Not on speaking terms: hallucinations and structural network disconnectivity in schizophrenia

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Not on speaking terms: hallucinations and structural network disconnectivity in schizophrenia

Branislava Ćurčić-Blake­a, Luca Nanetti­a, Lisette van der Meerab, Leonardo Cerliani­ac, Remco Renken­a, Marieke Pijnenborgcd and André Aleman­a


a Department of Neuroscience, Neuroimaging center, University Medical Center Groningen, University of Groningen, A. Deusinglaan 2, 9713AW, Groningen, The Netherlands; b Lentis, Department of Rehabilitation, PO box 128, 9470 AC Zuidlaren, The Netherlands; c Netherlands Institute for Neuroscience, Amsterdam, The Netherlands; d Department of Clinical Psychology, University of Groningen, Grote Kruisstraat 2/1 9712 TS, Groningen, The Netherlands; e Department of psychotic disorders, GGZ Drenthe, Dennenweg 9, 9404 LA, Assen, The Netherlands

Corresponding author:

Branislava Ćurčić-Blake,

Cognitive Neuropsychiatry group

Department of Neuroscience, Neuroimaging Center (NIC)

University Medical Center Groningen, University of Groningen


Auditory verbal hallucinations in schizophrenia have previously been associated with functional deficiencies in language networks, specifically with functional disconnectivity in fronto-temporal connections in the left hemisphere and in interhemispheric connections between frontal regions. Here we investigate whether auditory verbal hallucinations are accompanied by white matter abnormalities in tracts connecting the frontal, parietal and temporal lobes, also engaged during language tasks. We combined diffusion tensor imaging with tract-based spatial statistics and found white matter abnormalities in patients with schizophrenia as compared with healthy controls: the patients showed reduced fractional anisotropy bilateral: in the anterior thalamic radiation, body of the corpus callosum (forceps minor), cingulum, temporal part of the superior longitudinal fasciculus and a small area in inferior fronto-occipital fasciculus (IFOF); and in the right hemisphere: in the visual cortex, forceps major, body of the corpus callosum (posterior parts) and inferior parietal cortex. Compared to patients without current hallucinations, patients with hallucinations revealed decreased fractional anisotropy in the left inferior fronto-occipital fasciculus, uncinate fasciculus, arcuate fasciculus with superior longitudinal fasciculus, corpus callosum (posterior parts – forceps major), cingulate, cortiospinal tract and antherior thalamic radiation. The severity of hallucinations correlated negatively with white matter integrity in tracts connecting the left frontal lobe with temporal regions (uncinate fasciculus, inferior fronto-occipital fasciculus, cingulum, aruate faciculus anterior and long part and superior long fasciculus frontal part) and in interhemispheric connections (anterior corona radiata).

These findings support the hypothesis that hallucinations in schizophrenia are accompanied by a complex pattern of white matter alterations that negatively affect the language, emotion and attention/perception networks.

Key words:

Hallucinations, anatomical connectivity, diffusion tensor imaging (DTI), language network, thalamo-cortical connectivity, fronto-temporal connectivity


Auditory verbal hallucinations (AVH1, or “hearing voices”), are distressing symptoms of schizophrenia that affect the majority of patients and diminish their quality of life (Aleman and Larøi 2008). Recent developments in brain imaging reveal that AVH in schizophrenia are associated with a complicated brain connectivity dysfunction (Jardri et al. 2011; Brown and Thompson 2010) that incorporates both functional and anatomical connections (Allen et al. 2008). Diffusion tensor imaging (DTI) is a relatively novel non-invasive technique to investigate the integrity of anatomical connections in the brain. This method uses a specific sequence of MR imaging to estimate white matter structural integrity as reflected by regional water diffusivity in white matter tracts (Beaulieu 2002). Although several studies have used DTI to investigate anatomical connectivity in schizophrenia, only a handful of them have extended this research to find the underlying associations with hallucinations. In addition, previous studies have chiefly focused on the integrity of only one tract the arcuate fasciculus (AF), neglecting the full network. Even more, the variety of techniques used in these studies has led to inconsistent results (Shergill et al. 2007; Catani et al. 2011; Hubl et al. 2004; de Weijer et al. 2011).

