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Original Investigation |

Brain Connectivity Abnormalities Predating the Onset of Psychosis Correlation With the Effect of Medication FREE

André Schmidt, PhD1,2; Renata Smieskova, PhD1,2; Jacqueline Aston, MD1; Andor Simon, MD1,3,4; Paul Allen, PhD5; Paolo Fusar-Poli, MD, PhD6; Philip K. McGuire, MD, PhD5,6; Anita Riecher-Rössler, MD, PhD1; Klaas E. Stephan, MD, PhD7,8; Stefan Borgwardt, MD, PhD1,2,5
[+] Author Affiliations
1Department of Psychiatry, University of Basel, Basel, Switzerland
2Medical Image Analysis Centre, University Hospital Basel, Basel, Switzerland
3University Hospital of Psychiatry, University of Bern, Bern, Switzerland
4Department of Psychiatry, Specialized Early Psychosis Outpatient Service for Adolescents and Young Adults, Bruderholz, Switzerland
5Institute of Psychiatry, Department of Psychosis Studies, King’s College London, London, England
6OASIS Team, National Health Service South London Foundation Trust, London, England
7Wellcome Trust Centre for Neuroimaging, London, England
8Translational Neuromodeling Unit, Institute for Biomedical Engineering, and ETH Zurich, Laboratory for Social and Neural Systems Research, University of Zurich, Zurich, Switzerland
JAMA Psychiatry. 2013;70(9):903-912. doi:10.1001/jamapsychiatry.2013.117.
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Published online

Importance  Brain imaging studies have identified robust changes in brain structure and function during the development of psychosis, but the contribution of abnormal brain connectivity to the onset of psychosis is unclear. Furthermore, antipsychotic treatment can modulate brain activity and functional connectivity during cognitive tasks.

Objectives  To investigate whether dysfunctional brain connectivity during working memory (WM) predates the onset of psychosis and whether connectivity parameters are related to antipsychotic treatment.

Design  Dynamic causal modeling study of functional magnetic resonance imaging data.

Setting  Participants were recruited from the specialized clinic for the early detection of psychosis at the Department of Psychiatry, University of Basel, Basel, Switzerland.

Participants  Seventeen participants with an at-risk mental state (mean [SD] age, 25.24 [6.3] years), 21 individuals with first-episode psychosis (mean [SD] age, 28.57 [7.2] years), and 20 healthy controls (mean [SD] age, 26.5 [4] years).

Main Outcome and Measure  Functional magnetic resonance imaging data were recorded while participants performed an N-back WM task. Functional interactions among brain regions involved in WM, in particular between frontal and parietal brain regions, were characterized using dynamic causal modeling. Bayesian model selection was performed to evaluate the likelihood of alternative WM network architectures across groups, whereas bayesian model averaging was used to examine group differences in connection strengths.

Results  We observed a progressive reduction in WM-induced modulation of connectivity from the middle frontal gyrus to the superior parietal lobule in the right hemisphere in healthy controls, at-risk mental state participants, and first-episode psychosis patients. Notably, the abnormal modulation of connectivity in first-episode psychosis patients was normalized by treatment with antipsychotics.

Conclusions and Relevance  Our findings suggest that the vulnerability to psychosis is associated with a progressive failure of functional integration of brain regions involved in WM processes, including visual encoding and rule updating, and that treatment with antipsychotics may have the potential to counteract this.

Figures in this Article

Abnormal functional integration among brain areas has been proposed as a pathophysiologic hallmark of psychosis.1,2 Indeed, recent studies,3,4 which estimated effective connectivity from functional magnetic resonance imaging (fMRI) data, reported dysfunctional integration of regional brain activity in patients with psychoses. The extent of this dysconnectivity has often been linked to the severity of psychotic symptoms,5,6 suggesting a mechanistic relation between the degree of functional network integrity and the clinical expression of schizophrenia.2

In the last decade, new research paradigms have investigated individuals at increased clinical risk of developing psychosis. These patients, mostly those with schizophrenia spectrum disorders,7 with an at-risk mental state (ARMS) have a high probability of transitioning to overt psychosis within a short period (36% within 3 years after presentation).8 The ARMS criteria require 1 of 3 presentations: attenuated psychotic symptoms, full-blown psychotic symptoms that are brief and self-limiting, or a significant decrease in functioning in the context of a family history of schizophrenia.9,10 Some evidence suggests that vulnerability to psychosis is associated with qualitatively similar, but less severe, dysfunctional connectivity as in patients with full-blown psychosis. For example, Crossley and colleagues11 demonstrated a progressive increase in dysfunctional frontotemporal connectivity during a working memory (WM) task from healthy controls (HCs) to ARMS individuals and further to patients with first-episode psychosis (FEP). However, this study did not analyze the modulation of effective connectivity by WM, a putative index of executive (dys)function and, thus, of pathophysiologic relevance for psychotic symptoms.12 Beyond connectivity, ARMS is also associated with abnormalities of regional brain structure,13,14 activity,15 and neurochemistry16,17 that are qualitatively similar to but less severe than those in patients with overt psychosis.18 There is also evidence suggesting that within ARMS individuals, prefrontal dysfunction during WM is related to gray matter abnormalities in the same regions.19

Neurocognitive deficits are considered to be a central manifestation of the pathophysiology of schizophrenia.20 The most consistent findings are usually observed in WM and higher-order executive function and are evident not only at the time of the first episode of the disease21 but also in ARMS individuals.22 Deficits in WM may vary with illness progression during the development of psychosis, reflecting dynamic changes in the underlying neurobiology. However, the results of previous studies23 have been inconsistent, with some studies reporting significant alterations and others reporting no significant differences to matched controls. These conflicting findings across studies prevent clinical applications of neurocognitive assessment and neuroimaging methods in prodromal psychosis because it remains unclear to what extent the observed deficits are consistently associated with vulnerability to psychosis and whether they may be predictive markers of the illness. In the current study, we aimed to explore whether modulation of effective connectivity during WM predates the onset of psychosis. We applied dynamic causal modeling (DCM)24 to fMRI data from ARMS individuals, FEP patients, and HCs during the performance of an N-back task. Significant WM-associated frontoparietal activations during the N-back were previously observed in these groups, whereas ARMS individuals and FEP patients had reduced activations in these regions compared with HCs,25 suggesting differences in the underlying brain connectivity. We hypothesized that FEP patients would have reduced WM-induced frontoparietal connectivity compared with HCs, whereas the ARMS group would have an intermediate value. In addition, we also examined modulation of interhemispheric interactions by WM, given previous reports suggesting altered callosal connectivity during WM processing among prefrontal and parietal areas in patients with schizophrenia.26,27 Finally, we also tested for the effect of antipsychotic treatment on modulation of connectivity by WM in FEP patients because it has been reported that antipsychotic treatment can alter neuroanatomical brain structures28 and modulate brain activity during the N-back task.29,30

The presented DCM analyses are based on the fMRI data previously published by Smieskova et al.25 Details of the mass-univariate statistical parametric mapping (SPM) analyses are described in the previous publication. In this article, we provide a summary of the participants’ characteristics and details of data analysis with DCM and bayesian model selection (BMS). In addition, a brief summary of the N-back task and fMRI data acquisition protocol is given in the eMethods in the Supplement.

Participants

We recruited ARMS individuals and FEP patients in our specialized clinic for the early detection of psychosis at the Department of Psychiatry, Psychiatric University Hospital Basel, Basel, Switzerland. These patients were allocated to either the FEP or ARMS group according to the Program Approval for Continuing Education criteria. Given the possibility of FEP resulting from an initial (and possibly unnoticed) ARMS, it would have been interesting to address the question of how many of our FEP patients have transitioned from ARMS. However, because of our cross-sectional design, this was not possible in the present study. All participants provided written informed consent, and the study had research ethics committee permission.

