Schizophrenia Research 160 (2014) 35–42

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Association between symptoms of psychosis and reduced functional connectivity of auditory cortex Viola Oertel-Knöchel a,⁎, Christian Knöchel a, Silke Matura a, Michael Stäblein a, David Prvulovic a, Konrad Maurer a, David E.J. Linden c, Vincent van de Ven b a b c

Laboratory of Neurophysiology and Neuroimaging, Dept. of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe Univ., Frankfurt/Main, Germany School of Psychology, University of Maastricht, Maastricht, The Netherlands MRC Centre for Neuropsychiatric Genetics & Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, United Kingdom

a r t i c l e

i n f o

Article history: Received 10 June 2014 Received in revised form 26 September 2014 Accepted 25 October 2014 Available online 11 November 2014 Keywords: Resting-state fMRI Schizophrenia Schizophrenia spectrum Heschl's gyrus

a b s t r a c t We have previously reported altered functional asymmetry of the primary auditory cortex (Heschl's gyrus) of patients with schizophrenia (SZ) and their relatives during auditory processing. In this study, we investigated whether schizophrenia patients have altered intrinsic functional organization of Heschl's gyrus (HG) during rest. Using functional magnetic resonance imaging (fMRI), we measured functional connectivity between bilateral HG and the whole brain in 24 SZ patients, 22 unaffected first-degree relatives and 24 matched healthy controls. SZ patients and relatives showed altered functional asymmetry in HG and altered connectivity between temporal and limbic areas in the auditory network during resting-state in comparison with healthy controls. These changes in functional connectivity correlated with predisposition towards hallucinations in patients and relatives and with acute positive symptoms in patients. The results are in line with the results from task-related and symptom-mapping studies that investigated the neural correlates of positive symptoms, and suggest that individual psychopathology is associated with aberrant intrinsic organization of auditory regions in schizophrenia. This might be evidence that reduced hemispheric lateralization and reduced functional connectivity of the auditory network are trait markers of schizophrenia. © 2014 Elsevier B.V. All rights reserved.

1. Introduction One notable human characteristic is the hemispheric laterality of language and speech processing. For example, in most healthy righthanders, the left Heschl's gyrus (HG) is larger than its right hemisphere counterpart (Chance et al., 2008). Abnormal hemispheric asymmetry has been proposed to be a key morphological correlate of the manifestation of schizophrenia (SZ) symptoms (Stephane et al., 2001; Frith, 2005), being associated with impaired language processing and the experience of auditory verbal hallucinations (AVH) (Oertel-Knochel and Linden, 2011; OertelKnochel et al., 2012). Moreover, numerous neuroimaging studies showed correlations between AVHs and abnormal structure and function within the temporal lobe, including the HG (e.g. Chance et al., 2006; Dierks et al., 1999; Oertel et al., 2010; Sommer et al., 2004; van de Ven et al., 2005).

⁎ Corresponding author at: Laboratory of Neurophysiology and Neuroimaging, Dept. of Psychiatry, Psychosomatic Medicine and Psychotherapy, Heinrich-Hoffmann-Str. 10, Goethe-University, 60528 Frankfurt, Germany. Tel.: + 49 69 6301 7181; fax: +49 69 6301 3833. E-mail address: [email protected] (V. Oertel-Knöchel).

http://dx.doi.org/10.1016/j.schres.2014.10.036 0920-9964/© 2014 Elsevier B.V. All rights reserved.

Functional neural networks, such as the language processing network, are mostly investigated using two different approaches: (1) through assessment of activity patterns during experimental tasks (Meyer-Lindenberg et al., 2001; Lawrie et al., 2002; Calhoun et al., 2004; Ford et al., 2007; Garrity et al., 2007) or (2) through restingstate functional imaging (e.g. resting-state fMRI). Resting-state fMRI measures brain activation in the absence of any active or attentional task performance. This makes the intrinsic characteristic of the neural architecture accessible while avoiding potential task-related betweengroup biases. In the current study, we examined resting-state functional connectivity between the HG and whole-brain in SZ patients, their firstdegree relatives and matched healthy control participants. Seed regions (regions of interest — ROIs) within the HG were identified using functional activation clusters related with an auditory perceptual language task (Oertel et al., 2010). Most previous resting-state fMRI studies in schizophrenia focused on the default mode network (DMN) (Fox and Raichle, 2007) and related regions. Comparatively, few resting-state fMRI studies investigated the intrinsic functional connectivity within the auditory network or between speech or language regions and other brain networks in schizophrenia (Gavrilescu et al., 2010; Rotarska-Jagiela et al., 2010; Vercammen et al., 2010; Hoffman et al., 2011; Wolf et al., 2011; Sommer

