by Helbling, Saskia, Teki, Sundeep, Callaghan, Martina F., Sedley, William, Mohammadi, Siawoosh, Griffiths, Timothy D., Weiskopf, Nikolaus and Barnes, Gareth R.
Abstract:
We present an approach for combining high resolution MRI-based myelin mapping with functional information from electroencephalography (EEG) or magnetoencephalography (MEG). The main contribution to the primary currents detectable with EEG and MEG comes from ionic currents in the apical dendrites of cortical pyramidal cells, aligned perpendicularly to the local cortical surface. We provide evidence from an in-vivo experiment that the variation in MRI-based myeloarchitecture measures across the cortex predicts the variation of the current density over individuals and thus is of functional relevance. Equivalent current dipole locations and moments due to pitch onset evoked response fields (ERFs) were estimated by means of a variational Bayesian algorithm. The myeloarchitecture was estimated indirectly from individual high resolution quantitative multi-parameter maps (MPMs) acquired at 800μm isotropic resolution. Myelin estimates across cortical areas correlated positively with dipole magnitude. This correlation was spatially specific: regions of interest in the auditory cortex provided significantly better models than those covering whole hemispheres. Based on the MPM data we identified the auditory cortical area TE1.2 as the most likely origin of the pitch ERFs measured by MEG. We can now proceed to exploit the higher spatial resolution of quantitative MPMs to identify the cortical origin of M/EEG signals, inform M/EEG source reconstruction and explore structure-function relationships at a fine structural level in the living human brain.
Reference:
Helbling, Saskia, Teki, Sundeep, Callaghan, Martina F., Sedley, William, Mohammadi, Siawoosh, Griffiths, Timothy D., Weiskopf, Nikolaus and Barnes, Gareth R. (2015). Structure predicts function: Combining non-invasive electrophysiology with in-vivo histology. NeuroImage, 108, 377–385.
Bibtex Entry:
@article{helbling_structure_2015,
abstract = {We present an approach for combining high resolution MRI-based myelin mapping with functional information from electroencephalography (EEG) or magnetoencephalography (MEG). The main contribution to the primary currents detectable with EEG and MEG comes from ionic currents in the apical dendrites of cortical pyramidal cells, aligned perpendicularly to the local cortical surface. We provide evidence from an in-vivo experiment that the variation in MRI-based myeloarchitecture measures across the cortex predicts the variation of the current density over individuals and thus is of functional relevance. Equivalent current dipole locations and moments due to pitch onset evoked response fields (ERFs) were estimated by means of a variational Bayesian algorithm. The myeloarchitecture was estimated indirectly from individual high resolution quantitative multi-parameter maps (MPMs) acquired at 800μm isotropic resolution. Myelin estimates across cortical areas correlated positively with dipole magnitude. This correlation was spatially specific: regions of interest in the auditory cortex provided significantly better models than those covering whole hemispheres. Based on the MPM data we identified the auditory cortical area TE1.2 as the most likely origin of the pitch ERFs measured by MEG. We can now proceed to exploit the higher spatial resolution of quantitative MPMs to identify the cortical origin of M/EEG signals, inform M/EEG source reconstruction and explore structure-function relationships at a fine structural level in the living human brain.},
author = {Helbling, Saskia and Teki, Sundeep and Callaghan, Martina F. and Sedley, William and Mohammadi, Siawoosh and Griffiths, Timothy D. and Weiskopf, Nikolaus and Barnes, Gareth R.},
date-added = {2015-02-17 21:19:28 +0000},
date-modified = {2015-02-17 21:19:28 +0000},
doi = {10.1016/j.neuroimage.2014.12.030},
issn = {1095-9572},
journal = {NeuroImage},
language = {eng},
month = mar,
pages = {377--385},
pmid = {25529007},
shorttitle = {Structure predicts function},
title = {Structure predicts function: {Combining} non-invasive electrophysiology with in-vivo histology},
volume = {108},
year = {2015}}