Dysfunction of the language processing network, constituted of Broca’s and Wernicke’s areas, their right hemisphere homologues and the anterior cingulate gyrus (ACC), has been associated with AVH (Allen et al. 2008; Mechelli et al. 2007; Jardri et al. 2011; Kuhn and Gallinat 2010). Several theories of hallucinations rely on the idea of a lack of synchronization between language areas such as Broca’s and Wernicke’s areas (Grossberg 2000; Aleman and Larøi 2008; Hoffman 2007). Indeed, in two previous studies we found decreased functional connectivity among these regions during rest (Vercammen et al. 2010) and during a phonological language task (Curcic-Blake et al. 2012). More specifically, when inner speech is initiated, the strength of the effective connectivity between Wernicke’s and Broca’s areas is negatively associated with hallucinations, and the interhemispheric connections from Broca’s homologue (the equivalent area in the right hemisphere) to Broca’s area are weakened (Curcic-Blake et al. 2012). In the present article we aim to investigate the anatomical underpinnings of these findings, focusing on pathways connecting the regions involved in language processing, namely fronto-temporo-parietal, bilateral frontal (Broca’s area and the right hemisphere homologue) and ACC.

Finding the anatomical pathways that connect specific functional regions is not straightforward. Most available methods investigate separately either functional activation (fMRI) or anatomical connections (DTI), but do not combine the two. However, using intraoperative direct electrostimulation, Duffau and colleagues (Duffau 2008; Mandonnet et al. 2007) were able to distinguish pathways relevant for language tasks by selectively disrupting particular connections during the execution of a task. They identified the inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), AF and uncinate fasciculus (UF; relayed to the inferior longitudinal fasciculus) as pathways that are both chiefly involved in language and in connecting the frontal and temporo-parietal language areas. Also important are the genu of the corpus callosum and the anterior corona radiate (ACR) which connect the left and right frontal cortex (Ranson 1947), and the cingulum which connects the ACC with the posterior brain regions.

We used DTI combined with tract-based spatial statistics (TBSS) (Smith et al. 2006) to investigate these white matter pathways in the context of AVH. DTI-derived measures such as fractional anisotropy (FA) have been proven to reflect the integrity of the axonal membrane and myelin sheet (Beaulieu 2002), as well as the local coherence of myelinated axons (Cercignani and Horsfield 2001). Focusing on FA in the framework of TBSS for assessing the relationship between white matter integrity and the presence of hallucinations in patients with schizophrenia puts the present study in line with previous work on neuropsychiatric conditions  (Benedetti et al. 2011).



Patients with a DSM-IV diagnosis of schizophrenia and healthy controls participating in a study of insight into psychosis (van der Meer et al. 2012) were included in our DTI study. We selected only right handed subjects, because handedness has previously been proven to influence the brain lateralization and the thickness of fibers (Parker et al. 2005) but not consistently for left-handed people (Knecht et al. 2000). To confirm the diagnosis of patients and the absence of psychiatric disorders in healthy controls, all subjects were assessed by the Mini International Neuropsychiatric Interview-Plus (Sheehan et al. 1998). The patients were divided into two groups according to item M6 of this interview: patients who either never experienced and/or did not have any AVH in the last month (NoAVH group), and patients who had AVH in the past month (AVH group). The severity of symptoms was determined by the Positive and Negative Syndrome Scale (PANSS) interview (Kay et al. 1987). Two patients were excluded because their reported presence of AVH was inconsistent with the severity (PANSS P3 item score of less than 3, indicating the absence of hallucinations  (Kay et al. 1987), together with a MINI interview item M6 indicating the presence of hallucinations). To clarify this issue, we aimed to ensure that the patients in the AVH group had hallucinations in the auditory verbal modality, regardless of other modalities (visual or tactile). Similarly, we aimed to ensure that the NoAVH group had no AVH in the month prior to the study and some of them have never experienced AVH (n=6). Insight into psychosis was assessed using the Schedule for the Assessment of Insight - Expanded Version - SAI-E (Kemp and David 1997). All but two patients were taking at least one type of antipsychotic medication. Medication effects were included in additional analysis according to a standardized quantitative method for comparing dosages of different drugs  (Andreasen et al. 2010). Each medication dose was expressed in equivalent doses of haloperidol.

