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Brain structure in people at ultra-high risk of psychosis, patients with first-episode schizophrenia, and healthy controls: a VBM study

Schizophrenia Research, 2-3, 161, pages 169 - 176


Early intervention research in schizophrenia has suggested that brain structural alterations might be present in subjects at high risk of developing psychosis. The heterogeneity of regional effects of these changes, which is established in schizophrenia, however, has not been explored in prodromal or high-risk populations. We used high-resolution MRI and voxel-based morphometry (VBM8) to analyze grey matter differences in 43 ultra high-risk subjects for psychosis (meeting ARMS criteria, identified through CAARMS interviews), 24 antipsychotic–naïve first-episode schizophrenia patients and 49 healthy controls (groups matched for age and gender). Compared to healthy controls, resp., first-episode schizophrenia patients had reduced regional grey matter in left prefrontal, insula, right parietal and left temporal cortices, while the high-risk group showed reductions in right middle temporal and left anterior frontal cortices. When dividing the ultra-high-risk group in those with a genetic risk vs. those with attenuated psychotic symptoms, the former showed left anterior frontal, right caudate, as well as a smaller right hippocampus, and amygdala reduction, while the latter subgroup showed right middle temporal cortical reductions (each compared to healthy controls). Our findings in a clinical psychosis high-risk cohort demonstrate variability of brain structural changes according to subgroup and background of elevated risk, suggesting frontal and possibly also hippocampal/amygdala changes in individuals with genetic susceptibility. Heterogeneity of structural brain changes (as seen in schizophrenia) appears evident even at high-risk stage, prior to potential onset of psychosis.

Keywords: At-risk mental state (ARMS), Magnetic resonance imaging (MRI), Psychosis, Schizophrenia, Ultra high-risk (UHR), Voxel-based morphometry (VBM).

1. Introduction

Early intervention and detection of people at risk of developing psychosis has become a major focus of clinical research on schizophrenia (Yung and Nelson, 2011 and Stafford et al, 2013). There are now well-validated clinical instruments ( Daneault et al., 2013 ), which have been used to screen young people at high risk for later onset of psychosis, both with the aim of identifying early intervention strategies, as well as enabling biological research into well-defined high-risk populations. Most of these assessments rely on clinical signs and symptoms, including basic symptoms, the occurrence of brief or attenuated psychotic symptoms, psychometric schizotypy, or biological factors such as familial liability (Addington and Heinssen, 2012, Schultze-Lutter et al, 2012, and Daneault et al, 2013). Among the most widely used clinical and research instruments, the CAARMS interview (comprehensive assessment of at-risk mental state ( Yung et al., 2002 )), for example, considers several clinical factors, including higher genetic load (e.g. first-degree relatives of patients with schizophrenia with drop in functioning), attenuated psychotic symptoms, or brief self-limiting intermittent psychotic symptoms (BLIPS). Any of these factors or “routes” towards an at-risk mental state (ARMS) is considered, and screened subjects might meet criteria for ARMS based on one or more of these aspects (Yung et al, 2002 and Daneault et al, 2013). People at ultra-high risk (UHR) for psychosis can therefore be assumed to be a heterogeneous group, independent of whether they eventually convert to schizophrenia or develop another psychiatric condition, since they vary in degrees of genetic liability, symptom profiles, and other phenotypic variables such as cognitive function ( Kohler et al., 2014 ).

Along with the established and validated research criteria for high-risk states for psychosis, there have been several studies investigating neurobiological changes in high-risk populations, including volumetric and voxel-based morphometry (VBM) approaches (for review, see (Jung et al, 2010, Lawrie et al, 2008, and Wood et al, 2013)) as well as functional MRI ( Fusar-Poli, 2012 ). Reviews and meta-analyses in this area, however, differ, with regards to the definition and inclusion of high-risk subjects: while some have provided overviews on studies in genetic high-risk relatives ( Palaniyappan et al., 2012 ), others have included studies with a broader spectrum of the high-risk paradigm, including individuals at risk for psychosis not only through affected relatives, but also through either psychometric or subclinical symptom profiles (Chan et al, 2011 and Wood et al, 2013).

