The American Psychiatric Association (APA) has updated its Privacy Policy and Terms of Use, including with new information specifically addressed to individuals in the European Economic Area. As described in the Privacy Policy and Terms of Use, this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences.

Please read the entire Privacy Policy and Terms of Use. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy and Terms of Use, including the utilization of cookies.




The purpose of this study was to determine whether microglial activity, measured using translocator-protein positron emission tomography (PET) imaging, is increased in unmedicated persons presenting with subclinical symptoms indicating that they are at ultra high risk of psychosis and to determine whether microglial activity is elevated in schizophrenia after controlling for a translocator-specific genetic polymorphism.


The authors used the second-generation radioligand [11C]PBR28 and PET to image microglial activity in the brains of participants at ultra high risk for psychosis. Participants were recruited from early intervention centers. The authors also imaged a cohort of patients with schizophrenia and matched healthy subjects for comparison. In total, 56 individuals completed the study. At screening, participants were genotyped to account for the rs6971 polymorphism in the gene encoding the 18Kd translocator protein. The main outcome measure was total gray matter [11C]PBR28 binding ratio, representing microglial activity.


[11C]PBR28 binding ratio in gray matter was elevated in ultra-high-risk participants compared with matched comparison subjects (Cohen’s d >1.2) and was positively correlated with symptom severity (r=0.730). Patients with schizophrenia also demonstrated elevated microglial activity relative to matched comparison subjects (Cohen’s d >1.7).


Microglial activity is elevated in patients with schizophrenia and in persons with subclinical symptoms who are at ultra high risk of psychosis and is related to at-risk symptom severity. These findings suggest that neuroinflammation is linked to the risk of psychosis and related disorders, as well as the expression of subclinical symptoms.

Schizophrenia is a severe psychiatric disorder characterized by psychotic and cognitive symptoms, and it is a leading cause of global disease burden (1). It is generally preceded by a prodromal phase of attenuated psychotic symptoms and functional impairment (2). Individuals meeting standardized criteria for this phase have an ultra high risk for developing a psychotic disorder, in most cases schizophrenia (3). Approximately 35% of high-risk persons will develop a psychotic disorder within 24 months (4).

While the pathoetiology of schizophrenia is not fully understood, there is increasing evidence for the involvement of neuroinflammatory processes. Microglia are the resident immune cells of the CNS, and several lines of evidence indicate microglial involvement in the pathology of psychosis (57). In ultra-high-risk individuals, there are elevations in the levels of proinflammatory cytokines (8), which are also elevated in patients with schizophrenia (9). The levels of such peripheral markers have also been associated with reductions in gray matter volume in both ultra-high-risk individuals (10) and patients with schizophrenia (11). Postmortem investigation of brain tissue has found elevated microglial cell density (with a hypertrophic morphology) in persons with schizophrenia compared with control subjects (5), particularly in the frontal and temporal lobes (12), although some studies have found no differences (13). However, since microglial activity is dynamic, postmortem studies may miss alterations early in the development of the disease.

Elevations in microglial activity can be measured in vivo with positron emission tomography (PET) using radioligands specific for the 18kD translocator-protein (TSPO), which is expressed on microglia (14). Investigations using the first-generation radiotracer (R)-[11C]PK11195 have revealed an increase in TSPO binding in medicated patients with schizophrenia when compared with healthy control subjects (6, 7). The first investigation of microglia using PET in schizophrenia, in a cohort of 10 patients, revealed a total gray matter elevation of microglial activity in the 5 years following diagnosis (6). The most recent investigation in seven chronically medicated patients with schizophrenia using (R)-[11C]PK11195 demonstrated an elevation in hippocampal binding potential and a nonsignificant 30% increase in total gray matter binding potential (7).

While these studies indicate elevated microglial activity in schizophrenia, they included patients in whom the disorder was already established. It is therefore unknown whether this elevation predates the onset of, or becomes evident after, the first episode of frank psychosis.

Therefore, in the present investigation, we sought to determine whether microglial activity was elevated in ultra-high-risk individuals using the novel TSPO radioligand [11C]PBR28. Our a priori hypothesis was that microglial activity would be elevated in the total gray matter in ultra-high-risk individuals relative to matched comparison subjects. An additional prediction was that this elevation would be evident in frontal and temporal cortical regions, brain areas that have been particularly implicated in ultra-high-risk pathophysiology (15). [11C]PBR28 is a second-generation TSPO tracer with a higher affinity for TSPO than (R)-[11C]PK11195 (16). Recent in situ binding evidence shows that a genetic polymorphism (a C/T substitution at rs6971) influences the binding of TSPO tracers, including [11C]PBR28. This results in three TSPO binding profiles: high-affinity binders (C/C) have the greatest tracer affinity; low-affinity binders (T/T) have a 50-fold reduction in affinity; and mixed-affinity binders (C/T) express both high-affinity binder and low-affinity binder TSPO in approximately equal proportion (17). In view of this, we included a cohort of patients to test the hypothesis that TSPO binding is elevated in schizophrenia after adjusting for this polymorphism, since this has not been taken into account previously. We also tested the secondary hypothesis that there would be a positive relationship between total gray matter microglial activity and symptom severity.


The study was approved by the local research ethics committee and was conducted in accordance with the Declaration of Helsinki. After complete description of the study to the subjects, written informed consent was obtained.


