Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves

PLoS One. 2016 Jul 6;11(7):e0156436. doi: 10.1371/journal.pone.0156436. eCollection 2016.

Abstract

At rest, healthy human brain activity is characterized by large electroencephalography (EEG) fluctuations in the 8-13 Hz range, commonly referred to as the alpha band. Although it is well known that EEG alpha activity varies across individuals, few studies have investigated how this may be related to underlying morphological variations in brain structure. Specifically, it is generally believed that the lateral geniculate nucleus (LGN) and its efferent fibres (optic radiation, OR) play a key role in alpha activity, yet it is unclear whether their shape or size variations contribute to its inter-subject variability. Given the widespread use of EEG alpha in basic and clinical research, addressing this is important, though difficult given the problems associated with reliably segmenting the LGN and OR. For this, we employed a multi-modal approach and combined diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI) and EEG in 20 healthy subjects to measure structure and function, respectively. For the former, we developed a new, semi-automated approach for segmenting the OR and LGN, from which we extracted several structural metrics such as volume, position and diffusivity. Although these measures corresponded well with known morphology based on previous post-mortem studies, we nonetheless found that their inter-subject variability was not significantly correlated to alpha power or peak frequency (p >0.05). Our results therefore suggest that alpha variability may be mediated by an alternative structural source and our proposed methodology may in general help in better understanding the influence of anatomy on function such as measured by EEG or fMRI.

MeSH terms

  • Brain Mapping / methods
  • Diffusion Magnetic Resonance Imaging
  • Electroencephalography*
  • Female
  • Geniculate Bodies / anatomy & histology*
  • Geniculate Bodies / diagnostic imaging
  • Healthy Volunteers
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Nerve Fibers
  • Pattern Recognition, Automated
  • Reproducibility of Results
  • Rest
  • Thalamus / anatomy & histology
  • Thalamus / diagnostic imaging

Grants and funding

This work was supported by The Natural Sciences and Engineering Research Council of Canada (NSERC) (http://www.nserc-crsng.gc.ca/); The Fonds Quebecois de Recherche en Nature et Technologie (FQRNT) (http://www.frqnt.gouv.qc.ca/en/bourses-et-subventions); and The Faculty of Medicine of the University of Sherbrooke. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.