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Neural correlates of executive function and working memory in the ‘at-risk mental state’

Published online by Cambridge University Press:  02 January 2018

Matthew R. Broome*
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London, and Health Sciences Research Institute, Warwick Medical School, University of Warwick, Coventry, UK
Pall Matthiasson
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London
Paolo Fusar-Poli
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK, and Department of Applied and Psychobehavioural Health Sciences, University of Pavia, Italy
James B. Woolley
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Louise C. Johns
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Paul Tabraham
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Elvira Bramon
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Lucia Valmaggia
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK, and Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands
Steven C. R. Williams
Affiliation:
Neuroimaging Research Group, Department of Neurology, Institute of Psychiatry, King's College London, UK
Michael J. Brammer
Affiliation:
Brain Image Analysis Unit, Department of Biostatistics and Computing, Institute of Psychiatry, King's College London, UK
Xavier Chitnis
Affiliation:
Brain Image Analysis Unit, Department of Biostatistics and Computing, Institute of Psychiatry, King's College London, UK
Philip K. McGuire
Affiliation:
Section of Neuroimaging, Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
*
Matthew R. Broome, Warwick Medical School, University of Warwick, Gibbet Hill, Coventry CV4 7AL, UK. Email: m.r.broome@warwick.ac.uk
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Extract

Background

People with prodromal symptoms have a very high risk of developing psychosis.

Aims

To use functional magnetic resonance imaging to examine the neurocognitive basis of this vulnerability.

Method

Cross-sectional comparison of regional activation in individuals with an ‘at-risk mental state’ (at-risk group: n=17), patients with first-episode schizophreniform psychosis (psychosis group: n=10) and healthy volunteers (controls: n=15) during an overt verbal fluency task and an N-back working memory task.

Results

A similar pattern of between-group differences in activation was evident across both tasks. Activation in the at-risk group was intermediate relative to that in controls and the psychosis group in the inferior frontal and anterior cingulate cortex during the verbal fluency task and in the inferior frontal, dorsolateral prefrontal and parietal cortex during the N-back task.

Conclusions

The at-risk mental state is associated with abnormalities of regional brain function that are qualitatively similar to, but less severe than, those in patients who have recently presented with psychosis.

Type
Papers
Copyright
Copyright © Royal College of Psychiatrists 2009 

People with prodromal symptoms of psychosis have a 25–40% risk of developing a psychotic disorder in the following 12 months Reference Miller, McGlashan, Rosen, Somjee, Markovich and Stein1,Reference Yung, Phillips, Yuen, Francey, McFarlane and Hallgren2 and thus have an ‘at-risk mental state’. Neuropsychological studies indicate people with an at-risk mental state show impairments in executive and memory functions, with performance often intermediate between that in patients with schizophrenia and controls Reference Wagner, Frommann, Jessen, Pukrop, Bechdolf and Ruhrmann3 and with working memory performance predicting the onset of psychosis. Reference Pukrop, Ruhrmann, Schultze-Lutter, Bechdolf, Brockhaus-Dumke and Klosterkotter4 Structural magnetic resonance imaging (MRI) studies suggest that the at-risk mental state is associated with reduced grey matter volume in regions that are also abnormal in schizophrenia, Reference Pantelis, Velakoulis, McGorry, Wood, Suckling and Phillips5 and a recent functional MRI study reported differential prefrontal activation in individuals with an at-risk mental state relative to controls and patients with schizophrenia during a visual oddball paradigm. Reference Morey, Inan, Mitchell, Perkins, Lieberman and Belger6 Taken together, these findings suggest that individuals with an at-risk mental state display neurocognitive abnormalities that are qualitatively similar to, but less severe than, those seen in schizophrenia. We tested this hypothesis using functional MRI in conjunction with classic tasks of executive function and working memory.

Methods

Participants

At-risk mental state (at-risk) group (n=17)

Individuals meeting Personal Assessment and Crisis Evaluation Reference Yung, Phillips, McGorry, McFarlane, Francey and Harrigan7 (PACE) criteria for an at-risk mental state were recruited from Outreach and Support in South London (OASIS). Reference Broome, Woolley, Johns, Valmaggia, Tabraham and Gafoor8 The diagnosis was based on assessment by two experienced clinicians using the comprehensive assessment for the at-risk mental state, Reference Yung, Phillips, Yuen, Francey, McFarlane and Hallgren2 and a consensus meeting with the clinical team. None of the participants had ever received antipsychotic medication. Briefly, an individual meets PACE criteria for an at-risk mental state if they display one or more of the following: ‘attenuated’ positive symptoms; frank psychotic symptoms that last less than 1 week and resolve without treatment; a recent decline in function coupled with either schizotypal personality disorder or a first-degree relative with psychosis. The individuals recruited were representative of the local population of people presenting with an at-risk mental state in terms of age, gender, ethnicity, and duration and intensity of symptoms. Reference Broome, Woolley, Johns, Valmaggia, Tabraham and Gafoor8

First-episode (psychosis) group (n=10)

Participants were patients who had presented with a first episode of psychosis to Lambeth Early Onset Services. All met ICD–10 9 criteria for a schizophreniform psychosis at the time of scanning and subsequently met the Operationalized Criteria (OPCRIT) Reference McGuffin, Farmer and Harvey10 threshold for a diagnosis of schizophrenia when reassessed 12 months after first presentation. Three patients were medication naïve. Seven had been treated with either oral risperidone or quetiapine for a mean of 10 days (95% CI 4.7–16.3) at mean doses of 1.7 mg and 63.75 mg respectively. Patients were scanned as soon after presentation as was practicable, and all but one were scanned within 2 weeks of presentation.