A total of 56 individuals were recruited and completed the study. Fourteen participants who met ultra-high-risk criteria, as assessed on the Comprehensive Assessment of the At-Risk Mental States (2), were recruited from OASIS (Outreach and Support in South London) (18) (mean age=24.3 years [SD=5.40]; male:female ratio=7:7). Fourteen age-matched (SD=5 years) comparison subjects were recruited through newspaper and poster advertisements. Fourteen persons with schizophrenia (mean age=47.0 years [SD=9.31]; male:female ratio=11:3) were recruited from mental health centers in London (South London and Maudsley NHS Foundation Trust). An additional 14 age-matched (SD=5 years) healthy subjects were recruited for comparison with this second cohort (Table 1).

TABLE 1. Demographic Characteristics of Experimental and Comparison Groups

CharacteristicFirst CohortSecond Cohort
Comparison Group (N=14)Ultra-High Risk Group (N=14)pComparison Group (N=14)Schizophrenia Group (N=14)p
Age (years)28.147.9924.295.400.133a46.2113.6247.009.310.982a
Education (years)
18kD Translocator-protein genotype (high-affinity binder)1071.43750.000.352b14100.001392.860.541b
Comprehensive Assessment of the At-Risk Mental States/Positive and Negative Syndrome Scale scoresc

aThe data were compared using independent samples t test.

bThe data were compared using Mann-Whitney U test.

cSymptoms were measured in high-risk participants on the Comprehensive Assessment of the At-Risk Mental States and in schizophrenia patients on the Positive and Negative Syndrome Scale.

TABLE 1. Demographic Characteristics of Experimental and Comparison Groups

Enlarge table

All participants met the following inclusion criteria: 1) aged ≥18 years, 2) no significant physical or psychiatric health contraindications on assessment and medical examination by a trained physician.

Subjects were then screened based on the following exclusion criteria:


Exposure to any medications, including anti-inflammatory medications, in the last 1 month (see the data supplement accompanying the online version of this article).


History of substance abuse/dependence as determined by the Structured Clinical Interview for DSM-IV Axis I Disorders, Clinician Version (SCID-CV) (19).


Benzodiazepine use, whether prescribed or not (20). One subject was excluded due to a positive result for benzodiazepines on the scan day.


History of head injury resulting in unconsciousness, or any physical medical condition associated with inflammation.


At the time of screening, participants were tested for TSPO genotype to determine binding status (17). Those with a low-affinity binder genotype were excluded.


In ultra-high-risk individuals and comparison subjects: antipsychotic medication exposure.


Significant prior exposure to radiation.


Pregnancy or breast feeding.

Healthy comparison subjects with a personal history of a psychiatric disorder or a first-degree relative with schizophrenia or a psychotic illness were excluded.

Clinical Measures

At screening, all participants were assessed using SCID-CV (19). Ultra-high-risk individuals were assessed with the Comprehensive Assessment of the At-Risk Mental States (2) by a trained investigator, and patients with a diagnosis of schizophrenia were assessed with the Positive and Negative Syndrome Scale (PANSS) (21) by a clinician on the day of the PET scan. Depressive symptoms were assessed using the Beck Depression Inventory (BDI) (22).

PET Imaging

An initial low-dose transmission CT scan (0.34 mSv) was acquired for attenuation and scatter correction using a Siemens Biograph TruePoint PET/CT scanner (Siemens Medical Solutions, Erlangen, Germany). Participants then received a bolus injection of [11C]PBR28 (mean Mbq activity=325.31 [SD=27.03]) followed by a 90-minute emission scan. PET data were coregistered with whole-brain structural images acquired with a 3T MRI scanner (Trio, Siemens Medical Solutions, Erlangen, Germany). A 32-channel coil was used for all but one scan, where a 12-channel coil was used instead.

PET Acquisition

PET data were acquired dynamically over a 90-minute time window and binned into 26 frames (durations: 8×15 seconds, 3×1 minute, 5×2 minutes, 5×5 minutes, 5×10 minutes). Images were reconstructed using filtered back projection, which provides better data quality and signal-to-noise ratio over iterative methods (23), and corrected for attenuation and scatter. During the PET acquisition, arterial blood data were sampled via the radial artery using a combined automatic-manual approach. A continuous (one sample per second) sampling system (Allogg ABSS, Mariefred, Sweden) measured whole-blood activity for the first 15 minutes of each scan (see the online data supplement).

Structural MRI

Each subject underwent a T1 weighted MRI brain scan. MRI images were used for gray/white matter segmentation and region of interest (ROI) definition. A neuroanatomical atlas (24) was coregistered on each subject’s MRI scan and PET image using a combination of Statistical Parametric Mapping 8 ( and FSL ( functions, implemented in MIAKAT ( The primary region of interest was total gray matter. Secondary regions of interest were temporal and frontal lobe gray matter (12).

