Schizophrenia Bulletin Advance Access published March 11, 2014 Schizophrenia Bulletin doi:10.1093/schbul/sbu031
Visual Hallucinations Are Associated With Hyperconnectivity Between the Amygdala and Visual Cortex in People With a Diagnosis of Schizophrenia
Judith M. Ford*,1,2, Vanessa A. Palzes1,2, Brian J. Roach1,2, Steven G. Potkin3, Theo G. M. van Erp3, Jessica A. Turner4,5, Bryon A. Mueller6, Vincent D. Calhoun4,7, Jim Voyvodic8, Aysenil Belger8,9, Juan Bustillo10, Jatin G. Vaidya11, Adrian Preda3, Sarah C. McEwen12, Functional Imaging Biomedical Informatics Research Network13, and Daniel H. Mathalon1,2
*To whom correspondence should be addressed; Psychiatry Service (116D), San Francisco VA Medical Center, 4150 Clement Street, San Francisco, CA 94121, US; tel: 415-221-4810, ext. 4187, fax: 415-750-6622, e-mail: [email protected]
Introduction: While auditory verbal hallucinations (AH) are a cardinal symptom of schizophrenia, people with a diagnosis of schizophrenia (SZ) may also experience visual hallucinations (VH). In a retrospective analysis of a large sample of SZ and healthy controls (HC) studied as part of the functional magnetic resonance imaging (fMRI) Biomedical Informatics Research Network (FBIRN), we asked if SZ who endorsed experiencing VH during clinical interviews had greater connectivity between visual cortex and limbic structures than SZ who did not endorse experiencing VH. Methods: We analyzed resting state fMRI data from 162 SZ and 178 age- and gender-matched HC. SZ were sorted into groups according to clinical ratings on AH and VH: SZ with VH (VH-SZ; n = 45), SZ with AH but no VH (AH-SZ; n = 50), and SZ with neither AH nor VH (NoH-SZ; n = 67). Our primary analysis was seed based, extracting connectivity between visual cortex and the amygdala (because of its role in fear and negative emotion) and visual cortex and the hippocampus (because of its role in memory). Results: Compared with the other groups, VH-SZ showed hyperconnectivity between the amygdala and visual cortex, specifically BA18, with no differences in connectivity among the other groups. In a voxel-wise, whole brain analysis comparing VH-SZ with AH-SZ, the amygdala was hyperconnected to left temporal pole and inferior frontal gyrus in VH-SZ, likely due to their more severe thought broadcasting. Conclusions: VH-SZ have hyperconnectivity between subcortical areas subserving emotion and cortical areas subserving higher order visual
processing, providing biological support for distressing VH in schizophrenia. Introduction Auditory verbal hallucinations (AH) are experienced by about 75% of people with a diagnosis of schizophrenia (SZ).1 Although visual hallucinations (VH) are experienced by about half of those,2 VH are studied less frequently by clinical neuroscientists interested in schizophrenia. This may reflect their prominence in neurological conditions with known etiology, such as Lewy Body Dementia, temporal lobe epilepsy, Parkinson’s disease, and Charles Bonnet syndrome.3 Indeed, common instruments for assessing hallucinations in SZ either neglect the distinction between AH and VH4 or primarily focus on AH.5 Brain imaging studies of hallucinations typically take 2 forms: “state” and “trait”. State studies, or “symptomcapture studies,” involve participants signaling the start and end of a hallucination, and brain activity during periods with and without hallucinations is compared. Trait studies involve comparing groups who tend to have hallucinations to groups who do not,6,7 or involve using hallucination severity scores as regressors in analyses of brain activity across groups.8 State studies suggest involvement of sensory specific areas in the visual hallucinatory experiences of people with Charles Bonnet syndrome9 and in the auditory hallucinatory experiences in SZ.10 In addition, in people with
© The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected]
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Psychiatry Service, San Francisco VA Medical Center, San Francisco, CA; 2Department of Psychiatry, University of California, San Francisco, CA; 3Department of Psychiatry and Human Behavior, University of California, Irvine, CA; 4Mind Research Network, Albuquerque, NM; 5Departments of Psychology and Neuroscience, Georgia State University, Atlanta GA; 6Department of Psychiatry, University of Minnesota, Twin Cities, Minneapolis, MN; 7Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM; 8Brain Imaging and Analysis Center, University of North Carolina-Duke University, Durham, NC; 9Department of Psychiatry, University of North Carolina, Durham, NC; 10Department of Psychiatry, University of New Mexico, Albuquerque, NM; 11 Department of Psychiatry, University of Iowa, Iowa City, IA; 12Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA; 13Functional Imaging Biomedical Informatics Research Network (FBIRN), http://www.birncommunity.org 1
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of interest (SOI) and visual cortical target regions of interest (ROI; BA17, BA18, BA19, fusiform gyrus). We chose the hippocampus because of its reported involvement in VH11,25 and in memory retrieval,26 arguably a critical element of hallucinations. The amygdala was chosen because of its role in emotion and fear processing,27 perhaps contributing to the negative emotional tone characteristic of hallucinations experienced by many SZ.1 Furthermore, direct brain stimulation studies of people undergoing surgical planning for intractable epilepsy indicate that stimulation of both the hippocampus and amygdala provoke AH and VH,23,28 lending further support for the hippocampus and amygdala seeds. We predicted that hyperconnectivity in SZ who experienced VH would be specific to VH and would not be seen in SZ with only AH. We also predicted hyperconnectivity to visual cortex would not be a general feature of schizophrenia and thus would not be seen in SZ who experienced neither VH nor AH. Further, we predicted that hyperconnectivity in SZ with VH would not be seen in HC. Finally, we predicted no difference in connectivity between the other groups because hyperconnectivity to visual areas should only be relevant to the experience of VH, a phenomenon not experienced by the others. To confirm the functional connectivity findings of Amad et al,25 we extracted connectivity values between the hippocampus and MPFC and caudate. Because this analysis was retrospective and detailed symptom data were not collected as part of FBIRN, we were unable to test hypotheses about the content and nature of specific hallucinatory experiences. Methods Participants Data from 180 SZ and 178 HC matched for age, sex, handedness, and race distributions, collected from 7 sites, are included in this analysis. Demographic information about these participants is provided in table 1. Written informed consent, including permission to share deidentified data between centers and with the wider research community, approved by University of California Irvine, University of California Los Angeles, University of California San Francisco, Duke University/ University of North Carolina, University of New Mexico, University of Iowa, and University of Minnesota Institutional Review Boards, was obtained from all study participants. Inclusion criterion for SZ was a diagnosis of schizophrenia based on the Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID-I/P).29 SZ were clinically stable on antipsychotic medication for at least 2 months. SZ and HC were excluded for history of major medical illness, contraindications for MRI, insufficient eyesight to see with normal acuity with MRI compatible corrective lenses, drug dependence in the last 5 years or
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psychosis, areas involved in emotional experience and memory retrieval, such as the amygdala and hippocampus, are also activated by hallucinations.10 For example, a symptom-capture study of VH in 1 person with schizophrenia found activity in higher visual areas corresponding to the content of the hallucinations (faces, bodies, scenes), as well as the hippocampus, possibly related to retrieval of visual images from memory.11 Amygdala activity was not reported, perhaps due to the lack of negative content in the specific VH. Trait studies of people with Parkinson’s disease reveal decreased visual cortical activation to external visual stimulation in people with visual hallucinators,12–14 perhaps suggesting competition for visual resources. These studies did not report involvement of the amygdala or the hippocampus, perhaps because the hallucinatory experiences were not emotional because people with Parkinson’s disease typically have retained insight into the origin of VH.15 However, one study found evidence of selective atrophy of the hippocampal head in Parkinson’s disease patients with VH.16 Functional connectivity reflects temporal correlation of activity across brain regions and has been used to understand the pathophysiology of hallucinations. Connectivity may reflect the ability of areas to communicate efficiently with each other and underpin conscious experience,17–19 with hyperconnectivity perhaps resulting in excessive conscious experiences.20 Connectivity may reflect the scaffolding set up between different brain areas, when they are repeatedly coactive, recalling Hebb’s rule: “units that fire together, wire together.”21 Using resting state functional connectivity analyses, Jardri et al22 combined state and trait approaches to compare people with first-episode psychosis suffering from AH, VH, or concomitant audio-visual hallucination. They noted increased signal in modality-dependent association sensory cortices during AH, VH, and multisensory hallucination. Because of the hippocampal role in VH11,23 and AH,24 Amad et al used a hippocampal seed and reported hyperconnectivity to medial prefrontal cortex (MPFC) and caudate in people with severe AH and VH compared with people with only severe AH. Furthermore, they reported higher white matter connectivity between the hippocampus and visual cortical areas and greater hippocampal hypertrophy. It is unknown how the visual hallucinators would compare to healthy controls (HC) and SZ who have neither AH nor VH. By taking advantage of a large sample of SZ collected as part of the functional magnetic resonance imaging (fMRI) Biomedical Informatics Network (FBIRN) study, we did a retrospective, trait-based, analysis of resting state data to ask whether visual cortex is hyperconnected to limbic areas in SZ who experienced VH within 1 month of scanning. To this end, we did a seed-based connectivity analysis, using the hippocampus and amygdala seeds