There has been little research into the biological diversity of subgroups of people within at-risk mental state for the psychosis spectrum, i.e. testing the hypothesis that distinct brain structural changes characterize subgroups of at-risk populations depending on their risk profile. So far, subgroups of UHR subjects have been defined in longitudinal studies according to clinical outcome, i.e. whether brain structural parameters might predict eventual conversion into psychosis, and in particular schizophrenia (Lawrie et al, 2008, Wood et al, 2008, Koutsouleris et al, 2009a, Koutsouleris et al, 2009b, Koutsouleris et al, 2012, Sprooten et al, 2013, and Cooper et al, 2014). Although such a distinction is relevant for using brain imaging for prediction or monitoring, it still leaves unanswered the question of heterogeneity within this population of subjects who are at risk of developing a disorder, which in itself is highly heterogeneous with regards to clinical presentation, long-term outcomes, and treatment. Only two studies have divided subgroups according to family history of psychosis, which might be a biologically more plausible discriminant of subgroups: one study, using volumetry and assessment of gross morphological features, found differences with reduced hippocampal volume in those UHR subjects without family history ( Wood et al., 2005 ), another assessed cavum septum pellucidum prevalence and its features, but failed to find differences in UHR subgroups ( Takahashi et al., 2008 ). Hence, to our knowledge, there is no study to assess which regions that are structurally compromised in UHR would be related to or specific for subgroups.

In this study, we aim to test the hypothesis that the biological “route” into high-risk status, i.e. whether through genetic liability or attenuated psychotic symptom profiles, differs with regards to regional grey matter. Using a cross-sectional design, we compared a group of ultra-high risk (UHR) individuals (defined by CAARMS criteria ( Yung et al., 2002 )) with both healthy controls and people with first-episode schizophrenia; in a second set of analyses, we then divided the UHR group into two subgroups, which were compared to identify changes (each compared to healthy controls and head-on) that would distinguish the two groups. Specifically, we hypothesized diverging effects in the lateral prefrontal, lateral temporal and hippocampal areas identified in the studies mentioned above, while testing voxel-wise across the whole brain to additionally provide an explorative analysis of other brain regions.

2. Methods

2.1. Subjects

For this study we included a total of 116 subjects: 43 subjects (22 women, 21 men; mean age 23.7 yrs, SD 3.3) at ultra-high risk (UHR) for psychosis, as defined by CAARMS screening criteria, 24 first-episode antipsychotic–naïve schizophrenia (SZ) patients (12 women, 12 men; mean age 24.9 yrs, SD 3.1), and 49 healthy subjects (HC; 23 women, 26 men; mean age 23.8 yrs, SD 3.0) recruited from the community. Groups did not differ in gender (Chi-square, chi2 = 0.172, p = 0.917) and age (ANOVA, F = 1.348, p = 0.264). UHR subjects included 32 UHR with attenuated psychotic symptoms (UHR-attenuated: 15 women, 17 men; mean age 23.7 yrs, SD 3.5) and 11 UHR subjects with genetic risk profile (UHR-genetic: 7 women, 4 men; mean age 23.5 yrs, SD 2.6). Subgroup demographics (testing four groups: the two UHR subgroups, Sz group, HC group) revealed no significant age effect (F = 0.911; p = 0.438) or gender effect (Chi-square, chi2 = 1.093, p = 0.779). All subjects gave written informed consent to the study protocol, which was approved by the Ethics Committee of the Friedrich-Schiller-University's Medical School, and in accordance with the Declaration of Helsinki (current revision). Subjects were compensated financially for their participation in the study; there was no debriefing after the study. General exclusion criteria for all participants were: active substance abuse or dependence, neurological or major medical conditions, traumatic brain injury, and intellectual impairment (defined as IQ below 80).