Control group (n=15)

Healthy volunteers were recruited via advertisements in the local media.

All individuals lived in the same borough of London as the clinical participants (Lambeth), were native speakers of English and were right-handed.

Individuals were excluded if there was a history of neurological disorder or they met DSM–IV 11 criteria for a substance misuse disorder. General intellectual function was estimated in all participants using the National Adult Reading Test. Reference Nelson12 The severity of symptoms in the clinical groups was assessed with the Positive and Negative Syndrome Scale (PANSS) Reference Kay, Fiszbein and Opler13 on the day of scanning. Additionally, individuals were excluded from the analysis after data collection if they were unable to perform the cognitive tasks during image acquisition as detailed below. For the at-risk group, 19 participants underwent functional MRI, with 2 being excluded because they did not perform the task, resulting in n=17; 1 participant was excluded from the psychosis group, and one from the control group, leaving data being reported for n=10 and n=15 respectively.

There were no significant group differences in socio-demographic variables or IQ. Both positive and general PANSS scores were higher in the psychosis group than in the at-risk group, but these differences were not significant (Table 1).

Table 1 Age, IQ, gender and psychopathology ratings across groups

Variable Controls (n=15) At-risk group (n=17) Psychosis group (n=10)
Age, years: mean (s.d.) 25.4 (4.9) 24.2 (4.1) 25.5 (5.9)
Gender, male:female 11:4 12:5 7:3
NART IQ: mean (s.d.) 111.2 (7.2) 102.9 (11.9) 103.6 (9.2)
PANSS total: mean (s.d.) N/A 51.9 (12.7) 58.1 (9.5)
PANSS positive: mean (s.d.) N/A 11.7 (3.4) 18.5 (4.6)
PANSS negative: mean (s.d.) N/A 10.6 (4.1) 10.0 (2.3)
PANSS general: mean (s.d.) N/A 20.9 (9.2) 29.6 (5.9)

Image acquisition

Images were acquired on a 1.5 T Signa (GE) system at the Maudsley Hospital, London; T2*-weighted images were acquired with a repetition time (TR) of 2 s, 38 × 3 mm slices, with a 0.3 mm gap, in 14 axial planes. During the verbal fluency task a gradient-echo sequence (TR=4000 ms, echo time (TE)=40 ms) was used with the acquisition of each volume compressed into the first 1250 ms of the repetition time, creating a 2750 ms window in which participants could articulate a response in the absence of scanner noise. Reference Fu, Morgan, Suckling, Williams, Andrew and Vythelingum14 The other tasks (which did not involve speech) were studied using TR=2000 ms and TE=40 ms. To facilitate anatomical localisation of activation, a high-resolution inversion recovery image data-set was also acquired, with 3 mm contiguous slices and an in-plane resolution of 3 mm (TR=1600 ms, inversion time (TI)=180 ms, TE=80 ms).

Cognitive tasks

N-back memory task

In all conditions participants were presented with a series of letters which they viewed using a prismatic mirror. The interstimulus interval was 2 s. During the baseline (0-back) condition, individuals were required to move a joystick to the left when the letter ‘X’ appeared. During the 1-back and 2-back conditions, participants were required to press a button on the joystick with their right index finger if the currently presented letter was the same as that presented one or two letters beforehand respectively. The three conditions were presented in 10 alternating 30 s blocks matched for the number of target letters per block (i.e. two or three), in pseudorandom order. Reaction time and the accuracy of the responses were recorded electronically by computer.

Overt verbal fluency task

Participants were required to say aloud a word beginning with a visually presented letter. The stimuli, each subtending an angle of 5°, were presented visually on a black screen, viewed through a mirror. Cognitive load was modulated with two levels of task difficulty, ‘easy’ and ‘hard’ conditions, which involved letters that differed with respect to the ease with which volunteers can usually generate words beginning with them. The ‘easy’ condition involved the letters L, T, C, P, S; the ‘hard’ condition: O, N, E, F, G. Reference Fu, Morgan, Suckling, Williams, Andrew and Vythelingum14 Incorrect responses were defined as words that were proper names, repetitions or grammatical variations of the previous word, and ‘pass’ responses. Letters were presented in 28 s blocks of seven stimuli at 4 s intervals. The control condition of word repetition comprised 28 s blocks of 7 presentations of the word ‘rest’ at 4 s intervals, which participants were required to read aloud. Five blocks of each condition (hard/easy/repetition) were presented in random order.

Verbal responses were recorded via an MRI-compatible microphone on Cool Edit 2000 for Windows. To ensure that participants heard their responses clearly, their speech was transmitted by an MRI-compatible microphone, amplified by a computer sound card and relayed back through an acoustic MRI sound system (Ward Ray, Hampton Court, UK), and noise-insulated, stereo headphones at a volume of 91 plus or minus 2 dB.