Image Analysis

All PET images were corrected for head movement using nonattenuation-corrected images, since they include greater scalp signal, which improves realignment compared with attenuation-corrected images (25). Frames were realigned to a single “reference” space identified by the individual T1-weighted MRI scan. The transformation parameters were then applied to the corresponding attenuation-corrected PET frames, creating a movement-corrected dynamic image for analysis. Regional time-activity curves were obtained by sampling the image with the coregistered atlas. Hence quantification of [11C]PBR28 tissue distribution was performed using the two-tissue compartmental model accounting for endothelial vascular TSPO binding (2TCM-1K) (26), since this has been shown to have improved performance compared with the two-tissue compartmental model not accounting for endothelial binding (2TCM) (26). Nevertheless, for completeness, we analyzed the data using the 2TCM as well (see Table S1 in the online data supplement). Even after accounting for genotype, high intersubject variability is seen in imaging with TSPO tracers. With PK11195, plasma protein binding is evident and may account for some levels of variability with TSPO imaging (27). Indeed, TSPO ligand quantification approaches mostly use tissue reference methodologies (28). Analysis of PK11195 is conducted using simplified reference tissue models and supervised cluster analysis (29). This method is not applicable to second-generation TSPO tracers, including PBR28, since the higher ligand affinity leads to appreciable endothelial binding in the blood brain barrier (26). As a result, it is not possible to identify a supervised cluster for reference. Our outcome measure therefore was the distribution volume ratio (the ratio of the VT [volume of distribution] in the region of interest to VT in the whole brain) because this accounts for intersubject variability in the input function. In the secondary analyses, we tested the regional specificity of group changes by comparing distribution volume ratio between groups in regions (the cerebellum and brainstem) where we did not expect marked inflammatory changes based on the postmortem studies and gray matter changes seen in people at risk of psychosis (30). To test for nonspecific effects of normalization, our parameters used to normalize the PET signal were tested for group differences. There are no differences in blood input function or VT in normalization regions used (see Table S2 in the online data supplement).

Statistical Analysis

Data, other than for gender and genotype, were shown to have a normal distribution following a Shapiro-Wilk test (31). Hence parametric tests were implemented for all but gender and affinity analyses, where a Mann-Whitney U test was used. Demographic data and tracer activity data were analyzed using independent-samples t tests. Multiple analysis of covariance with Bonferroni correction (32) was used to determine whether there was an effect of group on [11C]PBR28 binding associated microglial activity in the total gray matter, frontal lobe, and temporal lobe. There are data to suggest that cortical microglial density, hence TSPO binding, is elevated with aging (33), which is also evident in our data (see Table S3 in the data supplement). For this reason, we performed group-level analysis using age as a covariate. There is a significantly higher binding of tracer in high-affinity binders than mixed-affinity binders, and the variation in signal from high-affinity binder and mixed-affinity binder subjects is high, with a large degree of overlap across the binding statuses (see Figure S3 in the data supplement). Hence we pooled the binding statuses in our groups and covaried for genotype in our analysis (17). For all statistical comparisons, alpha was set at a 0.05 threshold (two-tailed) for significance. Statistical analysis was performed using SPSS 21 (IBM, Armonk, N.Y.). Partial correlation analysis was used to test the association of microglial activity with symptom severity and total gray matter volumes, with age and affinity as covariates of no interest.


Demographic Comparisons and Tracer Dosing

No significant demographic differences between the two groups of comparison subjects and respective patient groups were observed (Table 1). There were no differences in the injected dose, injected mass, specific activity, parent plasma fraction, or plasma over blood ratio between ultra-high-risk individuals or patients with schizophrenia and their respective comparison subjects (see Table S4 in the data supplement).

[11C]PBR28 Distribution in Total Gray Matter Regions

The [11C]PBR28 distribution volume ratios in total gray matter and frontal lobe and temporal lobe gray matter were significantly increased in ultra-high-risk individuals when compared with matched comparison subjects (Table 2, Figure 1A). Similarly, patients with a diagnosis of schizophrenia had elevated [11C]PBR28 distribution volume ratios in total, frontal lobe, and temporal lobe gray matter compared with matched comparison subjects (Table 2, Figure 1B). Secondary analysis to investigate regional specificity revealed no difference between ultra-high-risk individuals or schizophrenia patients and respective comparison subjects in cerebellar or brainstem distribution volume ratio (Table 2). Representative PET images of comparison subjects, ultra-high-risk individuals, and patients with schizophrenia are presented in Figure 1C. When comparing regions using VT, with either 2TCM or 2TCM-1K, no significant group difference was observed (see Table S5 in the data supplement).

TABLE 2. Microglial Activity (as Measured by PBR28 Distribution Volume Ratio) Elevated in Participants at Ultra High Risk of Psychosis and in Schizophrenia Patients in Total Gray Matter and Frontal and Temporal Cortical Regions of Interesta

RegionFirst CohortSecond Cohort
Comparison GroupUltra-High-Risk GroupAnalysisComparison GroupSchizophrenia GroupAnalysis
MeanbSDMeanSDFdfpCohen’s dMeanbSDMeanSDFdfpCohen’s d
Total gray matter2.0320.0172.0880.02110.33224, 30.0041.2442.4650.0202.5570.01420.80224, 3<0.0011.769
Frontal lobe2.0000.0382.0870.0265.33924, 30.0300.8942.4890.0372.6060.0259.88324, 30.0051.245
Temporal lobe1.9140.0412.0010.0284.41724, 30.0470.8292.2820.0652.5180.04413.08924, 30.0011.430
Cerebellum2.3070.0552.2870.0810.06224, 30.8052.8630.0602.8730.0630.01524, 30.905
Brain stem2.2910.1912.4890.280.50024, 30.4872.5140.1542.0970.2343.19424, 30.088

aMicroglial activity was not elevated in control regions (the cerebellum and brainstem). The mean regional distribution volume ratios are shown for each experimental group together with those for matched comparison subjects. The results of the analysis of covariance covarying for age and translocator-protein genotype are shown for each case-matched comparison cohort.

bThe data represent the mean regional distribution volume ratio of [11C]PBR28.