Hyperconnectivity Between the Amygdala and Visual Cortex in Visual Hallucinators
Table 1. Demographics of Healthy Controls (HC) and Schizophrenia Patients (SZ) [Mean (SD)]
Demographics Age (y) Socioeconomic scale—subject* Socioeconomic scale—caretaker* Gender (male/female) Handedness (right/left/ambidextrous) Smoking status (smoker/nonsmoker) Duration of illness (y) Diagnosis (n)
VH-SZ (n = 45)
NoH-SZ (n = 67)
HC (n = 178)
39.0 (11.9) 50.8 (12.3)d 34.5 (14.4)d 39 (m), 11 (f) 44 (r), 5 (l), 1 (a) 22 (s), 28 (n) 18.8 (11.3) 38 Paranoid 8 Undifferentiated 2 Residual 2 Disorganized
36.9 (10.8) 50.2 (14.0)d 35.9 (15.2)d 32 (m), 10 (f) 35 (r), 5 (l), 2 (a) 22 (s), 23 (n) 15.9 (12.1) 34 Paranoid 10 Undifferentiated 0 Residual 1 Disorganized
39.8 (12.0) 50.2 (12.5)d 37.3 (14.3)d 49 (m), 18 (f) 66 (r), 0 (l), 1 (a) 27 (s), 40 (n) 17.1 (11.5) 47 Paranoid 10 Undifferentiated 7 Residual 3 Disorganized
37.7 (11.2) 33.2 (12.9)a,b,c 29.9 (14.7)a,b,c 127 (m), 51 (f) 169 (r), 7 (l), 2 (a) 16 (s), 162 (n) n/a n/a
3.3 (1.4)c 2.7 (1.0)ac 1.0 (1.4)c 0.6 (1.1)c 3.3 (0.9)c 2.9 (1.2)c
0 (0)a,b 0 (0)b 0.3 (0.8)b 0.3 (0.9)a,b 0.2 (0.7)a,b 1.3 (1.3)a,b
n/a n/a n/a n/a n/a n/a
33.5 (19.1)
27.7 (17.0)
n/a
328.9 (282.7) 36 3 0 3 3
363.9 (342.6) 52 9 2 2 2
n/a n/a n/a n/a n/a n/a
Symptom measures Scales for the assessment of positive symptoms Auditory hallucinations (Item 1)* 3.5 (1.2)c Visual hallucinations (Item 6)* 0 (0)b Tactile hallucinations (Item 4)* 0.6 (1.1) Olfactory hallucinations (Item 5)* 0.4 (1.1)c Global hallucinations (Item 7)* 3.1 (1.0)c Global delusions (Item 20)* 2.5 (1.3)c Scales for the assessment of negative symptoms Overall total 29.9 (17.3) Antipsychotic medication Chlorpromazine equivalentse 424.0 (442.2) Aypical 43 Typical 3 Both atypical and typical 1 None 3 Unknown 0
*Group ANOVA is significant at P = .05. Significantly different than AH-SZ (t test, P < .05). b Significantly different than VH-SZ (t test, P < .05). c Significantly different than NoH-SZ (t test, P < .05). d Significantly different than HC (t test, P < .05). e Only have chlorpromazine equivalent data for 43 AH-SZ, 32 VH-SZ, and 56 NoH-SZ. a
a current substance abuse disorder, and intelligence quotient < 75. SZ with significant extrapyramidal symptoms were excluded. HC with a current or past history of major neurological or psychiatric illness (SCID-I/NP)29 or with a first-degree relative with an Axis-I psychotic disorder diagnosis were also excluded. Symptoms experienced within the last month were rated with scales for the assessment of negative (SANS)30 and positive (SAPS)5 symptoms. The protocol stipulated that symptom assessment ratings be done within 1 month of scanning. Clinicians at each center participated in training sessions to calibrate clinical ratings. Grouping of SZ SZ were sorted into 3 groups based on ratings on SAPS Item #1 that asks if she/he “reports voices, noises, or other sounds that no one else hears” and on SAPS Item #6 that asks if she/he “sees shapes or people that are not actually present.” One group (AH-SZ, n = 50) experienced AH (SAPS Item #1 > 1) but not VH (SAPS Item #6 = 0). One
group (VH-SZ, n = 45) experienced VH (SAPS Item #6 > 1), with most (42/45) also experiencing AH (SAPS Item #1 > 1). One group (NoH-SZ, n = 67) experienced neither (SAPS Item #1 = 0 and SAPS Item #6 = 0). Eighteen SZ were rated “questionable” AH or VH (SAPS Item #1 = 1 or SAPS Item #6 = 1) and were dropped from this analysis because we could not be certain whether or not they experienced AH or VH. As can be seen in table 1, SZ groups did not differ on illness duration, current smoking status, patient socioeconomic status (SES), global delusions, total SANS, or age. These variables were not included as covariates in analyses of connectivity data. AH-SZ and VH-SZ did not differ on severity of AH, tactile hallucination, or olfactory hallucination. Data Acquisition Data were acquired at 7 sites: 6 3T Siemens TIM Trio scanners and 1 3T GE MR750 scanner. The imaging protocol was T2*-weighted AC-PC aligned echo planar Page 3 of 10
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AH-SZ (n = 50)
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imaging sequence (TR/TE 2 s/30 ms, flip angle 77°, 32 slices collected sequentially from superior to inferior, 3.4 × 3.4 × 4 mm with 1 mm gap, 162 frames, 5:38 min). Subjects were instructed to lie still with eyes closed and to stay awake. This scan followed an emotional working memory task. fMRI Image Processing
Statistical Analysis Four separate functional connectivity maps between 4 SOIs (left and right, amygdala [AMYG] and hippocampus [HIPPO]) and all the voxels in the brain were generated using CONN toolbox implemented in Matlab.33 Mean connectivity values (beta weights) in 8 visual cortical ROIs (left and right, BA17, BA18, BA19, fusiform gyrus [FUSI]) were extracted for each SOI for each participant. The ROIs and SOIs were made using WFU-PickAtlas, which provides a method for generating regional masks based on Talairach Daemon database.34 ROIs were dilated by 1° in 3D space, and voxels belonging to adjacent regions were removed. The SOIs (AMYG and HIPPO) were not dilated or edited. The SOIs and ROIs are shown in figure 1. Because of possible site differences in SZ cohorts, particularly in terms of the relative number of SZ reporting VH, we did not want to use site means that included SZ data during modeling and statistical removal of site effects. Instead, we modeled site effects based only on HC data because the inclusion/exclusion criteria led to relatively
Fig. 1. The seeds of interest (SOIs) used in connectivity analysis appear on the left, with amygdala (AMYG) shown in orange and hippocampus (HIPPO) shown in pink. The target regions of interest (ROIs) appear on the right, with BA17 shown in red, BA18 in yellow, BA19 in green, and fusiform (FUSI) in cyan. Details of how these were constructed appear in the text.
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Imaging preprocessing was done using Statistical Parametric Mapping (SPM5). Processing was done as follows: (1) realignment of functional images to the first slice was done using INRIalign. (2) Slice-time correction was done to adjust for timing differences of individual slices within each TR. (3) A method similar to “aCompCor” (anatomic component based noise correction) was used to identify nuisance regressors.31 To this end, time series from each voxel within noise ROIs (white matter, identified in FreeSurfer and eroded by 2 voxels, and CSF derived using SPM anatomical segmentation with P > .95 threshold and 3D nearest neighbor clustering criteria of at least two neighbors) were submitted to principal component analysis (PCA). The number of components to be used as nuisance regressors was calculated using a version of the broken stick method.31 Denoising with these PCA component time series reduced the global signal change of all volumes in the time series of all subjects below the threshold previously used for “motion scrubbing” volumes.32 (4) Normalization of the mean functional image from the realignment step to the Montreal Neurological Institute echo planar image template was done using a 12-parameter affine transformation and 4 × 5 × 4 nonlinear basis functions (the resulting parameters are used to normalize all individual functional images in the time series). (5) Image reslicing to 3 × 3 × 3 mm3 using fourth
Degree B-Spline (SPM5) was done during normalization. (6) Finally, data were spatially smoothed with a Gaussian filter, 8 mm full width half max (FWHM). The influence of head motion as quantified by 6 realignment parameters, and their derivatives and the effect of rest and aCompCor noise components were modeled as nuisance variables in a linear regression to generate a residual time series for every voxel. The residual data were temporally filtered using band pass filters with 0.008–0.09 Hz cutoffs. The same regression and filtering steps were separately applied to averaged, unsmoothed time series of voxels contained within each SOI.
