Elsevier

NeuroImage

Volume 99, 1 October 2014, Pages 269-280
NeuroImage

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

https://doi.org/10.1016/j.neuroimage.2014.05.030Get rights and content

Highlights

  • The left anterior insula is part of language, memory and socio-emotional networks

  • Three connectivity approaches were systematically compared examining this region

  • Resting state specifically highlighted regions involved in internal cognition

  • MACM specifically highlighted regions involved in active perception/language

  • Structural covariance specifically highlighted regions involved in social cognition

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.

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).

References (109)

  • N.U.F. Dosenbach et al.

    A core system for the implementation of task sets

    Neuron

    (2006)
  • S.B. Eickhoff et al.

    Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation

    Neuroimage

    (2011)
  • S.B. Eickhoff et al.

    Activation likelihood estimation meta-analysis revisited

    Neuroimage

    (2012)
  • A.C. Evans

    Networks of anatomical covariance

    Neuroimage

    (2013)
  • O. Jakobs et al.

    Across-study and within-subject functional connectivity of a right temporo-parietal junction subregion involved in stimulus-context integration

    Neuroimage

    (2012)
  • C. Kelly et al.

    A convergent functional architecture of the insula emerges across imaging modalities

    Neuroimage

    (2012)
  • S. Kousta

    Mapping the structural and functional architecture of the brain

    Trends Cogn. Sci.

    (2013)
  • A.R. Laird et al.

    Networks of task co-activations

    Neuroimage

    (2013)
  • J.S. Lee et al.

    Involvement of the mirror neuron system in blunted affect in schizophrenia

    Schizophr. Res.

    (2014)
  • J.P. Lerch et al.

    Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI

    Neuroimage

    (2006)
  • S.A. Mitelman et al.

    Correlations between MRI-assessed volumes of the thalamus and cortical Brodmann’s areas in schizophrenia

    Schizophr. Res.

    (2005)
  • I. Mutschler et al.

    Functional organization of the human anterior insular cortex

    Neurosci. Lett.

    (2009)
  • T. Nichols et al.

    Valid conjunction inference with the minimum statistic

    Neuroimage

    (2005)
  • M.L. Phillips et al.

    A differential neural response to threatening and non-threatening negative facial expressions in paranoid and non-paranoid schizophrenics

    Psychiatry Res.

    (1999)
  • K. Reetz et al.

    Investigating function and connectivity of morphometric findings–exemplified on cerebellar atrophy in spinocerebellar ataxia 17 (SCA17)

    Neuroimage

    (2012)
  • G.R. Ridgway et al.

    Ten simple rules for reporting voxel-based morphometry studies

    Neuroimage

    (2008)
  • C. Rottschy et al.

    Modelling neural correlates of working memory: a coordinate-based meta-analysis

    Neuroimage

    (2012)
  • T.D. Satterthwaite et al.

    An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data

    Neuroimage

    (2013)
  • W.W. Seeley et al.

    Neurodegenerative diseases target large-scale human brain networks

    Neuron

    (2009)
  • T. Singer et al.

    A common role of insula in feelings, empathy and uncertainty

    Trends Cogn. Sci.

    (2009)
  • P. Skudlarski et al.

    Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations

    Neuroimage

    (2008)
  • S.M. Smith et al.

    Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference

    Neuroimage

    (2009)
  • S.M. Smith et al.

    Functional connectomics from resting-state fMRI

    Trends Cogn. Sci.

    (2013)
  • A. Alexander-Bloch et al.

    The convergence of maturational change and structural covariance in human cortical networks

    J. Neurosci.

    (2013)
  • T.J. Andrews et al.

    Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract

    J. Neurosci.

    (1997)
  • P.A. Bandettini et al.

    Endogenous oscillations and networks in functional magnetic resonance imaging

    Hum. Brain Mapp.

    (2008)
  • M. Beckmann et al.

    Connectivity-based parcellation of human cingulate cortex and its relation to functional specialization

    J. Neurosci.

    (2009)
  • B.C. Bernhardt et al.

    Structural covariance networks of the dorsal anterior insula predict females’ individual differences in empathic responding

    Cereb. Cortex

    (2013)
  • B. Biswal et al.

    Functional connectivity in the motor cortex of resting human brain using echo-planar MRI

    Magn. Reson. Med.

    (1995)
  • F. Cauda et al.

    Functional connectivity and coactivation of the nucleus accumbens: a combined functional connectivity and structure-based meta-analysis

    J. Cogn. Neurosci.

    (2011)
  • L. Cerliani et al.

    Probabilistic tractography recovers a rostrocaudal trajectory of connectivity variability in the human insular cortex

    Hum. Brain Mapp.

    (2012)
  • L.J. Chang et al.

    Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference

    Cereb. Cortex

    (2013)
  • M. Clos et al.

    Aberrant connectivity of areas for decoding degraded speech in patients with auditory verbal hallucinations

    Brain Struct. Funct.

    (2014)
  • A.D.B. Craig

    How do you feel–now? The anterior insula and human awareness

    Nat. Rev. Neurosci.

    (2009)
  • D. Crepaldi et al.

    Clustering the lexicon in the brain: a meta-analysis of the neurofunctional evidence on noun and verb processing

    Front. Hum. Neurosci.

    (2013)
  • B. Crespo-Facorro et al.

    Neural mechanisms of anhedonia in schizophrenia: a PET study of response to unpleasant and pleasant odors

    JAMA

    (2001)
  • V.A. Curtis et al.

    Attenuated frontal activation during a verbal fluency task in patients with schizophrenia

    Am. J. Psychiatry

    (1998)
  • G. Deco et al.

    The dynamical balance of the brain at rest

    Neuroscientist

    (2011)
  • B. Deen et al.

    Three systems of insular functional connectivity identified with cluster analysis

    Cereb. Cortex

    (2011)
  • L.E. DeLisi

    Speech disorder in schizophrenia: review of the literature and exploration of its relation to the uniquely human capacity for language

    Schizophr. Bull.

    (2001)
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