This study was approved by the Medical Ethical Committee of the University Medical Hospital in Groningen. All the participants have signed written informed consent. They received a monetary compensation (45 euros) upon their participation.

Imaging and data analysis

Diffusion-weighted images were acquired using a 3T Philips Intera MRI scanner (Philips, Best, The Netherlands) with an 8-channel SENSE head coil using a single-shot pulsed gradient spin echo EPI sequence (TR=7621 ms, TE=113 ms, SENSE factor 3, P-reduction, and POS factor of 1). Fifty-one slices were acquired with the following parameters: field of view 240 x 240 mm; matrix size 128 x 128; image resolution 1.875x1.875x2 mm3 without gap. In total, 67 volumes were acquired per subject, seven without diffusion weighting (b0=0 s/mm2; averaged automatically by the scanner) and 60 volumes with diffusion weighting (b=4000 s/mm2) along 60 isotropically distributed directions.

Eddy current and motion artifacts were corrected for, and all diffusion images were aligned to the reference volume (first b0 image) using the FSL toolbox ( Subsequently, voxels related to the brain were separated from the skull using the FSL Brain Extraction Tool (BET) and the diffusion tensor was fitted to the images using FSL FDT Diffusion to estimate diffusion-tensor parameters and related indices of white matter integrity (FA). Maps of the FA parameter were fed to the TBSS processing pipeline (Smith et al. 2006).

The TBSS method involves the normalization of diffusion weighted images in such a way that the center of the main tracts is aligned for each subject, where between-group differences are most informative and less likely due to partial volume artifacts. The use of this method decreases the likelihood of observing differences between groups that arise from partial volume artifacts. This increases the statistical power of the analysis, enabling the detection of medium and small effects, which are more frequent in white matter studies of neuropsychiatric syndromes in comparison to neurological conditions. At this point one subject was excluded from further analysis since its overall FA (within the white matter skeleton) was more than three standard deviations lower than the average of the overall FA value within the patient group. FA is very sensitive to unspecific noise (Bastin et al. 1998; Alexander et al. 2007), thus such extreme value is usually indicative of some unknown measurement artifacts such as movement. We carried out a) voxelwise and b) regions of interest (ROI) analysis, as described below.

        1. Voxelwise comparison

Within the white matter tract skeleton we estimated the voxelwise FA differences between controls and schizophrenia patients, as well as within the patient group. Inference was carried out using the FSL randomise tool for nonparametric permutation testing: between-group FA values were compared with the corresponding null distributions estimated by exchanging subject labels 5000 times. As a standard procedure, the age of the subjects was entered as an additional regressor. The additional covariates that were determined significant by ANCOVA (see below) were entered in comparisons between two patient groups. The final p threshold of 0.05 was corrected for multiple comparisons using Threshold-Free Cluster Enhancement  (Smith and Nichols 2009) (TFCE) , corresponding to a FWE correction (p=0.05).
        1. ROI analysis and covariates

Masks for regions of interest (ROI; see Figure 1) were constructed as the intersection of the mean FA skeleton with the masks for particular white matter tracts. Masks for most of the ROIs were obtained from the following JHU DTI-based white-matter atlases (Wakana et al. 2007): ICBM-DTI-81 white matter labels for ACR left and right, genu and cingulum; JHU-white matter tractography atlas for IFOF, UF and SLF. The AF is not listed in the atlases available with the FSL software. Therefore, masks for the AF were obtained from the Natbrainlab atlas for white matter tracts (Thiebaut de Schotten et al. 2011) consisting of three AF sections: the anterior, long and posterior AF (Figure 1). The average FA per ROI was then calculated for each subject from their FA skeletons. In order to determine which covariates to take into account in the follow-up correlation analysis, ANCOVA was performed on the averaged FA per ROI with hallucination group (AVH or NoAVH) as a fixed factor and age, duration of illness, medication effects and SAI-E scores as additional explanatory variables. Age is standardly used as a covariate in white matter tract analysis because it strongly influences integrity of white matter  (Jones et al. 2006; de Weijer et al. 2011). Duration of illness was recently acknowledged as an important confound as there is evidence that the duration of schizophrenia illness affects the FA values  (Mori et al. 2007; Rotarska-Jagiela et al. 2009). Furthermore, medication was previously reported as a possible source of influence on the white matter integrity, although this is still under debate (Kubicki et al. 2005). To include it as a covariate, for each subject we calculated the medication quantity equivalent dose of haloperidol according to Andreasens’s formula for antipsychotic medication (Andreasen et al. 2010). For 9 patients, for one type of medication (often patients used combined medication) the dosage was unknown. In those cases we averaged quantity for this particular medication and then calculated the haloperidol equivalent. In 4 cases, patients also received antipsychotics not listed in the article (Andreasen et al. 2010), efexor (venlafaxine; 3x) and orap (1x). For them we used averaged medication quantities across all subjects. Finally, because the patients were selected according to their insight rating – SAI-E score, we also entered SAI-E scores in ANCOVA.