TABLE 2. Microglial Activity (as Measured by PBR28 Distribution Volume Ratio) Elevated in Participants at Ultra High Risk of Psychosis and in Schizophrenia Patients in Total Gray Matter and Frontal and Temporal Cortical Regions of Interesta

Enlarge table

FIGURE 1. Microglial Activity Measured With Positron Emission Tomography (PET) in Ultra-High-Risk Participants, Patients With Schizophrenia, and Matched Comparison Subjectsa

a Significant differences were found between experimental (red) and comparison (blue) groups, according to analysis of covariance (covarying for age and genotype). The images in part C are representative [11C]PBR28 PET images from a participant from each group. * p<0.05. ** p≤0.001. *** p≤0.005.

Two subjects in the ultra-high-risk group had taken citalopram in the past. However, only one was using the medication at the time of the scan, and no other ultra-high-risk participants had taken psychotropic drugs. Reanalysis excluding the two subjects who had taken citalopram did not alter the significant elevation in the [11C]PBR28 distribution volume ratio in the high-risk group in the total (F=6.601, df=22, 3, p=0.018) and frontal lobe (F=5.392, df=22, 3, p=0.030) gray matter, but the finding in the temporal cortex was no longer significant (p=0.149).

Genotype-specific analysis.

In further sensitivity analyses, we repeated the analyses separately for high-affinity binders and mixed-affinity binders in the ultra-high-risk group. This showed that the elevation in the ultra-high-risk individuals was present irrespective of whether the analysis was restricted to high-affinity binders or mixed-affinity binders (see Table S6 and Figure S3 in the data supplement).

Relationship Between [11C]PBR28 Distribution and Symptom Severity

In ultra-high-risk participants, there was a positive correlation between the total Comprehensive Assessment of the At-Risk Mental States symptom severity score and [11C]PBR28 distribution volume ratio in total gray matter (r=0.730, p=0.011) (Figure 2). No correlation was observed between [11C]PBR28 distribution volume ratio in total gray matter and duration of ultra-high-risk symptoms (r=−0.086, p=0.802). In patients with schizophrenia, there was no significant correlation between total gray matter distribution volume ratio and total PANSS score (see Figure S2 in the data supplement). There was no relationship between depressive symptom severity (Beck Depression Inventory score) and total gray matter distribution volume ratio in either patients with schizophrenia (r=0.478, p=0.162) or ultra-high-risk participants (r=−0.339, p=0.506).


FIGURE 2. Microglial Activity and Symptom Scores in Ultra-High-Risk Participantsa

a Significant correlation between measures was observed. Partial correlation including age and genotype as covariates (N=13; data were missing for one subject; r=0.730, p=0.011). The large circle indicates the subject who has subsequently developed schizophrenia to date.

Exploratory analysis of distribution volume ratio normalization.

To evaluate whether our findings were influenced by the signal used for normalization, we conducted exploratory analyses using the cerebellum and white matter as alternative normalization regions. Cerebellar normalization did not alter the major regional findings (frontal lobe: p=0.001; temporal lobe: p=0.006). White matter normalization performed similarly to the cerebellum (see Table S7 in the data supplement).


Our main finding is that [11C]PBR28 binding ratio, a marker of microglial activity, is elevated in people at ultra high risk of psychosis, with a large effect size (Cohen’s d >1.2). Furthermore, [11C]PBR28 binding ratio was associated with the severity of symptoms in ultra-high-risk individuals, linking elevated microglial activity to the expression of subclinical psychotic symptoms. Importantly, we found no relationship with depressive symptoms, suggesting elevated microglial activity is specific to the development of psychotic-like symptoms, rather than psychiatric symptoms in general. It would be valuable to examine change in [11C]PBR28 signal during the course of the prodromal period to determine whether there is a change during the prodromal phase. Because the ultra-high-risk group, who had recently presented to psychiatric services, was medication naive and had no history of psychotic disorder, these findings cannot be attributed to effects of previous illness or its treatment. Interestingly, at the time of this writing, one ultra-high-risk participant transitioned to first-episode psychosis. This participant had the highest total gray matter [11C]PBR28 signal in the cohort (distribution volume ratio=2.14). Follow-up of the remaining participants is required to determine the role of elevated TSPO availability in the onset of psychosis.

The present findings are consistent with recent evidence of elevated peripheral inflammatory markers in people at high risk of psychosis (8, 10) and suggest that elevated microglial activity predates the onset of frank psychosis. We also found evidence of elevated microglia activity in people with schizophrenia relative to comparison subjects, with a large effect size (Cohen’s d >1.7). This extends previous PET studies that have not controlled for TSPO genotype (34), a potential confound because genotype influences binding, by showing that TSPO binding is elevated after controlling for TSPO genotype. Our findings are also consistent with the findings of a postmortem study in schizophrenia, which also used PBR28. However, because it was in vitro, a two-point assay was used to quantify specific PBR28 binding to show elevated PBR28 binding in schizophrenia (35). We did not find the same symptom correlation in schizophrenia patients as we did in ultra-high-risk individuals. This may be due to the fact that these patients were not acutely unwell.


Antipsychotic treatment is a potential confound in the schizophrenia group (see Table S6 in the online data supplement) but not the ultra-high-risk group. There is growing evidence to suggest an influence of antipsychotic medication on microglial cell dynamics, including evidence that antipsychotics may reduce microglial activity (3638). Hence, in future studies it would be preferable to investigate patients with schizophrenia who are medication naive.

