonance characteristics of networks above and beyond baseline oscillations can be investigated. A more distributed (frontal and occipital) deficit was seen with resting eyes-closed, but only the frontal deficit remained with SSVEP stimulation in the alpha range (i.e., the occipital deficits were partially reverted). This finding is suggestive of an intrinsic deficit in frontal regions to entrain to the alpha rhythm. While the present study only offers direct insight into scalp EEG specificity, reduced activation of the frontal cortex in schizophrenia has been widely reported in neuroimaging studies (Andreasen et al., 1994; Erkwoh et al., 1997; Hill et al., 2004; Glahn et al., 2005). Furthermore, metabolic activity in the frontal cortex has been associated with power deficits in schizophrenia (Alper et al., 1998). Neuroimaging investigations show functional and anatomical deficits particularly in the dorsolateral prefrontal cortex (DLPFC) (Andreasen et al., 1994; Sullivan et al., 1998; Radhu et al., 2015), a region also reported to have altered thalamic excitatory inputs in schizophrenia (Glantz et al., 2000). In addition to specific frontal deficits, current theories of schizophrenia posit dysfunctions in distributed widespread neuronal networks (Andreasen et al., 1998; Friston, 1998, 2005; Breakspear et al., 2003). Several studies have reported connectivity issues between frontal and occipital cortices (Fornito et al., 2011; Liu et al., 2012; Cheng et al., 2015), including fractional anisotropy deficits of inferior and superior longitudinal fasciculus pathways (Lener et al., 2015; Prasad et al., 2015). Therefore, although from EEG recordings we can only indirectly infer the cortical sources underlying scalp recorded activity, the current findings of decreased alpha resonance in frontal regions may reflect resonance deficits particularly in the frontal cortex, perhaps mediated by thalamo-cortical abnormalities, in addition to dysfunction in connectivity between occipital and frontal cortices preventing alpha oscillations from reaching frontal regions. Indeed, the thalamus plays an important role in the generation and modulation of scalp recorded EEG alpha oscillations (Andersen and Andersson, 1968; Lopes da Silva et al., 1980; Liu et al., 2012; Omata et al., 2013), and several lines of research converge to further indicate the thalamus as a key mechanism in schizophrenia pathophysiology. First, thalamic volume and metabolism are reduced in schizophrenia (Danos et al., 2003; Buchmann et al., 2014). These deficits may in part contribute to thalamo-cortical dysrhythmia (TCD), which has been proposed as an underlying mechanism across multiple psychiatric and neurological conditions that manifests as alterations in theta and alpha band oscillations of EEG and MEG recordings (Schulman et al., 2011). Integrating findings from sleep studies, deficits have been observed in both slow and fast sleep spindles (Hiatt et al., 1985; Ferrarelli et al.,