Each of the UHR subjects was screened by a trained psychiatrist or psychologist of the Department's Early Psychosis Intervention Unit using the CAARMS inventory. All were neuroleptic–naïve, i.e. had never taken antipsychotics. Positive symptoms as assessed with the Brief Psychiatric Rating Scale (BPRS) subscores showed a mean value of 49.57 (SD 14.909) for the genetic risk group (n = 7 with complete scores) and 49.58 (SD 9.106) for the attenuated symptoms (n = 19 with complete scores) subgroups, respectively. Negative symptoms as assessed with the Scale for Assessment of Negative Symptoms (SANS) showed a total mean of 49 (SD 30.627) for the genetic risk group (complete scoring available for n = 7) and 43.94 (SD 15.144) for the attenuated symptoms subgroup (n = 18).

The schizophrenia first-episode subjects were recruited from patients of the in- or out-patient department or day clinic. They were all neuroleptic–naïve, i.e. had never received any antipsychotic treatment, and none of them received antidepressant treatment. Patients were followed-up to verify initial diagnosis of schizophrenia. First-episode schizophrenia patients were assessed using the Positive and Negative Syndrom Scale (PANSS) to characterize psychopathology (n = 18 available full data sets), showing a mean total score of 53.2 (SD 6.3), mean positive subscale of 30.4 (SD 4.9), mean negative subscale of 29.1 (SD 8.1) and mean global subscale of 44.3 (SD 7.2). Duration of untreated psychosis was estimated to a mean of 4.1 (SD 1.9) months.

Healthy controls (HC) were recruited from the community, and were screened by a psychologist or psychiatrist for absence of current or previous psychiatric disorders or treatment, absence of previous psychotropic medication, and absence of first-degree relatives' history of psychosis or schizophrenia.

2.2. MR imaging and voxel-based morphometry

High-resolution T1-weighted MRI scans were acquired on a 3 Tesla Siemens Tim Trio scanner (Siemens, Erlangen, Germany) using the1H channel of a combined1H/31P-head-coil with a MPRAGE sequence (TR 2300 ms, TE 3.03 ms, flip angle 9°, FOV 256 × 256mm, 192 contiguous sagittal slices, resulting voxel resolution of 1 × 1 × 1mm).

We used VBM8, a freely available software toolbox for voxel-based morphometry ( http://dbm.neuro.uni-jena.de/vbm8/ ), implemented in SPM8 (Statistical Parametric Mapping; FIL, Institute of Neurology, UCL, London, UK; http// www.fil.ion.ucl.ac.uk/spm/ ), which is based on Matlab ( www.mathworks.com ). First, MRI data sets were visually inspected for gross image artifacts, and all individual scans passed an automated quality assurance protocol, implemented in the VBM8 package. Next, we applied VBM8 using segmentation and non-linear normalization with the DARTEL algorithm, as provided in SPM8. Segmented grey matter maps were finally smoothed with a Gaussian kernel of 12 mm FWHM. Statistical maps were all thresholded at p < 0.001 (uncorrected) based on the anatomical hypotheses from the literature.

In addition to the regional voxel-wise analysis, we also used a total brain grey matter and total brain white matter estimate derived from VBM analysis, based on the automated counts of grey matter and white matter voxels, resp., multiplied by voxel volume.

2.3. Statistics

We performed general linear model analyses for both the total brain grey matter and white matter values, as well as the voxel-wise analyses. First, we analyzed a three-group comparison, contrasting the (total) UHR group, Sz group, and HC group. Second, we performed the UHR subgroup analysis by using general linear models with a four-group comparison, i.e. UHR-genetic (UHR subjects with genetic risk profile), UHR-attenuated (UHR subjects with attenuated symptoms), Sz group, and HC group. While groups were matched for age and gender, we used both age and gender as co-variates in the analyses to remove variance related to these variables.

3. Results

3.1. Total brain grey matter and white matter

For the three-group analysis, we found a trend level group effect for grey matter (F = 2.68; p = 0.073), but no significant effect for white matter (F = 2.002; p = 0.140). For the four-group analysis, we found a trend level group effect for grey matter (F = 2.333; p = 0.078), but no significant effect for white matter (F = 1.660; p = 0.180).

3.2. Voxel-based morphometry (VBM) analysis

For the three-group analysis (UHR, Sz, HC; for overview, see Table 1 ), we found grey matter reductions of UHR vs. healthy controls in left superior frontal, right middle/superior temporal, and right postcentral cortices, and relative grey matter increases in left temporal cluster including temporal pole and fusiform cortex, and a right fusiform cortex cluster (see Fig. 1 a). Compared to healthy controls, first-episode schizophrenia patients had grey matter reductions in left inferior frontal, left lingual/fusiform, right postcentral/inferior parietal, right middle frontal/precentral, and right postcentral cortices (see Fig. 1 b). Direct comparison of UHR and first-episode schizophrenia did not yield significant differences in regional grey matter.

Table 1 VBM findings in the three-group analysis (UHR vs. Sz vs. healthy controls); only clusters with k = 10 or greater are listed.

Co-ordinates for maximum voxel Anatomical region k (number of voxels)
A) Ultra-high risk subjects (UHR) > healthy controls
− 18; 9; − 35 Left temporal pole (middle and superior) 305
8; − 22; 25 Right posterior cingulate cortex/corpus callosum 26
22; − 1; − 50 Right fusiform cortex 25
B) Healthy controls > ultra-high risk subjects (UHR)
58; − 45; 7 Right middle/superior temporal gyrus 219
51; − 31; 57 Right postcentral gyrus 18
− 21; 53; 1 Left superior frontal gyrus 65
C) Healthy controls > first-episode schizophrenia patients (Sz)
− 40; 24; − 2 Let inferior frontal cortex 423
− 33; − 66; − 5 Left lingual cortex 79
56; − 27; 52 Right postcentral gyrus 167
51; − 9; 54 Right precentral and middle frontal gyrus 14
40; − 27; 51 Right postcentral gyrus 36

Fig. 1 VBM grey matter analysis (p < 0.001, uncorr.) comparing ultra high-risk (UHR) subjects vs. healthy controls (1a), and first-episode schizophrenia patients vs. healthy controls (1b) with significant differences.

The subgroup analysis differentiating UHR subjects with genetic risk vs. attenuated symptoms (four groups: UHR-genetic, UHR-attenuated, Sz, HC) revealed that most of the regional effects seen for UHR subjects in the three-group analysis could be assigned to one of the two subgroups. An overview of findings is given in Table 2 .

Table 2 VBM findings in the four-group analysis (UHR-genetic vs. UHR-attenuated vs. Sz vs. healthy controls); only clusters with k = 10 or greater are listed.

Co-ordinates for maximum voxel Anatomical region k (number of voxels)
A) Healthy controls > ultra-high risk, genetic subgroup
− 26; 53; 1 Left superior/middle frontal gyri 259
12; 12; 13 Right caudate 152
26; 46; − 21 Right middle/superior orbital frontal cortex 76
26; − 10; − 12 Right hippocampus/amygdala 41
B) Healthy controls > ultra-high risk, attenuated symptoms subgroup
57; − 43; 9 Right middle/superior temporal cortex 164
C) Ultra-high risk, attenuated symptoms subgroup > healthy controls
− 20; 11; − 36 Left temporal pole (middle/superior), parahippocampal cortex 480
22; − 1; − 50 Right fusiform cortex 180
22; − 34; 6 Right hippocampus 61
D) First-episode schizophrenia > ultra-high risk, genetic subgroup
− 22; − 45; − 32 Left cerebellum 48
E) Ultra-high risk, attenuated symptoms subgroup > first-episode schizophrenia
26; 18; 0 Right putamen 501
12; − 21; 19 Right thalamus 100
− 26; 17; − 5 Left insular, left putamen 801
− 30; − 13; − 14 Left hippocampus 36
F) Ultra-high risk (UHR) subjects with attenuated symptoms profile > UHR with genetic risk profile
24; − 9; − 9 Right hippocampus/amygdala 709
26; 12; 10 Right putamen/caudate 701
24; − 42; − 30 Right cerebellum 555
− 22; − 40; − 30 Left cerebellum 141
− 6; 24; − 3 Left olfactory cortex 111
G) Ultra-high risk (UHR) subjects with genetic risk profile > UHR with attenuated symptoms profile
− 58; − 19; 45 Left inferior parietal cortex 22