Hyperconnectivity Between the Amygdala and Visual Cortex in Visual Hallucinators
Results ROI extraction Main Effects. Although ROI and SOI were both factors in the 4-way interaction described below, simple main effects of these variables are described here, as well as simple contrasts between groups for each ROI and SOI. There was a main effect of SOI, revealing greater connectivity with AMYG than HIPPO. There was a main effect of ROI, with weaker connectivity to BA17 than BA18 (P = .005) and weaker connectivity to BA18 than BA19 (P = .001). Connectivity to FUSI was not different from other ROIs (all P’s > .10). Without FUSI connectivity in the analysis, we found a significant linear trend (BA17 < BA18 < BA19; F(1,336) = 18.19, P < .0001), which was not different in the different Groups (Group × ROI linear trend: F(1,336) = 0.51, P = .60). Because of the interest in simple effects of Group for AMYG and HIPPO connectivity, we performed simple contrasts between VH-SZ and other groups for AMYG and HIPPO separately. For AMYG, VH-SZ had greater connectivity than AH-SZ (P = .02), NoH-SZ (P = .024), and HC (P = .005). For HIPPO, VH-SZ tended to have greater connectivity than AH-SZ (P = .069) but not than NoH-SZ (P = .39) or HC (P = .45). Finally, we performed simple contrasts between VH-SZ and the other groups for each of the SOIs and ROIs. In
general, VH-SZ had greater connectivity to visual cortex than AH-SZ. VH-SZ had greater connectivity than the other groups to BA18, specifically. Group effects were stronger for AMYG than HIPPO. This is explored in more detail below. Interactions. The 5-way ANOVA results appear in table 2. Only the highest order interactions are parsed, with successive follow-up tests shown beneath significant interactions. There was a 4-way interaction of Group × ROI × ROI-L/R × SOI. This can be seen in the plots of the mean connectivity values in figure 2, where SOI means for AMYG and HIPPO are collapsed across hemisphere because SOI-L/R was not involved in the interaction. This interaction was parsed by inspecting the 3-way interaction (Group × ROI × ROI-L/R) for the 2 SOIs separately. As can be seen in table 2, the 3-way interaction was not significant for HIPPO but was for AMYG. This was parsed by inspecting the 2-way (ROI-L/R × Group) interaction for AMYG connectivity to each of the 4 ROIs separately. The 2-way interaction was only significant for BA18. This was parsed by assessing the effect of Group for the left and right BA18 separately. The effect of Group was significant for right BA18 connectivity (P < .04) though the Group effect was not quite significant for left BA18 (P < .07). Importantly, when considering only the VH-SZ vs AH-SZ comparison, there was greater connectivity between BA18 and AMYG in VH-SZ than AH-SZ in both left (P = .017) and right (P = .014) hemispheres. Inspection of HC connectivity before z-scoring revealed that HC had hyperconnectivity between AMYG and FUSI (P < .001) but not between other visual cortical areas. They also had hyperconnectivity between HIPPO and all the visual ROIs (all P < .001). Although some connectivity values for AH-SZ are negative, as can be seen in figure 2, a 1-sample t test against zero showed none was significant. Symptom Correlations. To test the relationship between VH severity and connectivity values, we performed Pearson’s correlation tests between scores on SAPS Item #6 and connectivity from bilateral AMYG and HIPPO to bilateral visual cortex, summed across ROIs. VH severity was not related to AMYG (r = .19, P = .20) or HIPPO (r = .14, P = .35) connectivity. Additional Analyses. To account for possible effects of antipsychotic medications on AMYG hyperconnectivity in VH-SZ, we included chlorpromazine equivalents37 as a covariate. Because we did not have dosage data for all SZ, this analysis included a smaller sample: AH-SZ = 43, VH-SZ = 32, and NoH-SZ = 57. To simplify this analysis, we collapsed across ROI, ROI-L/R, and SOI-L/R and used a single value for AMYG connectivity for each Page 5 of 10
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more homogenous groups across sites, leaving scanner differences as the main contributor to variation across sites. To achieve this, connectivity data (beta weights) extracted from ROIs were regressed on site (using dummy coding) in a multiple regression model estimated in HC, and the resulting predicted value for each site was saved, as was the standard error of regression from the overall regression model. Subsequently, voxel connectivity data from all subjects were transformed to site-corrected z-scores by subtracting sitespecific HC predicted values from observed connectivity values, with this difference score then being divided by the standard error of regression from the HC site model. In effect, site-corrected z-scores reexpressed all subject connectivity values as deviations, in standard units, from the value expected for a HC at a given site. These site-corrected z-scores served as the unit of analysis for subsequent group comparisons and correlational analyses. Similar methods have been used previously to correct for normal aging effects in structural35 and functional36 MRI data. Group differences in connectivity z-scores were tested for significance in SPSS using a 5-way ANOVA for repeated factors SOI (AMYG, HIPPO), SOI-L/R (Left, Right), ROI (BA17, BA18, BA19, FUSI), and ROI-L/R (Left, Right) and between subjects factor of Group (VHSZ, AH-SZ, NoH-SZ, HC). Groups were compared using simple contrasts. To address connectivity in HC before z-scoring, connectivity values were compared with zero, with Site as a covariate in 1-way ANOVAs.
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Table 2. ANOVA for Connectivity Values Between Seeds of Interest (SOI) and Regions of Interest (ROI) df
F ratio
P Value
Group ROI ROI × Group SOI SOI × Group ROI-L/R ROI-L/R × Group SOI-L/R SOI-L/R × Group SOI × ROI SOI × ROI × Group ROI-L/R × ROI ROI-L/R × ROI × Group SOI × SOI-L/R × ROI SOI × SOI-L/R × ROI × Group SOI-L/R × ROI-L/R SOI-L/R × ROI-L/R × Group SOI-L/R × ROI-L/R × ROI SOI-L/R × ROI-L/R × ROI × Group SOI-L/R × ROI SOI-L/R × ROI × Group SOI × SOI-L/R SOI × SOI-L/R × Group SOI × SOI-L/R × ROI-L/R SOI × SOI-L/R × ROI-L/R × Group SOI × SOI-L/R × ROI-L/R × ROI SOI × SOI-L/R × ROI-L/R × ROI × Group SOI × ROI-L/R SOI × ROI-L/R × Group SOI × ROI-L/R × ROI SOI × ROI-L/R × ROI × Group SOI Hippocampus: ROI-L/R × ROI × Group Amygdala: ROI-L/R × ROI × Group ROI BA17: ROI-L/R × Group BA18: ROI-L/R × Group ROI-L/R Left BA18: Groupa Right BA18: Groupb BA19: ROI-L/R × Group Fusiform: ROI-L/R × Group
3, 336 3, 1008 9, 1008 1, 336 3, 336 1, 336 3, 1008 1, 336 3, 336 3, 1008 9, 1008 3, 336 9, 1008 3, 336 9, 1008 1, 336 3, 336 3, 1008 9, 1008 3, 336 9, 1008 1, 336 3, 336 3, 336 3, 1008 3, 1008 9, 1008 3, 336 3, 336 3, 1008 9, 1008
2.15 5.48 1.37 6.83 1.83 0.05 1.17 3.09 1.22 3.35 1.11 1.01 1.07 0.78 0.39 0.05 1.30 2.29 1.02 0.78 0.29 0.60 1.03 1.93 0.52 1.25 0.53 1.20 1.62 14.65 3.35
.09 .01 .23 .01 .14 .83 .32 .08 .30 .04 .36 .36 .38 .45 .87 .83 .28 .11 .41 .45 .93 .44 .38 .17 .67 .28 .76 .27 .19 AH-SZ (P = .017), VH-SZ > NoH-SZ (P = .068), VH-SZ > HC (P = .014). VH-SZ > AH-SZ (P = .014), VH-SZ > NoH-SZ (P = .010), VH-SZ > HC (P = .013).
a
b
subject. When controlling for dosage, connectivity was greater in VH-SZ than AH-SZ (P = .003) and NoH-SZ (P = .01). In the same smaller sample, connectivity was greater in VH-SZ than AH-SZ (P = .021) and NoH-SZ (P = .025) when not controlling for dosage. Larger doses were associated with greater AMYG connectivity (P = .003), an effect that was not affected by Group (P = .44). To confirm Amad et al,25 we extracted connectivity values between bilateral HIPPO and MPFC and caudate ROIs. Connectivity to neither MPFC (P = .69) nor caudate (P = .90) was greater in VH-SZ than AH-SZ. We also compared AH-SZ with VH-SZ for whole brain connectivity from bilateral AMYG and HIPPO, using Page 6 of 10
significance and extent thresholds of P = .025, and k = 50. For AMYG, there were 2 significant clusters of hyperconnectivity; 1 contained mostly visual areas (P = .003, corrected), and the other contained frontal-temporal areas (P = .001, corrected), specifically inferior frontal gyrus and temporal pole. For HIPPO, there was only 1 cluster (P = .04, corrected), which contained mostly visual areas. These are shown in figure 3. No areas were more connected in AH-SZ than VH-SZ to AMYG and HIPPO. To understand AMYG hyperconnectivity to frontaltemporal areas in VH-SZ, we focused on symptoms that were more severe in VH-SZ than AH-SZ that are also related to auditory function. Thought broadcasting, a
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Source
Hyperconnectivity Between the Amygdala and Visual Cortex in Visual Hallucinators
symptom described as experiencing one’s thoughts being broadcast, was more severe in VH-SZ than AH-SZ (P = .008). It appeared to account for hyperconnectivity to the frontal-temporal cluster because when included as a covariate, only voxels in visual areas remained. Discussion We confirmed our hypothesis that SZ with VH have hyperconnectivity between visual cortex and the amygdala. Hyperconnectivity was strongest in BA18 though
other visual areas had varying degrees of hyperconnectivity to the amygdala. This hyperconnectivity may facilitate retrieval and reactivation of visual memories,38 arguably the raw material of VH. Hyperconnectivity between the amygdala and visual cortex may reflect the ability of these areas to communicate too efficiently with each other, perhaps resulting in excessive conscious experiences20 or VH of negatively charged, threatening images. While we predicted VH would be associated with hyperconnectivity to visual cortex from both the amygdala and Page 7 of 10
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Fig. 2. Means and standard errors are plotted for connectivity values (beta weights) between the amygdala (collapsed across hemispheres) and left and right visual cortex (top, left and right) and between the hippocampus (collapsed across hemispheres) and left and right visual cortex (bottom, left and right). Connectivity to specific ROIs are shown for BA17, BA18, BA19, and FUSI. Values are shown for SZ who reported auditory but not visual hallucinations (AH-SZ), SZ who experienced visual hallucinations (VH-SZ), SZ who experienced neither auditory nor visual hallucinations (NoH-SZ), and healthy controls.