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V. Oertel-Knöchel et al. / Schizophrenia Research 160 (2014) 35–42

et al., 2012; Oertel-Knochel et al., 2013; Shinn et al., 2013). These studies suggested abnormal interhemispheric auditory cortex connectivity. Further studies showed altered functional connectivity within motor and speech perception areas in hallucinating SZ patients (Ford et al., 2001, 2007; Shergill et al., 2005). An important factor in understanding intrinsic functional brain dynamics in schizophrenia is the investigation of neural networks in genetically-related non-clinical populations, such as prodromal patients, or non-clinical relatives of diagnosed patients. The investigation of first-degree relatives is useful for screening of inheritable or genetic effects on neuronal network function and may thus help identifying potential endophenotypes for schizophrenia (Repovs et al., 2011). A number of previous resting-state studies in schizophrenia showed functional or anatomical findings in non-clinical relatives similar to diagnosed patients (Repovs et al., 2011; Whitfield-Gabrieli et al.; Oertel et al.), which may indicate the presence of pathophysiological mechanisms or endophenotypes in populations in which symptoms or impairments are not (yet) manifested. However, investigation of HG-related connectivity in non-clinical phenotypical populations is currently missing. In this study, we examined potential associations between HGseeded functional connectivity and symptoms of psychosis in three groups of participants: diagnosed SZ patients, first-degree relatives and healthy controls. We hypothesized that patients and relatives show decreased functional connectivity, as well as laterality, in comparison to healthy controls. We hypothesized that the functional connectivity and asymmetry pattern of HG would predict symptoms of psychosis. We discuss our findings in light of the current literature, and suggest that aberrant HG functional connectivity may point to a clinical relevance in diagnosed patients as well as pre-clinical populations.

2. Methods 2.1. Participants In the current study, we included 24 SZ patients, all diagnosed with paranoid schizophrenia according to DSM-IV criteria (American Psychiatric Association, 1994). All were inpatients of the Department of Psychiatry of the Goethe University, Frankfurt am Main, Germany and were treated with neuroleptic medication (all with atypical antipsychotics and four patients additionally with typical neuroleptics) at the

time of testing. Table 1 lists demographic parameters, individual psychopathology and duration of disease of the patients. We further included twenty-two first-degree relatives (REL) of patients with paranoid SZ and twenty-four healthy controls (CON) in the study (Table 1). ANOVAs of variates of age and years of (parental) education, and Chi-square test of sex showed no significant differences between the groups. Contact to the relatives was established through participating patients, from a support group for relatives of SZ patients, and through local media advertisements. The relatives were requested to provide a letter from the attending psychiatrist confirming the patients' diagnosis. In addition, an interview was conducted with each SZ patient's psychiatrist (asking for symptoms, history and medication) to confirm the diagnosis. Additionally, all relatives were asked to bring their own identification as well as of the related patient in order to prove the relationship. We further conducted a semi-structured interview with the relatives, assessing premorbid adaptation of their related SZ patients in order to back-up the diagnosis. The relative group included parents (n = 8) and siblings (n = 11) of SZ patients. The relatives and control groups were matched to the SZ patient group in age, handedness (only right-handed participants) (assessed using the Edinburgh Inventory; Oldfield, 1971), gender and education (see Table 1). None of the participants in the control group had any positive family history of schizophrenia or any other psychiatric disorders. Exclusion criteria for control and relative participants were any psychiatric disorder including Axis I and Axis II disorders according to DSM-IV, left-handedness, current drug-abuse, neurological pathology and inability to provide informed consent. After complete description of the study to the subjects, written informed consent was obtained from all participants. Experimental procedures were approved by the ethical board of the medical department of the Goethe-University, Frankfurt/Main, Germany. Auditory analysis by an otologist revealed normal hearing. The anatomical MRI scans were reviewed by a neuroradiologist who did not find pathology in the auditory cortex or surrounding areas. All subjects were native German speakers and part of another study of the group (see Oertel-Knochel et al., 2013). 2.2. Assessment of psychopathology The German version of the Structured Clinical Interview for DSM-IV (‘Strukturiertes Klinisches Interview für DSM-IV’, Wittchen et al., 1996) was carried out with the participants of all three groups. All relatives

Table 1 Demographic and illness variables of samples. Continuous variates are presented by mean (SD), nominal variates are presented as frequencies. CON = controls, REL = relatives, SZ = schizophrenia patients.