Image processing and analysis

The data were realigned Reference Bullmore, Brammer, Rabe-Hesketh, Curtis, Morris and Williams15 then smoothed using a Gaussian filter (full width half maximum 7.2 mm). Responses to the experimental paradigms were detected by convolving each component of the design with each of two gamma variate functions (peak responses at 4 and 8 s respectively). The best fit between the weighted sum of these convolutions and the time series at each voxel was computed using the constrained blood oxygen level-dependent (BOLD) effect model. Reference Friman, Borga, Lundberg and Knutsson16 A goodness of fit statistic comprising the ratio of the sum of squares of deviations from the mean image intensity (over the whole time series) divided by the sum of squares of deviations due to the residuals (SSQ ratio) was then computed at each voxel.

The data were then permuted by a wavelet-based method Reference Bullmore, Long, Suckling, Fadili, Calvert and Zelaya17 to calculate the null distribution of SSQ ratios under the assumption of no experimentally determined response. This was used to calculate the critical value of SSQ ratio needed to threshold the maps at a type I error rate of <1. The detection of activated voxels was then extended from voxel to cluster level. Reference Bullmore, Suckling, Overmayer, Rabe-Hesketh, Taylor and Brammer18 To minimise the potential confounding effects of between-group and between-condition variation in task performance, in the analysis of data from the verbal fluency and N-back tasks the BOLD response in each person was modelled using only trials associated with correct responses.

In addition to the SSQ ratio, the size of the BOLD response to each experimental condition was computed for each individual at each voxel as a percentage of the mean resting image intensity level. In order to calculate the BOLD effect size, the difference between the maximum and minimum values of the fitted model for each condition was expressed as a percentage of the mean image intensity level over the whole time series.

The SSQ ratio maps for each individual were transformed into the standard space of Talairach and Tournoux Reference Talairach and Tournoux19 using a two-stage warping procedure. Reference Brammer, Bullmore, Simmons, Williams, Grasby and Howard20 Group activation maps were computed by determining the median SSQ ratio at each voxel (across all individuals) in the observed and permuted data maps. The distribution of median SSQ ratios from the permuted data was used to derive the null distribution of SSQ ratios and the critical SSQ ratio to threshold group activation maps at a cluster level threshold of <1 expected type I error cluster per brain.

Comparisons of responses between groups or experimental conditions was performed by fitting the data at each intracerebral voxel at which all participants had non-zero data using a linear model of the type:

\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[\ Y=a+bX+e\ \] \end{document}

where Y is the vector of BOLD effect sizes for each individual, X is the contrast matrix for the particular intercondition/group contrasts required, a is the mean effect across all individuals in the various conditions/groups, b is the computed group/condition difference and e is a vector of residual errors. The model was fitted by minimising the sum of absolute deviations rather than the sums of squares to reduce outlier effects. The null distribution of b was computed by permuting data between conditions/groups (assuming the null hypothesis of no effect of experimental condition or group membership) and refitting the above model.

In order to examine the data for a linear trend in activation across groups (controls, at-risk and psychosis) we carried out an orthogonal polynomial trend analysis in which the linear trend was coded as −1, 0, 1 (controls, at-risk and psychosis) and the orthogonal polynomial trend as −1, 2 and −1. Our hypothesis was that the linear trend would be significant but the quadratic trend would not be (i.e. there would be a linear trend but no significant departure from linearity). This would indicate that the order of responses would be controls>at-risk>psychosis or psychosis>at-risk>controls. This analysis was carried out using the effect size (beta) maps (which represented percentage changes in BOLD response) for each individual in each group after these had been transformed into standard space.

Voxel- and cluster-level maps of voxels and clusters showing significant linear and quadratic effects were computed using permutation testing as described above. The threshold for cluster-level analysis was chosen to give <1 false activated cluster per brain.

The method of analysis we employed (XBAM) uses median statistics to control outlier effects and permutation rather than normal theory-based inference. The main test statistic is computed by standardising for individual differences in residual noise before embarking on second-level, multiperson testing using robust permutation-based methods. Approaches using a mixed effects analysis, and permutation-based and cluster-level inference appear to be more valid than analyses involving simple random effects and voxel-level inference. Reference Thirion, Pinel, Meriaux, Roche, Dehaene and Poline21

Results

Task performance

In the N-back task there were no significant group differences in mean reaction time (P=0.44), and no differences in the number of errors during the 1- and 2-back conditions (P=0.49).

In the verbal fluency task there were no significant group differences in mean reaction time (P=0.81). There was a group difference in the proportion of movements made to the right (F=4.05, d.f.=2, P=0.028): controls made more such movements than the at-risk group, with the psychosis group intermediate between them. There were no group differences in the number of errors produced during either the ‘easy’ (P=0.45) or ‘hard’ versions of the verbal fluency task (P=0.82).

Regional activation

N-back tasks, within-group activation (voxel P<0.05, cluster P<0.01)

1-back. In the control group, there was activation in the left inferior frontal gyrus and the posterior parietal cortex bilaterally. In the at-risk group, activation was evident in the inferior and middle frontal gyri bilaterally, the left inferior parietal and right inferior temporal cortex, and the left fusiform gyrus. The psychosis group displayed activation in the middle and superior frontal gyri bilaterally, the right inferior frontal gyrus, the left insula, the medial parietal cortex bilaterally, the right middle temporal gyrus and the right thalamus.