The averaged FA values per subject for each region were subsequently entered into a partial correlation analysis in order to investigate the correlation between the P3 score and ROI value using SPSS package software. An FDR correction for multiple comparisons was performed on each p-value from the correlation analysis.



The three groups of subjects (healthy controls, schizophrenia patients with and without current hallucinations) did not significantly differ in age, gender, education level, negative PANSS score or general symptom severity ( details in Table 1). However, the two patient groups differed in their positive PANSS scores. This was expected as this scale contains the P3 item for hallucinations. In the group of patients without current hallucinations, 6 patients had never experienced AVH before.

White matter integrity differences between schizophrenia patients and healthy subjects

The results of voxel-wise TBSS comparison (p<0.05 TFCE corrected) were in accordance with previous reports (Ellison-Wright and Bullmore 2009). The schizophrenia patients exhibited bilaterally decreased FA values in the following regions: anterior thalamic radiation (ATR, site of the IFOF / UF / anterior corona radiate (ACR)); body of the corpus callosum (forceps minor); cingulum (posterior parts); temporal part of the SLF and a small area in the IFOF. In the right hemisphere, schizophrenia patients had decreased FA in the visual cortex- optic radiation, forceps major, body of the corpus callosum (posterior parts), and inferior parietal lobule / auditory cortex. The results are summarized in Figure 2.

Hallucinations and FA

TBSS tracts of 32 right-handed schizophrenia patients were entered for the voxel-wise and ROI patient comparison. We first compared patients using the voxel-wise TBSS comparison (p<0.05 TFCE corrected). The confounding effects of age and duration of illness were accounted for by introducing their respective scores as covariates. Patients with current hallucinations had significantly decreased FA on higher threshold (p<0.02 TFCE corrected) in the left hemisphere (see Figure 3): anterior IFOF, UF, ACR, AF especially the anterior and long parts of the AF (branches reaching frontal regions such as BA44, and temporal regions), callosal body (medial and posterior part - the forceps major), the cingulum, corticospinal tract and the anterior thalamic radiation. In addition, on a lower threshold (p<0.05 TFCE corrected) patients with current hallucinations had decreased FA in several regions in the right hemisphere including anterior part of AF, parts of SLF, ACR, cingulum and corticospinal tract.

Covariate analysis revealed that age affect the FA in IFOF and anterior AF and only marginally in other tracts (see Online Resource 1). However, the duration of illness was significant for several tracts: the anterior AF, the IFOF, the cingulum and posterior and long portions of the AF (trend). Insight and medication revealed no effect on the FA. Therefore, in addition to the standard age covariate we also included the duration of illness in all our analysis. Post-hoc contrast of group differences between patients with and without current hallucinations revealed a significant decrease in FA in the left IFOF (F(1,27)=10.85; pFDR=0.027), left ACR (F(1,27)=847; pFDR=0.036), left UF (F(1,27)=7.84; pFDR=0.021), left cingulum (F(1,27)=6.78; pFDR=0.037), anterior part of AF (F(1,27)=6.35; pFDR=0.036), posterior part of AF (F(1,27)=5.81; pFDR=0.038) and SLF (F(1,27)=5.79; pFDR=0.033).

Partial correlation ROI analysis (right handed hallucinations patients, n=17; no hallucinations, n=14) revealed a negative correlation of FA and P3 in the left IFOF, UF, cingulum, left and right ACR, anterior and long part of AF and SLF (see Table 2 for r values and significance). We found that the association between AF impairment and hallucination severity is most evident in the anterior part of the AF. Details of partial correlation analysis and FDR correction calculation are given in Online Resource 2. The distribution of FA values in the ROIs is depicted in Figure 4.