N Age (y) M (SD) Gender (m/f) Handedness (l/r) Education (y) M (SD) Parental education (y) M (SD) Mother Father RHS

REL

CON

24 37.9 (7.84)

22 39.35 (10.75)

24 40.84 (10.23)

3.05

12/12 0/24 15.08 (2.51)

10/12 0/22 15.10 (4.76)

13/11 0/24 16.14 (2.98)

12.87 (2.31) 12.95 (2.83)

12.57 (2.18) 13.43 (2.98)

33.0 (7.88)

25.67 (1.39)

Illness Onset (y) Duration of disease (y) ns = not significant. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

F/χ2

SZ

Atypical/typical CZ equivalence (mg/day)

Post-hoc CON N REL

CON N SZ

REL N SZ

ns

ns

ns

ns

0.66 – 2.5

ns – ns

ns ⁎

ns

ns

ns

ns

ns

12.92 (2.68) 13.13 (2.97)

2.13 2.45

ns ns

ns ns

ns ns

ns ns

23.01 (2.98)

28.12

⁎⁎⁎

⁎⁎

⁎⁎⁎

⁎⁎

Medication 24.12 (5.58) 13.52 (6.54)

P

PANSS 21/4 610.42 (387.3)

Total Pos. Neg.

63.29 (5.24) 15.45 (3.07) 15.19 (1.97)

Hallucinations General

3.14 (1.23) 32.65 (4.01)

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and controls who met criteria for psychiatric or neurological disorders were excluded from the study. All participants completed the Revised Hallucination Scale (RHS), a self-report questionnaire that screened for predisposition towards hallucinations (Morrison et al., 2002). This questionnaire contained 20 statements about hallucinatory experiences. The participants rated the frequency of experiencing the statements on a four-point scale (1 = never; 4 = almost always). The predisposition towards hallucinations (RHS) was significantly different between groups (ANOVA: F(2, 60) = 28.12, p b 0.001), such that patients reported a higher predisposition to hallucinate (mean [SD] = 33.00 [7.88]), compared to first-degree relatives (25.67 [1.39]) and healthy controls (23.01 [2.98]). These values were similar to RHS values of patients, relatives and healthy controls from a previous study with a different sample of participants (Oertel et al., 2009). We complemented the screening for individual psychopathology with the assessment of acute positive and negative symptoms (fourteen days prior to the moment of assessment) in the patient group. We used the Positive and Negative Symptom Scale (PANSS; Kay et al., 1987), a semi-structured interview lasting approximately 45 min. The measured PANSS scores (mean [SD]) of the patient group were: global scale: 63.29 (5.24), positive symptoms: 15.45 (3.07), negative symptoms: 15.19 (1.97) and general psychopathology: 32.65 (4.01). Additionally, all patients reported a history of auditory hallucinations, the last period ranged from 13 days to 8 months before the study. None of the patients reported any hallucinations while they were scanned (see Table 1). 2.3. Data acquisition and image preprocessing Images acquisition was done on a Siemens Magnetom Allegra 3 T MRI system (Siemens Medical Systems, Erlangen, Germany) at the Goethe University Brain Imaging Center, Frankfurt am Main, Germany. The sessions started with a resting-state functional measurement with the following parameters: EPI-sequence, 400 volumes, voxel size: 3.1 × 3.1 × 5.0 mm 3 , TR = 1000 ms, TE = 30 ms, 16 slices, slice thickness = 5 mm, distance factor = 10%, inter-slice time 62 ms, and flip angle = 60°, followed by a high-resolution T1-weighted anatomical measurement (MDEFT sequence (Deichmann et al., 2004), 176 slices, 1 × 1 × 1 mm3). The resting-state measurements were done instructing all participants to have the eyes in darkness continuously open, to lie still, to do not engage in any speech, to think nothing specially and to look at a white fixation cross (presented in the center of the visual field) during the whole scan. The presentation protocols including the fixation cross were synchronized with the fMRI sequence at the beginning of each trial. The whole session lasted approximately 25 min. Preprocessing and co-registration of functional and anatomical MR images were done using the BrainVoyager QX software, version 2.2 (Goebel et al., 2006). Preprocessing of functional data included slice-time correction, rigid-body motion correction (Levenberg– Marquardt algorithm), spatial smoothing (Gaussian kernel of 4 mm full-width at half-maximum), linear trend removal and high-pass temporal filtering (3 cycles per time course, cutoff = 0.0075 Hz). Threedimensional (3D) anatomical scans were transformed into Talairach space (Talairach and Tournoux, 1988) using a 12-point affine transformation. We used automated routines of the BrainVoyager QX software to co-register the functional data to the anatomical scans of the same participant, and resampled the functional data to an iso-voxel size of 3 × 3 × 3 mm3. 5. Statistical analysis 2.5. Resting-state data We computed a seed-regions analysis with bilateral HG as seedregions. The location of HG seeds were obtained from a previous study, in which we identified HG volumes-of-interest (VOIs) based on an auditory processing task in schizophrenia, first-degree relatives and