2-back. In the control group there was activation in the left precentral and medial frontal gyrus, the right inferior frontal gryus, and the left posterior and right medial parietal cortex. In the at-risk group, activation was evident in the right inferior frontal and the left middle frontal gyrus, and in the right posterior cortex and left precuneus. The psychosis group displayed activation in the inferior and middle frontal gyri bilaterally, the middle temporal gyrus bilaterally, and in the left thalamus and caudate.

N-back tasks, between-group differences in activation (voxel P<0.05, cluster P<0.01)

1-back. There was differential activation across the three groups in the left inferior parietal lobule and the right angular gyrus. In both these areas the at-risk group showed less activation than controls but more activation than the psychosis group (post hoc t-tests, P<0.05) (Fig. 1 and Table 2).

Fig. 1 Group differences in cluster activation during the 1-back and 2-back conditions of the N-back task. For the 1-back condition, activation was greatest in controls, weakest in the psychosis group and intermediate in the at-risk group in the left inferior parietal lobule and in the right angular gyrus. Differential activation during the 2-back condition was greatest in controls, weakest in the psychosis group and intermediate in the at-risk group in the lateral prefrontal, insular and parietal cortex, and in the precuneus, The left side of the brain is shown on the left of the figure (voxel P<0.05, cluster P<0.01). SSQRs, sum of squares of deviations due to the residuals.

Table 2 1-back task between-group differences in activation: controls > at-risk > psychosis

Talairach and Tournoux coordinates (x, y, z) Anatomical region Brodmann area Cluster size (number of voxels)
32, -59, 17 Posterior part of right middle temporal gyrus 39 37
-40, -48, 37 Left inferior parietal lobule 40 31
29, -63, 31 Right precuneus 7 16
40, -48, 42 Right inferior parietal lobule 40 9
-22, -59, 26 Left precuneus 31 7

2-back. Differential activation across the three groups was evident in the right insula and left inferior frontal gyrus, the right inferior parietal lobule, the left precuneus and right medial/superior frontal gyrus. In each of these areas the at-risk group showed less activation than controls but more activation than the psychosis group (post hoc t-tests, P<0.05) (Fig. 1 and Table 3).

Table 3 2-back task between-group differences in activation: controls > at-risk > psychosis

Talairach and Tournoux coordinates (x, y, z) Anatomical region Brodmann area Cluster size (number of voxels)
40, -44, 42 Right inferior parietal lobule 40 38
-22, -70, 42 Left precuneus 7 35
-40, -41, 37 Left inferior parietal lobule 40 31
29, -59, 31 Right precuneus 7 29
-18, -74, 17 Left calcarine sulcus 17 26
32, 22, -2 Right post insula/claustrum 22
-40, 11, 26 Left inferior frontal gyrus 44 19
4, 11, 48 Medial part right superior frontal gyrus 6 18
36, -56, 48 Right superior parietal lobule 7 15
0, 15, 42 Anterior cingulate 32 12
-22, -4, 48 Left superior frontal gyrus 6 12

Verbal fluency task, within-group activation (voxel P<0.05, cluster P<0.01)

‘Easy’ condition. Controls showed activation in the left inferior and superior frontal gyri, the at-risk group activated the left inferior frontal and left fusiform gyri, right insula, and left superior frontal gyrus, and the psychosis group activated the left precentral gyrus, right insula, and the left inferior parietal and fusiform cortex.

‘Hard’ condition. Controls displayed activation in the left inferior frontal gyrus and inferior parietal lobule, and the right posterior cerebellar cortex. The at-risk group activated the left inferior frontal gyrus, the left superior frontal gyrus, and the psychosis group activated the left precentral gyrus and insula, and the right inferior frontal gyrus, insula and anterior cingulate gyrus.

Verbal fluency tasks between-group differences in activation (voxel P<0.05, cluster P<0.01)

‘Easy’ condition. There was differential activation across the three groups in a region which included both the opercular and dorsal parts of the left inferior frontal gyrus (Fig. 2 and Table 4). The at-risk group showed less activation in this region than controls but more activation than the psychosis group (post hoc t-tests, P<0.05).

Fig. 2 Group differences in left inferior frontal cluster activation during ‘easy’ verbal fluency. The at-risk group showed greater activation than the psychosis group but less than the controls. The left side of the brain is shown on the left of the figure (voxel P<0.05, cluster P<0.01). SSQ ratio, sum of squares of deviations due to the residuals.

Table 4 Controls < at-risk < psychosis: ‘easy’ verbal fluency task between-group differences in activation

Talairach and Tournoux coordinates (x, y, z) Anatomical region Brodmann area Cluster size (number of voxels)
-36, 30, 15 Left inferior frontal gyrus.(anterior portion) 45 36
-40, 7, 20 Left inferior frontal gyrus (dorsal portion) 44 34
-47, 11, 9 Left inferior frontal gyrus (frontal operculum) 44 26

‘Hard’ condition. Differential activation across the three groups was evident in a region which extended superiorly from the dorsal part of inferior frontal gyrus to adjacent middle frontal and precentral gyri (Fig. 3 and Table 5). In this region, the at-risk group showed less activation than the controls but greater activation than the psychosis group (post hoc t-tests, P<0.05).

Fig. 3 Group difference in cluster activation during ‘hard’ verbal fluency. When the task demands were high, there was a differential engagement of dorsolateral prefrontal cortex with activation greatest in the control group, weakest in the psychosis group, and intermediate in the at-risk group. However, on the same version of the task, there was differential engagement of the left anterior insula. When task demands were high activation in this region was greatest in the psychosis group, weakest in the controls and intermediate in the at-risk group. The left side of the brain is shown on the left of the figure (voxel P<0.05, cluster P<0.01). SSQRs, sum of squares of deviations due to the residuals.

Table 5 Controls > at-risk > psychosis: ‘hard’ verbal fluency task between-group differences in activation

Talairach and Tournoux coordinates (x, y, z) Anatomical region Brodmann area Cluster size (number of voxels)
-43, 11, 15 Left inferior Frontal gyrus (frontal operculum). 44 18

The reverse pattern of differential activation was evident in a more ventral region focused on the left anterior insula. In this region activation was again intermediate in the at-risk group, but was greatest in the psychosis group and weakest in the controls (Fig. 3 and Table 6). Post hoc pairwise comparisons confirmed that in this region the at-risk group showed greater activation than controls, with a trend for less activation than the psychosis group (t-tests, P<0.05).

Table 6 Psychosis > at-risk > controls: ‘hard’ verbal fluency task between-group differences in activation

Talairach and Tournoux coordinates (x, y, z) Anatomical region Brodmann area Cluster size (number of voxels)
-32, 15, -2 Left anterior insula 47 24

Effects of medication

Within the psychosis group (the only group which included participants who had received antipsychotic medication), there was no significant correlation (voxel P<0.05, cluster P<0.01) between activation in the regions that were differentially engaged across groups during each task and either the daily or cumulative dose (in chlorpromazine equivalents) of antipsychotic treatment, or the duration of antipsychotic treatment.

Discussion

The present study used functional MRI to study the neural substrate of executive functions and working memory in individuals with an at-risk mental state. The N-back task engages verbal working memory and requires the suppression of responses to currently presented stimuli. Verbal fluency entails the intrinsic generation of a verbal response, suppression of inappropriate responses and recalling and utilising previous responses.

In line with our hypothesis, there was a consistent pattern of differential activation across the groups for both tasks: during the N-back and verbal fluency paradigms, the level of regional activation in the at-risk group was intermediate between that in the psychosis group and controls. This is the first study to demonstrate statistically intermediate patterns of activation in an at-risk group, compared with controls and participants with psychosis. These differences were evident in brain regions that are normally activated during these paradigms in volunteers: the prefrontal and parietal cortex during the N-back task, and the prefrontal and anterior cingulate cortex during verbal fluency. Reference Callicott, Egan, Mattay, Bertolino, Bone and Verchinksi22Reference Yurgelun-Todd, Waternaux, Cohen, Gruber, English and Renshaw28 The differential activation was not attributable to impairments in task performance, as there were no significant differences in the speed or accuracy of responses across groups, and the analysis selectively modelled the BOLD response to those trials associated with correct responses. The lack of difference in behavioural performance allows the interpretation of activations to proceed knowing that the psychological task is being carried out to an equal level by all participants and hence, any remaining difference in activation is likely to be due to the disorder of interest, rather than a non-specific correlate of poor performance. The lack of behavioural difference is due both to excluding from the analysis individuals who perform the task very badly and to the study being powered to detect physiological changes, rather than neuropsychological differences, between the groups.

Similarly, the findings are unlikely to be related to effects of antipsychotic medication as both the at-risk group and controls were medication naïve, and in the psychosis group there was no relationship between medication exposure and activation in the regions that were differentially engaged across groups. Further, when quadratic trend analysis was carried out, there were no significant clusters activated differentially across the groups: again, this indicates that there was a predominantly linear relationship in activation across the groups on all tasks.

The brain regions where we observed differential activation in the at-risk group correspond to those that have previously been reported as sites of abnormal activation in functional imaging studies of schizophrenia. Thus, patients with schiziophrenia show reduced activation in the prefrontal and parietal cortex during the N-back task, Reference Honey, Honey, O'Loughlin, Sharar, Kumaran and Bullmore24 in the parietal cortex during random movement generation, Reference Spence, Brooks, Hirsch, Liddle, Meehan and Grasby29 and in the left prefrontal cortex during verbal fluency. Reference Curtis, Bullmore, Brammer, Wright, Williams and Morris30 There has only been one previous functional imaging study involving participants with an at-risk mental state. This reported differential prefrontal activation during a visual oddball paradigm in an at-risk group relative to controls and patients with schizophrenia. Reference Morey, Inan, Mitchell, Perkins, Lieberman and Belger6

During the 1-back condition of the N-back task, the at-risk group showed attenuated activation in the parietal cortex relative to controls. These differences became more extensive during the more demanding 2-back condition, and were accompanied by additional reductions in prefrontal activation. Nevertheless, the magnitude of activation in the at-risk group remained intermediate to that in the control and psychosis groups when the task demands were increased. Similarly, although during the ‘hard’ verbal fluency task the pattern of activation differences in the insula was reversed (discussed further below), the magnitude of activation in the at-risk group remained intermediate relative to that in the other groups, as during the ‘easy’ version of the task, and did not more closely resemble that in the psychosis group.

During the ‘hard’ verbal fluency task, engagement of the left insula was greatest in the psychosis group, intermediate in the at-risk group and weakest in controls. In the dorsal part of the left inferior frontal gyrus the opposite applied, with greatest activation in controls and least in the psychosis group. Relatively greater engagement of the insula in the psychosis group in the context of increased demands on controlled word retrieval Reference Wagner, Pare-Blagoev, Clark and Poldrack31 and selection among competing words Reference Moss, Abdallah, Fletcher, Bright, Pilgrim and Acres32 might reflect a compensatory response in the group in whom processing was most compromised and who showed the weakest engagement of the inferior frontal gyrus.

The overall pattern of the findings is consistent with data from neuropsychological studies of the at-risk mental state. These indicate that individuals who are at risk display impairments on tasks of executive functions and memory (including N-back and verbal fluency) that are qualitatively similar, but less severe, than those evident in patients with schizophrenia. Reference Brewer, Francey, Wood, Jackson, Pantelis and Phillips33Reference Broome, Woolley, Tabraham, Johns, Bramon and Murray38 Similarly, structural MRI studies suggest that the at-risk mental state is associated with reductions in grey-matter volume in similar regions that show volume reductions in schizophrenia, including the inferior frontal, cingulate and temporal cortex. Reference Pantelis, Velakoulis, McGorry, Wood, Suckling and Phillips5,Reference Borgwardt, Riecher-Rössler, Dazzan, Chitnis, Aston and Drewe39

As the at-risk group had a high risk of developing a psychotic disorder but did not have psychosis, the functional abnormalities they displayed can be seen as a correlate of their increased vulnerability to psychosis. It is unlikely that the findings reflected the erroneous inclusion of individuals who already had psychosis, or who were already progressing towards schizophrenia, as inclusion required detailed assessment by at least two clinicians experienced in the management of the at-risk mental state. In addition, participants were closely monitored for signs of frank psychosis subsequent to scanning.

Limitations of the study

This study reports cross-sectional data on three groups: those at risk of developing a psychotic disorder, those with psychosis and controls. As noted above, the findings in the at-risk group may be a correlate of their increased vulnerability to psychosis. However, to determine this formally will require a longitudinal study: a study informed by the findings presented here and in particular whether the pattern and degree of activation during executive and working memory tasks predicts transition to psychosis in a clinical high-risk group.

Conclusions

The at-risk mental state is associated with abnormalities of regional brain function that are qualitatively similar but less severe than those seen in patients who have just developed schizophrenia. These may underlie the impairments in executive function and working memory that are evident in this group and can be seen as correlates of their increased vulnerability to psychosis.

Acknowledgements

OASIS is supported by the Guy's and St Thomas' Charitable Foundation and the South London and Maudsley NHS Trust. E.B. is a Wellcome research fellow. Thanks go to all the clients, staff and referrers of both OASIS and Lambeth Early Onset Services. The authors are grateful to Dr Paul Allen for advice on interpretation of the verbal fluency data.

Footnotes

Declaration of interest

None. Funding detailed in Acknowledgements.

