Spectral Dynamic Causal Modeling Research Articles

Hierarchical Dynamic Causal Modeling of Resting-State fMRI Reveals Longitudinal Changes in Effective Connectivity in the Motor System after Thalamotomy for Essential Tremor.

Thalamotomy at the ventralis intermedius nucleus for essential tremor is known to cause changes in motor circuitry, but how a focal lesion leads to progressive changes in connectivity is not clear. To understand the mechanisms by which thalamotomy exerts enduring effects on motor circuitry, a quantitative analysis of directed or effective connectivity among motor-related areas is required. We characterized changes in effective connectivity of the motor system following thalamotomy using (spectral) dynamic causal modeling (spDCM) for resting-state fMRI. To differentiate long-lasting treatment effects from transient effects, and to identify symptom-related changes in effective connectivity, we subject longitudinal resting-state fMRI data to spDCM, acquired 1 day prior to, and 1 day, 7 days, and 3 months after thalamotomy using a non-cranium-opening MRI-guided focused ultrasound ablation technique. For the group-level (between subject) analysis of longitudinal (between-session) effects, we introduce a multilevel parametric empirical Bayes (PEB) analysis for spDCM. We found remarkably selective and consistent changes in effective connectivity from the ventrolateral nuclei and the supplementary motor area to the contralateral dentate nucleus after thalamotomy, which may be mediated via a polysynaptic thalamic–cortical–cerebellar motor loop. Crucially, changes in effective connectivity predicted changes in clinical motor-symptom scores after thalamotomy. This study speaks to the efficacy of thalamotomy in regulating the dentate nucleus in the context of treating essential tremor. Furthermore, it illustrates the utility of PEB for group-level analysis of dynamic causal modeling in quantifying longitudinal changes in effective connectivity; i.e., measuring long-term plasticity in human subjects non-invasively.

Erratum to: GABA content within medial prefrontal cortex predicts the variability of fronto-limbic effective connectivity.

The amygdala-medial prefrontal cortex (mPFC) circuit plays a key role in social behavior. The amygdala and mPFC are bidirectionally connected, functionally and anatomically, via the uncinate fasciculus. Recent evidence suggests that GABA-ergic neurotransmission within the mPFC could be central to the regulation of amygdala activity related to emotions and anxiety processing. However, the functional and neurochemical interactions within amygdala-mPFC circuits are unclear. In the current study, multimodal magnetic resonance imaging techniques were combined to investigate effective connectivity within the amygdala-mPFC network and its relationship with mPFC neurotransmission in 22 healthy subjects aged between 41 and 88 years. Effective connectivity in the amygdala-mPFC circuit was assessed on resting-state functional magnetic resonance imaging data using spectral dynamic causal modelling. State and trait anxiety were also assessed. The mPFC was shown to be the target of incoming outputs from the amygdalae and the source of exciting inputs to the limbic system. The amygdalae were reciprocally connected by excitatory projections. About half of the variance relating to the strength of top–down endogenous connection between right amygdala and mPFC was explained by mPFC GABA levels. State anxiety was correlated with the strength of the endogenous connections between right amygdala and mPFC. We suggest that mPFC GABA content predicts variability in the effective connectivity within the mPFC-amygdala circuit, providing new insights on emotional physiology and the underlying functional and neurochemical interactions.

Abnormal resting state effective connectivity within the default mode network in major depressive disorder: A spectral dynamic causal modeling study.

IntroductionUnderstanding the neural basis underlying major depressive disorder (MDD) is essential for the diagnosis and treatment of this mental disorder. Aberrant activation and functional connectivity of the default mode network (DMN) have been consistently found in patients with MDD. It is not known whether effective connectivity within the DMN is altered in MDD.ObjectsThe primary object of this study is to investigate the effective connectivity within the DMN during resting state in MDD patients before and after eight weeks of antidepressant treatment.MethodsWe defined four regions of the DMN (medial frontal cortex, posterior cingulate cortex, left parietal cortex, and right parietal cortex) for each participant using a group independent component analysis. The coupling parameters reflecting the causal interactions among the DMN regions were estimated using spectral dynamic causal modeling (DCM).ResultsTwenty‐seven MDD patients and 27 healthy controls were included in the statistical analysis. Our results showed declined influences from the left parietal cortex to other DMN regions in the pre‐treatment patients as compared with healthy controls. After eight weeks of treatment, the influence from the right parietal cortex to the posterior cingulate cortex significantly decreased.ConclusionThese findings suggest that the reduced excitatory causal influence of the left parietal cortex is the key alteration of the DMN in patients with MDD, and the disrupted causal influences that parietal cortex exerts on the posterior cingulate cortex is responsive to antidepressant treatment.