2007, 2010), waveforms known to involve thalamo-cortical generators with GABAergic inputs (Steriade, 2003), also reported to be altered in schizophrenia (Perry et al., 1979; De Gennaro and Ferrara, 2003; Ferrarelli and Tononi, 2011). Thus, the current findings may reflect a common thalamo-cortical mechanism of deficits in both waking alpha oscillatory activity and sleep spindles (Robinson et al., 2001). Findings from in vitro (Lörincz et al., 2008), rodent model (Koch, 2013), and neuroimaging studies (Goldman et al., 2002) have demonstrated that functional states of thalamic nuclei and connected networks modulate alpha EEG rhythms. Oscillatory generators and resonance characteristics implicating the thalamus as well as cortico-cortical and thalamo-cortical circuits, including feed-forward dynamics, must all be considered (Hindriks and van Putten, 2013). Future studies are warranted to further apply computational models and experimental paradigms to delineate the oscillatory characteristics involved in schizophrenia pathophysiology. 4.4. Limitations A number of limitations of the present study merit discussion. First, the schizophrenia patients in this study were comprised of a heterogeneous symptom profile, with the majority qualifying as an undifferentiated subtype. It is possible that a more homogeneous sample of specific subtype or symptom profile, particularly in regards to reports of visual disturbances, may alter the results. All schizophrenia patients were taking antipsychotic medication of varying doses, which is likely to influence the networks involved in alpha generation and propagation. However, the inclusion of a psychiatric control group also taking antipsychotic medications, not statistically different from the schizophrenia group in terms of mean dose, offers a unique strength to the current study and novel information regarding the specificity of effects to schizophrenia illness versus antipsychotic medication or other psychiatric illness. Although this study benefits from the strength of high-density EEG to explore topographic specificity of resting-state and SSVEP effects, caution is warranted in interpreting their significance in terms of cortical and subcortical mechanisms, pending replication by both scalp EEG and other imaging studies. It would also be important to perform source modeling analysis to better localize frontal cortical regions underlying scalp-recorded EEG activity. Furthermore, while a significant difference was only observed here for a frontal area in 10 Hz SSVEP, topographic patterns were similar in the occipital region and for the other two SSVEP conditions. Whether due to insufficient statistical power, lower variability in alpha power during 10 Hz SSVEP for frontal regions relative to occipital areas, or a larger effect size for frontal effects, these results suggest that relative deficits in SZ are most pronounced in frontal areas with visual stimulation centered in the alpha range and thus have implications for our understanding of schizophrenia pathophysiology. As suggested above, while the specific mechanisms contributing to the specific results observed here remain unclear, these findings appear consistent with dysfunction in thalamic or thalamo-cortical activity. Future studies utilizing specific cortical and subcortical imaging capability (e.g., fMRI–EEG) with combined resting and SSVEP recordings would help elucidate these mechanisms. Finally, small sample sizes in the current study limit statistical power and warrant replication. 4.5. Conclusion In sum, the present findings build on prior research and offer novel perspectives on frontal deficits in alpha oscillations in schizophrenia and their potential role in illness chronicity. Future studies that replicate and extend these findings may have important implications for understanding the neural mechanisms of these deficits and provide biomarkers to gauge the effectiveness of treatment interventions in patients with schizophrenia.

Please cite this article as: Goldstein, M.R., et al., Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia, Schizophr. Res. (2015), http://dx.doi.org/10.1016/j.schres.2015.06.012

M.R. Goldstein et al. / Schizophrenia Research xxx (2015) xxx–xxx Role of funding source This work was supported by the Lane's Schizophrenia Research Fund; a National Institutes of Health/National Institute of Mental Health Conte Center grant, 1P20MH077967-01A1 (to GT); a European Union Marie Curie International Reintegration grant, FP7-PEOPLE-2007-5-4-3-IRG-No208779 (to FF); and the National Science Foundation Graduate Research Fellowship (to MRG). Contributors MRG contributed to data processing, analyses, and drafting the manuscript. MJP assisted with study design, data collection, and drafting the manuscript. JLS assisted with data analyses and drafting the manuscript. GT contributed to study design and drafting the manuscript. FF led the study design process, assisted with data processing, data analyses, and drafting the manuscript. Conflict of interest Dr. Peterson has received research grant support from Sanofi-Aventis. Dr. Tononi has served as a consultant for Tikvah Therapeutics and Respironics; he has received speaker's honoraria from Sanofi-Aventis and Respironics; he has received research support from Sanofi-Aventis as well as from Respironics (Philips). The other authors report no financial relationships with commercial interests. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j. schres.2015.06.012.

Acknowledgments The authors would like to thank the research assistants for assistance with data collection and data processing. The authors are also grateful for the feedback provided by Dr. David Plante during data analyses and manuscript drafting.

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Please cite this article as: Goldstein, M.R., et al., Topographic deficits in alpha-range resting EEG activity and steady state visual evoked responses in schizophrenia, Schizophr. Res. (2015), http://dx.doi.org/10.1016/j.schres.2015.06.012