Effective Connectivity within the Distributed Cortical Network for Face Perception

Institute of Neuroradiology, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
Cerebral Cortex (Impact Factor: 8.67). 11/2007; 17(10):2400-6. DOI: 10.1093/cercor/bhl148
Source: PubMed


Face perception elicits activation within a distributed cortical network in the human brain. The network includes visual ("core") regions, as well as limbic and prefrontal ("extended") regions, which process invariant facial features and changeable aspects of faces, respectively. We used functional Magnetic Resonance Imaging and Dynamic Causal Modeling to investigate effective connectivity and functional organization between and within the core and the extended systems. We predicted a ventral rather than dorsal connection between the core and the extended systems during face viewing and tested whether valence and fame would alter functional coupling within the network. We found that the core system is hierarchically organized in a predominantly feed-forward fashion, and that the fusiform gyrus (FG) exerts the dominant influence on the extended system. Moreover, emotional faces increased the coupling between the FG and the amygdala, whereas famous faces increased the coupling between the FG and the orbitofrontal cortex. Our results demonstrate content-specific dynamic alterations in the functional coupling between visual-limbic and visual-prefrontal face-responsive pathways.

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    • "The OFA provides input to higher face selective cortical regions, such as the FFA (Haxby et al., 2000), in which more complex features are processed (Haxby et al., 2000; Pitcher et al., 2011). In turn, the FFA is thought to be involved in a later stage of more complex information processing and is assumed to exert the dominant influence (as compared to the OFA) to the extended face processing system, which, among others, includes the amygdala (Fairhall and Ishai, 2007; Herrington et al., 2011; Vuilleumier et al., 2003, 2004; Vuilleumier and Pourtois, 2007). "
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    ABSTRACT: The application of global signal regression (GSR) to resting-state functional magnetic resonance imaging data and its usefulness is a widely discussed topic. In this article, we report an observation of segregated distribution of amygdala resting-state functional connectivity (rs-FC) within the fusiform gyrus (FFG) as an effect of GSR in a multi-center-sample of 276 healthy subjects. Specifically, we observed that amygdala rs-FC was distributed within the FFG as distinct anterior versus posterior clusters delineated by positive versus negative rs-FC polarity when GSR was performed. To characterize this effect in more detail, post hoc analyses revealed the following: first, direct overlays of task-functional magnetic resonance imaging derived face sensitive areas and clusters of positive versus negative amygdala rs-FC showed that the positive amygdala rs-FC cluster corresponded best with the fusiform face area, whereas the occipital face area corresponded to the negative amygdala rs-FC cluster. Second, as expected from a hierarchical face perception model, these amygdala rs-FC defined clusters showed differential rs-FC with other regions of the visual stream. Third, dynamic connectivity analyses revealed that these amygdala rs-FC defined clusters also differed in their rs-FC variance across time to the amygdala. Furthermore, subsample analyses of three independent research sites confirmed reliability of the effect of GSR, as revealed by similar patterns of distinct amygdala rs-FC polarity within the FFG. In this article, we discuss the potential of GSR to segregate face sensitive areas within the FFG and furthermore discuss how our results may relate to the functional organization of the face-perception circuit. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
    Human Brain Mapping 07/2015; 36(10). DOI:10.1002/hbm.22900 · 5.97 Impact Factor
    • "We selected a priori volumes of interest for DCM (Amy, DLPFC, ACC, fusiform gyrus FG, and visual cortex VC) by considering their relevance in facial and emotional processing (Dima et al., 2011; Fairhall and Ishai, 2007; Radaelli et al., 2015; Stein et al., 2007), and antidepressant response (Benedetti et al., 2007; Canli et al., 2004; Hamann, 2005; Maddock et al., 2003; Mayberg et al., 1997, 2005; Seminowicz et al., 2004). "