In the present investigation, we have used an approach to analysis (accounting for endothelial and vascular binding) that has been shown to be more reliable than alternative approaches (26). This was applied in a standardized automated manner across groups and also applied to control regions (brain stem and cerebellum) to examine the specificity for our findings. A limitation of all current approaches to imaging microglia, including with [11C]PBR28, is that the outcome measure is VT. Thus, the elevation in gray matter could reflect increased nonspecific tracer binding as well as biological signal. However, blocking studies have shown that a substantial proportion of VT for [11C]PBR28 is specific binding to the TSPO (39), although the proportion in schizophrenia remains to be determined.

In our sample, plasma input analysis resulted in an approximate 30% level of VT variability using the 2TCM approach. This variability is due in part to a small free fraction (fp), which has been reported to introduce 29% variability (see Table S8 in the data supplement [also see reference 26]). This being the case, the noise and measurement error from fp appears to be greater than the component it represents (40) and obscures the signal difference in our cohorts. Indeed, the variability of fp in our cohorts reaches 35% (9.9 [SD=3.5]), resulting in a large VT variability. Further work is needed to assess the full consequences of fp variability. Our normalization approach does, however, remove individual variation in fp, thus reducing intersubject variability.

We used the distribution volume ratio, in this case with whole-brain signal as the normalization region, as our outcome measure. We also showed that the main findings remained significant when other regions were used, suggesting that the findings are robust to the method of normalization. The use of distribution volume ratio analysis is a standard approach in PET imaging that has recently been applied to second-generation TSPO tracers (41, 42), including using whole-brain normalization (43), as well as to the first-generation TSPO tracer PK11195 (44, 45). Preclinical studies have demonstrated that the distribution volume ratio approach is able to detect microglial changes due to inflammatory stimuli and have confirmed that elevated distribution volume ratio signal corresponds to elevated levels of TSPO and other markers of microglia measured ex vivo using immunohistochemistry and/or autoradiography (4649). These preclinical studies thus indicate the functional significance of elevated [11C]PBR28 distribution volume ratio and support further in vivo investigation in patients to confirm the functional significance.

Patient Perspective

Patient 1: Clinical vignette

“Well I like the idea of taking part in research. It’s nice to try to help people find out what’s wrong.”

Patient 1 was the first patient I (P.S.B.) had met while conducting clinical academic research. I had spoken to her care coordinator with the local mental health trust in South London, and after talking on the telephone, she visited the Hammersmith Hospital. We had a long chat about the research, and I, with support from Dr. Selvaraj, screened her for the study. Patient 1 had lived in London for her adult life and relied greatly on support from her family and the local mental health trust; she had been diagnosed with schizophrenia 13 years ago, at which point she was placed under section (involuntary admission). She spoke of her experience of schizophrenia and her struggles to find a suitable medication; she was experiencing extrapyramidal side effects from her current regime of paliperidone; however, she was “sticking with it for now.” Following the MRI scan, she seemed glad to be out of such a confined space and responded in a more positive way to the open space of the positron emission tomography scanner. I telephoned her after 24 hours to check on the arterial site condition, which was “a little bruised but getting better.” We stayed in touch during the study, and she asked to be informed of more research opportunities.

Normalization by the whole-brain VT raises a conceptual issue because the whole-brain VT includes the gray matter VT as one constituent. The whole-brain VT also includes the VT in other tissue compartments, including white matter and subcortical structures. Thus, the elevation we see in the ultra-high-risk and schizophrenia groups could indicate a reduced VT in one or more of these other compartments. Further work to investigate changes in these compartments, for example, using selective TSPO blockers, is required to test this interpretation.

The normalization approach would likely account for global group differences in nonspecific binding, but we cannot exclude a gray matter selective increase in nonspecific binding contributing to the elevations seen. While the signal-to-noise ratio of [11C]PBR28 PET imaging is better relative to first-generation tracers, it remains relatively low. However, this noise would obscure a difference between groups and thus is unlikely to account for our findings. In this study, we did not correct for possible partial volume effects. Given that brain volume is generally reduced in schizophrenia, these would tend to underestimate the elevations observed here and not account for our group differences. There is a relatively higher binding in comparison subjects matched to patients with schizophrenia over those matched to the ultra-high-risk group. This can be explained in part by age-associated increases in TSPO but also by an increased number of mixed-affinity binders in the ultra-high-risk matched comparison subjects. Finally, it is important to note that not all the ultra-high-risk participants will go on to develop a psychotic disorder, and we will conduct clinical follow-up to determine whether the elevated microglial activity is specific to the development of the disorder or risk factors for psychosis.


While TSPO may be expressed on astrocytes (49) and some neuronal subtypes (50), it is predominantly expressed on microglia (51). The direct biological relationship between microglia and TSPO binding in vivo is not fully understood. However, in nonhuman primates inflammation-induced increases in microglial activity cause marked increases in [11C]PBR28 signal, confirmed postmortem to be largely a result of microglial binding (52). Microglia perform immune surveillance roles, mount inflammatory response to injury (53), and are involved in synaptic modulation in experience-dependent plasticity (54). Interpretation of elevated activity is therefore complex and not defined by “activated” or “resting” states. The elevations presented here might reflect a protective response triggered by associated pathology, such as glutamatergic excitotoxicity (55), or indicate a primary neuroinflammatory process linked to risk factors for psychosis and the development of subclinical symptoms. When our findings are interpreted with evidence that anti-inflammatory drugs are effective in schizophrenia (56), particularly in addressing early negative symptoms (57), they suggest that a neuroinflammatory process is involved in the development of psychotic disorders. While this indicates that anti-inflammatory treatment may be effective in preventing the onset of the disorder, further studies are required to determine the clinical significance of elevated microglial activity.