Comparing the UHR-genetic subgroup vs. healthy controls, we found grey matter reductions in left superior and middle frontal and right middle and superior frontal gyri, as well as right caudate and a cluster including right amygdala and hippocampus (see Fig. 2 a), but no relative grey matter increases compared to healthy controls. In contrast, the UHR-attenuated subgroup (compared to healthy controls) showed grey matter reductions in a right middle/superior temporal gyrus cluster, and relative grey matter increases in a left temporal cluster including the temporal pole and extending towards the left parahippocampal cortex, as well as clusters in the right fusiform cortex, and right hippocampus (see Fig. 2 b); the latter cluster also showed trend-level significance (p = 0.098) at FWE correction levels (peak-level). Comparing the two UHR subgroups to the first-episode schizophrenia sample, we found the following differences: UHR-genetic subjects had no relative grey matter increase compared to Sz subjects, but grey matter reductions in the left cerebellum and a very small left inferior frontal cluster (see Fig. 3 a); UHR-attenuated subjects compared to Sz subjects had higher grey matter density in the right putamen, right thalamus, left insular cortex/putamen, and left hippocampus, while there was no grey matter loss in the other direction (see Fig. 3 b).


Fig. 2 VBM grey matter analysis (p < 0.001, uncorr.) comparing ultra high-risk (UHR) subjects with genetic liability (UHR-genetic) vs. healthy controls (2a), and ultra high-risk (UHR) subjects with attenuated psychotic symptoms vs. healthy controls (2b) with significant differences.


Fig. 3 VBM grey matter analysis (p < 0.001, uncorr.) comparing ultra high-risk (UHR) subjects with genetic liability (UHR-genetic) vs. first-episode schizophrenia patients (3a), and ultra high-risk (UHR) subjects with attenuated psychotic symptoms vs. first-episode schizophrenia patients (3b) with significant differences.

A direct comparison of the two UHR subgroups showed only one cluster with lower grey matter in the attenuated symptoms subgroup compared to the genetic subgroup (see Fig. 4 b) in the left inferior parietal cortex; however, the genetic subgroup had lower grey matter values compared to the attenuated symptoms subgroup in several areas (see Fig. 4 a), including a right hippocampus/amygdala cluster, right putamen/striatum, and cerebellum bilaterally. While none of these direct subgroup comparisons survived correction for multiple comparisons, the right hippocampus/amygdala cluster showed trend-level findings for FWE correction on the peak-level.


Fig. 4 VBM grey matter analysis (p < 0.001, uncorr.) comparing the two subgroups within the ultra-high risk (UHR) cohort, i.e. those with attenuated symptoms (AS) vs. those with genetic (GE) liability (4a, left), and vice versa (4b, right).

4. Discussion

In this study, we provide a first account of how different brain structural changes might contribute to different risk profiles in people with high liability to develop psychosis. Using cross-sectional data, we compared UHR subgroups with either genetic risk vs. those with attenuated psychotic symptoms, each versus healthy controls and a first-episode schizophrenia sample. Our results suggest that prefrontal and temporal grey matter changes in UHR are differentially related to genetic liability versus a clinical psychosis-prone phenotype. This expands on a very recent literature on differential effects of genetic vs. symptom-related risk factors in subjects at high risk for schizophrenia (Aiello et al, 2012 and Smieskova et al, 2013), as outlined in a most recent meta-analysis of hippocampal and amygdala volumes ( Ganzola et al., 2014 ).

Recent reviews of structural imaging studies in high-risk populations have shown that reduced hippocampal volume, as demonstrated in our analysis of UHR subjects with a attenuated vs. genetic risk profile, is a rather consistent feature of high-risk subjects (Jung et al, 2010, Bois et al, 2014, and Ganzola et al, 2014). In fact, one of the recent reviews points out that there is some evidence ( Wood et al., 2005 ) to suggest that hippocampal abnormalities in high-risk subjects “may be more environmental than genetically driven” ( Bois et al., 2014 ). More interestingly, a most recent review ( Ganzola et al., 2014 ) has aimed to separate UHR subjects along genetic vs. symptom risk profiles, and has suggested that these two subgroups might differ with respect to localization of tissue loss within the hippocampus, i.e. anterior hippocampus in the former and posterior hippocampus in the latter. Our own findings, while limited by the very small sample size of the genetic risk UHR subgroup, do not support this suggestion. In fact, they rather suggest that the genetic UHR subgroup, but not the attenuated symptoms UHR subgroup show right hippocampal tissue loss (as compared to healthy controls), which would actually be more consistent with subgroup effect on hippocampal structure per se, as the relatively larger attenuated symptoms subgroup (n = 32) failed to show significant hippocampal effects when compared to healthy controls. Yet, this interpretation will need further replication and extension, as our other UHR subgroup was too small to draw definitive conclusions. Also, our subgroup analyses suggest divergent effects within the UHR sample for left prefrontal volume loss, which was seen in the UHR genetic risk subgroup (when compared to healthy controls), but not in the UHR attenuated symptoms subgroup. These results, which so far have not been featured in previous studies (Bois et al, 2014 and Ganzola et al, 2014), deserve further exploration and confirmation.

Overall, the pattern of morphometric difference suggests greater similarity between the first-episode schizophrenia sample and the UHR-genetic subgroup than compared to the UHR attenuated symptoms subgroup. This includes differences in the left inferior frontal/insular cortex, right thalamus, left hippocampus, and right striatum (see Fig. 3 b), where UHR-attenuated symptoms and schizophrenia patients diverge markedly. While the left prefrontal/insular cortex changes were also significant for the comparison of healthy controls with schizophrenia, it is noteworthy that the above regions are among the best-replicated following psychosis onset ( Chan et al., 2011 ). It is, however, the left prefrontal and right middle temporal finding that distinguishes the UHR subgroups best in comparison to healthy controls.

We found dorsolateral prefrontal changes in both the genetic UHR subgroup and also the first-episode schizophrenia sample. This suggests these changes to reflect genetic liability rather than only the expression of the disease phenotype or changes associated with disease onset. Prefrontal grey matter reduction has been among the most intensively studied structural changes in schizophrenia, especially in chronic patients ( Glahn et al., 2008 ). While some of the prefrontal grey matter changes have been linked to neuroleptic exposure ( Torres et al., 2013 ), our subjects had not received antipsychotic treatment, thus excluding medication as a potential confound. Recent meta-analyses in fact show that several of the lateral and medial prefrontal grey matter reductions are already seen in unmedicated first-episode schizophrenia samples ( Leung et al., 2011 ), similar to the results in our first-episode group. This is also consistent with studies in relatives of patients with schizophrenia, whose pattern of structural alterations only partially resembles the reductions in their affected relatives ( Palaniyappan et al., 2012 ). Conversely, the lack of such prefrontal deficits in the UHR subgroup with attenuated symptoms suggests that this psychosis-prone phenotype might completely lack such prefrontal changes.

The right temporal lobe grey matter reduction, in contrast, was significant in the UHR attenuated symptoms group compared to controls, but not in the first-episode schizophrenia sample. This clusters appeared to be unique to this UHR subgroup. It is unclear, however, whether it might be related to the clinical phenotype, or whether it might reflect more unspecific changes that subjects in this sub-sample may have in common. Clinically, many of the patients with attenuated psychotic symptoms do not progress to frank psychosis or schizophrenia, but develop or will be later diagnosed with either spectrum disorders or specific personality disorders, whose biological intermediate phenotype does not resemble schizophrenia. Consequently, the biological significance of this particular finding remains incompletely understood and warrants further replication.