We found that alterations of white matter in tracts that connect frontal and temporal areas (especially in the left hemisphere), as well as in tracts involved in inter-hemispheric communication, are associated with hallucination severity in schizophrenia patients. Here we point that anatomical impairment does not merely correspond to the presence/absence of hallucinations. Rather, the extent of the anatomical impairment is directly correlated with the severity of the hallucinations. More specifically, patients with hallucinations had smaller FA values than patients without current hallucinations in tracts connecting frontal and temporal areas involved in language (the uncinate fasciculus (UF), inferior fronto-occipital fasciculus (IFOF), the superior longitudinal fasciculus (SLF) and anterior and long arcuate fasciculus (AF), as well as in tracts involved in interhemispheric communication, namely the bilateral anterior corona radiate (ACR) and posterior parts of the corpus callosum. The finding of fronto-temporal and inter-hemispherical deficiencies (see Figure 3 and Table2) supports theories in which hallucinations arise from the erroneous attribution of internally generated information (Friston and Frith 1995) such as inner speech, accompanied by disrupted fronto-temporal connections between language processing areas (Allen et al. 2008).

FA is a measure of the overall directionality of water diffusion and is largest when there is a clear diffusion direction (such as in white matter tracts) and lowest in media where there is little restriction to diffusion (such as cerebrospinal fluid). Thus, a decrease in FA values between groups reflects diminished connections, most probably due to the orientation or number of axons, or reduced integrity of the axonal or myelin sheet (Beaulieu 2002). It has previously been argued that decreased FA reflects a decrease in anatomical connectivity (Rotarska-Jagiela et al. 2009). We found not only that all schizophrenia patients exhibited significantly decreased FA values in comparison to healthy controls, but also that this decrease became more pronounced as the hallucinations score increased. This suggests that the anatomical connections are disrupted even more in patients with hallucinations. The abnormalities in the fronto-parietal tracts and interhemispheric tract involved in language processing (Figure 2) are consistent with our previous investigation of functional connectivity in the language network (Curcic-Blake et al. 2012). We found specifically that during a phonological task, patients with AVH showed significantly reduced connectivity from Wernicke’s to Broca’s areas and a trend toward a reduction in connectivity from the homologues of Broca’s and Wernicke’s areas to Broca’s area in the right hemisphere. In other words, patients without AVH had intermediate functional connectivity strengths, similar to the anatomical connectivity strengths found in the current study.

Our finding of diminished connectivity between frontal and temporo-parietal regions also yields information that may complement the so-called corollary discharge hypothesis (Feinberg and Guazzelli 1999; Frith 2005). This hypothesis states that the improper source attribution of voices in patients with hallucinations can be explained in terms of insufficient top-down feedback and control from higher cognitive areas (such as Broca’s region) toward the primary and secondary auditory areas (such as Wernicke’s region). Indeed, recent experimental studies point toward a delay in this top-down control (Hoffman et al. 2011; Whitford et al. 2011). Consequently, our findings may suggest that abnormal fronto-temporoparietal connections in patients with hallucinations are responsible for the decreased functional connectivity between these areas, a putative underlying mechanism of reduced corollary discharge.

Two fasciculi of the left hemisphere, the IFOF and the UF, both belong to the language network and were found significantly altered in schizophrenia patients. The IFOF bundle connects the frontal and temporal regions, more specifically the frontal lobe with the posterior portion of the occipital gyri via the posterior temporo-basal area (Martino et al. 2010). The IFOF has only recently been identified as an important connection for the semantic processing of language (Duffau 2008). Next to the AF, which is involved in phonological language processing, Duffau describes the IFOF as the ventral route for language processing. A clear anatomical, also DTI-oriented, description of this important connection appeared only recently in literature (Forkel et al. 2012), who combined a thorough literature review with DTI tractography of the IFOF. IFOF has also important role in both primary visual processing (ffytche and Catani, 2005) visual imaginery such as visual hallucinations (Chechlacz et al. 2012) or visualization of music (Zamm et al. 2013) , visual awareness (Amad et al. 2013) and processing of emotions (Baggio et al., 2012; Xu et al., 2012). Specifically, Forkel and colleagues outlined how IFOF connects two branches of the frontal lobe, namely the inferior frontal gyrus (where Broca’s area is placed) and the medial frontal gyrus. They further described the route of this fasciculus as ventral, passing the temporal lobe and ending in the occipital lobe. Although previous studies found indications that the IFOF plays a role for schizophrenia (Clark et al. 2011), the direct involvement of the IFOF in AVH is investigated in our current work is the first time.