Here we provide, to our knowledge, the first evidence of elevated brain microglial activity in people at ultra high risk of psychosis and show that greater microglial activity is associated with greater symptom severity. We also demonstrate the first in vivo elevations of TSPO binding in schizophrenia with a second-generation tracer after adjusting for TSPO genotyping. These findings are consistent with increasing evidence that there is a neuroinflammatory component in the development of psychotic disorders, raising the possibility that it plays a role in its pathogenesis.

From the Institute of Clinical Science, Imperial College London; the Department of Psychiatric Imaging, Imperial College London; Centre for Neuroimaging Sciences, King’s College London; University of Padova, Padova, Italy; King’s College London; the Department of Neuroplasticity and Disease, Imperial College London; and the University of Texas Health Science Center, Houston.
Address correspondence to Mr. Bloomfield () or Dr. Howes ().

Mr. Bloomfield and Drs. Selvaraj, de Paola, and Howes contributed equally to this study.

Supported by a Medical Research Council (United Kingdom) grant to Dr. Howes (grant number MC-A656-5QD30) and a Maudsley Charity grant (grant number 666) to Dr. Howes, as well as the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London.

The views expressed are those of the authors and do not necessarily represent those of the NHS, the NIHR, or the Department of Health.

Mr. Bloomfield has conducted research funded by the Medical Research Council (United Kingdom), the National Institute of Health Research (United Kingdom), and the British Medical Association. Dr. Kalk received funding for a Ph.D. degree by a Wellcome Trust GlaxoSmithKline fellowship, and she has received grant support from GlaxoSmithKline to attend educational events and conferences. Dr. Howes has received investigator-initiated research funding from and/or participated in advisory/speaker meetings organized by AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen, Lundbeck, Lyden-Delta, Otsuka, Servier, and Roche. All other authors report no financial relationships with commercial interests.

The authors thank all the clinical imaging staff at Imanova for their assistance with this study, with particular thanks to Qi Guo for her initial input and assistance with positron emission tomography methodology.


1 Howes OD, Murray RM: Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet 2014; 383:1677–1687Crossref, MedlineGoogle Scholar

2 Yung AR, Yuen HP, McGorry PD, et al.: Mapping the onset of psychosis: the Comprehensive Assessment of At-Risk Mental States. Aust N Z J Psychiatry 2005; 39:964–971Crossref, MedlineGoogle Scholar

3 Fusar-Poli P, Bechdolf A, Taylor MJ, et al.: At risk for schizophrenic or affective psychoses? A meta-analysis of DSM/ICD diagnostic outcomes in individuals at high clinical risk. Schizophr Bull 2013; 39:923–932Crossref, MedlineGoogle Scholar

4 Fusar-Poli P, Bonoldi I, Yung AR, et al.: Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. Arch Gen Psychiatry 2012; 69:220–229Crossref, MedlineGoogle Scholar

5 Bayer TA, Buslei R, Havas L, et al.: Evidence for activation of microglia in patients with psychiatric illnesses. Neurosci Lett 1999; 271:126–128Crossref, MedlineGoogle Scholar

6 van Berckel BN, Bossong MG, Boellaard R, et al.: Microglia activation in recent-onset schizophrenia: a quantitative (R)-[11C]PK11195 positron emission tomography study. Biol Psychiatry 2008; 64:820–822Crossref, MedlineGoogle Scholar

7 Doorduin J, de Vries EFJ, Willemsen ATM, et al.: Neuroinflammation in schizophrenia-related psychosis: a PET study. J Nucl Med 2009; 50:1801–1807Crossref, MedlineGoogle Scholar

8 Perkins DO, Jeffries CD, Addington J, et al.: Towards a psychosis risk blood diagnostic for persons experiencing high-risk symptoms: preliminary results from the NAPLS Project. Schizophr Bull 2015; 41:419–428Crossref, MedlineGoogle Scholar

9 Miller BJ, Buckley P, Seabolt W, et al.: Meta-analysis of cytokine alterations in schizophrenia: clinical status and antipsychotic effects. Biol Psychiatry 2011; 70:663–671Crossref, MedlineGoogle Scholar

10 Cannon TD, Chung Y, He G, et al.: Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol Psychiatry 2015; 77:147–157Crossref, MedlineGoogle Scholar

11 Meisenzahl EMRD, Rujescu D, Kirner A, et al.: Association of an interleukin-1β genetic polymorphism with altered brain structure in patients with schizophrenia. Am J Psychiatry 2001; 158:1316–1319LinkGoogle Scholar

12 Radewicz K, Garey LJ, Gentleman SM, et al.: Increase in HLA-DR immunoreactive microglia in frontal and temporal cortex of chronic schizophrenics. J Neuropathol Exp Neurol 2000; 59:137–150Crossref, MedlineGoogle Scholar

13 Steiner J, Mawrin C, Ziegeler A, et al.: Distribution of HLA-DR-positive microglia in schizophrenia reflects impaired cerebral lateralization. Acta Neuropathol 2006; 112:305–316Crossref, MedlineGoogle Scholar

14 Karlstetter M, Nothdurfter C, Aslanidis A, et al.: Translocator protein (18-kDa) (TSPO) is expressed in reactive retinal microglia and modulates microglial inflammation and phagocytosis. J Neuroinflammation 2014; 11:3Crossref, MedlineGoogle Scholar