GABA content within medial prefrontal cortex predicts the variability of fronto-limbic effective connectivity.

The amygdala-medial prefrontal cortex (mPFC) circuit plays a key role in social behavior. The amygdala and mPFC are bidirectionally connected, functionally and anatomically, via the uncinate fasciculus. Recent evidence suggests that GABA-ergic neurotransmission within the mPFC could be central to the regulation of amygdala activity related to emotions and anxiety processing. However, the functional and neurochemical interactions within amygdala-mPFC circuits are unclear. In the current study, multimodal magnetic resonance imaging techniques were combined to investigate effective connectivity within the amygdala-mPFC network and its relationship with mPFC neurotransmission in 22 healthy subjects aged between 41 and 88 years. Effective connectivity in the amygdala-mPFC circuit was assessed on resting-state functional magnetic resonance imaging data using spectral dynamic causal modelling. State and trait anxiety were also assessed. The mPFC was shown to be the target of incoming outputs from the amygdalae and the source of exciting inputs to the limbic system. The amygdalae were reciprocally connected by excitatory projections. About half of the variance relating to the strength of top-down endogenous connection between right amygdala and mPFC was explained by mPFC GABA levels. State anxiety was correlated with the strength of the endogenous connections between right amygdala and mPFC. We suggest that mPFC GABA content predicts variability in the effective connectivity within the mPFC-amygdala circuit, providing new insights on emotional physiology and the underlying functional and neurochemical interactions.

Prefrontal dysconnectivity links to working memory deficit in first-episode schizophrenia.

Working memory (WM) deficit is a core feature of schizophrenia and is characterized by abnormal functional integration in the prefrontal cortex, including the dorsolateral prefrontal cortex (dLPFC), dorsal anterior cingulate cortex (dACC), and ventrolateral prefrontal cortex (vLPFC). However, the specific mechanism by which the abnormal neuronal circuits that involve these brain regions contribute to this deficit is still unclear. Therefore, this study focused on these regions and sought to answer which abnormal causal relationships in these regions can be linked to impaired WM in schizophrenia. We used spectral dynamic causal modeling to estimate directed (effective) connectivity between these regions based on resting-state functional magnetic resonance imaging data from healthy control (HC) subjects and patients with first-episode schizophrenia (FES). By comparing these effective connections in the controls and patients, we found that the effective connectivity from the dACC to the dLPFC and from the right dLPFC to the left vLPFC was weaker in the FES group than in the HC group. Furthermore, these effective connections displayed a positive correlation with WM performance in the HCs. However, in the FES patients, the effective connectivity from the dACC to the dLPFC was not correlated with WM performance, and the effective connectivity from the right dLPFC to the left vLPFC was negatively correlated with WM performance. These results could be explained by an aberrant top-down mechanism of WM processing and provide new evidence for the dysconnectivity hypothesis of schizophrenia.

A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures

This paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. The performance of the proposed strategy in terms of effective connectivity identification is assessed using simulated data generated by a neuronal mass model (simulating unidirectional and bidirectional flows) and real epileptic intracerebral Electroencephalographic signals. Results show the efficiency of proposed approach compared to the conventional Dynamic Causal Modelling and the one wherein a deterministic annealing scheme is employed.

P120 Asymmetric function of the right and left inferior parietal lobule in mind wandering – A tDCS and fMRI study

Recent researches have shown that inappropriate self-generated thought, namely mind wandering (MW), is decreased by transcranial direct current stimulation (tDCS) of the right inferior parietal lobule (IPL) through altering information flow within the default mode network (DMN). However, it remains unknown whether this effect is specific to the “right” IPL. We explored the difference of right and left IPL involvement in MW using tDCS and fMRI. As the right IPL has been indicated to regulate other core regions of the DMN, any reduction in MW resulting from tDCS should be observed only by right IPL stimulation. Twenty-seven non-clinical subjects were allocated to either a right- or left-tDCS group and completed two experiments with different tDCS conditions (real and sham) over an interval of more than a week (the experiment order was counterbalanced). On both days subjects underwent resting-state fMRI before and after tDCS. After the second scan they completed an attention task containing thought probes measuring their attentional state (on-/off-task). The ratio of off-task thoughts was defined as MW propensity. In the real tDCS condition, direct current (density: 0.043 mA/cm2) was applied through anodal electrodes placed on either the right or left IPL (reference: contralateral cheek) for 20 min. At the behavioral level, only stimulation of the right IPL decreased MW propensity compared to the sham condition. In parallel, directed connection strengths within the DMN core regions (calculated by spectral dynamic causal modeling) significantly changed only by stimulation to the right IPL. As anodal tDCS is thought to enhance original connectivity/function, this supports the possibility that the right but not the left IPL regulate other DMN regions. Mediation analysis further revealed that the changes to the recurrent connections of the posterior cingulate cortex and its afferent connections from other core regions of the DMN partially mediated the decrease in MW. Interestingly, in contrast with the inhibitory function of the right IPL connections, the left IPL connections were associated with increasing MW. In conclusion, the present study is the first to demonstrate the neural asymmetry of right and left IPL function in MW. Download : Download high-res image (281KB) Download : Download full-size image Download : Download high-res image (356KB) Download : Download full-size image