References

1 Miller, TJ, McGlashan, TH, Rosen, JL, Somjee, L, Markovich, PJ, Stein, K, et al. Prospective diagnosis of the initial prodrome for schizophrenia based on the Structured Interview for Prodromal Syndromes: preliminary evidence of interrater reliability and predictive validity. Am J Psychiatry 2002; 159: 863–5.CrossRefGoogle ScholarPubMed
2 Yung, AR, Phillips, LJ, Yuen, HP, Francey, SM, McFarlane, CA, Hallgren, M, et al. Psychosis prediction: 12-month follow up of a high-risk (‘prodromal’) group. Schizophr Res 2003; 60: 2132.CrossRefGoogle ScholarPubMed
3 Wagner, M, Frommann, I, Jessen, F, Pukrop, R, Bechdolf, A, Ruhrmann, S, et al. Cognitive and neurobiological risk indicators in early and late prodromal stages. Schizophr Res 2006; 86 (suppl): s356.CrossRefGoogle Scholar
4 Pukrop, R, Ruhrmann, S, Schultze-Lutter, F, Bechdolf, A, Brockhaus-Dumke, A, Klosterkotter, J. Neurocognitive indicators for a conversion to psychosis: Comparison of patients in a potentially initial prodromal state who did or did not convert to a psychosis. Schizophr Res 2007; 92: 116–25.Google Scholar
5 Pantelis, C, Velakoulis, D, McGorry, PD, Wood, SJ, Suckling, J, Phillips, LJ, et al. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet 2003; 361: 281–8.CrossRefGoogle ScholarPubMed
6 Morey, RA, Inan, S, Mitchell, TV, Perkins, DO, Lieberman, JA, Belger, A. Imaging frontostriatal function in ultra-high-risk, early, and chronic schizophrenia during executive processing. Arch Gen Psychiatry 2005; 62: 254–62.Google Scholar
7 Yung, AR, Phillips, LJ, McGorry, PD, McFarlane, CA, Francey, S, Harrigan, S, et al. Prediction of psychosis. A step towards indicated prevention of schizophrenia. Br J Psychiatry 1998; 172 (suppl 33): s1420.CrossRefGoogle ScholarPubMed
8 Broome, MR, Woolley, JB, Johns, LC, Valmaggia, LR, Tabraham, P, Gafoor, R, et al. Outreach and Support in South London (OASIS): implementation of a clinical service for prodromal psychosis and the at risk mental state. Eur Psychiatry 2005; 20: 372–8.CrossRefGoogle ScholarPubMed
9 World Health Organization. ICD–10: The ICD–10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. WHO, 1992.Google Scholar
10 McGuffin, P, Farmer, A, Harvey, I. A polydiagnostic application of operational criteria in studies of psychotic illness. Development and reliability of the OPCRIT system. Arch Gen Psychiatry 1991; 48: 764–70.Google Scholar
11 American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (4th edn) (DSM–IV). APA, 1994.Google Scholar
12 Nelson, HE. National Adult Reading Test (NART) Manual. nferNelson, 1982.Google Scholar
13 Kay, S, Fiszbein, A, Opler, L. The positive and negative symptom scale (PANSS) for schizophrenia. Schizophr Bull 1987; 13: 261–76.CrossRefGoogle ScholarPubMed
14 Fu, CH, Morgan, K, Suckling, J, Williams, SC, Andrew, C, Vythelingum, GN, et al. A functional magnetic resonance imaging study of overt letter verbal fluency using a clustered acquisition sequence: greater anterior cingulate activation with increased task demand. Neuroimage 2002; 17: 871–9.CrossRefGoogle ScholarPubMed
15 Bullmore, ET, Brammer, MJ, Rabe-Hesketh, S, Curtis, VA, Morris, RG, Williams, SC, et al. Methods for diagnosis and treatment of stimulus-correlated motion in generic brain activation studies using fMRI. Hum Brain Mapp 1999; 7: 3848.3.0.CO;2-Q>CrossRefGoogle ScholarPubMed
16 Friman, O, Borga, P, Lundberg, P, Knutsson, H. Adaptive analysis of fMRI data. Neuroimage 2003; 19: 837–45.CrossRefGoogle ScholarPubMed
17 Bullmore, ET, Long, C, Suckling, J, Fadili, J, Calvert, G, Zelaya, F, et al. Coloured noise and computational inference in neurophysiological (fMRI) time series analysis. Resampling methods in time and wavelet domains. Hum Brain Mapp 2001; 12: 6178.Google Scholar
18 Bullmore, ET, Suckling, J, Overmayer, S, Rabe-Hesketh, S, Taylor, E, Brammer, MJ. Global, voxel and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Trans Med Imaging 1999; 18: 3242.Google Scholar
19 Talairach, J, Tournoux, P. Co-Planar Stereotaxic Atlas of the Human Brain 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Thieme Publishing Group, 1988.Google Scholar
20 Brammer, MJ, Bullmore, ET, Simmons, A, Williams, SCR, Grasby, PM, Howard, RJ, et al. Generic brain activation mapping in fMRI: a nonparametric approach. Magn Reson Imaging 1997; 15: 763–70.Google Scholar
21 Thirion, B, Pinel, P, Meriaux, S, Roche, A, Dehaene, S, Poline, J-B. Analysis of a large fMRI cohort: statistical and methodological issues for group analysis. Neuroimage 2007; 35: 105–20.Google Scholar
22 Callicott, JH, Egan, MF, Mattay, VS, Bertolino, A, Bone, AD, Verchinksi, B, et al. Abnormal fMRI response of the dorsolateral prefrontal cortex in cognitively intact siblings of patients with schizophrenia. Am J Psychiatry 2003; 160: 709–19.CrossRefGoogle ScholarPubMed
23 Callicott, JH, Mattay, VS, Verchinski, BA, Marenco, S, Egan, MF, Weinberger, DR. Complexity of prefrontal cortical dysfunction in schizophrenia: more than up or down. Am J Psychiatry 2003; 160: 2209–15.Google Scholar
24 Honey, R, Honey, G, O'Loughlin, C, Sharar, SR, Kumaran, D, Bullmore, ET, et al. Acute ketamine administration alters the brain responses to executive demands in a verbal working memory task: an fMRI study. Neuropsychopharmacology 2004; 29: 1203–14.Google Scholar
25 Crespo-Facorro, B, Paradiso, S, Andreasen, N, O'Leary, DS, Watkins, GL, Boles Ponto, LL, et al. Recalling word lists reveals ‘cognitive dysmetria’ in schizophrenia: a positron emission tomography study. Am J Psychiatry 1999; 156: 386–92.Google Scholar
26 Curtis, V, Dixon, T, Morris, R, Bullmore, ET, Brammer, MJ, Williams, SC, et al. Differential frontal activation in schizophrenia and bipolar illness during verbal fluency. J Affect Disord 2001; 66: 111–21.Google Scholar
27 Fu, CH, Suckling, J, Williams, SC, Andrew, CM, Vythelingum, GN, McGuire, PK. Effects of psychotic state and task demand on prefrontal function in schizophrenia: an fMRI study of overt verbal fluency. Am J Psychiatry 2005; 162: 485–94.Google Scholar
28 Yurgelun-Todd, D, Waternaux, C, Cohen, B, Gruber, S, English, C, Renshaw, P. Functional magnetic resonance imaging of schizophrenic patients during word production. Am J Psychiatry 1996; 153: 200–5.Google ScholarPubMed
29 Spence, SA, Brooks, DJ, Hirsch, SR, Liddle, PF, Meehan, J, Grasby, PM. A PET study of voluntary movement in schizophrenic patients experiencing passivity phenomena (delusions of alien control). Brain 1997; 120 (Pt 11): 19972011.CrossRefGoogle ScholarPubMed
30 Curtis, V, Bullmore, ET, Brammer, MJ, Wright, IC, Williams, SC, Morris, RG, et al. Attenuated frontal activation during a verbal fluency task in patients with schizophrenia. Am J Psychiatry 1998; 155: 1056–63.Google Scholar
31 Wagner, AD, Pare-Blagoev, EJ, Clark, J, Poldrack, RA. Recovering meaning: left prefrontal cortex guides controlled semantic retrieval. Neuron 2001; 31: 329–38.CrossRefGoogle ScholarPubMed
32 Moss, HE, Abdallah, S, Fletcher, P, Bright, P, Pilgrim, L, Acres, K, et al. Selecting among competing alternatives: selection and retrieval in the left inferior frontal gyrus. Cereb Cortex 2005; 15: 1723–35.CrossRefGoogle ScholarPubMed
33 Brewer, WJ, Francey, SM, Wood, SJ, Jackson, HJ, Pantelis, C, Phillips, LJ, et al. Memory impairments identified in people at ultra-high risk for psychosis who later develop first-episode psychosis. Am J Psychiatry 2005; 162: 71–8.Google Scholar
34 Hawkins, KA, Addington, J, Keefe, RS, Christensen, B, Perkins, DO, Zipurksy, R, et al. Neuropsychological status of subjects at high risk for a first episode of psychosis. Schizophr Res 2004; 67: 115–22.Google Scholar
35 Gschwandtner, U, Aston, J, Borgwardt, S, Drewe, M, Feinendegen, C, Lacher, D, et al. Neuropsychological and neurophysiological findings in individuals suspected to be at risk for schizophrenia: preliminary results from the Basel early detection of psychosis study – Fruherkennung von Psychosen (FEPSY). Acta Psychiatr Scand 2003; 108: 152–5.Google Scholar
36 Pflueger, MO, Gschwandtner, U, Aston, J, Berger, G, Borgwardt, S, Drewe, M, et al. Cognitive capability of individuals at risk with and without transition to psychosis. Eur Psychiatry 2007; 22: s30.Google Scholar
37 Broome, MR, Johns, LC, Valli, I, Woolley, JB, Tabraham, P, Brett, C, et al. Delusion formation and reasoning biases in those at clinical high risk for psychosis. Br J Psychiatry 2007; 51: s3842.Google Scholar
38 Broome, MR, Woolley, JB, Tabraham, P, Johns, LC, Bramon, E, Murray, GK, et al. What causes the onset of psychosis? Schizophr Res 2005; 79: 2334.CrossRefGoogle ScholarPubMed
39 Borgwardt, S, Riecher-Rössler, A, Dazzan, P, Chitnis, X, Aston, J, Drewe, M, et al. Regional gray matter volume abnormalities in the at risk mental state. Biol Psychiatry 2007; 61: 1148–56.Google Scholar
Figure 0