37

controls (Oertel et al., 2010). VOIs were selected as seed-regions to do functional connectivity analysis during resting-state using freely available toolboxes and custom-written routines in Matlab (MathWorks, Natick, MA). We analyzed the data with a two-level general linear model (GLM) (Biswal et al., 1995; Fox and Raichle, 2007; Rotarska-Jagiela et al., 2010). On the first level, functional connectivity coefficients for each participant were estimated using the GLM and corrected for nuisance variables. This was done sampling by the time-series from the voxels that were tagged by the HG VOIs, averaged and standardized (Znormalization) within each VOI, and entering into the first-level analysis. The incorporated nuisance variables (Z-normalized) included fMRI signal sampled from the ventricles and from white matter, the global (whole-brain) signal, and six head movement parameters estimated from the motion correction procedure (Birn et al., 2006; Fox and Raichle, 2007). The nuisance-corrected functional connectivity coefficients were entered into a second-level analysis of covariance (ANCOVA), with group as between-subject factor and age, sex and education as subject-level nuisance covariates. The group effect of the ANCOVA model (F-map) was superimposed on an anatomical template and visualized using a False-discovery-rate (FDR, Genovese et al., 2002) threshold of q b 0.05 and a minimum cluster size of 200 mm3. Regions of effects that survived the visualization thresholds (i.e., significant group effect corrected at the voxel-level) were defined as regions-of-interest (ROIs). The voxel functional connectivity coefficients from these ROIs were sampled and averaged for each participant, and two-tailed posthoc two-sample t-tests were done with the ROI-averaged connectivity coefficients as dependent variables. In addition to the bilateral HG seed-region analysis, where we combined left and right HGs into one single bilateral seed, we also analyzed the data using left HG and right HG as separate seed-regions. 2.6. Lateralization To assess if functional connectivity of HG showed differences in brain laterality between the groups we calculated a Laterality Index (LI) as the left–right ratio of the number of functionally connected voxels in the left and right HGs (i.e., LI = [left − right] / [left + right]) (Fernandez et al., 2001; Seghier, 2008). The threshold for voxel selection for each participant in each region was adjusted through a calculation of the average of the 5% most strongly connected voxels. After that, the selection threshold was set at 50% of the ensuing value (Fernandez et al., 2001). Surviving voxels were summed within each hemisphere to obtain the number of connected voxels per hemisphere. LI values ranged between − 1 (exclusively right-lateralized) and 1 (exclusively leftlateralized). The LI values were then investigated using an ANCOVA with group as between-subject factor and age, sex and education as nuisance covariates. 2.7. Correlation analyses We calculated Spearman rank correlation coefficients (rho) in order to assess the relationship between HG functional connectivity and HG laterality and individual psychopathology (PANSS subscores, RHS). We used the following PANSS scores: PANSS positive scale, the PANSS hallucination scores, the PANSS negative scale, the PANSS general psychopathology scale and the PANSS total score. In addition, we computed the chlorpromazine equivalent doses for the patients' antipsychotic medication (Woods, 2003). Individual doses ranged from 200 to 1500 mg/day (see Table 1). To ensure that the results of our study were not due to a medication effect with typical neuroleptics (Dazzan et al., 2005), we repeated the analysis for group differences on the functional measurements without the four patients with typical medication, and found no differences in results. In addition, we computed a correlation analysis between the chlorpromazine equivalents and the functional connectivity indices (Pearson correlation, r).

