Comparison of structural covariance with functional connectivity approaches exemplified by an investigation of the left anterior insula

Abstract

The anterior insula is a multifunctional region involved in various cognitive, perceptual and socio-emotional processes. In particular, a portion of the left anterior insula is closely associated with working memory processes in healthy participants and shows gray matter reduction in schizophrenia. To unravel the functional networks related to this left anterior insula region, we here combined resting state connectivity, meta-analytic-connectivity modeling (MACM) and structural covariance (SC) in addition to functional characterization based on BrainMap meta-data. Apart from allowing new insight into the seed region, this approach moreover provided an opportunity to systematically compare these different connectivity approaches. The results showed that the left anterior insula has a broad response profile and is part of multiple functional networks including language, memory and socio-emotional networks. As all these domains are linked with several symptoms of schizophrenia, dysfunction of the left anterior insula might be a crucial component contributing to this disorder. Moreover, although converging connectivity across all three connectivity approaches for the left anterior insula were found, also striking differences were observed. RS and MACM as functional connectivity approaches specifically revealed functional networks linked with internal cognition and active perceptual/language processes, respectively. SC, in turn, showed a clear preference for highlighting regions involved in social cognition. These differential connectivity results thus indicate that the use of multiple forms of connectivity is advantageous when investigating functional networks as conceptual differences between these approaches might lead to systematic variation in the revealed functional networks.

Structural covariance mapping delineates medial and medio-lateral temporal networks in déjà vu

“…Our own metaanalytic measurements of co-activation within these two covariance patterns supports this proposition, revealing differences in their co-activation profile; a much higher probability of functional interaction is observed among the positive pattern. This assumption is also supported by an increasing number of studies reporting strong convergence between covariance-based analyses of structural (GM volume) and functional data, both in the healthy and diseased brain (e.g., Kelly et al 2012;Clos et al 2014;Hoffstaedter et al 2014Hoffstaedter et al , 2015Seeley et al 2009;Wolf et al 2014;Zielinski et al 2010). Our structure-function assumption does require formal testing, however, which requires neuroscientific techniques capable of assessing more directly the functional and structural connectivity profiles of these two covariance patterns.…”

“…org); a higher probability of co-activation reflecting greater functional interaction between them. This method has been shown to converge with direct measures of functional connectivity acquired from resting-state functional MRI data (e.g., Chase et al 2015;Clos et al 2014;Dogan et al 2015;Hoffstaedter et al 2014Hoffstaedter et al , 2015Rottschy et al 2013). …”

“…It remains unknown, however, whether the benign manifestation of DV experienced by healthy individuals results from a similar pattern of neural activity (Moulin 2014). Given the difficulty in studying DV in vivo with functional neuroimaging, the present study built on a growing body of research demonstrating that GM covaries among brain structures that interact frequently (e.g., Kelly et al 2012;Clos et al 2014;Hoffstaedter et al 2014Hoffstaedter et al , 2015; see also Evans 2013). By investigating GM covariance among brain structures implicated in non-pathological DV we observed two distinct patterns of structural co-variation related orthogonally with DV frequency: Pairwise correlations in GM among structures within MTL, and between MTL, dorsal striatum and insula cortex become more positively correlated with higher DV frequency.…”

“…This makes it difficult to map in vivo the pattern of neural interactions underlying the phenomenon. An alternative approach is to map structural covariance, with an increasing number of studies demonstrating that GM covaries among brain structures that co-activate, or interact, frequently as nodes of functional networks (see Evans 2013): Patterns of covarying GM volume resemble closely the brains intrinsic functional networks (Clos et al 2014;Kelly et al 2012;Zielinski et al 2010), follow similar developmental trajectories (Alexander-Bloch et al 2013;Khundrakpam et al 2013;Raznahan et al 2011) and exhibit age-and disease-related decline that parallel any overlapping specialized functional networks (Hoffstaedter et al 2015;Seeley et al 2009;Wolf et al 2014). This likely reflects the strong interplay between brain structure and function; while brain morphology will constrain the spatial and temporal dynamics of neural signalling within functional networks, neural activity can trigger molecular mechanisms supporting brain plasticity (e.g., growth factors) that can exert coordinated structural changes in two frequently interacting brain systems (Honey et al 2010, Power et al 2010).…”

Multimodal connectivity of motor learning-related dorsal premotor cortex

“…As previous studies indicate that functional connectivity is associated with correlated gray-matter volumes (Seeley et al, 2009), we therefore regard it as a ‘functional connectivity’ measure (or, more precisely, a proxy for long-term functional connectivity patterns) for the purposes of this paper. While the degree to which SC can be used to infer functional networks has yet to be established (Clos et al, 2014), it provides a further method for investigating coactivity with the seed region.…”