期刊
FRONTIERS IN INTEGRATIVE NEUROSCIENCE
卷 7, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnint.2013.00031
关键词
neuronmorphology; MRI; diffusion; simulation; kurtosis; cytoarchitecture; cerebralcortex
资金
- Danish Ministry of Science, Technology, and Innovation's University Investment Grant (MINDLab)
- NIH [R01 NS070022, P51 RR000163, T32AG023477]
- NATIONAL CENTER FOR RESEARCH RESOURCES [P51RR000163] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R01NS070022] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [T32AG023477] Funding Source: NIH RePORTER
Accurate mathematical modeling is integral to the ability to interpret diffusion magnetic resonance (MR) imaging data in terms of cellular structure in brain gray matter (GM). In previous work, we derived expressions to facilitate the determination of the orientation distribution of axonal and dendritic processes from diffusion MR data. Here we utilize neuron reconstructions available in the NeuroMorpho database (www.neuromorpho.org) to assess the validity of the model we proposed by comparing morphological properties of the neurons to predictions based on diffusion MR simulations using the reconstructed neuron models. Initially, the method for directly determining neurite orientation distributions is shown to not depend on the line length used to quantify cylindrical elements. Further variability in neuron morphology is characterized relative to neuron type, species, and laboratory of origin. Subsequently, diffusion MR signals are simulated based on human neocortical neuron reconstructions. This reveals a bias in which diffusion MR data predict neuron orientation distributions to have artificially low anisotropy. This bias is shown to arise from shortcomings (already at relatively low diffusion weighting) in the Gaussian approximation of diffusion, in the presence of restrictive barriers, and data analysis methods involving higher moments of the cumulant expansion are shown to be capable of reducing the magnitude of the observed bias.
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