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3. Results 3.1. Bilateral HG as seed region: resting-state functional connectivity Functional connectivity results were superimposed on a Talairachtransformed MNI template (courtesy of the Montreal Neurological Institute [MNI]), with visualization thresholds that included a falsediscovery rate of q = 0.05 and minimum cluster size of 200 mm3.

A

The multi-subject result map (voxel-by-voxel test of connectivity coefficients) with voxel clusters of significant bilateral HG-seeded functional connectivity included left and right STGs, left and right insulae, left and right anterior cingulate, left cingulate gyrus and left parahippocampal gyrus. For the bilateral HG-seeded functional connectivity, we found a significant effect of group (ANCOVA) in the left STG, in the right insula, in the left anterior cingulate, in the left middle cingulate gyrus and in the left parahippocampal gyrus (see Fig. 1A, C, Table 2).

Seed: Bilateral HG Group comparisons (F-test)

l. STG

B

Post-hoc contrast CON vs. PAT

r. Insula

C

l. STG

Functional connectivity (FC) parameters

Colour Code white: SZ grey: REL black: CON

l. STG

l. insula l.mid-cing. l. PHG gyrus

l. AC

Fig. 1. One-way ANCOVA of the functional connectivity scores with Group as between-subject factor and age, sex and education as covariates (ANCOVA F-map corrected for FDR and cluster size). Statistical threshold: q [FDR] b 0.05. CON = controls, REL = relatives, SZ = schizophrenia patients. Upper row (A): Significant regions with HG bilateral as seed-region, second row (B): Significant group differences in functional connectivity coefficients between controls and patients, third row (C): Beta values of significant areas in the group comparisons of HG bilateral: Left superior temporal gyrus (STG), right insula, left mid-cingulate gyrus, left parahippocampal gyrus (PHG), left anterior cingulate (AC). The left side in the figure indicates the right side of the brain (radiological convention).

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Table 2 Group effect of HG functional connectivity. The table lists the mean Talairach coordinates for each of the four areas, and one-sample t-test statistics of functional connectivity (FC) coefficients compared to 0, the Group effect F-statistic and post-hoc contrasts. CON = controls, REL = relatives, SZ = schizophrenia patients; STG = superior temporal gyrus. ROI

TAL x, y, z

k

Group

FC (t)

F(2, 61)

CON N REL

CON/PAT

REL N PAT

Bilateral HG Left STG

−45, −19, 7

62,239

⁎⁎⁎

⁎⁎⁎

ns

42, −19, 10

72,701

25.58⁎⁎⁎

⁎⁎⁎

⁎⁎⁎

ns

Left mid-cingulate gyrus

−3, −16, 42

16,785

2.75⁎

ns

ns

ns

Left parahippocampal gyrus

−23, −35, 59

0.12 (0.03)⁎⁎ 0.08 (0.03)⁎⁎ 0.05 (0.03)⁎⁎ 0.14 (0.03)⁎⁎ 0.08 (0.03)⁎⁎ 0.07 (0.02)⁎⁎ 0.07 (0.05)⁎⁎ 0.04 (0.04)⁎⁎ 0.03 (0.04)⁎⁎ 0.05 (0.10)⁎⁎

21.20⁎⁎⁎

Right insula

CON REL SZ CON REL SZ CON REL SZ CON REL SZ CON REL SZ

5.92⁎⁎⁎

⁎⁎

⁎⁎

ns

5.91⁎⁎

ns

⁎

⁎

Left anterior cingulate

0, 29, 7

2188

652

0.01 (0.06) 0.01 (0.05) 0.07 (0.09)⁎⁎ 0.07 (0.11) 0.09 (0.12)⁎⁎

ns = not significant. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

Post-hoc pairwise contrasts (two sample t-tests, corrected for nuisance variates) (see Fig. 1 and Table 2) showed significantly higher functional connectivity in CON compared with SZ and REL in the left STG, in the right insula and in the left parahippocampal gyrus. Functional connectivity of SZ and REL did not differ significantly in these areas. In left anterior cingulate, SZ showed significantly higher connectivity with the HG seed in comparison with REL and CON. In left mid-cingulate gyrus, post-hoc pairwise contrasts were non-significant (p N 0.05) (see Fig. 1B, Table 2).