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    ABSTRACT: The identification of antidepressant response predictors in bipolar disorder (BD) may provide new potential enhancements in treatment selection. Repeated total sleep deprivation combined with light therapy (TSD+LT) can acutely reverse depressive symptoms and has been proposed as a model antidepressant treatment. This study aims at investigating the effect of TSD+LT on effective connectivity and neural response in cortico-limbic circuitries during implicit processing of fearful and angry faces in patients with BD. fMRI and Dynamic Causal Modeling (DCM) were combined to study the effect of chronotherapeutics on neural responses in healthy controls (HC, n=35) and BD patients either responder (RBD, n=26) or non responder (nRBD, n=11) to 3 consecutive TSD+LT sessions. Twenty-four DCMs exploring connectivity between anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), Amygdala (Amy), fusiform gyrus and visual cortex were constructed. After treatment, patients significantly increased their neural responses in DLPFC, ACC and insula. nRBD showed lower baseline and endpoint neural responses than RBD. The increased activity in ACC and in medial prefrontal cortex, associated with antidepressant treatment, was positively associated with the improvement of depressive symptomatology. Only RBD patients increased intrinsic connectivity from DLPFC to ACC and reduced the modulatory effect of the task on Amy-DLPFC connection. A successful antidepressant treatment was associated with an increased functional activity and connectivity within cortico-limbic networks, suggesting the possible role of these measures in providing possible biomarkers for treatment efficacy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    07/2015; 233(2). DOI:10.1016/j.pscychresns.2015.07.015
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    • "Using DCM, researchers have gained deeper insight into the mechanisms underlying cognitive tasks, such as visuospatial attention (Kellermann et al., 2012; Siman-Tov et al., 2007), face perception (Fairhall and Ishai, 2007; Li et al., 2010; Nguyen et al., 2014), working memory (Dima et al., 2014), decision making (Stephan et al., 2007a; Summerfield et al., 2006; Summerfield and Koechlin, 2008), motor processes (Grefkes et al., 2008; Grol et al., 2007), and the " resting state " (Goulden et al., 2014; Di and Biswal, 2014; Friston et al., 2014). In this NeuroImage 117 (2015) 56–66 ⁎ Corresponding author at: University of Marburg, Section of Brainimaging, Department of Psychiatry, Rudolf-Bultmann-Straße 8, 35039 Marburg, Germany. "
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    ABSTRACT: Dynamic causal modeling (DCM) is a Bayesian framework for inferring effective connectivity among brain regions from neuroimaging data. While the validity of DCM has been investigated in various previous studies, the reliability of DCM parameter estimates across sessions has been examined less systematically. Here, we report results of a software comparison with regard to test-retest reliability of DCM for fMRI, using a challenging scenario where complex models with many parameters were applied to relatively few data points. Specifically, we examined the reliability of different DCM implementations (in terms of the intra-class correlation coefficient, ICC) based on fMRI data from 35 human subjects performing a simple motor task in two separate sessions, one month apart. We constructed DCMs of motor regions with fair to excellent reliability of conventional activation measures. Using classical DCM (cDCM) in SPM5, we found that the test-retest reliability of DCM results was high, both concerning the model evidence (ICC = 0.94) and the model parameter estimates (median ICC = 0.47). However, when using a more recent DCM version (DCM10 in SPM8), test-retest reliability was reduced notably. Analyses indicated that, in our particular case, the prior distributions played a crucial role in this change in reliability across software versions. Specifically, when using cDCM priors for model inversion in DCM10, this not only restored reliability but yielded even better results than in cDCM. Analyzing each component of the objective function in DCM, we found a selective change in the reliability of posterior mean estimates. This suggests that tighter regularization afforded by cDCM priors reduces the possibility of local extrema in the objective function. We conclude this paper with an outlook to ongoing developments for overcoming the software-dependency of reliability observed in this study, including global optimization and empirical Bayesian procedures.
    NeuroImage 05/2015; 117. DOI:10.1016/j.neuroimage.2015.05.040 · 6.36 Impact Factor
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