Table 1 Age, IQ, gender and psychopathology ratings across groups

Figure 1

Fig. 1 Group differences in cluster activation during the 1-back and 2-back conditions of the N-back task. For the 1-back condition, activation was greatest in controls, weakest in the psychosis group and intermediate in the at-risk group in the left inferior parietal lobule and in the right angular gyrus. Differential activation during the 2-back condition was greatest in controls, weakest in the psychosis group and intermediate in the at-risk group in the lateral prefrontal, insular and parietal cortex, and in the precuneus, The left side of the brain is shown on the left of the figure (voxel P<0.05, cluster P<0.01). SSQRs, sum of squares of deviations due to the residuals.

Figure 2

Table 2 1-back task between-group differences in activation: controls > at-risk > psychosis

Figure 3

Table 3 2-back task between-group differences in activation: controls > at-risk > psychosis

Figure 4

Fig. 2 Group differences in left inferior frontal cluster activation during ‘easy’ verbal fluency. The at-risk group showed greater activation than the psychosis group but less than the controls. The left side of the brain is shown on the left of the figure (voxel P<0.05, cluster P<0.01). SSQ ratio, sum of squares of deviations due to the residuals.

Figure 5

Table 4 Controls < at-risk < psychosis: ‘easy’ verbal fluency task between-group differences in activation

Figure 6

Fig. 3 Group difference in cluster activation during ‘hard’ verbal fluency. When the task demands were high, there was a differential engagement of dorsolateral prefrontal cortex with activation greatest in the control group, weakest in the psychosis group, and intermediate in the at-risk group. However, on the same version of the task, there was differential engagement of the left anterior insula. When task demands were high activation in this region was greatest in the psychosis group, weakest in the controls and intermediate in the at-risk group. The left side of the brain is shown on the left of the figure (voxel P<0.05, cluster P<0.01). SSQRs, sum of squares of deviations due to the residuals.

Figure 7

Table 5 Controls > at-risk > psychosis: ‘hard’ verbal fluency task between-group differences in activation

Figure 8

Table 6 Psychosis > at-risk > controls: ‘hard’ verbal fluency task between-group differences in activation

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