The left UF is also a pathway that connects the temporal regions and the frontal lobe, in particular the inferior frontal gyrus. The UF is traditionally considered to be part of the limbic lobe (Von Der Heide et al. 2013) and its function is not entirely clear. Recently, it has been found that the UF is involved in the semantic processing of language (Catani et al. 2013), an alternative route to the IFOF for language processing (Duffau et al. 2009). However, the role of the UF is still under debate, with evidence suggesting that it acts as a pathway for creating mnemonic associations involving the processing of language, visual and auditory information (von der Heide et al. 2013). Our findings of abnormalities in the left UF in schizophrenia patients compared to healthy controls are consistent with the results of Hubl et al. (2004) and de Weijer et al. (de Weijer et al. 2011). Our finding that the degree of FA reduction in the UF is associated with the severity of hallucinations is, however, an original and novel finding of the present study.

We found a negative correlation between FA in both the SLF and the anterior and long part of AF, and the severity of hallucinations. The SLF is a complex association tract that links multiple frontal, temporo-parietal and occipital regions (Makris et al. 2005). It is considered the most important fasciculus in language processing because it provides direct connections between the language regions Broca’s and Wernicke’s areas. According to Makris (Makris et al. 2005), the AF, which is often investigated in studies of auditory hallucinations, is part of the SLF originating in the dorsal prefrontal cortex (Brodmann areas 6 and 46), passing around the caudal Sylvian fissure and ending in the caudal area of the superior temporal sulcus.

Our results are in agreement with the findings of Catani and colleagues (Catani et al. 2011) who performed exhaustive tractography analysis on the AF and observed that schizophrenia patients with hallucinations showed reduced FA in long segment of the left AF consistent with our findings. They were able to examine details of fibers within the AF tract, and to pinpoint the posterior temporal and anterior regions in the inferior frontal and parietal lobes where the differences occurred. However, the findings of this study are not entirely consistent with the previous literature. For example, Shergill et al. (Shergill et al. 2007) reported a positive association of FA along the AF with hallucinations, where Hubl et al. found both increased and decreased FA values in the left AF when patients with and without hallucinations were compared (evident from Figure 5A (1 and 2) in their article) (Hubl et al. 2004). On the other hand, de Weijer and colleagues (de Weijer et al. 2011) did not find any direct correlation between the severity of hallucinations and the FA values in the left AF after excluding the influence of age. However, they found that the magnetic transfer ratio (MTR) was increased in patients with hallucinations while FA was negatively correlated with MTR, thus supporting our findings.

The reasons for these discrepancies might lie in the different data acquisition methods used in the above studies (line-by-line spin echo sampling by Hubl et al.), the difference in analysis methods and the different questionnaires (BPRS was used by Shergill et al.). Another important aspect is that the duration of illness may partially explain the reduced FA in schizophrenia patients (Mori et al. 2007). Indeed, Rotarska-Jagiella et al. (Rotarska-Jagiela et al. 2009) found positive correlations of the duration of illness with the FA values in the bilateral arcuate fasciculus and negative correlations in the corpus callosum, occipitofrontal fasciculus and other areas.

The anterior corona radiata (ACR) and the posterior corpus callosum are also the loci of some significant differences between hallucinating and non-hallucinating schizophrenia patients. The ACR consists of a mixture of various association, projection and callosal fibers, and partially connects the corpus callosum with the cortex (Wakana et al. 2004), thus providing an extension to the interhemispheric connection. It has been implied that the ACR connects the anterior cingulate cortex (ACC) with other brain structures (Tang et al. 2010) and is involved in attention networks (Niogi et al. 2010). We found an association between the bilateral ACR and hallucination severity. The majority of the left ACR mask overlapped with the differences between schizophrenia patients and healthy controls, suggesting that these differences in the left hemisphere are not only a characteristic of schizophrenia but also to some extent of hallucinations. However, the differences observed in the right ACR are specific for hallucinations. Similarly, we found significant differences in the voxel-wise comparison of the two patient groups in the posterior parts of the corpus callosum. These findings are consistent with that of Knochel and colleagues who reported that hypoconnectivity in the corpus callosum is associated with the severity of hallucinations (Knochel et al. 2012).