Within and between subject variability of effective connectivity in (small) resting state networks: A spectral dynamic causal modeling study

Within and between subject variability of effective connectivity in (small) resting state networks: A spectral dynamic causal modeling study

Large-scale DCMs for resting-state fMRI.

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

Effective Connectivity within the Mesocorticolimbic System during Resting-State in Cocaine Users.

Objective: Although effective connectivity between brain regions has been examined in cocaine users during tasks, no effective connectivity study has been conducted on cocaine users during resting-state. In the present functional magnetic resonance imaging study, we examined effective connectivity in resting-brain, between the brain regions within the mesocorticolimbic dopamine system, implicated in reward and motivated behavior, while the chronic cocaine users and controls took part in a resting-state scan by using a spectral Dynamic causal modeling (spDCM) approach.Method: As part of a study testing cocaine cue reactivity in cocaine users (Ray et al., 2015b), 20 non-treatment seeking cocaine-smoking (abstinent for at least 3 days) and 17 control participants completed a resting state scan and an anatomical scan. A mean voxel-based time series data extracted from four key brain areas (ventral tegmental area, VTA; nucleus accumbens, NAc; hippocampus, medial frontal cortex) within the mesocorticolimbic dopamine system during resting-state from the cocaine and control participants were used as input to the spDCM program to generate spDCM analysis outputs.Results: Compared to the control group, the cocaine group had higher effective connectivity from the VTA to NAc, hippocampus and medial frontal cortex. In contrast, the control group showed a higher effective connectivity from the medial frontal cortex to VTA, from the NAc to medial frontal cortex, and on the hippocampus self-loop.Conclusions: The present study is the first to show that during resting-state in abstaining cocaine users compared to controls, the VTA initiates an enhanced effective connectivity to NAc, hippocampus and medial frontal cortex areas within the mesocorticolimbic dopamine system, the brain’s reward system. Future studies of effective connectivity analysis during resting-state may eventually be used to monitor treatment outcome.

Alterations in amygdala-prefrontal circuits in infants exposed to prenatal maternal depression.

Prenatal exposure to maternal depression is common and puts offspring at risk for developing a range of neuropsychiatric disorders. Despite its prevalence and adverse associations, neurobiological processes by which prenatal maternal depression (PMD) confers risk remain poorly understood. Maternal mood and fetal behavior were assessed between 34 and 37 gestational weeks. Using resting-state functional magnetic resonance imaging (fMRI) and diffusion MRI, we examined functional and structural connectivity within amygdala–prefrontal circuits in 64 infants (mean age=5.8±1.7 weeks) with (n=20) and without (n=44) in utero exposure to PMD. Resting fMRI and diffusion MRI both indicated atypical amygdala–prefrontal connectivity in PMD-exposed infants: Resting fMRI indicated increased inverse, or negative, functional connectivity between the amygdala and the dorsal prefrontal cortex (PFC), bilaterally, and diffusion MRI indicated decreased structural connectivity between the right amygdala and the right ventral PFC. Spectral dynamic causal modeling supported these findings suggesting altered amygdala–PFC effective (or directed) connectivity in PMD-exposed infants. Last, path analyses supported a mechanistic account relating PMD to a third-trimester fetal behavior: PMD alters amygdala–PFC connectivity, which in turn, is associated with an increase in fetal heart rate reactivity to in utero perturbation. These data suggest that the maturation and coordination of central and peripheral physiology are altered by prenatal exposure to maternal depression. To the best of our knowledge, this is the first study to directly associate infant MRI measures with a behavior—fetal heart rate response, and supports hypotheses that PMD-associated variations in the development of amygdala–PFC circuits are relevant for future neurobehavioral maturation.