15 Bose SK, Turkheimer FE, Howes OD, et al.: Classification of schizophrenic patients and healthy controls using [18F] fluorodopa PET imaging. Schizophr Res 2008; 106:148–155Crossref, MedlineGoogle Scholar

16 Kreisl WC, Fujita M, Fujimura Y, et al.: Comparison of [(11)C]-(R)-PK 11195 and [(11)C]PBR28, two radioligands for translocator protein (18 kDa) in human and monkey: implications for positron emission tomographic imaging of this inflammation biomarker. Neuroimage 2010; 49:2924–2932Crossref, MedlineGoogle Scholar

17 Owen DR, Yeo AJ, Gunn RN, et al.: An 18-kDa translocator protein (TSPO) polymorphism explains differences in binding affinity of the PET radioligand PBR28. J Cereb Blood Flow Metab 2012; 32:1–5Crossref, MedlineGoogle Scholar

18 Fusar-Poli P, Byrne M, Badger S, et al. Outreach and support in South London (OASIS), 2001–2011: ten years of early diagnosis and treatment for young individuals at high clinical risk for psychosis. European psychiatry 2013; 28:315–326Crossref, MedlineGoogle Scholar

19 First MB, Spitzer RL, Gibbon M, et al.: Structured Clinical Interview for DSM-IV Axis I Disorders, Clinician Version (SCID-CV). Washington, DC, American Psychiatric Publishing, 1996Google Scholar

20 Rao VLR, Butterworth RF: Characterization of binding sites for the ω3 receptor ligands [3H]PK11195 and [3H]RO5-4864 in human brain. Eur J Pharmacol 1997; 340:89–99Crossref, MedlineGoogle Scholar

21 Kay SR, Fiszbein A, Opler LA: The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophr Bull 1987; 13:261–276Crossref, MedlineGoogle Scholar

22 Beck AT, Ward CH, Mendelson M, et al.: An inventory for measuring depression. Arch Gen Psychiatry 1961; 4:561–571Crossref, MedlineGoogle Scholar

23 Reilhac A, Tomeï S, Buvat I, et al.: Simulation-based evaluation of OSEM iterative reconstruction methods in dynamic brain PET studies. Neuroimage 2008; 39:359–368Crossref, MedlineGoogle Scholar

24 Tziortzi AC, Searle GE, Tzimopoulou S, et al.: Imaging dopamine receptors in humans with [11C]-(+)-PHNO: dissection of D3 signal and anatomy. Neuroimage 2011; 54:264–277Crossref, MedlineGoogle Scholar

25 Montgomery AJ, Thielemans K, Mehta MA, et al.: Correction of head movement on PET studies: comparison of methods. J Nucl Med 2006; 47:1936–1944MedlineGoogle Scholar

26 Rizzo G, Veronese M, Tonietto M, et al.: Kinetic modeling without accounting for the vascular component impairs the quantification of [lsqb]11C[rsqb]PBR28 brain PET data. J Cereb Blood Flow Metab 2014; 34:1060–1069Crossref, MedlineGoogle Scholar

27 Lockhart A, Davis B, Matthews JC, et al.: The peripheral benzodiazepine receptor ligand PK11195 binds with high affinity to the acute phase reactant alpha1-acid glycoprotein: implications for the use of the ligand as a CNS inflammatory marker. Nucl Med Biol 2003; 30:199–206Crossref, MedlineGoogle Scholar

28 Turkheimer FE, Edison P, Pavese N, et al.: Reference and target region modeling of [11C]-(R)-PK11195 brain studies. J Nucl Med 2007; 48:158–167MedlineGoogle Scholar

29 Yaqub M, van Berckel BN, Schuitemaker A, et al.: Optimization of supervised cluster analysis for extracting reference tissue input curves in (R)-[(11)C]PK11195 brain PET studies. J Cereb Blood Flow Metab 2012; 32:1600–1608Crossref, MedlineGoogle Scholar

30 Wood SJ, Pantelis C, Velakoulis D, et al.: Progressive changes in the development toward schizophrenia: studies in subjects at increased symptomatic risk. Schizophr Bull 2008; 34:322–329Crossref, MedlineGoogle Scholar

31 Shapiro S, Wilk MB: An analysis of variance test for normality (complete samples). Biometrika 1965; 52:591–611CrossrefGoogle Scholar

32 Dunn OJ: Multiple Comparisons among Means. J Am Stat Assoc 1961; 56:52–64CrossrefGoogle Scholar

33 Schuitemaker A, van der Doef TF, Boellaard R, et al.: Microglial activation in healthy aging. Neurobiol Aging 2012; 33:1067–1072Crossref, MedlineGoogle Scholar

34 Takano A, Arakawa R, Ito H, et al.: Peripheral benzodiazepine receptors in patients with chronic schizophrenia: a PET study with [11C]DAA1106. Int J Neuropsychopharmacol 2010; 13:943–950Crossref, MedlineGoogle Scholar

35 Kreisl WC, Jenko KJ, Hines CS, et al.: A genetic polymorphism for translocator protein 18 kDa affects both in vitro and in vivo radioligand binding in human brain to this putative biomarker of neuroinflammation. J Cereb Blood Flow Metab 2013; 33:53–58Crossref, MedlineGoogle Scholar

36 Zhu F, Zheng Y, Ding YQ, et al.: Minocycline and risperidone prevent microglia activation and rescue behavioral deficits induced by neonatal intrahippocampal injection of lipopolysaccharide in rats. PLoS One 2014; 9:e93966Crossref, MedlineGoogle Scholar