3.2. Left and right HGs as seed region: resting-state functional connectivity The multi-subject result map (voxel-by-voxel test of connectivity coefficients) with voxel clusters of significant left HG-seeded functional connectivity included similar clusters as the bilateral HG analysis. Moreover, the group contrasts of left HG seed analysis revealed a comparable pattern to bilateral HG seed analysis, showing decreased functional connectivity in REL and SZ in the left STG, in the right insula and in the left parahippocampal gyrus, and increased functional connectivity in the left anterior cingulate in SZ and REL compared with CON. Right HGseeded functional connectivity showed no significant group differences (see Table 2). We also did a visual conjunction analysis in order to test which voxels are overlaying in the resting-state network of left HG and right HG. Fig. 2 shows a conjunction map, with regions-of-interest maps of the functional connectivity scores for HG left (marked in red), HG right (marked in yellow) and conjunction map (joined region-of-interest for HG left and HG right) (marked in green).

3.3. Laterality of functional connectivity We computed the distribution of functional connectivity coefficients within the left–right ratio of the HG regions. ANCOVA of the laterality index of the HG-seeded functional connectivity revealed a significant effect of group (F(2, 61) = 6.95, p b 0.005). Post-hoc Scheffe contrast showed significant differences for all pairwise comparisons (all p values b 0.01). CON showed the strongest left-ward laterality (0.10 (0.02)), followed by a weaker left lateralization for REL (0.06 (0.03)) and SZ (0.01 (0.008)) (see Fig. 3).

3.4. Correlation between connectivity & laterality and psychopathology For the correlation analysis, we extracted individual functional connectivity (FC) coefficients of the ROIs with significant group effect, and correlated these coefficients with scores of predisposition to hallucinate (RHS) for each group. For HG-seeded connectivity, RHS scores of SZ correlated negatively with left STG (rho = −0.56, p b 0.01) and right insula (rho = −0.45, p b 0.01). In relatives, RHS scores correlated negatively with left STG (rho = −0.47, p = 0.02). In controls, RHS scores did not correlate with any of the ROI connectivity coefficients. We also correlated individual functional connectivity coefficients of the ROIs with the PANSS scores in the patient group. For HG-seeded connectivity, there was a significant negative correlation in PANSS positive scale and PANSS hallucination scores with the left superior temporal gyrus (PANSS positive: rho = −0.32, p b 0.05; PANSS hallucinations: rho = −0.41, p b 0.05) (Table 3). We also correlated LI values of the bilateral PT-seeded functional connectivity with the self-reported RHS scores and the PANSS hallucination scores. However, none of the individual psychopathology parameters (RHS, PANSS) showed any significant correlation with HG laterality scores (all p-values N 0.05). The correlations between the chlorpromazine equivalent doses of the patients and functional connectivity scores were not significant, either (p N 0.05).

4. Discussion Our results indicated that there are significant group differences across schizophrenia patients, first-degree relatives and controls in the functional connectivity of the region of the primary auditory cortex (HG) during rest. Overall, our results demonstrate functional connectivity of bilateral HG with bilateral superior temporal gyrus (STG), bilateral insula, bilateral anterior cingulate, left cingulate and left parahippocampal gyrus. Importantly, we showed that in SZ patients and in first degree relatives, resting-state functional connectivity of the HG was reduced in the left STG, the right insula and the left parahippocampal gyrus in comparison with controls. This finding is in line with previous findings of reduced network connectivity in SZ patients (Malaspina et al., 2004; Liang et al., 2006; Bluhm et al., 2007; Zhou et al., 2007), in particular within the auditory network (Gavrilescu et al., 2010; Rotarska-Jagiela

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Conjunction Analysis

Colour Code HG left HG right Joined regions HG left + right Fig. 2. Regions-of-interest maps of the functional connectivity scores for HG left (marked in red), HG right (marked in yellow) and conjunction map (joined region-of-interest for HG left and HG right) (marked in green). CON = controls, REL = relatives, SZ = schizophrenia patients.