Dynamic Causal Modeling of Hippocampal Links within the Human Default Mode Network: Lateralization and Computational Stability of Effective Connections.

The purpose of this paper was to study causal relationships between left and right hippocampal regions (LHIP and RHIP, respectively) within the default mode network (DMN) as represented by its key structures: the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and the inferior parietal cortex of left (LIPC) and right (RIPC) hemispheres. Furthermore, we were interested in testing the stability of the connectivity patterns when adding or deleting regions of interest. The functional magnetic resonance imaging (fMRI) data from a group of 30 healthy right-handed subjects in the resting state were collected and a connectivity analysis was performed. To model the effective connectivity, we used the spectral Dynamic Causal Modeling (DCM). Three DCM analyses were completed. Two of them modeled interaction between five nodes that included four DMN key structures in addition to either LHIP or RHIP. The last DCM analysis modeled interactions between four nodes whereby one of the main DMN structures, PCC, was excluded from the analysis. The results of all DCM analyses indicated a high level of stability in the computational method: those parts of the winning models that included the key DMN structures demonstrated causal relations known from recent research. However, we discovered new results as well. First of all, we found a pronounced asymmetry in LHIP and RHIP connections. LHIP demonstrated a high involvement of DMN activity with preponderant information outflow to all other DMN regions. Causal interactions of LHIP were bidirectional only in the case of LIPC. On the contrary, RHIP was primarily affected by inputs from LIPC, RIPC, and LHIP without influencing these or other DMN key structures. For the first time, an inhibitory link was found from MPFC to LIPC, which may indicate the subjects’ effort to maintain a resting state. Functional connectivity data echoed these results, though they also showed links not reflected in the patterns of effective connectivity. We suggest that such lateralized architecture of hippocampal connections may be related to lateralization phenomena in verbal and spatial domains documented in human neurophysiology, neuropsychology, and neurolinguistics.

Abnormal reward circuitry in anorexia nervosa: A longitudinal, multimodal MRI study.

Anorexia nervosa (AN) is a debilitating illness and existing interventions are only modestly effective. This study aimed to determine whether AN pathophysiology is associated with altered connections within fronto-accumbal circuitry subserving reward processing. Diffusion and resting-state functional MRI scans were collected in female inpatients with AN (n = 22) and healthy controls (HC; n = 18) between the ages of 16 and 25 years. Individuals with AN were scanned during the acute, underweight phase of the illness and again following inpatient weight restoration. HC were scanned twice over the same timeframe. Based on univariate and multivariate analyses of fronto-accumbal circuitry, underweight individuals with AN were found to have increased structural connectivity (diffusion probabilistic tractography), increased white matter anisotropy (tract-based spatial statistics), increased functional connectivity (seed-based correlation in resting-state fMRI), and altered effective connectivity (spectral dynamic causal modeling). Following weight restoration, fronto-accumbal structural connectivity continued to be abnormally increased bilaterally with large (partial η2 = 0.387; right NAcc-OFC) and moderate (partial η2 = 0.197; left NAcc-OFC) effect sizes. Increased structural connectivity within fronto-accumbal circuitry in the underweight state correlated with severity of eating disorder symptoms. Taken together, the findings from this longitudinal, multimodal neuroimaging study offer converging evidence of atypical fronto-accumbal circuitry in AN. Hum Brain Mapp 37:3835-3846, 2016. © 2016 Wiley Periodicals, Inc.

Mapping Smoking Addiction Using Effective Connectivity Analysis.

Prefrontal and parietal cortex, including the default mode network (DMN; medial prefrontal cortex (mPFC), and posterior cingulate cortex, PCC), have been implicated in addiction. Nonetheless, it remains unclear which brain regions play a crucial role in smoking addiction and the relationship among these regions. Since functional connectivity only measures correlations, addiction-related changes in effective connectivity (directed information flow) among these distributed brain regions remain largely unknown. Here we applied spectral dynamic causal modeling (spDCM) to resting state fMRI to characterize changes in effective connectivity among core regions in smoking addiction. Compared to nonsmokers, smokers had reduced effective connectivity from PCC to mPFC and from RIPL to mPFC, a higher self-inhibition within PCC and a reduction in the amplitude of endogenous neuronal fluctuations driving the mPFC. These results indicate that spDCM can differentiate the functional architectures between the two groups, and may provide insight into the brain mechanisms underlying smoking addiction. Our results also suggest that future brain-based prevention and intervention in addiction should consider the amelioration of mPFC-PCC-IPL circuits.