37 Bian Q, Kato T, Monji A, et al.: The effect of atypical antipsychotics, perospirone, ziprasidone and quetiapine on microglial activation induced by interferon-gamma. Prog Neuropsychopharmacol Biol Psychiatry 2008; 32:42–48Crossref, MedlineGoogle Scholar

38 Kato T, Mizoguchi Y, Monji A, et al.: Inhibitory effects of aripiprazole on interferon-γ-induced microglial activation via intracellular Ca2+ regulation in vitro. J Neurochem 2008; 106:815–825Crossref, MedlineGoogle Scholar

39 Owen DR, Guo Q, Kalk NJ, et al.: Determination of [(11)C]PBR28 binding potential in vivo: a first human TSPO blocking study. J Cereb Blood Flow Metab 2014; 34:989–994Crossref, MedlineGoogle Scholar

40 Hines CS, Fujita M, Zoghbi SS, et al.: Propofol decreases in vivo binding of 11C-PBR28 to translocator protein (18 kDa) in the human brain. J Nucl Med 2013; 54:64–69Crossref, MedlineGoogle Scholar

41 Coughlin JM, Wang Y, Ma S, et al.: Regional brain distribution of translocator protein using [(11)C]DPA-713 PET in individuals infected with HIV. J Neurovirol 2014; 20:219–232Crossref, MedlineGoogle Scholar

42 Dimber R, Vera Rojas J, Guo Q, et al.: Imaging brain TSPO availability with [11C]PBR28 PET in patients with retroviral (HTLV1 and HIV-1) infection. J Nucl Med meeting abstracts. 2014; 55:376Google Scholar

43 Loggia ML, Chonde DB, Akeju O, et al.: Evidence for brain glial activation in chronic pain patients. Brain 2015; 138(pt 3)604–615Crossref, MedlineGoogle Scholar

44 Arias L, Rissanen E, Tuisku J, et al: In vivo PET imaging of activated microglial cells can be used to differentiate between inactive and active chronic lesions in progressive multiple sclerosis. Neurology 2014; 82:(10 supplement) S44.003Google Scholar

45 Rissanen E, Tuisku J, Rokka J, et al.: In vivo detection of diffuse inflammation in secondary progressive multiple sclerosis using PET imaging and the radioligand ¹¹C-PK11195. J Nucl Med 2014; 55:939–944Crossref, MedlineGoogle Scholar

46 Maeda J, Zhang M-R, Okauchi T, et al.: In vivo positron emission tomographic imaging of glial responses to amyloid-β and tau pathologies in mouse models of Alzheimer’s disease and related disorders. J Neurosci 2011; 31:4720–4730Crossref, MedlineGoogle Scholar

47 Imaizumi M, Kim H-J, Zoghbi SS, et al.: PET imaging with [11C]PBR28 can localize and quantify upregulated peripheral benzodiazepine receptors associated with cerebral ischemia in rat. Neurosci Lett 2007; 411:200–205Crossref, MedlineGoogle Scholar

48 Converse AK, Larsen EC, Engle JW, et al.: 11C-(R)-PK11195 PET imaging of microglial activation and response to minocycline in zymosan-treated rats. J Nucl Med 2011; 52:257–262Crossref, MedlineGoogle Scholar

49 Martín A, Boisgard R, Thézé B, et al.: Evaluation of the PBR/TSPO radioligand [(18)F]DPA-714 in a rat model of focal cerebral ischemia. J Cereb Blood Flow Metab 2010; 30:230–241Crossref, MedlineGoogle Scholar

50 Varga B, Markó K, Hádinger N, et al.: Translocator protein (TSPO 18kDa) is expressed by neural stem and neuronal precursor cells. Neurosci Lett 2009; 462:257–262Crossref, MedlineGoogle Scholar

51 Taylor RA, Sansing LH: Microglial responses after ischemic stroke and intracerebral hemorrhage. Clin Dev Immunol 2013; 2013:10CrossrefGoogle Scholar

52 Hannestad J, Gallezot J-D, Schafbauer T, et al.: Endotoxin-induced systemic inflammation activates microglia: [¹¹C]PBR28 positron emission tomography in nonhuman primates. Neuroimage 2012; 63:232–239Crossref, MedlineGoogle Scholar

53 Kettenmann H, Hanisch U-K, Noda M, et al.: Physiology of microglia. Physiol Rev 2011; 91:461–553Crossref, MedlineGoogle Scholar

54 Tremblay MÈ, Lowery RL, Majewska AK: Microglial interactions with synapses are modulated by visual experience. PLoS Biol 2010; 8:e1000527Crossref, MedlineGoogle Scholar

55 Howes O, McCutcheon R, Stone J: Glutamate and dopamine in schizophrenia: an update for the 21st century. J Psychopharmacol 2015; 29:97–115Crossref, MedlineGoogle Scholar

56 Müller N, Riedel M, Scheppach C, et al.: Beneficial antipsychotic effects of celecoxib add-on therapy compared to risperidone alone in schizophrenia. Am J Psychiatry 2002; 159:1029–1034LinkGoogle Scholar

57 Chaudhry IB, Hallak J, Husain N, et al.: Minocycline benefits negative symptoms in early schizophrenia: a randomised double-blind placebo-controlled clinical trial in patients on standard treatment. J Psychopharmacol 2012; 26:1185–1193Crossref, MedlineGoogle Scholar