Amygdala tractography predicts functional connectivity and learning during feedback-guided decision-making

Department of Epileptology, University of Bonn, Germany.
NeuroImage (Impact Factor: 6.36). 03/2008; 39(3):1396-407. DOI: 10.1016/j.neuroimage.2007.10.004
Source: PubMed


Flexibly adapting behavior in dynamic environments relies on fronto-limbic networks that include the amygdala, orbitofrontal cortex, and striatum. Animal work demonstrates that interactions among these regions are critical for flexible feedback-guided learning, but it remains unknown to what extent such anatomical-functional interactions operate in humans. Here, we use connectivity analyses in both structural and functional MRI to further our understanding of how brain circuits work in conjunction to promote goal-directed behavior. In particular, fiber tracking based on diffusion-weighted imaging provides information about anatomical connectivity between brain structures, and functional MRI provides estimates of functional connectivity between structures. We found that, during a feedback-guided reversal learning task, the strength of estimated white matter tracts from the amygdala to the hippocampus, orbitofrontal cortex, and ventral striatum predicted both how subjects adapted their behavior following positive and negative feedback, and the functional connectivity (estimated from functional MRI time series) between the amygdala and these regions. In addition, we identified a dissociation between an amygdala-hippocampus circuit that predicted response switching, and an amygdala-orbitofrontal cortex circuit that predicted learning following rule reversals. These findings provide novel insights into how the anatomy and functioning of amygdala-related brain circuits mediate different aspects of feedback-guided learning behavior.

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    • "Although it is clear that FC relies upon AC, the exact relation that exists between the anatomical and FC in networks is still an open question (see Damoiseaux and Greicius 2009; Guye et al. 2010; Rykhlevskaia et al. 2008 for reviews). Early research on this topic focused on relating FC to AC within few regions in the brain (Koch et al. 2002; Guye et al. 2003; Boorman et al. 2007; Cohen et al. 2008; Zhou et al. 2008; Takahashi et al. 2008). There is also strong evidence that anatomical networks support the formation of functional patterns of resting state connectivity (van den Heuvel et al. 2008; Skudlarski et al. 2008; Greicius et al. 2009; van den Heuvel et al. 2009; Teipel et al. 2010). "
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    ABSTRACT: Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical connections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 different brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very-large-scale integration circuits analyses, shows that functional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrangements for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal-ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organizations that can only be identified when the physical locations of the nodes are included in the analysis.
    Brain Topography 09/2014; 28(2). DOI:10.1007/s10548-014-0393-3 · 3.47 Impact Factor
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    • "A central goal in systems neuroscience is to unveil how cognitive abilities result from dynamic interactions in large-scale cortical networks, and to further identify how development, aging and brain diseases contribute to reshaping this complex organization. The notion that structural networks represent the physical substrate of functional connectivity patterns in the human brain has received considerable attention over the last few years [Cohen et al., 2008; Greicius et al., 2009; Koch et al., 2002; Van den Heuvel et al., 2009]. This relationship is reinforced with cerebral maturation [Hagmann et al., 2010], suggesting that associations between structural and functional connectivity (in the next, functional-structural coupling or F-S coupling) play a role in creating patterns of neural synchronization in the human brain. "
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    ABSTRACT: Understanding how the mammalian neocortex creates cognition largely depends on knowledge about large-scale cortical organization. Accumulated evidence has illuminated cortical substrates of cognition across the lifespan, but how topological properties of cortical networks support structure-function relationships in normal aging remains an open question. Here we investigate the role of connections (i.e., short/long and direct/indirect) and node properties (i.e., centrality and modularity) in predicting functional-structural connectivity coupling in healthy elderly subjects. Connectivity networks were derived from correlations of cortical thickness and cortical glucose consumption in resting state. Local-direct connections (i.e., nodes separated by less than 30 mm) and node modularity (i.e., a set of nodes highly interconnected within a topological community and sparsely interconnected with nodes from other modules) in the functional network were identified as the main determinants of coupling between cortical networks, suggesting that the structural network in aging is mainly constrained by functional topological properties involved in the segregation of information, likely due to aging-related deficits in functional integration. This hypothesis is supported by an enhanced connectivity between cortical regions of different resting-state networks involved in sensorimotor and memory functions in detrimental to associations between fronto-parietal regions supporting executive processes. Taken collectively, these findings open new avenues to identify aging-related failures in the anatomo-functional organization of the neocortical mantle, and might contribute to early detection of prevalent neurodegenerative conditions occurring in the late life. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.
    Human Brain Mapping 06/2014; 35(6). DOI:10.1002/hbm.22362 · 5.97 Impact Factor
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    • "The rats with disconnected OFC and BLA, however, did not differ from controls in their press rates or response latencies, suggesting an impairment specific to altering the value of a particular reward rather than a general reduction in motivation. Similar effects have been observed in humans where structural and functional connectivity between the OFC and BLA was found to correlate with rate of acquisition on a reversal learning task (Cohen et al., 2008). The nucleus accumbens (NAc) also receives excitatory afferents from the OFC and BLA (amongst other regions), and selectively gates information projecting to basal ganglia output nuclei (Figure 1A; Alheid and Heimer, 1988; Groenewegen et al., 1999). "
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    ABSTRACT: The ability to learn contingencies between actions and outcomes in a dynamic environment is critical for flexible, adaptive behavior. Goal-directed actions adapt to changes in action-outcome contingencies as well as to changes in the reward-value of the outcome. When networks involved in reward processing and contingency learning are maladaptive, this fundamental ability can be lost, with detrimental consequences for decision-making. Impaired decision-making is a core feature in a number of psychiatric disorders, ranging from depression to schizophrenia. The argument can be developed, therefore, that seemingly disparate symptoms across psychiatric disorders can be explained by dysfunction within common decision-making circuitry. From this perspective, gaining a better understanding of the neural processes involved in goal-directed action, will allow a comparison of deficits observed across traditional diagnostic boundaries within a unified theoretical framework. This review describes the key processes and neural circuits involved in goal-directed decision-making using evidence from animal studies and human neuroimaging. Select studies are discussed to outline what we currently know about causal judgments regarding actions and their consequences, action-related reward evaluation, and, most importantly, how these processes are integrated in goal-directed learning and performance. Finally, we look at how adaptive decision-making is impaired across a range of psychiatric disorders and how deepening our understanding of this circuitry may offer insights into phenotypes and more targeted interventions.
    Frontiers in Systems Neuroscience 05/2014; 8:101. DOI:10.3389/fnsys.2014.00101
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