Testing Proposed Neuronal Models of Effective Connectivity Within the Cortico-basal Ganglia-thalamo-cortical Loop During Loss of Consciousness.

In recent years, a number of brain regions and connectivity patterns have been proposed to be crucial for loss and recovery of consciousness but have not been compared in detail. In a 3 T resting-state functional magnetic resonance imaging paradigm, we test the plausibility of these different neuronal models derived from theoretical and empirical knowledge. Specifically, we assess the fit of each model to the dynamic change in effective connectivity between specific cortical and subcortical regions at different consecutive levels of propofol-induced sedation by employing spectral dynamic causal modeling. Surprisingly, our findings indicate that proposed models of impaired consciousness do not fit the observed patterns of effective connectivity. Rather, the data show that loss of consciousness, at least in the context of propofol-induced sedation, is marked by a breakdown of corticopetal projections from the globus pallidus. Effective connectivity between the globus pallidus and the ventral posterior cingulate cortex, present during wakefulness, fades in the transition from lightly sedated to full loss of consciousness and returns gradually as consciousness recovers, thereby, demonstrating the dynamic shift in brain architecture of the posterior cingulate "hub" during changing states of consciousness. These findings highlight the functional role of a previously underappreciated direct pallido-cortical connectivity in supporting consciousness.

Resting-State Coupling between Core Regions within the Central-Executive and Salience Networks Contributes to Working Memory Performance.

Previous studies investigated the distinct roles played by different cognitive regions and suggested that the patterns of connectivity of these regions are associated with working memory (WM). However, the specific causal mechanism through which the neuronal circuits that involve these brain regions contribute to WM is still unclear. Here, in a large sample of healthy young adults, we first identified the core WM regions by linking WM accuracy to resting-state functional connectivity with the bilateral dorsolateral prefrontal cortex (dLPFC; a principal region in the central-executive network, CEN). Then a spectral dynamic causal modeling (spDCM) analysis was performed to quantify the effective connectivity between these regions. Finally, the effective connectivity was correlated with WM accuracy to characterize the relationship between these connections and WM performance. We found that the functional connections between the bilateral dLPFC and the dorsal anterior cingulate cortex (dACC) and between the right dLPFC and the left orbital fronto-insular cortex (FIC) were correlated with WM accuracy. Furthermore, the effective connectivity from the dACC to the bilateral dLPFC and from the right dLPFC to the left FIC could predict individual differences in WM. Because the dACC and FIC are core regions of the salience network (SN), we inferred that the inter- and causal-connectivity between core regions within the CEN and SN is functionally relevant for WM performance. In summary, the current study identified the dLPFC-related resting-state effective connectivity underlying WM and suggests that individual differences in cognitive ability could be characterized by resting-state effective connectivity.

Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data.

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078–0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p < 0.05). Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain’s functioning at resting state.

Disrupted effective connectivity of the sensorimotor network in amyotrophic lateral sclerosis

Although dysfunctional sensorimotor network (SMN) has been frequently involved in the pathogenesis of amyotrophic lateral sclerosis (ALS), the causal relationship within this network remains unexplored. In this study, spectral dynamic causal modeling was applied to resting-state functional magnetic resonance imaging data to estimate the causal relationship of SMN in a cohort of 20 ALS patients and 21 healthy controls. The SMN components were first extracted using an independent component analysis, and then compared between the two groups to identify the abnormalities in SMN. In ALS patients, we found significant regional activity alterations in the left primary motor cortex (M1), the left primary somatosensory cortex (S1), and the right supplementary motor cortex (SMA). Among these regions, spectral DCM revealed missing closed-loop circuit between the left M1 and the right SMA, and lost projection from the right SMA to the left S1 in ALS. These findings may reflect the influences of the loss of motor neurons on motor function in ALS, and provide compelling evidence for a breakdown of the sensorimotor neural circuits in ALS. In conclusion, this study elucidates a neurobiological model that may explain the functional impairments of the SMN in ALS, and provides much deeper insights into the pathophysiology of this disease.

Graph theoretic analysis on large DCM models

Graph theoretic analysis on large DCM models

Anterior cingulate cortex-related connectivity in first-episode schizophrenia: a spectral dynamic causal modeling study with functional magnetic resonance imaging.

Understanding the neural basis of schizophrenia (SZ) is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and medial prefrontal cortex (MPFC) have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI). Forty-nine patients with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs) were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM) to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA) in addition to classical inference (t-test). In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, spectral DCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions.