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Anatomical integration and rich-club connectivity in euthymic bipolar disorder

  • S. O'Donoghue (a1), L. Kilmartin (a2), D. O'Hora (a3), L. Emsell (a4), C. Langan (a1), S. McInerney (a5), N. J. Forde (a6), A. Leemans (a7), B. Jeurissen (a8), G. J. Barker (a9), P. McCarthy (a10), D. M. Cannon (a1) and C. McDonald (a1)...
Abstract
Background

Although repeatedly associated with white matter microstructural alterations, bipolar disorder (BD) has been relatively unexplored using complex network analysis. This method combines structural and diffusion magnetic resonance imaging (MRI) to model the brain as a network and evaluate its topological properties. A group of highly interconnected high-density structures, termed the ‘rich-club’, represents an important network for integration of brain functioning. This study aimed to assess structural and rich-club connectivity properties in BD through graph theory analyses.

Method

We obtained structural and diffusion MRI scans from 42 euthymic patients with BD type I and 43 age- and gender-matched healthy volunteers. Weighted fractional anisotropy connections mapped between cortical and subcortical structures defined the neuroanatomical networks. Next, we examined between-group differences in features of graph properties and sub-networks.

Results

Patients exhibited significantly reduced clustering coefficient and global efficiency, compared with controls globally and regionally in frontal and occipital regions. Additionally, patients displayed weaker sub-network connectivity in distributed regions. Rich-club analysis revealed subtly reduced density in patients, which did not withstand multiple comparison correction. However, hub identification in most participants indicated differentially affected rich-club membership in the BD group, with two hubs absent when compared with controls, namely the superior frontal gyrus and thalamus.

Conclusions

This graph theory analysis presents a thorough investigation of topological features of connectivity in euthymic BD. Abnormalities of global and local measures and network components provide further neuroanatomically specific evidence for distributed dysconnectivity as a trait feature of BD.

Copyright
Corresponding author
*Address for correspondence: S. O'Donoghue, The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, College of Medicine, Nursing and Health Sciences, National University of Ireland Galway, Galway, Republic of Ireland. (Email: s.odonoghue9@nuigalway.ie)
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