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Dysconnectivity Within the Default Mode in First-Episode Schizophrenia: a Stochastic Dynamic Causal Modeling Study with Functional Magnetic Resonance Imaging

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Dysconnectivity Within the Default Mode in First-Episode Schizophrenia: a Stochastic Dynamic Causal Modeling Study with Functional Magnetic Resonance Imaging Schizophrenia Bulletin Advance Access published June 17, 2014 Schizophrenia Bulletin doi:10.1093/schbul/sbu080 Dysconnectivity Within the Default Mode in First-Episode Schizophrenia: A Stochastic Dynamic Causal Modeling Study With Functional Magnetic Resonance Imaging António J. Bastos-Leite*,1,6, Gerard R. Ridgway2,6, Celeste Silveira3,4,6, Andreia Norton5, Salomé Reis3, and Karl J. Friston2 1Department of Medical Imaging, Faculty of Medicine, University of Porto, Porto, Portugal; 2Wellcome Trust Centre for Neuroimaging, 3 Institute of Neurology, University College London, London, UK; Department of Psychiatry, Hospital de São João, Porto, Portugal; Downloaded from 4Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal; 5Hospital de Magalhães Lemos, Porto, Portugal 6These authors contributed equally to the article. *To whom correspondence should be addressed; Department of Medical Imaging, Faculty of Medicine, University of Porto, Alameda do Professor Hernâni Monteiro, 4200-319 Porto, Portugal; tel: +351938382287, fax: +351225500531, e-mail: [email protected] http://schizophreniabulletin.oxfordjournals.org/ We report the first stochastic dynamic causal modeling magnetic resonance imaging (fMRI)/resting (sDCM) study of effective connectivity within the default state/stochastic dynamic causal modeling (DCM) mode network (DMN) in schizophrenia. Thirty-three patients (9 women, mean age = 25.0 years, SD = 5) with a first episode of psychosis and diagnosis of schizophre- Introduction nia—according to the Diagnostic and Statistic Manual Schizophrenia is a complex psychiatric disorder of of Mental Disorders, 4th edition, revised criteria—were unknown etiology, with significant clinical and patho- studied. Fifteen healthy control subjects (4 women, mean physiological heterogeneity, for which biomarkers are still age = 24.6 years, SD = 4) were included for comparison. lacking.1 Schizophrenia is generally thought to result from All subjects underwent resting state functional magnetic pathological interactions among gray matter structures. In resonance imaging (fMRI) interspersed with 2 periods brief, there are 2 versions of this hypothesis. One is implied by guest on June 18, 2014 of continuous picture viewing. The anterior frontal (AF), by Wernicke’s “sejunction” hypothesis, which postulated an posterior cingulate (PC), and the left and right parietal anatomical disruption or “disconnection” of association nodes of the DMN were localized in an unbiased fash- fibers between regions. The other postulates abnormalities ion using data from 16 independent healthy volunteers at the level of synaptic efficacy and plasticity, leading to (using an identical fMRI protocol). We used sDCM “dysfunctional” integration or connectivity among cortical to estimate directed connections between and within and subcortical systems.2,3 Neuroimaging studies of effective nodes of the DMN, which were subsequently compared connectivity—defined as the causal influence of one neural with t tests at the between subject level. The excitatory system (eg, a network node) over another (or itself)—may, effect of the PC node on the AF node and the inhibi- therefore, help to identify abnormalities in neural circuits tory self-connection of the AF node were significantly whose dysfunction contributes to schizophrenia. weaker in patients (mean values = 0.013 and −0.048 Hz, The default mode network (DMN) has been proposed SD = 0.09 and 0.05, respectively) relative to healthy sub- as a system4 that may underlie introspective brain func- jects (mean values = 0.084 and −0.088 Hz, SD = 0.15 tion and consciousness. Previous functional magnetic and 0.77, respectively; P < .05). In summary, sDCM resonance imaging (fMRI) studies have demonstrated revealed reduced effective connectivity to the AF node aberrant temporal correlations (functional connectiv- of the DMN—reflecting a reduced postsynaptic efficacy ity) of the DMN in schizophrenia.5,6 It has been sug- of prefrontal afferents—in patients with first-episode gested that this abnormality could be attributable to an schizophrenia. altered modulation of the anterior and posterior cingu- late (PC) cortices or to result from abnormal interac- Key words: brain connectivity/default mode network/ tions between those regions and other functional brain dysconnectivity/first-episode schizophrenia/functional networks.5 In addition, differences between