et al., 2010; Vercammen et al., 2010; Sommer et al., 2012). We observed that group differences were mainly driven by left-sided HG seeded functional connectivity parameters. Regarding the findings by (Shinn et al., 2013), who observed no group differences between AVH prone and AVH not prone patients in right HG-seeded functional connectivity, we suggest that mainly left HG seeded functional connectivity may be affected by dysfunctional connectivity with other brain regions during rest. Our suggestion is line with previous research showing altered left-hemisphere volume and activity in schizophrenia patients compared to controls (Stephane et al., 2001; Frith, 2005), and may be indicative of impaired language processing or hemispheric dominance in schizophrenia (Crow, 1990; Sommer et al., 2012). Moreover, temporal and limbic functional alterations have been reported frequently to be involved in the pathophysiology of schizophrenia (Jang et al., 2011). In these areas, we found significant group differences between patients and controls, and between unaffected relatives and controls, but no significant differences between relatives and patients. This finding may indicate that unaffected first-degree relatives of SZ patients have a pattern of resting-state activation comparable to that of SZ patients, which also conforms to several other resting-state (Whitfield-Gabrieli et al., 2009; Gavrilescu et al., 2010; Jang et al., 2011; Repovs et al., 2011) and task-related fMRI studies (Kasai et al., 2003; Kawasaki et al., 2008; Oertel et al., 2010). In contrast, left anterior cingulate cortex showed higher HG-seeded functional connectivity in SZ patients in comparison to relatives and controls.

Laterality (LI)

Colour Code White: SZ Grey: REL Black: CON

Fig. 3. Statistical distribution of mean (standard deviation) lateralization indices (LI) across all groups (seed regions: PT bilaterally). CON = controls, REL = relatives, SZ = schizophrenia patients.

Our results demonstrated significant associations between psychotic symptoms and aberrant intrinsic functional connectivity within the auditory network. In particular, resting-state activity in the left STG was significantly associated with the predisposition towards hallucinations (RHS) in SZ patients and first-degree relatives. In the patient group, RHS correlated significantly with HG connectivity with right insula; PANSS positive and hallucination subscale were significantly correlated with left STG connectivity. Functional (Ford et al., 2001; Lawrie et al., 2002; Jardri et al., 2009; Whitfield-Gabrieli et al., 2009; Jang et al., 2011; Repovs et al., 2011) and structural (diffusion tensor imaging) (Wolkin et al., 2003; Mendelsohn et al., 2006; Mitelman et al., 2007; Skelly et al., 2008) auditory cortex connectivity parameters have been reported to be directly associated with psychotic symptoms in schizophrenia. This led to the assumption that functional and structural abnormalities in the auditory cortex pathways, and particularly an imbalance between language production and language perception areas, might be causally involved in subclinical and clinical psychotic symptoms across the schizophrenia spectrum (Rotarska-Jagiela et al., 2009). However, our finding of hyper-connectivity between a limbic structure (left anterior cingulate) and HG in the patient group in comparison to relatives and controls may be explained as a compensatory mechanism of the brain to balance dysfunction in temporal regions. Alternatively, aberrant connectivity between limbic and auditory cortical areas may be due to an increased emotional involvement and processing related with abnormal auditory processing in schizophrenia (Dierks et al., 1999). Gavrilescu et al. (2010) proposed that the disconnectivity of the auditory network is the reason for a failure of multiple auditory functions, including integration of basic auditory information between hemispheres (between primary auditory cortices) and language processing abilities (secondary auditory cortices). Moreover, our data showed abnormal FC between HG and language areas within the HG-seeded resting-state functional network. Abnormal cerebral asymmetry has been suggested to be a key cognitive factor in the manifestation of schizophrenia symptoms (Stephane et al., 2001; Frith, 2005). In contrast to our findings on PT connectivity (OertelKnochel et al., 2013), where we showed a direct relation between abnormal hemispheric asymmetry in a planum temporale-seeded network and predisposition towards hallucinations, HG-seeded language laterality was not associated with any psychotic symptoms (see also Samara and Tsangaris, 2011). This may indicate that HG-seeded language laterality is independent of acute symptoms.

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Table 3 Correlation analysis between significant areas during HG bilateral resting state analysis and Laterality Index (LI) with RHS and PANSS hallucination score. Seed bilateral HG