We assessed individuals using the Basel Screening Instrument for Psychosis,31 the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms, and the Global Assessment of Functioning. We additionally obtained information on current and previous psychotropic medication, nicotine, and illegal drug consumption using a semistructured interview adapted from the Early Psychosis Prevention and Intervention Centre Drug and Alcohol Assessment Schedule (eppic.org.au).

The following exclusion criteria for all groups applied: history of previous psychotic disorder, psychotic symptoms secondary to an organic disorder, substance abuse according to International Statistical Classification of Diseases, 10th Revision (ICD-10) research criteria, psychotic symptoms associated with an affective psychosis or a borderline personality disorder, age younger than 18 years, inadequate knowledge of the German language, and IQ less than 70 as measured by the Mehrfachwahl Wortschatz Test Form B.

In keeping with previous MRI studies8,13 of ARMS individuals, we recruited 17 ARMS individuals using these high-risk criteria. Inclusion into the present study required one or more of the following: (1) attenuated psychotic-like symptoms, (2) brief limited intermittent psychotic symptoms, or (3) a first- or second-degree relative with a psychotic disorder plus at least 2 additional risk factors for or indicators of beginning psychosis according to the Basel Screening Instrument for Psychosis. Inclusion because of attenuated psychotic symptoms required that change in mental state had to be present at least several times a week and for more than 1 week (a score of 2 or 3 on the BPRS hallucination item or 3 or 4 on BPRS items for unusual thought content or suspiciousness). Inclusion because of brief limited intermittent psychotic symptoms required scores of 4 or above on the hallucination item or 5 or above on the unusual thought content, suspiciousness, or conceptual disorganization items of the BPRS, with each symptom lasting less than 1 week before resolving spontaneously. A more detailed description of these ARMS criteria can be found in a previous publication.32,33 All ARMS individuals were antipsychotic naive.

The FEP patients (n = 21) were those who met the criteria for transition to psychosis according to Yung et al.34 These patients already fulfilled criteria for acute psychotic disorder according to ICD-10 or DSM-IV but not yet for schizophrenia. Inclusion required scores of 4 or above on the hallucination item or 5 or above on the unusual thought content, suspiciousness, or conceptual disorganization items of the BPRS.34 The symptoms must have occurred at least several times a week and persisted for more than 1 week. Seven FEP patients were completely antipsychotic naive, whereas 6 had previously been treated with antipsychotics but were antipsychotic free at the time of this study. Eight FEP patients were receiving antipsychotics at the time of scanning (5 patients receiving quetiapine and 2 patients receiving paliperidone for less than 6 months and 1 patient receiving olanzapine for less than 2 years).

We recruited 20 HCs from the same geographic area as the other groups. The HCs had no current psychiatric disorder; no history of psychiatric illness, head trauma, neurologic illness, serious medical or surgical illness, or substance abuse; and no family history of any psychiatric disorder as assessed by an experienced psychiatrist (J.A., S.J.B., A.R.-R.) in a detailed clinical semistructured interview.

Dynamic Causal Modeling

We used DCM-10 (revision No. 4290) as implemented in SPM-8 to analyze effective connectivity. In DCM for fMRI, the dynamics of the neural states underlying regional BOLD response are modeled by a bilinear differential equation that describes how the neural states change as a function of endogenous interregional connections, modulatory effects on these connections, and driving inputs.24,35 The endogenous connections represent coupling strengths in the absence of inputs to the system (independent of the task), whereas the modulatory effects represent context-specific and additive changes in coupling (eg, task-induced alterations in connectivity). The modeled neuronal dynamic is then related to the measured BOLD signal using a hemodynamic forward model.35 We explicitly examined how the coupling strengths between prefrontal and parietal regions are changed by the 2-back condition (modulatory effect).

Regions of Interest and Time Series Extraction

The regions of interest of our anatomical network were selected on the basis of 3 sources of information: (1) the previously published second-level SPM analysis of these data,25 (2) previous functional connectivity studies emphasizing the importance of frontoparietal connections for WM,36 and (3) previous DCM studies of WM in schizophrenia.3,11 The conventional second-level SPM analysis had revealed significant activation in the bilateral superior parietal lobule (SPL) and middle frontal gyrus (MFG) in all groups (Figure 1),25 whereas ARMS individuals and FEP patients had reduced SPL and MFG activations, suggesting differences in brain connectivity among groups. To test this hypothesis, we created an anatomical mask comprising the SPL and MFG taken from the automated Talairach atlas in the Wake Forest University Pick Atlas toolbox.37 Similar to previous DCM studies in ARMS individuals,38 regional time series from each participant were extracted from spherical volumes of interest with 12 mm in diameter that were centered on the group maxima of the 2-back greater than 0-back contrast within the anatomical mask using the first eigenvariate of voxels above a participant-specific F threshold of P < .001 (uncorrected). When a participant had no voxel above threshold at the group maxima (eTable 1 in the Supplement), we selected the nearest suprathreshold voxel within the mask. Four participants revealed no activated voxels under these criteria (1 HC, 2 ARMS individuals, and 1 FEP patients) and were therefore excluded from further analyses.

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Figure 1.
Regional Brain Activation During Working Memory Processing (2-Back Greater Than 0-Back) Within the Anatomical Mask

A, Healthy controls. B, Individuals with an at-risk mental state. C, Patients experiencing a first-episode psychosis. Activations are reported at a whole-brain–corrected cluster threshold of P < .001 and a voxel size threshold of 1400.

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Model Space

Across all models tested, we assumed the same network layout of connections between right and left SPL and MFG. Specifically, SPL and MFG were reciprocally connected within both hemispheres, with additional interhemispheric connections among all regions. Similar to a recent DCM study of WM,39 the visual input (driving) entered the SPL bilaterally.40 Starting from this basic layout, a factorially structured model space was derived by considering where the modulatory effect of the 2-back WM condition might be expressed within both hemispheres (for a graphic summary of the model space see Figure 2).

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Figure 2.
Model Space Tested in the Study

Numbers 1 through 4 indicate right superior parietal lobule, left superior parietal lobule, right middle frontal gyrus, and left middle frontal gyrus, respectively. We contrasted models in which the 2-back working memory (WM) condition was allowed to modulate within both hemispheres: the parietofrontal connections (top row), the frontoparietal connections (middle row), or both (bottom row). These 3 intrahemispheric options were crossed with 4 possibilities for which interhemispheric connections might be modulated by the 2-back WM condition: none (A), the interhemispheric connections between the parietal areas (B), the interhemispheric connections between the frontal areas (C), or both (D). As a result, our model space consisted of 12 alternative models, each of which was fitted to the data from each study participant.

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BMS and Bayesian Model Averaging

We used BMS to determine the most plausible models of the ones we considered. The BMS rests on comparing the (log) evidence of a predefined set of models (the model space). The model evidence is the probability of observing the empirical data, given a model, and represents a principled measure of model quality, derived from probability theory.41 Concretely, it represents the mean predicted data under random sampling from the model’s priors or, alternatively, the difference between the accuracy (fit) of a model and its complexity. We used a random-effects BMS approach for group studies, which is capable of quantifying the degree of heterogeneity in a population while being extremely robust to potential outliers.42 This method considers the model as a random variable and estimates the parameters of a Dirichlet distribution, which describes the probabilities of all models considered. One common way to summarize the results of random-effects BMS is to report the exceedance probability (EP) of each model (ie, the probability that this model is more likely than any other of the models tested, given the group data).42

In a first step, BMS was conducted treating HCs, ARMS individuals, and FEP patients as one group. Because data from the groups may be generated by different mechanisms and, thus, different models may explain the groupwise data best,3 we performed BMS also for each groups separately.