patients with © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Page 1 of 10 A. J. Bastos-Leite et al schizophrenia and healthy control subjects have also been (lamina III) cells of the cerebral neocortex reporting pre- found with respect to functional connectivity among dif- diction errors. ferent brain networks.7,8 In functional terms, hierarchical predictive coding Dynamic causal modeling (DCM) is a Bayesian casts recurrent message passing among different cortical scheme for assessing effective connectivity. DCM uses areas as the transmission of ascending prediction errors the Bayesian inversion of neuronal network models that and descending predictions (also known as top-down are grounded in neurophysiology and anatomy.9 The predictions or corollary discharges). Neural activity—at advantages of studying effective connectivity with DCM any level of the cortical hierarchy—is thought to encode include the ability to compare different models of brain expectations about the causes of a sensory input. These networks, as well as to characterize directed connectiv- expectations generate top-down predictions that are com- ity between cortical nodes at a neuronal level.10 Another pared with expectations at the level below. The resulting advantage of DCM is that it entails an explicit model of prediction error or mismatch is then passed forward to neuronal coupling, which enables regional variations in update expectations in the higher levels.14–17 The precision hemodynamic parameters to be estimated. This precludes of, or confidence in, prediction error—at any level—deter- difficulties in the interpretation of functional connectiv- mines how much that level constrains the expectations Downloaded from ity among hemodynamic signals in the presence of hemo- in the higher levels. Available evidence18 suggests that dynamic variability.11 the primary deficit in schizophrenia may be a failure to The particular advantage of stochastic DCM (sDCM) attenuate precision at the lowest (sensory) cortical lev- over the conventional (deterministic) DCM approach is els—leading to a failure of sensory attenuation and char- that one can model endogenous fluctuations in neuronal acteristic soft neurological signs (eg, abnormal pursuit eye activity. In other words, whereas deterministic DCM gen- movements).18 This primary deficit is assumed to induce http://schizophreniabulletin.oxfordjournals.org/ erates probabilistic and parametric measures of effective compensatory increases in the precision of higher levels connectivity as a response to experimental exogenous and consequent difficulties inferring the causes of sensa- inputs (eg, periods of “activity” during typical block or tions—causing, eg, hallucinations and delusions.19 This event-related fMRI experiments), sDCM accounts for means that many of the symptoms and signs of schizo- endogenous or random fluctuations in hidden neuro- phrenia can be understood as false perceptual inference, nal states that enable the analysis of resting state fMRI secondary to a failure of neuromodulation to optimize studies.9,12 precision (cf, aberrant salience)20 at different levels of the In contrast to functional connectivity analyses—based cortical hierarchy. Therefore, we hypothesized a reduc- upon correlations with a seed region or independent tion in the effective connectivity of extrinsic (ie, between component analysis (ICA)—sDCM has a number of node) afferents to the hierarchically highest node of the methodological and interpretational advantages. These DMN—the AF node—and an increase of its intrinsic (ie, include the ability to make inferences about directed and within node) excitability. Put simply, we anticipated that by guest on June 18, 2014 weighted (ie, excitatory or inhibitory) connections among the AF node would listen more to itself than to ascend- neuronal sources. This is clearly important in terms of ing messages from lower hierarchical levels. Associating understanding cortical hierarchies and distributed pro- the AF node with the highest level of the cortical hierar- cesses, which are usually cast in terms of forward and chy was based primarily on phylogenetic and ontogenetic backward connections. The disadvantages of sDCM are arguments.21 In summary, we hypothesized a reduction in largely computational in nature, because the estimation the effective connectivity of afferents to the AF node and (ie, model inversion) rests upon an iterative Bayesian a concomitant reduction of its recurrent self-inhibition. inversion or filtering. This can take several minutes or even hours for multiple subjects.9,12 Methods The purpose of the current study was to evaluate differences in effective connectivity, within the DMN, Subjects between patients with first-episode schizophrenia
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