Our findings bear two major implications for early psychosis research. First, we demonstrate heterogeneity of brain structural markers across two subgroups of UHR subjects. Similar to schizophrenia, where such heterogeneity has been shown in recent studies (Koutsouleris et al, 2008, Nenadic et al, 2010, and Nenadic et al, 2014), this corroborates the hypothesis that brain imaging in UHR reflects a group with different degrees of changes and may thus capture a mixture of genetic and phenotype-related effects. Second, studying subgroups might help in better identifying those subjects who are at higher risk for psychosis. For this purpose, longitudinal studies would be necessary to track changes not only on the symptom, but also on the brain structural level. Already now, there is accumulating evidence for biologically distinct subgroups, which also includes cognitive functions ( Kohler et al., 2014 ).

A major limitation of our study is its limited sample size. While sample size and composition reflect the catchment area (> 100,000 inhabitants) of our department's early psychosis service, sample size might have been too small for detection of more subtle effects, in particular in the subgroup analysis of UHR subjects. In particular, our UHR subgroup with a genetic risk profile comprised only 11 subjects, as compared to the 32 attenuated symptoms UHR subjects, and the very small sample size of the former group limits statistical inference; even though this reflects the composition of our high-risk population (without particular previous selection for risk profiles) in this catchment area, it means that conclusions from the UHR subgroup analyses are limited and not definitive. Future multi-center studies might be better suited to provide superior statistical power for delineating effects across larger UHR populations, which can then be stratified into sufficiently large subgroups. Although many confounding factors, especially antipsychotic medication, were eliminated, our results need further replication. Also, lack of longitudinal data did not allow us to assess the factor of later conversion to psychosis, which might be considered in a larger cohort analysis. Different approaches to classifying ultra-high risk subjects will obviously also have an impact on classification of subgroups (on clinical grounds); yet, our use of CAARMS has the advantage of applying one of the best validated instruments in the field ( Daneault et al., 2013 ).

In conclusion, our findings suggest that research on people at ultra-high risk of developing schizophrenia should consider the heterogeneity of biological markers, and that the “pathways” to UHR status might be based on different biological processes, which in turn might differentially predispose subjects to risk of conversion.

Role of funding source

The authors declare that the funding institutions had no influence on the analyses carried out and presented here.


I.N., St.S., C.G., and H.S. designed the study.

St.S., M.D., N.S., I.N., A.G., J.R.R., and H.S. contributed to patient recruitment and scanning.

I.N., M.D., N.S., C.L., C.G, H.S., and St.S. contributed to the data collection, processing, and pre-processing.

I.N., St.S., C.L., and C.G. contributed to implementation of the image processing pipeline and imaging data analysis.

I.N. wrote the first drafts of the manuscript, and all authors commented on/approved the final version.

Conflicts of interest

The authors declare that they have no conflicts of interest, in particular no relevant financial interests. The funding institutions had no influence on the analyses carried out and presented here.


IN (Grant number 21007087) was supported by grants from the Friedrich-Schiller-University of Jena (Junior Scientist Grant). StS and AG were supported by German Research Foundation (DFG), grant Sm 68/3-1. JRR and AG acknowledge support from the German Research Foundation (DFG) grant RE 1123/11-1.


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a Department of Psychiatry and Psychotherapy, Jena University Hospital, 07743 Jena, Germany

b Medical Physics Group, Institute for Diagnostic and Interventional Radiology (IDIR), Jena University Hospital, 07743 Jena, Germany

c Department of Neurology, Jena University Hospital, 07743 Jena, Germany

lowast Corresponding author at: Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743 Jena, Germany. Tel.: + 49 3641 9390127; fax: + 49 3641 9390122.