Comparison of structural covariance with functional connectivity approaches exemplified by an investigation of the left anterior insula
Introduction
The anterior insula (AI) is a multifunctional integration region that has been associated with various sensory, cognitive and socio-affective processes (Kurth et al., 2010, Mutschler et al., 2009) and is hypothesized to implement the integration of external and internal processes by large-scale interactions with other brain regions (Craig, 2009, Menon and Uddin, 2010, Singer et al., 2009). Moreover, two recent meta-analyses highlighted the left AI as a core region in working memory (Rottschy et al., 2012) and as a region displaying structural abnormalities in schizophrenia (Nickl-Jockschat et al., 2011). This left AI region thus seems to be a key component of cognitive functioning in healthy subjects and shows aberrations in a highly prevalent mental disorder, prompting questions about the functional networks associated with it.
When aiming to delineate the functional interactions of this region, it is noteworthy that functional connectivity analysis is actually a rather heterogeneous construct. In particular, there are several different approaches to detect functional networks on the basis of non-invasive neuroimaging. Firstly, task-free resting state (RS) connectivity can be used to reveal brain regions that display temporal correlations with the seed region in functional MRI time-series obtained while no explicit task is presented (Fox and Raichle, 2007, Smith et al., 2013). Secondly, task-based functional connectivity using meta-analytic co-activation modelling (MACM) has been established as another functional connectivity approach (Eickhoff et al., 2010, Laird et al., 2013). Here, co-activation of regions with a certain seed region across many experiments recorded in the BrainMap database (Fox and Lancaster, 2002, Laird et al., 2005, Laird et al., 2009, Laird et al., 2011) is used to identify functional networks. Furthermore, the meta-data specifying the kind of task and contrast employed by experiments activating the region of interest may be used to functionally characterize the resulting networks and thus reveal their functional implication. Thirdly, structural covariance (SC) is an analysis method to infer structural networks which in turn result from to a combination of genetic, maturational and functional interaction effects (Evans, 2013). As such, the examination of SC networks can possibly contribute to the understanding of functional connectivity, although it is not yet entirely clear to what degree structural covariance can directly infer functional networks. In particular, this approach is based on the correlation of gray matter characteristics such as volume or thickness across participants (Albaugh et al., 2013, Lerch et al., 2006). Conceptually, gray matter covariance is thought to reflect shared maturational and functional specialization processes of these regions in addition to genetic factors (Alexander-Bloch et al., 2013, Evans, 2013). Such structural covariance patterns have been shown to exist between brain regions belonging to the same functional system in healthy participants (Andrews et al., 1997, Mechelli et al., 2005). Moreover, the learning of specific skills has been demonstrated to lead to training-induced structural plasticity in the networks subserving these skills (Draganski et al., 2004, Driemeyer et al., 2008, Haier et al., 2009, Maguire et al., 2003). Also in patient populations specific structural covariance abnormalities have been observed (Bernhardt et al., 2008, Bullmore et al., 1998, Mitelman et al., 2005, Spreng and Turner, 2013). In this context, it needs to be stressed that all three approaches – RS, MACM and SC – share the same goal of delineating regions that interact with the seed. In spite of this shared goal, however, substantial conceptual differences are also evident and in particular for SC there is still some debate regarding the extent that anatomical covariance networks represent functional connectivity. While there has been some evidence for convergence between RS and MACM (Cauda et al., 2011, Hoffstaedter et al., 2014, Jakobs et al., 2012), between RS and SC (He et al., 2007, Seeley et al., 2009), as well as between RS, MACM and SC onto a common insular architecture (Kelly et al., 2012), a systematic comparison of all these three approaches is still lacking. Such a comparison, however, seems highly warranted given the increasing focus on network interactions in neuroimaging (Kousta, 2013) and the conceptual differences between the approaches. In particular, these raise the question to which degree these methods may reveal common but also differential interactions, i.e., whether there is a bias in the delineated networks. A multimodal region such as the left AI might be particularly suited to tackle this question as it offers the possibility to discern multiple functional networks that could be preferentially delineated by the different conceptual approaches to functional connectivity. We thus examined RS-, MACM- and SC-derived networks seeded from the left AI as defined by two previous meta-analyses on working memory activations (Rottschy et al., 2012) and atrophy in schizophrenia (Nickl-Jockschat et al., 2011).
Section snippets
VOI definition and functional characterization
The seed volume of interest (VOI) was based on converging findings in the left anterior insula reported in two meta-analyses. The first identified the anterior insula as one of four regions showing significant reductions in gray matter volume in patients with schizophrenia compared with healthy controls across 38 morphometric MRI studies (Nickl-Jockschat et al., 2011). Secondly, the anterior insula was one of the core regions for working memory as identified in a meta-analysis across 189
Results
The functional characterization of the left AI revealed no significant associations with any specific behavioral domain or paradigm class at p < 0.05 (FDR-corrected for multiple comparisons) indicating a broad functional response profile. At p < 0.05 uncorrected, the BrainMap meta-data pointed to a role of this region in (working) memory, emotions and particularly in language and speech processes (Fig. 1B), confirming a relatively broad involvement in different cognitive functions. Significant
Discussion
The aim of the current study was to delineate the function of a left anterior insula (AI) region and to compare structural covariance with task-free and task-based functional connectivity of this particular region. The choice of this left AI region as a seed for the connectivity analyses was motivated by its central role in working memory in healthy participants (Rottschy et al., 2012) and by findings of atrophy in patients with schizophrenia in this region (Nickl-Jockschat et al., 2011). Using
Acknowledgments
This work was supported by the National Institute of Mental Health (R01-MH074457), the Initiative and Networking Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology (Human Brain Model SBE, MC), and the DFG (IRTG 1328 to SBE).
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