Poster Presentation 9th GeneMappers Conference 2012

Heritability of resting-state functional connectivity with Broca’s language area (#102)

Baptiste Couvy-Duchesne 1 , Gabriëlla A. M. Blokland 1 2 , Paul M. Thompson 3 , Ian B. Hickie 4 , Nicholas G. Martin 1 , Katie L. McMahon 2 , Margaret J. Wright 1 , Greig I. de Zubicaray 5
  1. Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Queensland, Australia
  2. Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
  3. Laboratory of NeuroImaging, University of California, Los Angeles, USA
  4. Brain & Mind Research Institute, University of Sydney, Sydney, Australia
  5. School of Psychology, University of Queensland, Brisbane, Australia

In the last few years, functional connectivity (FC) studies based on resting state fMRI have provided a statistical approach for describing and studying the network organization of the human brain.  FC analysis has allowed a deeper understanding of the interactions between areas, using Blood Oxygenation Level Dependent (BOLD) signal correlations. Recently, variations in task-related FC strength impacting language areas (Brodmann’s areas [BA] 44 and 45 or Broca’s area) have been associated with stuttering1 , severe depression2 , dyslexia3  and auditory/verbal hallucination in schizophrenic patients4 .

We propose to go further by estimating the heritability of the correlations at rest. Even if resting state does not maximize the coherent fluctuations in the language system, spontaneous fluctuations, constrained by anatomic connectivity, should be observed5 . We focused on language related areas, as a first step in drawing a heritability map of FC.

Our study was performed on 732 healthy twins between 16 and 30 years old.

Resting state fMRI analysis was performed using the DPARSF-A toolbox6  in SPM8. BOLD signal, after classical pre-processing, was regressed against head-motion parameters, white matter, cerebrospinal fluid and global mean signals.

Functional connectivity analysis was performed by a seed-based correlation method using our regions of interest (BA 44 & 45) as seeds. Specific connectivity was investigated for each seed region.

Heritability analysis for each seed correlation map was performed in OpenMx7  using sex, age, performance IQ, head-motion statistics as covariates and correlation values (Fisher’s Z transformed) as continuous traits.

The heritability maps of functional correlations could provide new leads to investigate the genetic determinants and risk factors of the pathologies (or phenotypes) associated with disruption in FC networks, through genome-wide association.

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