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the hippocampus, hippocampal hyperconnectivity was not as strong as amygdala. Indeed, VH-SZ differed only marginally (P < .07) from AH-SZ and not at all from the other groups. This relative lack of connectivity between the hippocampus and visual cortex suggests that these VH relied less on retrieval of memories than on intrusive negative visual images. The literature generally supports the notion that the hallucinations’ content is consistent with activity in brain regions involved in the experience. For example, in the single patient symptom-capture study, Oertel et al11 documented both hippocampal and visual cortical activations but did not report amygdala activation, perhaps because the patient’s VH were neither negative nor threatening. It is possible that the amygdala is only involved when those conditions are met. Similarly, a symptom-capture study of 8 people with Charles Bonnet syndrome failed to find involvement of the amygdala, and similarly they did not experience negative or threatening hallucinations. Our data suggest hyperconnectivity between the amygdala and visual cortex provides the necessary scaffolding for visual images to have a negative tone. The weak link in this argument is that we did not ask about the content of hallucinations. Although we were unable to exactly replicate Amad et al,25 we similarly found hyperconnectivity in SZ who experienced VH. Although they did not find hyperconnectivity between the hippocampus and visual cortex in their fMRI data, they did report that the visual hallucinators had increased fractional anisotropy in white matter tracts connecting these areas. It is important to emphasize that we have no clinical data indicating whether VH and AH were simultaneous, or appeared to be “talking heads,” to those with both Page 8 of 10
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Fig. 3. Group contrast (VH-SZ > AH-SZ) brain maps showing significant clusters for connectivity from AMYG and HIPPO SOIs. Cross-hair coordinates: x = −54, y = 18, z = −4. No clusters were significant for AH-SZ > VH-SZ.
experiences. However, using auditory cortical SOIs (BA 41, 42, and 22) and our visual cortical ROIs, we found evidence of marginally significant hyperconnectivity (P < .10) between auditory and visual cortices in the 42 SZ who experienced both AH and VH, compared with the group experiencing neither. Perhaps the wiring is in place for the simultaneous experience of auditory and visual percepts in this sample, and, perhaps, this relationship would be significant in people who indeed had simultaneous AH and VH. The whole brain analysis additionally revealed hyperconnectivity between the amygdala and frontal-temporal areas in VH-SZ compared with AH-SZ. Given that both groups experience AH to a similar degree, AH severity could not explain this. However, the severity of thought broadcasting, which was greater in VH-SZ than AH-SZ, was responsible for this hyperconnectivity. Although we cannot disentangle effects of medication dose from the reason the dose was prescribed, it is worth noting that although people taking higher doses of antipsychotic medications had greater connectivity between the amygdala and visual cortex, it did not account for greater connectivity in VH-SZ. People with temporal lobe epilepsy experience AH and VH when the amygdala is stimulated during presurgical brain mapping, prompting the suggestion, “Limbic activation by seizure discharge or electrical stimulation may add an affective dimension to perceptual and mnemonic data processed by the temporal neocortex, which may be required for endowing them with experiential immediacy.”23 Although epilepsy and schizophrenia most likely differ in the etiology of the symptoms, it is possible that similar symptoms are underpinned by similar mechanisms.39 This is reminiscent of the Research Domain Criteria40 initiative to move beyond the boundaries of diagnoses to mechanisms of brain function and ask if the same experience implies the same mechanism, regardless of diagnosis. While data presented here further our understanding about the functional neuroanatomy of VH, future gains would be achieved through inclusion of other groups of people who also experience VH and a better characterization of their phenomenology. Specifically, the role of the amygdala in the emotional content of VH could be confirmed by comparing people with distressing hallucinations, typical of people with psychosis, with those with nondistressing hallucinations, typical of people with Parkinson’s disease.15 There are limitations to this analysis. First, as mentioned throughout, this is a retrospective analysis, and as such, detailed questions about symptoms were not asked. Second, this is not a symptom-capture study but a trait study involving the comparison of SZ with and without hallucinations. Thus, we can say nothing about the current experience of hallucinations. Third, because experience immediately preceding resting scans can influence
Hyperconnectivity Between the Amygdala and Visual Cortex in Visual Hallucinators
Funding Biomedical Informatics Research Network (U24RR021992). Acknowledgments The authors have declared that there are no conflicts of interest in relation to the subject of this study. References 1. David AS. Auditory hallucinations: phenomenology, neuropsychology and neuroimaging update. Acta Psychiatr Scand Suppl. 1999;395:95–104. 2. Blom JD. Hallucinations and other sensory deceptions in psychiatric disorders. In: Jardri R, Cachia A, Thomas P, Pins D, eds. The Neuroscience of Hallucinations. New York, NY: Springer; 2013:43–57. 3. Collerton D, Dudley R, Mosimann UP. Visual hallucinations. In: Blom JD, Sommer IE, eds. Hallucinations. New York, NY: Springer; 2013:75–90. 4. Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13:261–276. 5. Andreasen NC. Scale for the Assessment of Positive Symptoms. Iowa City, IA: University of Iowa; 1984. 6. Fisher DJ, Labelle A, Knott VJ. Auditory hallucinations and the mismatch negativity: processing speech and non-speech sounds in schizophrenia. Int J Psychophysiol. 2008;70:3–15. 7. Fisher DJ, Labelle A, Knott VJ. The right profile: mismatch negativity in schizophrenia with and without auditory hallucinations as measured by a multi-feature paradigm. Clin Neurophysiol. 2008;119:909–921.
8. Fisher DJ, Grant B, Smith DM, Borracci G, Labelle A, Knott VJ. Effects of auditory hallucinations on the mismatch negativity (MMN) in schizophrenia as measured by a modified ‘optimal’ multi-feature paradigm. Int J Psychophysiol. 2011;81:245–251. 9. Ffytche DH, Howard RJ, Brammer MJ, David A, Woodruff P, Williams S. The anatomy of conscious vision: an fMRI study of visual hallucinations. Nat Neurosci. 1998;1:738–742. 10. Allen P, Larøi F, McGuire PK, Aleman A. The hallucinating brain: a review of structural and functional neuroimaging studies of hallucinations. Neurosci Biobehav Rev. 2008;32:175–191. 11. Oertel V, Rotarska-Jagiela A, van de Ven VG, Haenschel C, Maurer K, Linden DE. Visual hallucinations in schizophrenia investigated with functional magnetic resonance imaging. Psychiatry Res. 2007;156:269–273. 12. Meppelink AM, de Jong BM, Renken R, Leenders KL, Cornelissen FW, van Laar T. Impaired visual processing preceding image recognition in Parkinson’s disease patients with visual hallucinations. Brain. 2009;132:2980–2993. 13. Stebbins GT, Goetz CG, Carrillo MC, et al. Altered cortical visual processing in PD with hallucinations: an fMRI study. Neurology. 2004;63:1409–1416. 14. Holroyd S, Wooten GF. Preliminary FMRI evidence of visual system dysfunction in Parkinson’s disease patients with visual hallucinations. J Neuropsychiatry Clin Neurosci. 2006;18:402–404. 15. Barnes J, David AS. Visual hallucinations in Parkinson’s disease: a review and phenomenological survey. J Neurol Neurosurg Psychiatry. 2001;70:727–733. 16. Ibarretxe-Bilbao N, Ramírez-Ruiz B, Tolosa E, et al. Hippocampal head atrophy predominance in Parkinson’s disease with hallucinations and with dementia. J Neurol. 2008;255:1324–1331. 17. Cosmelli D, David O, Lachaux JP, et al. Waves of consciousness: ongoing cortical patterns during binocular rivalry. Neuroimage. 2004;23:128–140. 18. Sergent C, Dehaene S. Neural processes underlying conscious perception: experimental findings and a global neuronal workspace framework. J Physiol Paris. 2004;98:374–384. 19. Melloni L, Molina C, Pena M, Torres D, Singer W, Rodriguez E. Synchronization of neural activity across cortical areas correlates with conscious perception. J Neurosci. 2007;27:2858–2865. 20. Hoffman RE, Fernandez T, Pittman B, Hampson M. Elevated functional connectivity along a corticostriatal loop and the mechanism of auditory/verbal hallucinations in patients with schizophrenia. Biol Psychiatry. 2011;69:407–414. 21. Hebb DO. Organization of Behavior: A Neuropsychological Theory. New York, NY: John Wiley and Sons; 1949. 22. Jardri R, Thomas P, Delmaire C, Delion P, Pins D. The neurodynamic organization of modality-dependent hallucinations. Cereb Cortex. 2013;23:1108–1117. 23. Gloor P, Olivier A, Quesney LF, Andermann F, Horowitz S. The role of the limbic system in experiential phenomena of temporal lobe epilepsy. Ann Neurol. 1982;12:129–144. 24. Jardri R, Pouchet A, Pins D, Thomas P. Cortical activations during auditory verbal hallucinations in schizophrenia: a coordinate-based meta-analysis. Am J Psychiatry. 2011;168:73–81. 25. Amad A, Cachia A, Gorwood P, et al. The multimodal connectivity of the hippocampal complex in auditory and visual hallucinations. Mol Psychiatry. 2014;19:184–191.
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connectivity during rest,41 it is possible that the emotional working memory task could have affected connectivity; however, given the pattern of findings, it would have to have impacted VH-SZ more. Fourth, SZ differ from HC on some key variables, such as SES and education, which could contribute to differences seen between them. However, the SZ groups do not differ from each other on these variables, making them unlikely contributors to hyperconnectivity in VH-SZ. Fifth, we were unable to find a significant correlation between VH severity and hyperconnectivity in the VH-SZ. This could reflect the poor assessment of the phenomenology, with only 1 item being devoted to VH in the interview or to other factors.42 Finally, we do not know about life-long history of symptoms, and possibly different results would emerge by altering the temporal window defining group membership. In summary, we present evidence that SZ who endorse having recently experienced VH have hyperconnectivity between the amygdala and visual cortex. We suggest this hyperconnectivity facilitates access to consciousness of emotional visual images. While VH are generally neglected in clinical assessments in this population, functional connectivity data provide biological support for their distressing reality.
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35. Pfefferbaum A, Lim KO, Zipursky RB, et al. Brain gray and white matter volume loss accelerates with aging in chronic alcoholics: a quantitative MRI study. Alcohol Clin Exp Res. 1992;16:1078–1089. 36. Fryer SL, Woods SW, Kiehl KA, et al. Deficient Suppression of Default Mode Regions during Working Memory in Individuals with Early Psychosis and at Clinical High-Risk for Psychosis. Front Psychiatry. 2013;4:92. 37. Woods SW. Chlorpromazine equivalent doses for the newer atypical antipsychotics. J Clin Psychiatry. 2003;64:663–667. 38. Slotnick SD, Schacter DL. The nature of memory related activity in early visual areas. Neuropsychologia. 2006;44:2874–2886. 39. Cicchetti D, Rogosch FA. Equifinality and multifinality in developmental psychopathology. Dev Psychopathol. 1996;8:597–600. 40. Insel T, Cuthbert B, Garvey M, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–751. 41. Tambini A, Ketz N, Davachi L. Enhanced brain correlations during rest are related to memory for recent experiences. Neuron. 2010;65:280–290. 42. Mathalon DH, Ford JM. Neurobiology of schizophrenia: search for the elusive correlation with symptoms. Front Hum Neurosci. 2012;6:136.
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26. Squire LR. The legacy of patient H.M. for neuroscience. Neuron. 2009;61:6–9. 27. Phelps EA, LeDoux JE. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron. 2005;48:175–187. 28. Vignal JP, Maillard L, McGonigal A, Chauvel P. The dreamy state: hallucinations of autobiographic memory evoked by temporal lobe stimulations and seizures. Brain. 2007;130:88–99. 29. First MB, Spitzer RL, Gibbon MG, Williams JBW. Structured Clinical Interview for DSM-IV-TR Axis I Disorders - Patient Edition (SCID-I/P, 11/2002 revision). New York, NY; 2002. 30. Andreasen NC. The Scale for the Assessment of Negative Symptoms (SANS). Iowa City, IA: University of Iowa; 1983. 31. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37:90–101. 32. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59:2142–2154. 33. Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2:125–141. 34. Lancaster JL, Woldorff MG, Parsons LM, et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10:120–131.
Visual Hallucinations Are Associated With Hyperconnectivity Between the Amygdala and Visual Cortex in People With a Diagnosis of Schizophrenia
Judith M. Ford*,1,2, Vanessa A. Palzes1,2, Brian J. Roach1,2, Steven G. Potkin3, Theo G. M. van Erp3, Jessica A. Turner4,5, Bryon A. Mueller6, Vincent D. Calhoun4,7, Jim Voyvodic8, Aysenil Belger8,9, Juan Bustillo10, Jatin G. Vaidya11, Adrian Preda3, Sarah C. McEwen12, Functional Imaging Biomedical Informatics Research Network13, and Daniel H. Mathalon1,2
*To whom correspondence should be addressed; Psychiatry Service (116D), San Francisco VA Medical Center, 4150 Clement Street, San Francisco, CA 94121, US; tel: 415-221-4810, ext. 4187, fax: 415-750-6622, e-mail: [email protected]
Introduction: While auditory verbal hallucinations (AH) are a cardinal symptom of schizophrenia, people with a diagnosis of schizophrenia (SZ) may also experience visual hallucinations (VH). In a retrospective analysis of a large sample of SZ and healthy controls (HC) studied as part of the functional magnetic resonance imaging (fMRI) Biomedical Informatics Research Network (FBIRN), we asked if SZ who endorsed experiencing VH during clinical interviews had greater connectivity between visual cortex and limbic structures than SZ who did not endorse experiencing VH. Methods: We analyzed resting state fMRI data from 162 SZ and 178 age- and gender-matched HC. SZ were sorted into groups according to clinical ratings on AH and VH: SZ with VH (VH-SZ; n = 45), SZ with AH but no VH (AH-SZ; n = 50), and SZ with neither AH nor VH (NoH-SZ; n = 67). Our primary analysis was seed based, extracting connectivity between visual cortex and the amygdala (because of its role in fear and negative emotion) and visual cortex and the hippocampus (because of its role in memory). Results: Compared with the other groups, VH-SZ showed hyperconnectivity between the amygdala and visual cortex, specifically BA18, with no differences in connectivity among the other groups. In a voxel-wise, whole brain analysis comparing VH-SZ with AH-SZ, the amygdala was hyperconnected to left temporal pole and inferior frontal gyrus in VH-SZ, likely due to their more severe thought broadcasting. Conclusions: VH-SZ have hyperconnectivity between subcortical areas subserving emotion and cortical areas subserving higher order visual
processing, providing biological support for distressing VH in schizophrenia. Introduction Auditory verbal hallucinations (AH) are experienced by about 75% of people with a diagnosis of schizophrenia (SZ).1 Although visual hallucinations (VH) are experienced by about half of those,2 VH are studied less frequently by clinical neuroscientists interested in schizophrenia. This may reflect their prominence in neurological conditions with known etiology, such as Lewy Body Dementia, temporal lobe epilepsy, Parkinson’s disease, and Charles Bonnet syndrome.3 Indeed, common instruments for assessing hallucinations in SZ either neglect the distinction between AH and VH4 or primarily focus on AH.5 Brain imaging studies of hallucinations typically take 2 forms: “state” and “trait”. State studies, or “symptomcapture studies,” involve participants signaling the start and end of a hallucination, and brain activity during periods with and without hallucinations is compared. Trait studies involve comparing groups who tend to have hallucinations to groups who do not,6,7 or involve using hallucination severity scores as regressors in analyses of brain activity across groups.8 State studies suggest involvement of sensory specific areas in the visual hallucinatory experiences of people with Charles Bonnet syndrome9 and in the auditory hallucinatory experiences in SZ.10 In addition, in people with
© The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected]
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Psychiatry Service, San Francisco VA Medical Center, San Francisco, CA; 2Department of Psychiatry, University of California, San Francisco, CA; 3Department of Psychiatry and Human Behavior, University of California, Irvine, CA; 4Mind Research Network, Albuquerque, NM; 5Departments of Psychology and Neuroscience, Georgia State University, Atlanta GA; 6Department of Psychiatry, University of Minnesota, Twin Cities, Minneapolis, MN; 7Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM; 8Brain Imaging and Analysis Center, University of North Carolina-Duke University, Durham, NC; 9Department of Psychiatry, University of North Carolina, Durham, NC; 10Department of Psychiatry, University of New Mexico, Albuquerque, NM; 11 Department of Psychiatry, University of Iowa, Iowa City, IA; 12Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA; 13Functional Imaging Biomedical Informatics Research Network (FBIRN), http://www.birncommunity.org 1
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of interest (SOI) and visual cortical target regions of interest (ROI; BA17, BA18, BA19, fusiform gyrus). We chose the hippocampus because of its reported involvement in VH11,25 and in memory retrieval,26 arguably a critical element of hallucinations. The amygdala was chosen because of its role in emotion and fear processing,27 perhaps contributing to the negative emotional tone characteristic of hallucinations experienced by many SZ.1 Furthermore, direct brain stimulation studies of people undergoing surgical planning for intractable epilepsy indicate that stimulation of both the hippocampus and amygdala provoke AH and VH,23,28 lending further support for the hippocampus and amygdala seeds. We predicted that hyperconnectivity in SZ who experienced VH would be specific to VH and would not be seen in SZ with only AH. We also predicted hyperconnectivity to visual cortex would not be a general feature of schizophrenia and thus would not be seen in SZ who experienced neither VH nor AH. Further, we predicted that hyperconnectivity in SZ with VH would not be seen in HC. Finally, we predicted no difference in connectivity between the other groups because hyperconnectivity to visual areas should only be relevant to the experience of VH, a phenomenon not experienced by the others. To confirm the functional connectivity findings of Amad et al,25 we extracted connectivity values between the hippocampus and MPFC and caudate. Because this analysis was retrospective and detailed symptom data were not collected as part of FBIRN, we were unable to test hypotheses about the content and nature of specific hallucinatory experiences. Methods Participants Data from 180 SZ and 178 HC matched for age, sex, handedness, and race distributions, collected from 7 sites, are included in this analysis. Demographic information about these participants is provided in table 1. Written informed consent, including permission to share deidentified data between centers and with the wider research community, approved by University of California Irvine, University of California Los Angeles, University of California San Francisco, Duke University/ University of North Carolina, University of New Mexico, University of Iowa, and University of Minnesota Institutional Review Boards, was obtained from all study participants. Inclusion criterion for SZ was a diagnosis of schizophrenia based on the Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID-I/P).29 SZ were clinically stable on antipsychotic medication for at least 2 months. SZ and HC were excluded for history of major medical illness, contraindications for MRI, insufficient eyesight to see with normal acuity with MRI compatible corrective lenses, drug dependence in the last 5 years or
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psychosis, areas involved in emotional experience and memory retrieval, such as the amygdala and hippocampus, are also activated by hallucinations.10 For example, a symptom-capture study of VH in 1 person with schizophrenia found activity in higher visual areas corresponding to the content of the hallucinations (faces, bodies, scenes), as well as the hippocampus, possibly related to retrieval of visual images from memory.11 Amygdala activity was not reported, perhaps due to the lack of negative content in the specific VH. Trait studies of people with Parkinson’s disease reveal decreased visual cortical activation to external visual stimulation in people with visual hallucinators,12–14 perhaps suggesting competition for visual resources. These studies did not report involvement of the amygdala or the hippocampus, perhaps because the hallucinatory experiences were not emotional because people with Parkinson’s disease typically have retained insight into the origin of VH.15 However, one study found evidence of selective atrophy of the hippocampal head in Parkinson’s disease patients with VH.16 Functional connectivity reflects temporal correlation of activity across brain regions and has been used to understand the pathophysiology of hallucinations. Connectivity may reflect the ability of areas to communicate efficiently with each other and underpin conscious experience,17–19 with hyperconnectivity perhaps resulting in excessive conscious experiences.20 Connectivity may reflect the scaffolding set up between different brain areas, when they are repeatedly coactive, recalling Hebb’s rule: “units that fire together, wire together.”21 Using resting state functional connectivity analyses, Jardri et al22 combined state and trait approaches to compare people with first-episode psychosis suffering from AH, VH, or concomitant audio-visual hallucination. They noted increased signal in modality-dependent association sensory cortices during AH, VH, and multisensory hallucination. Because of the hippocampal role in VH11,23 and AH,24 Amad et al used a hippocampal seed and reported hyperconnectivity to medial prefrontal cortex (MPFC) and caudate in people with severe AH and VH compared with people with only severe AH. Furthermore, they reported higher white matter connectivity between the hippocampus and visual cortical areas and greater hippocampal hypertrophy. It is unknown how the visual hallucinators would compare to healthy controls (HC) and SZ who have neither AH nor VH. By taking advantage of a large sample of SZ collected as part of the functional magnetic resonance imaging (fMRI) Biomedical Informatics Network (FBIRN) study, we did a retrospective, trait-based, analysis of resting state data to ask whether visual cortex is hyperconnected to limbic areas in SZ who experienced VH within 1 month of scanning. To this end, we did a seed-based connectivity analysis, using the hippocampus and amygdala seeds
Hyperconnectivity Between the Amygdala and Visual Cortex in Visual Hallucinators
Table 1. Demographics of Healthy Controls (HC) and Schizophrenia Patients (SZ) [Mean (SD)]
Demographics Age (y) Socioeconomic scale—subject* Socioeconomic scale—caretaker* Gender (male/female) Handedness (right/left/ambidextrous) Smoking status (smoker/nonsmoker) Duration of illness (y) Diagnosis (n)
VH-SZ (n = 45)
NoH-SZ (n = 67)
HC (n = 178)
39.0 (11.9) 50.8 (12.3)d 34.5 (14.4)d 39 (m), 11 (f) 44 (r), 5 (l), 1 (a) 22 (s), 28 (n) 18.8 (11.3) 38 Paranoid 8 Undifferentiated 2 Residual 2 Disorganized
36.9 (10.8) 50.2 (14.0)d 35.9 (15.2)d 32 (m), 10 (f) 35 (r), 5 (l), 2 (a) 22 (s), 23 (n) 15.9 (12.1) 34 Paranoid 10 Undifferentiated 0 Residual 1 Disorganized
39.8 (12.0) 50.2 (12.5)d 37.3 (14.3)d 49 (m), 18 (f) 66 (r), 0 (l), 1 (a) 27 (s), 40 (n) 17.1 (11.5) 47 Paranoid 10 Undifferentiated 7 Residual 3 Disorganized
37.7 (11.2) 33.2 (12.9)a,b,c 29.9 (14.7)a,b,c 127 (m), 51 (f) 169 (r), 7 (l), 2 (a) 16 (s), 162 (n) n/a n/a
3.3 (1.4)c 2.7 (1.0)ac 1.0 (1.4)c 0.6 (1.1)c 3.3 (0.9)c 2.9 (1.2)c
0 (0)a,b 0 (0)b 0.3 (0.8)b 0.3 (0.9)a,b 0.2 (0.7)a,b 1.3 (1.3)a,b
n/a n/a n/a n/a n/a n/a
33.5 (19.1)
27.7 (17.0)
n/a
328.9 (282.7) 36 3 0 3 3
363.9 (342.6) 52 9 2 2 2
n/a n/a n/a n/a n/a n/a
Symptom measures Scales for the assessment of positive symptoms Auditory hallucinations (Item 1)* 3.5 (1.2)c Visual hallucinations (Item 6)* 0 (0)b Tactile hallucinations (Item 4)* 0.6 (1.1) Olfactory hallucinations (Item 5)* 0.4 (1.1)c Global hallucinations (Item 7)* 3.1 (1.0)c Global delusions (Item 20)* 2.5 (1.3)c Scales for the assessment of negative symptoms Overall total 29.9 (17.3) Antipsychotic medication Chlorpromazine equivalentse 424.0 (442.2) Aypical 43 Typical 3 Both atypical and typical 1 None 3 Unknown 0
*Group ANOVA is significant at P = .05. Significantly different than AH-SZ (t test, P < .05). b Significantly different than VH-SZ (t test, P < .05). c Significantly different than NoH-SZ (t test, P < .05). d Significantly different than HC (t test, P < .05). e Only have chlorpromazine equivalent data for 43 AH-SZ, 32 VH-SZ, and 56 NoH-SZ. a
a current substance abuse disorder, and intelligence quotient < 75. SZ with significant extrapyramidal symptoms were excluded. HC with a current or past history of major neurological or psychiatric illness (SCID-I/NP)29 or with a first-degree relative with an Axis-I psychotic disorder diagnosis were also excluded. Symptoms experienced within the last month were rated with scales for the assessment of negative (SANS)30 and positive (SAPS)5 symptoms. The protocol stipulated that symptom assessment ratings be done within 1 month of scanning. Clinicians at each center participated in training sessions to calibrate clinical ratings. Grouping of SZ SZ were sorted into 3 groups based on ratings on SAPS Item #1 that asks if she/he “reports voices, noises, or other sounds that no one else hears” and on SAPS Item #6 that asks if she/he “sees shapes or people that are not actually present.” One group (AH-SZ, n = 50) experienced AH (SAPS Item #1 > 1) but not VH (SAPS Item #6 = 0). One
group (VH-SZ, n = 45) experienced VH (SAPS Item #6 > 1), with most (42/45) also experiencing AH (SAPS Item #1 > 1). One group (NoH-SZ, n = 67) experienced neither (SAPS Item #1 = 0 and SAPS Item #6 = 0). Eighteen SZ were rated “questionable” AH or VH (SAPS Item #1 = 1 or SAPS Item #6 = 1) and were dropped from this analysis because we could not be certain whether or not they experienced AH or VH. As can be seen in table 1, SZ groups did not differ on illness duration, current smoking status, patient socioeconomic status (SES), global delusions, total SANS, or age. These variables were not included as covariates in analyses of connectivity data. AH-SZ and VH-SZ did not differ on severity of AH, tactile hallucination, or olfactory hallucination. Data Acquisition Data were acquired at 7 sites: 6 3T Siemens TIM Trio scanners and 1 3T GE MR750 scanner. The imaging protocol was T2*-weighted AC-PC aligned echo planar Page 3 of 10
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AH-SZ (n = 50)
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imaging sequence (TR/TE 2 s/30 ms, flip angle 77°, 32 slices collected sequentially from superior to inferior, 3.4 × 3.4 × 4 mm with 1 mm gap, 162 frames, 5:38 min). Subjects were instructed to lie still with eyes closed and to stay awake. This scan followed an emotional working memory task. fMRI Image Processing
Statistical Analysis Four separate functional connectivity maps between 4 SOIs (left and right, amygdala [AMYG] and hippocampus [HIPPO]) and all the voxels in the brain were generated using CONN toolbox implemented in Matlab.33 Mean connectivity values (beta weights) in 8 visual cortical ROIs (left and right, BA17, BA18, BA19, fusiform gyrus [FUSI]) were extracted for each SOI for each participant. The ROIs and SOIs were made using WFU-PickAtlas, which provides a method for generating regional masks based on Talairach Daemon database.34 ROIs were dilated by 1° in 3D space, and voxels belonging to adjacent regions were removed. The SOIs (AMYG and HIPPO) were not dilated or edited. The SOIs and ROIs are shown in figure 1. Because of possible site differences in SZ cohorts, particularly in terms of the relative number of SZ reporting VH, we did not want to use site means that included SZ data during modeling and statistical removal of site effects. Instead, we modeled site effects based only on HC data because the inclusion/exclusion criteria led to relatively
Fig. 1. The seeds of interest (SOIs) used in connectivity analysis appear on the left, with amygdala (AMYG) shown in orange and hippocampus (HIPPO) shown in pink. The target regions of interest (ROIs) appear on the right, with BA17 shown in red, BA18 in yellow, BA19 in green, and fusiform (FUSI) in cyan. Details of how these were constructed appear in the text.
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Imaging preprocessing was done using Statistical Parametric Mapping (SPM5). Processing was done as follows: (1) realignment of functional images to the first slice was done using INRIalign. (2) Slice-time correction was done to adjust for timing differences of individual slices within each TR. (3) A method similar to “aCompCor” (anatomic component based noise correction) was used to identify nuisance regressors.31 To this end, time series from each voxel within noise ROIs (white matter, identified in FreeSurfer and eroded by 2 voxels, and CSF derived using SPM anatomical segmentation with P > .95 threshold and 3D nearest neighbor clustering criteria of at least two neighbors) were submitted to principal component analysis (PCA). The number of components to be used as nuisance regressors was calculated using a version of the broken stick method.31 Denoising with these PCA component time series reduced the global signal change of all volumes in the time series of all subjects below the threshold previously used for “motion scrubbing” volumes.32 (4) Normalization of the mean functional image from the realignment step to the Montreal Neurological Institute echo planar image template was done using a 12-parameter affine transformation and 4 × 5 × 4 nonlinear basis functions (the resulting parameters are used to normalize all individual functional images in the time series). (5) Image reslicing to 3 × 3 × 3 mm3 using fourth
Degree B-Spline (SPM5) was done during normalization. (6) Finally, data were spatially smoothed with a Gaussian filter, 8 mm full width half max (FWHM). The influence of head motion as quantified by 6 realignment parameters, and their derivatives and the effect of rest and aCompCor noise components were modeled as nuisance variables in a linear regression to generate a residual time series for every voxel. The residual data were temporally filtered using band pass filters with 0.008–0.09 Hz cutoffs. The same regression and filtering steps were separately applied to averaged, unsmoothed time series of voxels contained within each SOI.