Statistical comparison of model parameter estimates across groups is only valid if those estimates stem from the same model. Given that different models may be found to be optimal across groups, bayesian model averaging (BMA) has been recommended as the standard approach for clinical DCM studies.43 The BMA averages posterior parameter estimates over models, weighted by the posterior model probabilities.44 Thus, models with a low posterior probability contribute little to the estimation of the marginal posterior.

Group Statistic of DCM Parameters

After BMA, we used the resulting posterior means from the averaged DCM for examining differences among groups. In this article, we focused on WM-induced changes in connectivity. Thus, we test for group differences in the modulatory parameters only. We then used 1-way analysis of variance (ANOVA), asking which of the connectivity parameters differed across groups. In a first step, all 20 FEP individuals were treated as one group for ANOVA.

To address the effect of antipsychotics on effective connectivity, in a second step, 2 FEP subgroups were included in a subsequent ANOVA based on whether they were treated with antipsychotics (n = 8) or nontreated (n = 12). Bonferroni-corrected post hoc t tests were conducted after obtaining significant ANOVA results.

Statistical Analysis

Clinical and sociodemographic differences were previously analyzed and published by Smieskova et al25; we repeat them here for completeness. Beyond those previous analyses, we analyzed behavioral performances during the 2-back WM condition using signal detection theory.45 The sensitivity index was calculated using the formula z(Hits) − z(False Alarms). The equal weighting of hits and false alarms provides an objective measure of sensitivity that is independent of participant response bias. The sensitivity index values were further subjected to a 1-way ANOVA. When the ANOVA null hypothesis of equal means was rejected, we used Bonferroni-corrected post hoc t tests.

Participant Characteristics

Demographic and clinical group characteristics are summarized in eTable 2 in the Supplement.

Behavioral Performance During WM Processing

Working memory performance as operationalized by the sensitivity index differed across HCs, ARMS individuals, and FEP patients (F = 5.12; P = .009). Specifically, FEP patients (P = .008) had significantly lower WM performance than HCs. The WM performance in the ARMS group was also lower than in the control group, but this difference did not reach statistical significance (P = .14). In a subsequent ANOVA we distinguished antipsychotic-treated from nontreated FEP patients (F = 3.44; P = .02) and found that only nontreated (P = .03) but not antipsychotic-treated FEP individuals (P = .27) differed from HCs (Figure 3).

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Figure 3.
Mean Sensitivity Indexes

Sensitivity indexes during the 2-back conditions in healthy controls (HCs) (n = 19), individuals with an at-risk mental state (ARMS) (n = 15), and individuals with first-episode psychosis (FEP) who were nontreated (n = 12) or antipsychotic treated (n = 8). Asterisk indicates significant differences among the groups at P < .05. Error bars indicate SEs.

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DCM Results
Bayesian Model Selection

The BMS yielded variable results across groups (Figure 4). When testing all groups together, model 12 was found to be most likely (EP = 47.73%), followed by model 4 (EP = 33.73%), model 6 (EP = 8.55%), and model 9 (EP = 7.56%). When applying BMS to each group separately, however, slight variations emerged. In HCs, model 4 emerged as the most likely model (EP = 63.43%), whereas model 12 was only second best (EP = 22.41%) and model 9 the third best (EP = 6.44%). In ARMS individuals, model 12 was identified as most likely (EP = 27.63%), followed by model 2 (EP = 27.17%) and model 9 (EP = 21.22%). In all FEP patients together, model 6 was clearly superior to all other models (EP = 58.05%), whereas model 12 was second best (EP = 18.47%) and model 4 the third best (EP = 11.7%). These BMS results across all groups are summarized in Figure 4.

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Figure 4.
Bayesian Model Selection Among All 12 Dynamic Causal Models

Data are shown for all groups together and separately for healthy controls (HCs), at-risk mental state (ARMS) individuals, and first-episode psychosis (FEP) patients.

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Group Statistics of DCM Parameters
Differences in Effective Connectivity

Statistical analysis of group differences in connection strengths concerned the posterior means of coupling estimates, following BMA for all 12 models. In other words, we compared the parameter estimates from participant-specific DCMs that were averaged over the 12 models (by using BMA) separately within each group. Thus, in our final group-level analysis of effective connectivity, we were able to test for differences among groups in 8 parameters describing the modulation of frontoparietal connections, within and across hemispheres, by the 2-back WM condition.

For the modulatory effect, significant group differences were found for the right MFG to SPL connectivity (F = 3.70; P = .03). Bonferroni-corrected post hoc t tests revealed that the right MFG to SPL connectivity was significantly reduced in FEP patients compared with HCs (P = .048), whereas the MFG to SPL connectivity in ARMS individuals was lower than in the control group, although this finding was not statistically significant (P = .10) (Figure 5). In contrast to these intrahemispheric connectivity differences among groups, we did not find significant group differences in the modulation of interhemispheric connections by the 2-back WM condition.

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Figure 5.
The Modulatory Effect of the 2-Back Working Memory (WM) Condition on the Connection From the Right Middle Frontal Gyrus (MFG) to the Right Superior Parietal Lobule (SPL)

Data are shown for healthy controls (HCs) (n = 19), individuals with an at-risk mental state (ARMS) (n = 15), and all patients with first-episode psychosis (FEP) (n = 20), independent of antipsychotic treatment. The y-axis denotes the mean for all study participants and all 12 dynamic causal models (using bayesian model averaging) with regard to the posterior mean of the modulatory effect; this encodes changes in connection strength induced by the 2-back WM condition. Asterisk indicates significant differences among groups at P < .05; dagger, significant t test within each group (relative to zero) at P < .05. Error bars indicate SDs derived from bayesian parameter averages.

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Effects of Antipsychotics on Effective Connectivity

After dissociating antipsychotic-treated from nontreated FEP patients, a subsequent ANOVA on the modulatory effect revealed significant group differences for the right MFG to SPL connectivity across groups (F = 2.99; P = .04). In particular, Bonferroni-corrected post hoc t tests revealed that the right MFG to SPL connectivity in HCs is dissociable from nontreated FEP patients (P = .046) but not from antipsychotic-treated FEP patients (P > .99) (Figure 6). The Table summarized the DCM parameters for all groups. Furthermore, using a multiple regression analysis, we did not find any significant relationship between the right MFG to SPL connectivity and clinical and demographic variables (statistics are provided in eTable 3 in the Supplement).

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Figure 6.
The Modulatory Effect of the 2-Back Working Memory (WM) Condition on the Connection From the Right Middle Frontal Gyrus (MFG) to the Right Superior Parietal Lobule (SPL)

Data are shown for healthy controls (HCs) (n = 19), individuals with an at-risk mental state (ARMS) (n = 15), and individuals with a first-episode psychosis (FEP) who were antipsychotic treated (n = 8) or nontreated (n = 12) during scanning. The y-axis denotes the mean for all study participants and all 12 dynamic causal models (using bayesian model averaging) with regard to the posterior mean of the modulatory effect; this encodes changes in connection strength induced by the 2-back WM condition. Asterisk indicates significant differences among groups at P < .05; dagger, significant t test within each group (relative to zero) at P < .05. Error bars indicate SDs derived from bayesian parameter averages.

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Table Graphic Jump LocationTable.  Dynamic Causal Modeling Parameter Estimates: Modulatory Effects of the 2-Back Working Memory Conditiona

In the present study we investigated whether there are changes in the modulation of within- and across-hemisphere functional interactions between the SPL and MFG by WM that predate the onset of psychosis and are related to antipsychotic treatment. We found that the modulatory effect on the MFG to SPL connectivity induced by the 2-back WM condition was gradually reduced from HCs to ARMS individuals and further to nontreated FEP patients. Remarkably, nontreated but not antipsychotic-treated FEP patients differed significantly from HCs with regard to the modulatory effect of WM on the MFG to SPL connectivity.

The behavioral performance differed between nontreated FEP patients and HCs, whereas ARMS individuals showed an intermediate performance, which was not significantly altered compared with HCs. This finding is consistent with a previous study in ARMS individuals and FEP patients, which reported that in the FEP group the proportion of correct responses was significantly lower than in the control group, whereas in the ARMS group the proportion of correct responses was lower than in the control group but did not reach statistical significance.46 In contrast, other studies11,15 found no significant group differences among HCs, ARMS individuals, and FEP patients in the number of missed targets, misidentified nontargets, and error rates, respectively, while performing the N-back task. In the later study,15 the lack of behavioral differences among groups is due to excluding from the analysis individuals who performed the task badly and the study being powered to detect physiologic changes. Furthermore, instead of evaluating the number of omissions or error rates, in this study we analyzed the 2-back WM performances using signal-detection theory.45 Although this precludes a direct comparison with the previous studies mentioned, the advantage of using the sensitivity index is that it provides an objective measure independent of the individual’s response bias. Thus, in our study, when equally weighting hit rates and false alarms, the 2-back WM performance did significantly distinguish nontreated FEP patients from HCs, whereas the WM performance in ARMS individuals came out in the middle of both groups.

Moreover, contrary to nontreated FEP patients, no significant difference was found in antipsychotic-treated FEP individuals compared with HCs, indicating an improved cognitive performance after antipsychotic medication. This result is in line with recent studies in early-psychosis patients47 and in those with chronic schizophrenia,48 which reported significant improvements in neurocognition after the treatment with antipsychotics.

Our previously published conventional fMRI analysis revealed significant activations in the bilateral SPL and MFG during the N-back task in all groups,25 consistent with previous N-back studies in ARMS individuals.15,19 However, the ARMS and FEP individuals revealed reduced activations in these regions relative to HCs. Therefore, in this study we aimed to investigate whether these different SPL and MFG activations among HCs, ARM patients, and FEP patients could be mediated by aberrant task-induced modulation of connectivity, given that interactions between frontal and parietal regions are crucial for WM performance.49

We found a gradual reduction of the modulatory effect on the MFG to SPL connectivity from HCs to ARMS individuals and further to nontreated FEP patients. Although our result of a reduced right MFG to SPL connectivity in nontreated FEP patients agrees with reports on reduced frontoparietal connectivity in patients with schizophrenia,3 Crossley et al11 reported a progressive increase in the frontotemporal connectivity in individuals with FEP. However, the results of the 2 studies are not directly comparable because of several major methodologic differences, including different contrasts used to identify WM-associated activations and different regions of interest. Furthermore, and perhaps most importantly, Crossley and colleagues focused on endogenous connections and not on connectivity changes, reflecting changes in the endogenous coupling between regions that are induced by experimental manipulations (ie, 2-back) as in this study.

The N-back task comprises numerous simultaneous cognitive processes, including a continuous encoding of incoming visual letters and rule updating. Although WM research has traditionally focused on mechanisms for maintaining stimulus representations, more recently the importance of rules for relating those stimuli to pending responses has been emphasized.49 These rules embody the if-then relationships that underlie decisions (eg, “If the letter presently displayed corresponds to the letter shown 2 trials ago, press the right button”) and that may change in time, as in the N-back task. Connections from the parietal cortex to the frontal cortex may contribute to the encoding of incoming stimuli,39 whereas the connections from the frontal to the parietal cortex likely mediate the updating of rules (eg, 2-back).36 Our DCM analyses suggest a reduced modulation of MFG to SPL connectivity by the 2-back WM condition (relative to 0-back and 1-back) in nontreated FEP individuals. If the common interpretations of bottom-up and top-down connections between parietal and prefrontal areas mentioned above are correct, this result would indicate a specific failure in rule updating during WM in nontreated FEP individuals. However, this specific interpretation should be treated with caution because our experimental design did not separately manipulate demands on visual stimulus encoding and rule updating.

Notably, the reduction of the right MFG to SPL connectivity between HCs and nontreated FEP patients was not found between HCs and antipsychotic-treated FEP patients. This is consistent with previous fMRI studies reporting that antipsychotic treatment can modulate brain activity during cognitive tasks29,30 and, more specifically, functional connectivity in circuits that are involved in mediating the cognitive symptoms in schizophrenia.50 Furthermore, our result fits with a recent study51 in FEP patients reporting that after 6 weeks of treatment with second-generation antipsychotic drugs, frontoparietal-temporal network connectivity during resting state was no longer significantly different from controls. Our present results echo this finding and suggest that antipsychotic treatment also normalizes frontoparietal connectivity during a WM task in FEP patients, which might explain their improved cognitive performance relative to nonmedicated patients.

Dynamic causal modeling is a suitable technique for inferring on synaptic plasticity, and disordered brain connectivity in schizophrenia is thought to result from abnormal regulation of N-methyl-d-aspartate receptor–dependent synaptic plasticity by neuromodulatory transmitters such as dopamine.2 Accordingly, abnormal N-methyl-d-aspartate receptor signaling may contribute to alteration in prefrontal connectivity in patients with schizophrenia.52 Thus, the effects of antipsychotic treatment on WM-dependent modulation of brain connectivity found in this study might reflect antipsychotic-induced alterations of dopaminergic regulation of N-methyl-d-aspartate receptor–dependent synaptic plasticity of frontoparietal connections. This would be consistent with recent studies that reported that dopamine influences prefrontal control of parietal processing in WM53 and that altered prefrontal activation in patients with an ARMS for psychosis is related to elevated striatal dopamine function.16

There are some limitations to be considered in the present study. Because of the chosen block design in this study, we were not able to distinguish among the different components of the WM process, although patients with psychosis reveal different impairments during encoding, retrieval, and information manipulation.54,55 Furthermore, we used a fairly simplistic neuronal network underlying WM-dependent effective connectivity with bilinear modulation of connections.24 There are also other brain regions involved in WM tasks, such as the anterior cingulate cortex,39 whose structural abnormalities have also been reported to be predictive for development of psychosis13 but that we did not include in our present model for reasons of parsimony and comparability with previous studies3 of connectivity during WM tasks in schizophrenia. Furthermore, the 8 antipsychotic-treated FEP patients received different antipsychotic medication in terms of substance and duration of the treatment. With this relatively small sample size, it is impossible to further subdivide the group of antipsychotic-treated FEP patients and determine drug-specific effects. Finally, the short duration of clinical follow-up did not allow investigating transition outcome–associated connectivity abnormalities.

In conclusion, our present findings suggest that abnormalities in WM-induced modulation of connectivity may separate HCs from ARMS individuals and FEP patients and may thus serve as a physiologic marker that predates the onset of psychosis. Moreover, as demonstrated in previous studies,53 the current study further demonstrates the potential of quantitative connectivity analysis for inferring the effects of antipsychotic treatment. These findings may particularly benefit clinical efforts to predict the onset of psychosis from the extent of dysfunctional connectivity during WM.

Corresponding Author: Stefan J. Borgwardt, MD, PhD, Department of Psychiatry (UPK), University of Basel, Petersgraben 4, Basel 4031, Switzerland (stefan.borgwardt@upkbs.ch).

Published Online: July 3, 2013. doi:10.1001/jamapsychiatry.2013.117.

Author Contributions:Study concept and design: Schmidt, McGuire, Riecher-Rössler, and Borgwardt.

Acquisition of data: Smieskova, Aston, Simon, McGuire, Riecher-Rössler, and Borgwardt.

Analysis and interpretation of data: Schmidt, Smieskova, Simon, Allen, Fusar-Poli, McGuire, Riecher-Rössler, Stephan, and Borgwardt.