Similarly consideration can be made about the cingulum, which is related to the anterior cingulate (also involved in attention and sensory decision making, as mentioned above). An impeded connection along the left cingulum might contribute to the overall impedance of this sensory processing. The ACC provides ‘the interface for emotions’; it is known to be important in schizophrenia (Barch and Dowd 2010). In line with these findings Vercammen et al. revealed a reduction in coupling between temporo-parietal junction and ACC in patients with AVH. We found decreased FA values in the left cingulate of patients in the hallucination group compared to patients without current hallucinations, a result that is in agreement with Hubl and colleagues (Hubl et al. 2004). These studies are consistent with theories of decreased attribution of agency, thus decreased awareness of one’s own volitional actions (in this case inner speech production) in AVH patients  (Frith 1996).

A number of limitations of our study deserve discussion. First, besides acting as an indicator of white matter integrity, FA might be influenced by other factors such as the thickness of fibers (Beaulieu 2002). However, in our TBSS analysis the FA values were extracted from regions belonging to a mean estimated skeleton mask. Thus, variations in fiber thickness beyond the skeleton do not affect our results. This also implies that our method is unsuitable for the investigation of fiber thickness. Second, DTI does not differentiate between efferent and afferent projections, thus it does not provide any information about the causality and directionality of the connections. Third, we investigated differences between patients with and without current hallucinations according to the PANSS and MINI interviews. Patients were considered to be without current hallucinations if none were reported in the month prior to scanning. However, they may have experienced hallucinations earlier in their illness, and thus may still have had trait characteristics associated with hallucinations. This implies that our conclusions are limited to the severity of hallucinations rather than their presence/absence, especially because schizophrenia patients who have never experienced hallucinations are difficult to find. Fourth, we did correct for the dose of medication in schizophrenia patients rounding the doses that were missing for 9 patients. However, all these patients were medicated, and we used standard method for filling in unknown parameters (by means of specific mean values). This is recommended method because it minimizes bias while maintaining the degrees of freedom as compared to simple omitting the data  (Rubin et al. 2007). Furthermore, a previous study (Seok et al. 2007) found that controlling for medication dosage did not change the correlation between FA values in the SLF and the hallucination severity, in line with our findings that medication dosage did not affect the FA values in any ROI.

All the fasciculi investigated in this paper are considered to be involved in language processing. However, their function is often much broader and encompasses the processing of emotions (cingulate, ACR), visual information (IFOF) or episodic memory (UF). Consequently, AVHs are also associated with broader functional deficiencies, such as cognitive control and emotional processing (Aleman and Laroi, 2008). Future studies should investigate the role of these regions in more detail. For example, the AF seems to be more asymmetric in men (Catani and Mesulam, 2008) and the lateralization of the UF is still under debate (Highley et al. 2002; Thiebaut de Schotten et al. 2011). Therefore, an important future strategy will be to combine the volumetric asymmetries of these regions in relation to language

To conclude, our results suggest that hallucinations in schizophrenia patients are associated to a complex set of white matter abnormalities that involve at least three distinct systems: the language network, putatively in the 'inner speech' domain; and the central in attention and perception loop; and the limbic system, providing the emotional edge. Additionally, we advance the hypothesis that the severity of the hallucinations experienced by schizophrenia patients is a direct function of the extent of the impairment affecting the anatomical connectivity within these three systems.


Table 1

Mean (SD)



Schizophrenia patients


Schizophrenia patients





3 groups


Age in years

34 (11)

34 (12)

35 (11)

F(2,42) =0.02 (0.98)

Gender males




χ­­2(2,42)=0.87 (0.65)


5.7 (1.0)

5.2 (1.1)

5.2 (1.2)

F(2,42) = 0.7 (0.35)

PANSS pos.

11.4 (4.4)

16.9 (4.0)

U=38 (0.001)

PANSS neg.