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Results ROI extraction Main Effects. Although ROI and SOI were both factors in the 4-way interaction described below, simple main effects of these variables are described here, as well as simple contrasts between groups for each ROI and SOI. There was a main effect of SOI, revealing greater connectivity with AMYG than HIPPO. There was a main effect of ROI, with weaker connectivity to BA17 than BA18 (P = .005) and weaker connectivity to BA18 than BA19 (P = .001). Connectivity to FUSI was not different from other ROIs (all P’s > .10). Without FUSI connectivity in the analysis, we found a significant linear trend (BA17 < BA18 < BA19; F(1,336) = 18.19, P < .0001), which was not different in the different Groups (Group × ROI linear trend: F(1,336) = 0.51, P = .60). Because of the interest in simple effects of Group for AMYG and HIPPO connectivity, we performed simple contrasts between VH-SZ and other groups for AMYG and HIPPO separately. For AMYG, VH-SZ had greater connectivity than AH-SZ (P = .02), NoH-SZ (P = .024), and HC (P = .005). For HIPPO, VH-SZ tended to have greater connectivity than AH-SZ (P = .069) but not than NoH-SZ (P = .39) or HC (P = .45). Finally, we performed simple contrasts between VH-SZ and the other groups for each of the SOIs and ROIs. In
general, VH-SZ had greater connectivity to visual cortex than AH-SZ. VH-SZ had greater connectivity than the other groups to BA18, specifically. Group effects were stronger for AMYG than HIPPO. This is explored in more detail below. Interactions. The 5-way ANOVA results appear in table 2. Only the highest order interactions are parsed, with successive follow-up tests shown beneath significant interactions. There was a 4-way interaction of Group × ROI × ROI-L/R × SOI. This can be seen in the plots of the mean connectivity values in figure 2, where SOI means for AMYG and HIPPO are collapsed across hemisphere because SOI-L/R was not involved in the interaction. This interaction was parsed by inspecting the 3-way interaction (Group × ROI × ROI-L/R) for the 2 SOIs separately. As can be seen in table 2, the 3-way interaction was not significant for HIPPO but was for AMYG. This was parsed by inspecting the 2-way (ROI-L/R × Group) interaction for AMYG connectivity to each of the 4 ROIs separately. The 2-way interaction was only significant for BA18. This was parsed by assessing the effect of Group for the left and right BA18 separately. The effect of Group was significant for right BA18 connectivity (P < .04) though the Group effect was not quite significant for left BA18 (P < .07). Importantly, when considering only the VH-SZ vs AH-SZ comparison, there was greater connectivity between BA18 and AMYG in VH-SZ than AH-SZ in both left (P = .017) and right (P = .014) hemispheres. Inspection of HC connectivity before z-scoring revealed that HC had hyperconnectivity between AMYG and FUSI (P < .001) but not between other visual cortical areas. They also had hyperconnectivity between HIPPO and all the visual ROIs (all P < .001). Although some connectivity values for AH-SZ are negative, as can be seen in figure 2, a 1-sample t test against zero showed none was significant. Symptom Correlations. To test the relationship between VH severity and connectivity values, we performed Pearson’s correlation tests between scores on SAPS Item #6 and connectivity from bilateral AMYG and HIPPO to bilateral visual cortex, summed across ROIs. VH severity was not related to AMYG (r = .19, P = .20) or HIPPO (r = .14, P = .35) connectivity. Additional Analyses. To account for possible effects of antipsychotic medications on AMYG hyperconnectivity in VH-SZ, we included chlorpromazine equivalents37 as a covariate. Because we did not have dosage data for all SZ, this analysis included a smaller sample: AH-SZ = 43, VH-SZ = 32, and NoH-SZ = 57. To simplify this analysis, we collapsed across ROI, ROI-L/R, and SOI-L/R and used a single value for AMYG connectivity for each Page 5 of 10
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more homogenous groups across sites, leaving scanner differences as the main contributor to variation across sites. To achieve this, connectivity data (beta weights) extracted from ROIs were regressed on site (using dummy coding) in a multiple regression model estimated in HC, and the resulting predicted value for each site was saved, as was the standard error of regression from the overall regression model. Subsequently, voxel connectivity data from all subjects were transformed to site-corrected z-scores by subtracting sitespecific HC predicted values from observed connectivity values, with this difference score then being divided by the standard error of regression from the HC site model. In effect, site-corrected z-scores reexpressed all subject connectivity values as deviations, in standard units, from the value expected for a HC at a given site. These site-corrected z-scores served as the unit of analysis for subsequent group comparisons and correlational analyses. Similar methods have been used previously to correct for normal aging effects in structural35 and functional36 MRI data. Group differences in connectivity z-scores were tested for significance in SPSS using a 5-way ANOVA for repeated factors SOI (AMYG, HIPPO), SOI-L/R (Left, Right), ROI (BA17, BA18, BA19, FUSI), and ROI-L/R (Left, Right) and between subjects factor of Group (VHSZ, AH-SZ, NoH-SZ, HC). Groups were compared using simple contrasts. To address connectivity in HC before z-scoring, connectivity values were compared with zero, with Site as a covariate in 1-way ANOVAs.
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Table 2. ANOVA for Connectivity Values Between Seeds of Interest (SOI) and Regions of Interest (ROI) df
F ratio
P Value
Group ROI ROI × Group SOI SOI × Group ROI-L/R ROI-L/R × Group SOI-L/R SOI-L/R × Group SOI × ROI SOI × ROI × Group ROI-L/R × ROI ROI-L/R × ROI × Group SOI × SOI-L/R × ROI SOI × SOI-L/R × ROI × Group SOI-L/R × ROI-L/R SOI-L/R × ROI-L/R × Group SOI-L/R × ROI-L/R × ROI SOI-L/R × ROI-L/R × ROI × Group SOI-L/R × ROI SOI-L/R × ROI × Group SOI × SOI-L/R SOI × SOI-L/R × Group SOI × SOI-L/R × ROI-L/R SOI × SOI-L/R × ROI-L/R × Group SOI × SOI-L/R × ROI-L/R × ROI SOI × SOI-L/R × ROI-L/R × ROI × Group SOI × ROI-L/R SOI × ROI-L/R × Group SOI × ROI-L/R × ROI SOI × ROI-L/R × ROI × Group SOI Hippocampus: ROI-L/R × ROI × Group Amygdala: ROI-L/R × ROI × Group ROI BA17: ROI-L/R × Group BA18: ROI-L/R × Group ROI-L/R Left BA18: Groupa Right BA18: Groupb BA19: ROI-L/R × Group Fusiform: ROI-L/R × Group
3, 336 3, 1008 9, 1008 1, 336 3, 336 1, 336 3, 1008 1, 336 3, 336 3, 1008 9, 1008 3, 336 9, 1008 3, 336 9, 1008 1, 336 3, 336 3, 1008 9, 1008 3, 336 9, 1008 1, 336 3, 336 3, 336 3, 1008 3, 1008 9, 1008 3, 336 3, 336 3, 1008 9, 1008
2.15 5.48 1.37 6.83 1.83 0.05 1.17 3.09 1.22 3.35 1.11 1.01 1.07 0.78 0.39 0.05 1.30 2.29 1.02 0.78 0.29 0.60 1.03 1.93 0.52 1.25 0.53 1.20 1.62 14.65 3.35
.09 .01 .23 .01 .14 .83 .32 .08 .30 .04 .36 .36 .38 .45 .87 .83 .28 .11 .41 .45 .93 .44 .38 .17 .67 .28 .76 .27 .19 AH-SZ (P = .017), VH-SZ > NoH-SZ (P = .068), VH-SZ > HC (P = .014). VH-SZ > AH-SZ (P = .014), VH-SZ > NoH-SZ (P = .010), VH-SZ > HC (P = .013).
a
b
subject. When controlling for dosage, connectivity was greater in VH-SZ than AH-SZ (P = .003) and NoH-SZ (P = .01). In the same smaller sample, connectivity was greater in VH-SZ than AH-SZ (P = .021) and NoH-SZ (P = .025) when not controlling for dosage. Larger doses were associated with greater AMYG connectivity (P = .003), an effect that was not affected by Group (P = .44). To confirm Amad et al,25 we extracted connectivity values between bilateral HIPPO and MPFC and caudate ROIs. Connectivity to neither MPFC (P = .69) nor caudate (P = .90) was greater in VH-SZ than AH-SZ. We also compared AH-SZ with VH-SZ for whole brain connectivity from bilateral AMYG and HIPPO, using Page 6 of 10
significance and extent thresholds of P = .025, and k = 50. For AMYG, there were 2 significant clusters of hyperconnectivity; 1 contained mostly visual areas (P = .003, corrected), and the other contained frontal-temporal areas (P = .001, corrected), specifically inferior frontal gyrus and temporal pole. For HIPPO, there was only 1 cluster (P = .04, corrected), which contained mostly visual areas. These are shown in figure 3. No areas were more connected in AH-SZ than VH-SZ to AMYG and HIPPO. To understand AMYG hyperconnectivity to frontaltemporal areas in VH-SZ, we focused on symptoms that were more severe in VH-SZ than AH-SZ that are also related to auditory function. Thought broadcasting, a
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Source
Hyperconnectivity Between the Amygdala and Visual Cortex in Visual Hallucinators
symptom described as experiencing one’s thoughts being broadcast, was more severe in VH-SZ than AH-SZ (P = .008). It appeared to account for hyperconnectivity to the frontal-temporal cluster because when included as a covariate, only voxels in visual areas remained. Discussion We confirmed our hypothesis that SZ with VH have hyperconnectivity between visual cortex and the amygdala. Hyperconnectivity was strongest in BA18 though
other visual areas had varying degrees of hyperconnectivity to the amygdala. This hyperconnectivity may facilitate retrieval and reactivation of visual memories,38 arguably the raw material of VH. Hyperconnectivity between the amygdala and visual cortex may reflect the ability of these areas to communicate too efficiently with each other, perhaps resulting in excessive conscious experiences20 or VH of negatively charged, threatening images. While we predicted VH would be associated with hyperconnectivity to visual cortex from both the amygdala and Page 7 of 10
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Fig. 2. Means and standard errors are plotted for connectivity values (beta weights) between the amygdala (collapsed across hemispheres) and left and right visual cortex (top, left and right) and between the hippocampus (collapsed across hemispheres) and left and right visual cortex (bottom, left and right). Connectivity to specific ROIs are shown for BA17, BA18, BA19, and FUSI. Values are shown for SZ who reported auditory but not visual hallucinations (AH-SZ), SZ who experienced visual hallucinations (VH-SZ), SZ who experienced neither auditory nor visual hallucinations (NoH-SZ), and healthy controls.