Drafting of the manuscript: Schmidt and Smieskova.

Critical revision of the manuscript for important intellectual content: Schmidt, Aston, Simon, Allen, Fusar-Poli, McGuire, Riecher-Rössler, Stephan, and Borgwardt.

Statistical analysis: Schmidt, Allen, and Stephan.

Obtained funding: McGuire, Riecher-Rössler, and Borgwardt.

Administrative, technical, and material support: Aston, Simon, McGuire, and Riecher-Rössler.

Study supervision: Fusar-Poli, McGuire, Riecher-Rössler, and Borgwardt.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported in part by grant 3232BO_119382 from the Swiss National Science Foundation (Drs Schmidt and Borgwardt) and from the René and Susanne Braginsky Foundation (Dr Stephan).

Additional Contributions: The Früherkennung von Psychosen Study Group recruited and managed the study participants. In addition, we acknowledge the contribution of the study participants.

Friston  KJ.  The disconnection hypothesis. Schizophr Res. 1998;30(2):115-125.
PubMed   |  Link to Article
Stephan  KE, Friston  KJ, Frith  CD.  Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophr Bull. 2009;35(3):509-527.
PubMed   |  Link to Article
Deserno  L, Sterzer  P, Wüstenberg  T, Heinz  A, Schlagenhauf  F.  Reduced prefrontal-parietal effective connectivity and working memory deficits in schizophrenia. J Neurosci. 2012;32(1):12-20.
PubMed   |  Link to Article
Mukherjee  P, Whalley  HC, McKirdy  JW,  et al.  Lower effective connectivity between amygdala and parietal regions in response to fearful faces in schizophrenia. Schizophr Res. 2012;134(2-3):118-124.
PubMed   |  Link to Article
Lawrie  SM, Buechel  C, Whalley  HC, Frith  CD, Friston  KJ, Johnstone  EC.  Reduced frontotemporal functional connectivity in schizophrenia associated with auditory hallucinations. Biol Psychiatry. 2002;51(12):1008-1011.
PubMed   |  Link to Article
Mechelli  A, Allen  P, Amaro  E  Jr,  et al.  Misattribution of speech and impaired connectivity in patients with auditory verbal hallucinations. Hum Brain Mapp. 2007;28(11):1213-1222.
PubMed   |  Link to Article
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 (published online May 15, 2012). Schizophr Bull. doi:10.1093/schbul/sbs060.
PubMed
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(3):220-229.
PubMed   |  Link to Article
Yung  AR, Nelson  B, Stanford  C,  et al.  Validation of “prodromal” criteria to detect individuals at ultra high risk of psychosis: 2 year follow-up. Schizophr Res. 2008;105(1-3):10-17.
PubMed   |  Link to Article
Fusar-Poli  P, Borgwardt  S, Bechdolf  A,  et al.  The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry. 2013;70(1):107-120.
PubMed   |  Link to Article
Crossley  NA, Mechelli  A, Fusar-Poli  P,  et al.  Superior temporal lobe dysfunction and frontotemporal dysconnectivity in subjects at risk of psychosis and in first-episode psychosis. Hum Brain Mapp. 2009;30(12):4129-4137.
PubMed   |  Link to Article
Perlstein  WM, Carter  CS, Noll  DC, Cohen  JD.  Relation of prefrontal cortex dysfunction to working memory and symptoms in schizophrenia. Am J Psychiatry. 2001;158(7):1105-1113.
PubMed   |  Link to Article
Smieskova  R, Fusar-Poli  P, Allen  P,  et al.  Neuroimaging predictors of transition to psychosis: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2010;34(8):1207-1222.
PubMed   |  Link to Article
Fusar-Poli  P, Radua  J, McGuire  P, Borgwardt  S.  Neuroanatomical maps of psychosis onset: voxel-wise meta-analysis of antipsychotic-naive VBM studies. Schizophr Bull. 2011;38(6):1297-1307.
PubMed   |  Link to Article
Broome  MR, Matthiasson  P, Fusar-Poli  P,  et al.  Neural correlates of executive function and working memory in the ‘at-risk mental state’. Br J Psychiatry. 2009;194(1):25-33.
PubMed   |  Link to Article
Fusar-Poli  P, Howes  OD, Allen  P,  et al.  Abnormal prefrontal activation directly related to pre-synaptic striatal dopamine dysfunction in people at clinical high risk for psychosis. Mol Psychiatry. 2011;16(1):67-75.
PubMed   |  Link to Article
Allen  P, Chaddock  CA, Howes  OD,  et al.  Abnormal relationship between medial temporal lobe and subcortical dopamine function in people with an ultra high risk for psychosis. Schizophr Bull. 2012;38(5):1040-1049.
PubMed   |  Link to Article
Fusar-Poli  P, Perez  J, Broome  M,  et al.  Neurofunctional correlates of vulnerability to psychosis: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2007;31(4):465-484.
PubMed   |  Link to Article
Fusar-Poli  P, Broome  MR, Woolley  JB,  et al.  Altered brain function directly related to structural abnormalities in people at ultra high risk of psychosis: longitudinal VBM-fMRI study. J Psychiatr Res. 2011;45(2):190-198.
PubMed   |  Link to Article
van Os  J, Kenis  G, Rutten  BP.  The environment and schizophrenia. Nature. 2010;468(7321):203-212.
PubMed   |  Link to Article
Mesholam-Gately  RI, Giuliano  AJ, Goff  KP, Faraone  SV, Seidman  LJ.  Neurocognition in first-episode schizophrenia: a meta-analytic review. Neuropsychology. 2009;23(3):315-336.
PubMed   |  Link to Article
Fusar-Poli  P, Deste  G, Smieskova  R,  et al.  Cognitive functioning in prodromal psychosis: a meta-analysis. Arch Gen Psychiatry. 2012;69(6):562-571.
PubMed   |  Link to Article
Fusar-Poli  P, Howes  OD, Allen  P,  et al.  Abnormal frontostriatal interactions in people with prodromal signs of psychosis: a multimodal imaging study. Arch Gen Psychiatry. 2010;67(7):683-691.
PubMed   |  Link to Article
Friston  KJ, Harrison  L, Penny  W.  Dynamic causal modelling. Neuroimage. 2003;19(4):1273-1302.
PubMed   |  Link to Article
Smieskova  R, Allen  P, Simon  A,  et al.  Different duration of at-risk mental state associated with neurofunctional abnormalities: a multimodal imaging study. Hum Brain Mapp. 2012;33(10):2281-2294.
PubMed   |  Link to Article
Schlösser  R, Gesierich  T, Kaufmann  B, Vucurevic  G, Stoeter  P.  Altered effective connectivity in drug free schizophrenic patients. Neuroreport. 2003;14(17):2233-2237.
PubMed   |  Link to Article
Schlösser  R, Gesierich  T, Kaufmann  B,  et al.  Altered effective connectivity during working memory performance in schizophrenia: a study with fMRI and structural equation modeling. Neuroimage. 2003;19(3):751-763.
PubMed   |  Link to Article
Fusar-Poli  P, Smieskova  R, Kempton  M, Ho  B, Andreasen  NC, Borgwardt  S.  Progressive brain changes in schizophrenia related to antipsychotic treatment? a meta-analysis of longitudinal MRI studies. Neurosci Biobehav Rev. In press.
Snitz  BE, MacDonald  A  III, Cohen  JD, Cho  RY, Becker  T, Carter  CS.  Lateral and medial hypofrontality in first-episode schizophrenia: functional activity in a medication-naive state and effects of short-term atypical antipsychotic treatment. Am J Psychiatry. 2005;162(12):2322-2329.
PubMed   |  Link to Article
Fusar-Poli  P, Broome  MR, Matthiasson  P, Williams  SC, Brammer  M, McGuire  PK.  Effects of acute antipsychotic treatment on brain activation in first episode psychosis: an fMRI study. Eur Neuropsychopharmacol. 2007;17(6-7):492-500.
PubMed   |  Link to Article
Riecher-Rössler  A, Aston  J, Ventura  J,  et al.  The Basel Screening Instrument for Psychosis (BSIP): development, structure, reliability and validity [in German]. Fortschr Neurol Psychiatr. 2008;76(4):207-216.
PubMed   |  Link to Article
Riecher-Rössler  A, Pflueger  MO, Aston  J,  et al.  Efficacy of using cognitive status in predicting psychosis: a 7-year follow-up. Biol Psychiatry. 2009;66(11):1023-1030.
PubMed   |  Link to Article
Riecher-Rössler  A, Gschwandtner  U, Aston  J,  et al.  The Basel early-detection-of-psychosis (FEPSY)-study--design and preliminary results. Acta Psychiatr Scand. 2007;115(2):114-125.
PubMed   |  Link to Article
Yung  AR, Phillips  LJ, McGorry  PD,  et al.  Prediction of psychosis. A step towards indicated prevention of schizophrenia. Br J Psychiatry Suppl. 1998;172(33):14-20.
PubMed
Stephan  KE, Weiskopf  N, Drysdale  PM, Robinson  PA, Friston  KJ.  Comparing hemodynamic models with DCM. Neuroimage. 2007;38(3):387-401.
PubMed   |  Link to Article
Gazzaley  A, Rissman  J, D’Esposito  M.  Functional connectivity during working memory maintenance. Cogn Affect Behav Neurosci. 2004;4(4):580-599.
PubMed   |  Link to Article
Maldjian  JA, Laurienti  PJ, Kraft  RA, Burdette  JH.  An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage. 2003;19(3):1233-1239.
PubMed   |  Link to Article
Allen  P, Stephan  KE, Mechelli  A,  et al.  Cingulate activity and fronto-temporal connectivity in people with prodromal signs of psychosis. Neuroimage. 2010;49(1):947-955.
PubMed   |  Link to Article
Ma  L, Steinberg  JL, Hasan  KM, Narayana  PA, Kramer  LA, Moeller  FG.  Working memory load modulation of parieto-frontal connections: Evidence from dynamic causal modeling. Hum Brain Mapp. 2012;33(8):1850-1867.
PubMed   |  Link to Article
Nakashita  S, Saito  DN, Kochiyama  T, Honda  M, Tanabe  HC, Sadato  N.  Tactile-visual integration in the posterior parietal cortex: a functional magnetic resonance imaging study. Brain Res Bull. 2008;75(5):513-525.
PubMed   |  Link to Article
Penny  WD, Stephan  KE, Mechelli  A, Friston  KJ.  Comparing dynamic causal models. Neuroimage. 2004;22(3):1157-1172.
PubMed   |  Link to Article
Stephan  KE, Penny  WD, Daunizeau  J, Moran  RJ, Friston  KJ.  Bayesian model selection for group studies. Neuroimage. 2009;46(4):1004-1017.
PubMed   |  Link to Article
Stephan  KE, Penny  WD, Moran  RJ, den Ouden  HE, Daunizeau  J, Friston  KJ.  Ten simple rules for dynamic causal modeling. Neuroimage. 2010;49(4):3099-3109.
PubMed   |  Link to Article
Penny  WD, Stephan  KE, Daunizeau  J,  et al.  Comparing families of dynamic causal models. PLoS Comput Biol. 2010;6(3):e1000709.
PubMed   |  Link to Article
Macmillan  N, Creelman  C. Detection Theory: A User's Guide. Cambridge, England: Cambridge University Press; 1991.
Benetti  S, Mechelli  A, Picchioni  M, Broome  M, Williams  S, McGuire  P.  Functional integration between the posterior hippocampus and prefrontal cortex is impaired in both first episode schizophrenia and the at risk mental state. Brain. 2009;132(pt 9):2426-2436.
PubMed   |  Link to Article
Keefe  RS, Sweeney  JA, Gu  H,  et al.  Effects of olanzapine, quetiapine, and risperidone on neurocognitive function in early psychosis: a randomized, double-blind 52-week comparison. Am J Psychiatry. 2007;164(7):1061-1071.
PubMed   |  Link to Article
Keefe  RS, Bilder  RM, Davis  SM,  et al; CATIE Investigators; Neurocognitive Working Group.  Neurocognitive effects of antipsychotic medications in patients with chronic schizophrenia in the CATIE Trial. Arch Gen Psychiatry. 2007;64(6):633-647.
PubMed   |  Link to Article
Montojo  CA, Courtney  SM.  Differential neural activation for updating rule versus stimulus information in working memory. Neuron. 2008;59(1):173-182.
PubMed   |  Link to Article
Stephan  KE, Magnotta  VA, White  T,  et al.  Effects of olanzapine on cerebellar functional connectivity in schizophrenia measured by fMRI during a simple motor task. Psychol Med. 2001;31(6):1065-1078.
PubMed   |  Link to Article
Lui  S, Li  T, Deng  W,  et al.  Short-term effects of antipsychotic treatment on cerebral function in drug-naive first-episode schizophrenia revealed by “resting state” functional magnetic resonance imaging. Arch Gen Psychiatry. 2010;67(8):783-792.
PubMed   |  Link to Article
Laruelle  M, Kegeles  LS, Abi-Dargham  A.  Glutamate, dopamine, and schizophrenia: from pathophysiology to treatment. Ann N Y Acad Sci. 2003;1003:138-158.
PubMed   |  Link to Article
Tan  HY, Chen  AG, Kolachana  B,  et al.  Effective connectivity of AKT1-mediated dopaminergic working memory networks and pharmacogenetics of anti-dopaminergic treatment. Brain. 2012;135(pt 5):1436-1445.
PubMed   |  Link to Article
Schlösser  RG, Koch  K, Wagner  G,  et al.  Inefficient executive cognitive control in schizophrenia is preceded by altered functional activation during information encoding: an fMRI study. Neuropsychologia. 2008;46(1):336-347.
PubMed   |  Link to Article
Potkin  SG, Turner  JA, Brown  GG,  et al; FBIRN.  Working memory and DLPFC inefficiency in schizophrenia: the FBIRN study. Schizophr Bull. 2009;35(1):19-31.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Regional Brain Activation During Working Memory Processing (2-Back Greater Than 0-Back) Within the Anatomical Mask

A, Healthy controls. B, Individuals with an at-risk mental state. C, Patients experiencing a first-episode psychosis. Activations are reported at a whole-brain–corrected cluster threshold of P < .001 and a voxel size threshold of 1400.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Model Space Tested in the Study

Numbers 1 through 4 indicate right superior parietal lobule, left superior parietal lobule, right middle frontal gyrus, and left middle frontal gyrus, respectively. We contrasted models in which the 2-back working memory (WM) condition was allowed to modulate within both hemispheres: the parietofrontal connections (top row), the frontoparietal connections (middle row), or both (bottom row). These 3 intrahemispheric options were crossed with 4 possibilities for which interhemispheric connections might be modulated by the 2-back WM condition: none (A), the interhemispheric connections between the parietal areas (B), the interhemispheric connections between the frontal areas (C), or both (D). As a result, our model space consisted of 12 alternative models, each of which was fitted to the data from each study participant.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.
Mean Sensitivity Indexes

Sensitivity indexes during the 2-back conditions in healthy controls (HCs) (n = 19), individuals with an at-risk mental state (ARMS) (n = 15), and individuals with first-episode psychosis (FEP) who were nontreated (n = 12) or antipsychotic treated (n = 8). Asterisk indicates significant differences among the groups at P < .05. Error bars indicate SEs.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 4.
Bayesian Model Selection Among All 12 Dynamic Causal Models

Data are shown for all groups together and separately for healthy controls (HCs), at-risk mental state (ARMS) individuals, and first-episode psychosis (FEP) patients.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 5.
The Modulatory Effect of the 2-Back Working Memory (WM) Condition on the Connection From the Right Middle Frontal Gyrus (MFG) to the Right Superior Parietal Lobule (SPL)

Data are shown for healthy controls (HCs) (n = 19), individuals with an at-risk mental state (ARMS) (n = 15), and all patients with first-episode psychosis (FEP) (n = 20), independent of antipsychotic treatment. The y-axis denotes the mean for all study participants and all 12 dynamic causal models (using bayesian model averaging) with regard to the posterior mean of the modulatory effect; this encodes changes in connection strength induced by the 2-back WM condition. Asterisk indicates significant differences among groups at P < .05; dagger, significant t test within each group (relative to zero) at P < .05. Error bars indicate SDs derived from bayesian parameter averages.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 6.
The Modulatory Effect of the 2-Back Working Memory (WM) Condition on the Connection From the Right Middle Frontal Gyrus (MFG) to the Right Superior Parietal Lobule (SPL)

Data are shown for healthy controls (HCs) (n = 19), individuals with an at-risk mental state (ARMS) (n = 15), and individuals with a first-episode psychosis (FEP) who were antipsychotic treated (n = 8) or nontreated (n = 12) during scanning. The y-axis denotes the mean for all study participants and all 12 dynamic causal models (using bayesian model averaging) with regard to the posterior mean of the modulatory effect; this encodes changes in connection strength induced by the 2-back WM condition. Asterisk indicates significant differences among groups at P < .05; dagger, significant t test within each group (relative to zero) at P < .05. Error bars indicate SDs derived from bayesian parameter averages.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable.  Dynamic Causal Modeling Parameter Estimates: Modulatory Effects of the 2-Back Working Memory Conditiona