13.9 (4.5)

12.8 (4.3)

U= 102 (0.19)

PANSS gen.

27.7 (4.7)

29.7 (6.4)

U= 99 (0.43)

PANSS tot.

53.1 (9.4)

59.4 (12.0)

U = 82 (0.14)


14.4 (5.4)

11.6 (6.5)

T(29) = 1.3 (0.20)

Duration of illness in years

12.6 (8.0)

15.3 (10.4)

T(29) = -0.8 (0.44)

Medication [mg] haloperidol equivalent

6.9 (5.7)

7.4 (4.8)

U = 102.5 (0.51)

Table 1. Demographic data of subjects. The left column lists the demographic variables. The second to fourth columns from the left show average values of the variables across the group, with their standard deviations in brackets. Education level was rated according to a six point scale defined by Verhage, which ranges from primary school (1) to university level (6). Non-parametric tests were used to test the group difference for PANSS (Mann-Whitney test) and gender (Chi-square).

        1. Table2













































Table 2. Results of ROI analysis. The mean FA values per subject were correlated with the P3 scores after the effects of age and illness duration were accounted for by means of partial correlations.


The authors thank G.R. Blake for his comments on earlier versions of the manuscript. We thank psychiatrists Dr. R. Bruggeman and Dr. H. Knegtering for their help with patient inclusion. Our thanks go especially to A. de Vos and J. van der Velde for help with data sorting and to S. Chalavi for discussions on analysis. This research was supported by a EURYI Award from the European Science Foundation (no. 044035001) awarded to A.A.

The authors declare that they have no competing financial interests in relation to the work described.

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Figure legends

Fig 1 Masks for ROI analysis: Uncinate fasciculus (yellow in A); Anterior Corona Radiata (ACR; Left red, Right blue); Genu (green); Cingum (light blue); SLF (Green in c); anterior AF (AFant; dark orange in C and D); longitudinal AF (AFlong; light orange in C and D); posterior AF (AFpost; light pink in C and D); overlap between SLF and longitudinal AF (yellow in C); Inferior fronto-occipital fasciculus (IFOF; pink). Cross-sections, MNI coordinates in (a) at z=-8, axial view; (b) at z=0, axial view; (c) at z=34, axial view; (d) at x=-45 coronal view.

Fig 2 Results of voxel-wise analysis: comparison of healthy controls and schizophrenia patients. In figure panels the following are depicted: AThR left / Anterior limb of internal caplsuae (­z = -3 & z = 5); IFOF (z = 5 & x = -24); Forceps minor / genu (z = 10 & z = 17); Forceps major (z = 17); cingulum (z = 17 & x = -16 & x = -12); Corpus callosum (z = 30 & x = -6 & x = 2 & x = 27); SLF right (z = 30 & x = -12); Primary auditory cortex (z = 5); optic radiation (x = 27).

Fig 3 Results of voxel-wise analysis: comparison of hallucinating and non-hallucinating patients. The following are depicted: z= -6: The anterior part of IFOF; z= -5: the UF; z= 20: The posterior part of IFOF, anterior thalamic radiation and SLF/AF branching frontal; z = 33: The cingulum and the SLF/AF branching temoral; y = -45: posterior callosal body; y = 15: The callosal body, SLF, anterior thalamic radiation, the anterior part of IFOF and UF; y = -20: Corticospinal tract and SLF temporal.

Fig 4 Block diagram of FA values in ROI for hallucinating (dark gray) and non-hallucinating group (light gray). Significant differences revealed by post hoc analysis of ANCOVA (the age and duration of illness were accounted for) are indicated by asterisk (*).

1Abbreviations, in alphabetical order: ACC: anterior cingulate cortex. ACR: anterior corona radiata. AF: anterior fasciculus. ATR: anterior thalamic radiation. AVH: auditory verbal hallucinations. DTI: diffusion tensor imaging. IFG, inferior frontal gyrus. IFOF: inferior fronto-occipital fasciculus. ILF: inferior longitudinal fasciculus. FA: fractional anysotropy. ROI: region of interest. SLF: superior longitudinal fasciculus. TBSS: tract-based spatial statistics. TFCE: threshold free cluster enhancement UF: uncinate fasciculus.

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