Left STG

Right insula

Left cingulate gyrus

Left parahippocampal gyrus

Left anterior cingulate

LI

RHS SZ REL CON

Rho = −0.56⁎⁎ Rho = −0.47⁎ Rho = 0.09, ns

Rho = −0.45⁎⁎ Rho = 0.17⁎⁎⁎ Rho = −0.01, ns

Rho = −0.12, ns Rho = 0.14, ns Rho = 0.31, ns

Rho = 0.20, ns Rho = −0.17, ns Rho = 0.16, ns

Rho = 0.25, ns Rho = 0.23, ns Rho = 0.16, ns

Rho = 0.11, ns Rho = −0.26, ns Rho = −0.24, ns

PANSS pos SZ

Rho = −0.32⁎

Rho = −0.18, ns

Rho = −0.10, ns

Rho = 0.08, ns

PANSS neg SZ

Rho = −0.11, ns

Rho = −0.10, ns

Rho = −0.01, ns

Rho = 0.01, ns

Rho = 0.10, ns

Rho = −0.20, ns

PANSS gen SZ

Rho = −0.05, ns

Rho = −0.08, ns

Rho = −0.07, ns

Rho = 0.05, ns

Rho = 0.16, ns

Rho = 0.15, ns

Rho = 0.08, ns

Rho = −0.18, ns

Rho = −0.15, ns

Rho = 0.13, ns

Rho = −0.05, ns

Rho = 0.18, ns

Rho = −0.41⁎

Rho = −0.19, ns

Rho = 0.05, ns

Rho = 0.12, ns

Rho = −0.13, ns

Rho = 0.26, ns

PANSS total score

PANSS hallu SZ ns = not significant. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

Several authors have suggested explanations for an aberrant functional architecture of auditory and limbic regions in schizophrenia (Stephane et al., 2001; Crow, 2000; Friston, 1998; van de Ven, 2012) (Wible et al., 2009; Van de Ven, 2012). However, the cross-sectional nature of our study does not allow definite conclusions about the causes of the observed functional changes. Friston (1998) explained misconnectivity by the so-called disconnectivity model of schizophrenia, in which pathological hyper- and hypoconnectivity between key brain areas may disrupt information processing and lead to a dynamic display of various symptoms and impairments. Recent findings of aberrant functional connectivity along a healthy controls– relatives–SZ (Whitfield-Gabrieli et al., 2009; Jang et al., 2011; Wolf et al., 2011) also fit to this model, although the model is not specific with respect to the nature of the disconnectivity invoked. Possible explanations for disconnected intrinsic functional networks include disruptions in the wiring of association fibers in the course of brain development and impaired synaptic plasticity (Feinberg, 1982, 1990; Bullmore et al., 1997). However, these models do not specifically account for the asymmetries that we and others have documented. One possibility is that psychosis and failure of language lateralization may share genetic variability (Crow, 2000). To discern potential associations between asymmetry findings and genetic liability to schizophrenia, longitudinal studies as well as studies of at-risk subjects are necessary. Numerous studies indicate that disturbed asymmetry of temporal regions represents a central disease mechanism, and our current results and others (see for review: Oertel-Knochel and Linden, 2011) – showing abnormal temporal lobe asymmetry also in relatives of schizophrenia patients – support the link with disease-associated genetic risk factors. Crow et al. (1989) has suggested that schizophrenia results from a failure of normal cerebral lateralization and that this failure is genetically determined. However, current knowledge from genome-wide association studies (GWAS) indicates that there are many genes with small effects and diverse functions, but no lateralization gene with large effect has been found so far. Nonetheless, to our knowledge, none of the current studies has so far looked at asymmetry as quantitative trait. In sum, we suggest that functional asymmetry alterations in the auditory cortex may be linked to disease evolution and clinical severity in schizophrenia, but this marker presently does not stand alone in the multimodal causal models of the illness. Further studies directly assessing laterality and potential genetic liability are needed to draw any further conclusion about the trait nature of abnormal functional laterality of the temporal lobe.

Role of the funding source There was no funding for this manuscript. Contributors Viola Oertel-Knöchel: recruitment, preparing the data, correcting and writing the manuscript. Christian Knöchel: preparing the data, writing the manuscript. Silke Matura: preparing the data, correcting the manuscript. Michael Stäblein: preparing the data, correcting the manuscript. David Prvulovic: writing the manuscript. David E.J. Linden: preparing the data, correcting the manuscript. Konrad Maurer: correcting the manuscript. Vincent van de Ven: methods, writing the manuscript. Conflict of interest David Linden has received consulting honoraria from Actelion Pharmaceuticals. All other authors report no financial relationships with commercial interests. Acknowledgments MRI was performed at the Frankfurt Brain Imaging Centre, supported by the German Research Council (DFG) and the German Ministry for Education and Research (BMBF; Brain Imaging Center Frankfurt/Main, DLR 01GO0203).

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