References

Friston  KJ.  The disconnection hypothesis. Schizophr Res. 1998;30(2):115-125.
PubMed   |  Link to Article
Stephan  KE, Friston  KJ, Frith  CD.  Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophr Bull. 2009;35(3):509-527.
PubMed   |  Link to Article
Deserno  L, Sterzer  P, Wüstenberg  T, Heinz  A, Schlagenhauf  F.  Reduced prefrontal-parietal effective connectivity and working memory deficits in schizophrenia. J Neurosci. 2012;32(1):12-20.
PubMed   |  Link to Article
Mukherjee  P, Whalley  HC, McKirdy  JW,  et al.  Lower effective connectivity between amygdala and parietal regions in response to fearful faces in schizophrenia. Schizophr Res. 2012;134(2-3):118-124.
PubMed   |  Link to Article
Lawrie  SM, Buechel  C, Whalley  HC, Frith  CD, Friston  KJ, Johnstone  EC.  Reduced frontotemporal functional connectivity in schizophrenia associated with auditory hallucinations. Biol Psychiatry. 2002;51(12):1008-1011.
PubMed   |  Link to Article
Mechelli  A, Allen  P, Amaro  E  Jr,  et al.  Misattribution of speech and impaired connectivity in patients with auditory verbal hallucinations. Hum Brain Mapp. 2007;28(11):1213-1222.
PubMed   |  Link to Article
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 (published online May 15, 2012). Schizophr Bull. doi:10.1093/schbul/sbs060.
PubMed
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(3):220-229.
PubMed   |  Link to Article
Yung  AR, Nelson  B, Stanford  C,  et al.  Validation of “prodromal” criteria to detect individuals at ultra high risk of psychosis: 2 year follow-up. Schizophr Res. 2008;105(1-3):10-17.
PubMed   |  Link to Article
Fusar-Poli  P, Borgwardt  S, Bechdolf  A,  et al.  The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry. 2013;70(1):107-120.
PubMed   |  Link to Article
Crossley  NA, Mechelli  A, Fusar-Poli  P,  et al.  Superior temporal lobe dysfunction and frontotemporal dysconnectivity in subjects at risk of psychosis and in first-episode psychosis. Hum Brain Mapp. 2009;30(12):4129-4137.
PubMed   |  Link to Article
Perlstein  WM, Carter  CS, Noll  DC, Cohen  JD.  Relation of prefrontal cortex dysfunction to working memory and symptoms in schizophrenia. Am J Psychiatry. 2001;158(7):1105-1113.
PubMed   |  Link to Article
Smieskova  R, Fusar-Poli  P, Allen  P,  et al.  Neuroimaging predictors of transition to psychosis: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2010;34(8):1207-1222.
PubMed   |  Link to Article
Fusar-Poli  P, Radua  J, McGuire  P, Borgwardt  S.  Neuroanatomical maps of psychosis onset: voxel-wise meta-analysis of antipsychotic-naive VBM studies. Schizophr Bull. 2011;38(6):1297-1307.
PubMed   |  Link to Article
Broome  MR, Matthiasson  P, Fusar-Poli  P,  et al.  Neural correlates of executive function and working memory in the ‘at-risk mental state’. Br J Psychiatry. 2009;194(1):25-33.
PubMed   |  Link to Article
Fusar-Poli  P, Howes  OD, Allen  P,  et al.  Abnormal prefrontal activation directly related to pre-synaptic striatal dopamine dysfunction in people at clinical high risk for psychosis. Mol Psychiatry. 2011;16(1):67-75.
PubMed   |  Link to Article
Allen  P, Chaddock  CA, Howes  OD,  et al.  Abnormal relationship between medial temporal lobe and subcortical dopamine function in people with an ultra high risk for psychosis. Schizophr Bull. 2012;38(5):1040-1049.
PubMed   |  Link to Article
Fusar-Poli  P, Perez  J, Broome  M,  et al.  Neurofunctional correlates of vulnerability to psychosis: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2007;31(4):465-484.
PubMed   |  Link to Article
Fusar-Poli  P, Broome  MR, Woolley  JB,  et al.  Altered brain function directly related to structural abnormalities in people at ultra high risk of psychosis: longitudinal VBM-fMRI study. J Psychiatr Res. 2011;45(2):190-198.
PubMed   |  Link to Article
van Os  J, Kenis  G, Rutten  BP.  The environment and schizophrenia. Nature. 2010;468(7321):203-212.
PubMed   |  Link to Article
Mesholam-Gately  RI, Giuliano  AJ, Goff  KP, Faraone  SV, Seidman  LJ.  Neurocognition in first-episode schizophrenia: a meta-analytic review. Neuropsychology. 2009;23(3):315-336.
PubMed   |  Link to Article
Fusar-Poli  P, Deste  G, Smieskova  R,  et al.  Cognitive functioning in prodromal psychosis: a meta-analysis. Arch Gen Psychiatry. 2012;69(6):562-571.
PubMed   |  Link to Article
Fusar-Poli  P, Howes  OD, Allen  P,  et al.  Abnormal frontostriatal interactions in people with prodromal signs of psychosis: a multimodal imaging study. Arch Gen Psychiatry. 2010;67(7):683-691.
PubMed   |  Link to Article
Friston  KJ, Harrison  L, Penny  W.  Dynamic causal modelling. Neuroimage. 2003;19(4):1273-1302.
PubMed   |  Link to Article
Smieskova  R, Allen  P, Simon  A,  et al.  Different duration of at-risk mental state associated with neurofunctional abnormalities: a multimodal imaging study. Hum Brain Mapp. 2012;33(10):2281-2294.
PubMed   |  Link to Article
Schlösser  R, Gesierich  T, Kaufmann  B, Vucurevic  G, Stoeter  P.  Altered effective connectivity in drug free schizophrenic patients. Neuroreport. 2003;14(17):2233-2237.
PubMed   |  Link to Article
Schlösser  R, Gesierich  T, Kaufmann  B,  et al.  Altered effective connectivity during working memory performance in schizophrenia: a study with fMRI and structural equation modeling. Neuroimage. 2003;19(3):751-763.
PubMed   |  Link to Article
Fusar-Poli  P, Smieskova  R, Kempton  M, Ho  B, Andreasen  NC, Borgwardt  S.  Progressive brain changes in schizophrenia related to antipsychotic treatment? a meta-analysis of longitudinal MRI studies. Neurosci Biobehav Rev. In press.
Snitz  BE, MacDonald  A  III, Cohen  JD, Cho  RY, Becker  T, Carter  CS.  Lateral and medial hypofrontality in first-episode schizophrenia: functional activity in a medication-naive state and effects of short-term atypical antipsychotic treatment. Am J Psychiatry. 2005;162(12):2322-2329.
PubMed   |  Link to Article
Fusar-Poli  P, Broome  MR, Matthiasson  P, Williams  SC, Brammer  M, McGuire  PK.  Effects of acute antipsychotic treatment on brain activation in first episode psychosis: an fMRI study. Eur Neuropsychopharmacol. 2007;17(6-7):492-500.
PubMed   |  Link to Article
Riecher-Rössler  A, Aston  J, Ventura  J,  et al.  The Basel Screening Instrument for Psychosis (BSIP): development, structure, reliability and validity [in German]. Fortschr Neurol Psychiatr. 2008;76(4):207-216.
PubMed   |  Link to Article
Riecher-Rössler  A, Pflueger  MO, Aston  J,  et al.  Efficacy of using cognitive status in predicting psychosis: a 7-year follow-up. Biol Psychiatry. 2009;66(11):1023-1030.
PubMed   |  Link to Article
Riecher-Rössler  A, Gschwandtner  U, Aston  J,  et al.  The Basel early-detection-of-psychosis (FEPSY)-study--design and preliminary results. Acta Psychiatr Scand. 2007;115(2):114-125.
PubMed   |  Link to Article
Yung  AR, Phillips  LJ, McGorry  PD,  et al.  Prediction of psychosis. A step towards indicated prevention of schizophrenia. Br J Psychiatry Suppl. 1998;172(33):14-20.
PubMed
Stephan  KE, Weiskopf  N, Drysdale  PM, Robinson  PA, Friston  KJ.  Comparing hemodynamic models with DCM. Neuroimage. 2007;38(3):387-401.
PubMed   |  Link to Article
Gazzaley  A, Rissman  J, D’Esposito  M.  Functional connectivity during working memory maintenance. Cogn Affect Behav Neurosci. 2004;4(4):580-599.
PubMed   |  Link to Article
Maldjian  JA, Laurienti  PJ, Kraft  RA, Burdette  JH.  An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage. 2003;19(3):1233-1239.
PubMed   |  Link to Article
Allen  P, Stephan  KE, Mechelli  A,  et al.  Cingulate activity and fronto-temporal connectivity in people with prodromal signs of psychosis. Neuroimage. 2010;49(1):947-955.
PubMed   |  Link to Article
Ma  L, Steinberg  JL, Hasan  KM, Narayana  PA, Kramer  LA, Moeller  FG.  Working memory load modulation of parieto-frontal connections: Evidence from dynamic causal modeling. Hum Brain Mapp. 2012;33(8):1850-1867.
PubMed   |  Link to Article
Nakashita  S, Saito  DN, Kochiyama  T, Honda  M, Tanabe  HC, Sadato  N.  Tactile-visual integration in the posterior parietal cortex: a functional magnetic resonance imaging study. Brain Res Bull. 2008;75(5):513-525.
PubMed   |  Link to Article
Penny  WD, Stephan  KE, Mechelli  A, Friston  KJ.  Comparing dynamic causal models. Neuroimage. 2004;22(3):1157-1172.
PubMed   |  Link to Article
Stephan  KE, Penny  WD, Daunizeau  J, Moran  RJ, Friston  KJ.  Bayesian model selection for group studies. Neuroimage. 2009;46(4):1004-1017.
PubMed   |  Link to Article
Stephan  KE, Penny  WD, Moran  RJ, den Ouden  HE, Daunizeau  J, Friston  KJ.  Ten simple rules for dynamic causal modeling. Neuroimage. 2010;49(4):3099-3109.
PubMed   |  Link to Article
Penny  WD, Stephan  KE, Daunizeau  J,  et al.  Comparing families of dynamic causal models. PLoS Comput Biol. 2010;6(3):e1000709.
PubMed   |  Link to Article
Macmillan  N, Creelman  C. Detection Theory: A User's Guide. Cambridge, England: Cambridge University Press; 1991.
Benetti  S, Mechelli  A, Picchioni  M, Broome  M, Williams  S, McGuire  P.  Functional integration between the posterior hippocampus and prefrontal cortex is impaired in both first episode schizophrenia and the at risk mental state. Brain. 2009;132(pt 9):2426-2436.
PubMed   |  Link to Article
Keefe  RS, Sweeney  JA, Gu  H,  et al.  Effects of olanzapine, quetiapine, and risperidone on neurocognitive function in early psychosis: a randomized, double-blind 52-week comparison. Am J Psychiatry. 2007;164(7):1061-1071.
PubMed   |  Link to Article
Keefe  RS, Bilder  RM, Davis  SM,  et al; CATIE Investigators; Neurocognitive Working Group.  Neurocognitive effects of antipsychotic medications in patients with chronic schizophrenia in the CATIE Trial. Arch Gen Psychiatry. 2007;64(6):633-647.
PubMed   |  Link to Article
Montojo  CA, Courtney  SM.  Differential neural activation for updating rule versus stimulus information in working memory. Neuron. 2008;59(1):173-182.
PubMed   |  Link to Article
Stephan  KE, Magnotta  VA, White  T,  et al.  Effects of olanzapine on cerebellar functional connectivity in schizophrenia measured by fMRI during a simple motor task. Psychol Med. 2001;31(6):1065-1078.
PubMed   |  Link to Article
Lui  S, Li  T, Deng  W,  et al.  Short-term effects of antipsychotic treatment on cerebral function in drug-naive first-episode schizophrenia revealed by “resting state” functional magnetic resonance imaging. Arch Gen Psychiatry. 2010;67(8):783-792.
PubMed   |  Link to Article
Laruelle  M, Kegeles  LS, Abi-Dargham  A.  Glutamate, dopamine, and schizophrenia: from pathophysiology to treatment. Ann N Y Acad Sci. 2003;1003:138-158.
PubMed   |  Link to Article
Tan  HY, Chen  AG, Kolachana  B,  et al.  Effective connectivity of AKT1-mediated dopaminergic working memory networks and pharmacogenetics of anti-dopaminergic treatment. Brain. 2012;135(pt 5):1436-1445.
PubMed   |  Link to Article
Schlösser  RG, Koch  K, Wagner  G,  et al.  Inefficient executive cognitive control in schizophrenia is preceded by altered functional activation during information encoding: an fMRI study. Neuropsychologia. 2008;46(1):336-347.
PubMed   |  Link to Article
Potkin  SG, Turner  JA, Brown  GG,  et al; FBIRN.  Working memory and DLPFC inefficiency in schizophrenia: the FBIRN study. Schizophr Bull. 2009;35(1):19-31.
PubMed   |  Link to Article

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Supplement.

eMethods

eTable 1. MNI Coordinates (x, y, z) of the Group Maxima During the Working Memory Task Across All 3 Groups (2-Back Greater Than 0-Back)

eTable 2. Characteristics of Study Groups

eTable 3. Regression Analysis Between the Right MFG to SPL Connectivity Estimate and Demographic and Clinical Parameters

eReferences

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