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the hippocampus, hippocampal hyperconnectivity was not as strong as amygdala. Indeed, VH-SZ differed only marginally (P < .07) from AH-SZ and not at all from the other groups. This relative lack of connectivity between the hippocampus and visual cortex suggests that these VH relied less on retrieval of memories than on intrusive negative visual images. The literature generally supports the notion that the hallucinations’ content is consistent with activity in brain regions involved in the experience. For example, in the single patient symptom-capture study, Oertel et al11 documented both hippocampal and visual cortical activations but did not report amygdala activation, perhaps because the patient’s VH were neither negative nor threatening. It is possible that the amygdala is only involved when those conditions are met. Similarly, a symptom-capture study of 8 people with Charles Bonnet syndrome failed to find involvement of the amygdala, and similarly they did not experience negative or threatening hallucinations. Our data suggest hyperconnectivity between the amygdala and visual cortex provides the necessary scaffolding for visual images to have a negative tone. The weak link in this argument is that we did not ask about the content of hallucinations. Although we were unable to exactly replicate Amad et al,25 we similarly found hyperconnectivity in SZ who experienced VH. Although they did not find hyperconnectivity between the hippocampus and visual cortex in their fMRI data, they did report that the visual hallucinators had increased fractional anisotropy in white matter tracts connecting these areas. It is important to emphasize that we have no clinical data indicating whether VH and AH were simultaneous, or appeared to be “talking heads,” to those with both Page 8 of 10
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Fig. 3. Group contrast (VH-SZ > AH-SZ) brain maps showing significant clusters for connectivity from AMYG and HIPPO SOIs. Cross-hair coordinates: x = −54, y = 18, z = −4. No clusters were significant for AH-SZ > VH-SZ.
experiences. However, using auditory cortical SOIs (BA 41, 42, and 22) and our visual cortical ROIs, we found evidence of marginally significant hyperconnectivity (P < .10) between auditory and visual cortices in the 42 SZ who experienced both AH and VH, compared with the group experiencing neither. Perhaps the wiring is in place for the simultaneous experience of auditory and visual percepts in this sample, and, perhaps, this relationship would be significant in people who indeed had simultaneous AH and VH. The whole brain analysis additionally revealed hyperconnectivity between the amygdala and frontal-temporal areas in VH-SZ compared with AH-SZ. Given that both groups experience AH to a similar degree, AH severity could not explain this. However, the severity of thought broadcasting, which was greater in VH-SZ than AH-SZ, was responsible for this hyperconnectivity. Although we cannot disentangle effects of medication dose from the reason the dose was prescribed, it is worth noting that although people taking higher doses of antipsychotic medications had greater connectivity between the amygdala and visual cortex, it did not account for greater connectivity in VH-SZ. People with temporal lobe epilepsy experience AH and VH when the amygdala is stimulated during presurgical brain mapping, prompting the suggestion, “Limbic activation by seizure discharge or electrical stimulation may add an affective dimension to perceptual and mnemonic data processed by the temporal neocortex, which may be required for endowing them with experiential immediacy.”23 Although epilepsy and schizophrenia most likely differ in the etiology of the symptoms, it is possible that similar symptoms are underpinned by similar mechanisms.39 This is reminiscent of the Research Domain Criteria40 initiative to move beyond the boundaries of diagnoses to mechanisms of brain function and ask if the same experience implies the same mechanism, regardless of diagnosis. While data presented here further our understanding about the functional neuroanatomy of VH, future gains would be achieved through inclusion of other groups of people who also experience VH and a better characterization of their phenomenology. Specifically, the role of the amygdala in the emotional content of VH could be confirmed by comparing people with distressing hallucinations, typical of people with psychosis, with those with nondistressing hallucinations, typical of people with Parkinson’s disease.15 There are limitations to this analysis. First, as mentioned throughout, this is a retrospective analysis, and as such, detailed questions about symptoms were not asked. Second, this is not a symptom-capture study but a trait study involving the comparison of SZ with and without hallucinations. Thus, we can say nothing about the current experience of hallucinations. Third, because experience immediately preceding resting scans can influence
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Funding Biomedical Informatics Research Network (U24RR021992). Acknowledgments The authors have declared that there are no conflicts of interest in relation to the subject of this study. References 1. David AS. Auditory hallucinations: phenomenology, neuropsychology and neuroimaging update. Acta Psychiatr Scand Suppl. 1999;395:95–104. 2. Blom JD. Hallucinations and other sensory deceptions in psychiatric disorders. In: Jardri R, Cachia A, Thomas P, Pins D, eds. The Neuroscience of Hallucinations. New York, NY: Springer; 2013:43–57. 3. Collerton D, Dudley R, Mosimann UP. Visual hallucinations. In: Blom JD, Sommer IE, eds. Hallucinations. New York, NY: Springer; 2013:75–90. 4. Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13:261–276. 5. Andreasen NC. Scale for the Assessment of Positive Symptoms. Iowa City, IA: University of Iowa; 1984. 6. Fisher DJ, Labelle A, Knott VJ. Auditory hallucinations and the mismatch negativity: processing speech and non-speech sounds in schizophrenia. Int J Psychophysiol. 2008;70:3–15. 7. Fisher DJ, Labelle A, Knott VJ. The right profile: mismatch negativity in schizophrenia with and without auditory hallucinations as measured by a multi-feature paradigm. Clin Neurophysiol. 2008;119:909–921.
8. Fisher DJ, Grant B, Smith DM, Borracci G, Labelle A, Knott VJ. Effects of auditory hallucinations on the mismatch negativity (MMN) in schizophrenia as measured by a modified ‘optimal’ multi-feature paradigm. Int J Psychophysiol. 2011;81:245–251. 9. Ffytche DH, Howard RJ, Brammer MJ, David A, Woodruff P, Williams S. The anatomy of conscious vision: an fMRI study of visual hallucinations. Nat Neurosci. 1998;1:738–742. 10. Allen P, Larøi F, McGuire PK, Aleman A. The hallucinating brain: a review of structural and functional neuroimaging studies of hallucinations. Neurosci Biobehav Rev. 2008;32:175–191. 11. Oertel V, Rotarska-Jagiela A, van de Ven VG, Haenschel C, Maurer K, Linden DE. Visual hallucinations in schizophrenia investigated with functional magnetic resonance imaging. Psychiatry Res. 2007;156:269–273. 12. Meppelink AM, de Jong BM, Renken R, Leenders KL, Cornelissen FW, van Laar T. Impaired visual processing preceding image recognition in Parkinson’s disease patients with visual hallucinations. Brain. 2009;132:2980–2993. 13. Stebbins GT, Goetz CG, Carrillo MC, et al. Altered cortical visual processing in PD with hallucinations: an fMRI study. Neurology. 2004;63:1409–1416. 14. Holroyd S, Wooten GF. Preliminary FMRI evidence of visual system dysfunction in Parkinson’s disease patients with visual hallucinations. J Neuropsychiatry Clin Neurosci. 2006;18:402–404. 15. Barnes J, David AS. Visual hallucinations in Parkinson’s disease: a review and phenomenological survey. J Neurol Neurosurg Psychiatry. 2001;70:727–733. 16. Ibarretxe-Bilbao N, Ramírez-Ruiz B, Tolosa E, et al. Hippocampal head atrophy predominance in Parkinson’s disease with hallucinations and with dementia. J Neurol. 2008;255:1324–1331. 17. Cosmelli D, David O, Lachaux JP, et al. Waves of consciousness: ongoing cortical patterns during binocular rivalry. Neuroimage. 2004;23:128–140. 18. Sergent C, Dehaene S. Neural processes underlying conscious perception: experimental findings and a global neuronal workspace framework. J Physiol Paris. 2004;98:374–384. 19. Melloni L, Molina C, Pena M, Torres D, Singer W, Rodriguez E. Synchronization of neural activity across cortical areas correlates with conscious perception. J Neurosci. 2007;27:2858–2865. 20. Hoffman RE, Fernandez T, Pittman B, Hampson M. Elevated functional connectivity along a corticostriatal loop and the mechanism of auditory/verbal hallucinations in patients with schizophrenia. Biol Psychiatry. 2011;69:407–414. 21. Hebb DO. Organization of Behavior: A Neuropsychological Theory. New York, NY: John Wiley and Sons; 1949. 22. Jardri R, Thomas P, Delmaire C, Delion P, Pins D. The neurodynamic organization of modality-dependent hallucinations. Cereb Cortex. 2013;23:1108–1117. 23. Gloor P, Olivier A, Quesney LF, Andermann F, Horowitz S. The role of the limbic system in experiential phenomena of temporal lobe epilepsy. Ann Neurol. 1982;12:129–144. 24. Jardri R, Pouchet A, Pins D, Thomas P. Cortical activations during auditory verbal hallucinations in schizophrenia: a coordinate-based meta-analysis. Am J Psychiatry. 2011;168:73–81. 25. Amad A, Cachia A, Gorwood P, et al. The multimodal connectivity of the hippocampal complex in auditory and visual hallucinations. Mol Psychiatry. 2014;19:184–191.
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connectivity during rest,41 it is possible that the emotional working memory task could have affected connectivity; however, given the pattern of findings, it would have to have impacted VH-SZ more. Fourth, SZ differ from HC on some key variables, such as SES and education, which could contribute to differences seen between them. However, the SZ groups do not differ from each other on these variables, making them unlikely contributors to hyperconnectivity in VH-SZ. Fifth, we were unable to find a significant correlation between VH severity and hyperconnectivity in the VH-SZ. This could reflect the poor assessment of the phenomenology, with only 1 item being devoted to VH in the interview or to other factors.42 Finally, we do not know about life-long history of symptoms, and possibly different results would emerge by altering the temporal window defining group membership. In summary, we present evidence that SZ who endorse having recently experienced VH have hyperconnectivity between the amygdala and visual cortex. We suggest this hyperconnectivity facilitates access to consciousness of emotional visual images. While VH are generally neglected in clinical assessments in this population, functional connectivity data provide biological support for their distressing reality.
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26. Squire LR. The legacy of patient H.M. for neuroscience. Neuron. 2009;61:6–9. 27. Phelps EA, LeDoux JE. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron. 2005;48:175–187. 28. Vignal JP, Maillard L, McGonigal A, Chauvel P. The dreamy state: hallucinations of autobiographic memory evoked by temporal lobe stimulations and seizures. Brain. 2007;130:88–99. 29. First MB, Spitzer RL, Gibbon MG, Williams JBW. Structured Clinical Interview for DSM-IV-TR Axis I Disorders - Patient Edition (SCID-I/P, 11/2002 revision). New York, NY; 2002. 30. Andreasen NC. The Scale for the Assessment of Negative Symptoms (SANS). Iowa City, IA: University of Iowa; 1983. 31. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37:90–101. 32. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59:2142–2154. 33. Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2:125–141. 34. Lancaster JL, Woldorff MG, Parsons LM, et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10:120–131.
