Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging

Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to bridge the spatial scales from microscopic fiber orientation measurements based on 3D-Polarized Light Imaging (3D-PLI) to meso- or macroscopic dimensions. By creating orientation distribution functions (pliODFs) from high-resolution vector data via series expansion with spherical harmonics utilizing high performance computing and supercomputing technologies, data fusion with Diffusion Magnetic Resonance Imaging has become feasible, even for a large-scale dataset such as the human brain. Validation of our approach was done effectively by means of two types of datasets that were transferred from fiber orientation maps into pliODFs: simulated 3D-PLI data showing artificial, but clearly defined fiber patterns and real 3D-PLI data derived from sections through the human brain and the brain of a hooded seal.

[1]  Oliver Bücker,et al.  Towards a Multiscale, High-Resolution Model of the Human Brain , 2013, BrainComp.

[2]  K Amunts,et al.  A Jones matrix formalism for simulating three-dimensional polarized light imaging of brain tissue , 2015, Journal of the Royal Society Interface.

[3]  N. Makris,et al.  High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity , 2002, Magnetic resonance in medicine.

[4]  R. Jones,et al.  A New Calculus for the Treatment of Optical SystemsII. Proof of Three General Equivalence Theorems , 1941 .

[5]  R. Woods,et al.  Direct Visualization and Mapping of the Spatial Course of Fiber Tracts at Microscopic Resolution in the Human Hippocampus , 2016, Cerebral cortex.

[6]  M. Catani,et al.  Can spherical deconvolution provide more information than fiber orientations? Hindrance modulated orientational anisotropy, a true‐tract specific index to characterize white matter diffusion , 2013, Human brain mapping.

[7]  J. Tournier,et al.  High Angular Resolution Diffusion Imaging , 2016 .

[8]  Katrin Amunts,et al.  A multiscale approach for the reconstruction of the fiber architecture of the human brain based on 3D-PLI , 2015, Front. Neuroanat..

[9]  A. Dale,et al.  Quantitative Histological Validation of Diffusion MRI Fiber Orientation Distributions in the Rat Brain , 2010, PloS one.

[10]  Susanne Schnell,et al.  Global fiber reconstruction becomes practical , 2011, NeuroImage.

[11]  P. Osten,et al.  Mapping brain circuitry with a light microscope , 2013, Nature Methods.

[12]  Kaleem Siddiqi,et al.  Recent advances in diffusion MRI modeling: Angular and radial reconstruction , 2011, Medical Image Anal..

[13]  S. Arridge,et al.  Detection and modeling of non‐Gaussian apparent diffusion coefficient profiles in human brain data , 2002, Magnetic resonance in medicine.

[14]  R. Jones.  A New Calculus for the Treatment of Optical SystemsI. Description and Discussion of the Calculus , 1941 .

[15]  Timo Dickscheid,et al.  High-Resolution Fiber Tract Reconstruction in the Human Brain by Means of Three-Dimensional Polarized Light Imaging , 2011, Front. Neuroinform..

[16]  P. Hagmann,et al.  Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[17]  Martha Elizabeth Shenton,et al.  Directional functions for orientation distribution estimation , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[18]  H. Bunge.  Texture Analysis in Materials Science: Mathematical Methods , 2013 .

[19]  Li Bai,et al.  Brain tractography using Q-ball imaging and graph theory: Improved connectivities through fibre crossings via a model-based approach , 2010, NeuroImage.

[20]  Alan C. Evans,et al.  BigBrain: An Ultrahigh-Resolution 3D Human Brain Model , 2013, Science.

[21]  Duan Xu,et al.  Q‐ball reconstruction of multimodal fiber orientations using the spherical harmonic basis , 2006, Magnetic resonance in medicine.

[22]  Aaron S. Andalman,et al.  Structural and molecular interrogation of intact biological systems , 2013, Nature.

[23]  Katrin Amunts,et al.  Understanding fiber mixture by simulation in 3D Polarized Light Imaging , 2015, NeuroImage.

[24]  Tipu Z. Aziz,et al.  Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner , 2011, NeuroImage.

[25]  Christoph Palm,et al.  A novel approach to the human connectome: Ultra-high resolution mapping of fiber tracts in the brain , 2011, NeuroImage.

[26]  A. Scheibel,et al.  Fiber composition of the human corpus callosum , 1992, Brain Research.

[27]  Anna Letizia Allegra Mascaro,et al.  A versatile clearing agent for multi-modal brain imaging , 2015, Scientific Reports.

[28]  Behrens,et al.  Validation of Tractography , 2009 .

[29]  Katrin Amunts,et al.  Chapter 18 – High-Resolution Fiber and Fiber Tract Imaging Using Polarized Light Microscopy in the Human, Monkey, Rat, and Mouse Brain , 2016 .

[30]  K. Amunts,et al.  Architectonic Mapping of the Human Brain beyond Brodmann , 2015, Neuron.

[31]  Markus Axer,et al.  Finite-Difference Time-Domain Simulation for Three-Dimensional Polarized Light Imaging , 2015, BrainComp.

[32]  Matthew D. Budde,et al.  Examining brain microstructure using structure tensor analysis of histological sections , 2012, NeuroImage.

[33]  Katrin Amunts,et al.  Target sites for transcallosal fibers in human visual cortex – A combined diffusion and polarized light imaging study , 2015, Cortex.

[34]  Alard Roebroeck,et al.  Unraveling the multiscale structural organization and connectivity of the human brain: the role of diffusion MRI , 2015, Front. Neuroanat..

[35]  K. Amunts,et al.  Towards Ultra-High Resolution Fibre Tract Mapping of the Human Brain – Registration of Polarised Light Images and Reorientation of Fibre Vectors , 2009, Front. Hum. Neurosci..

[36]  Daniel C Alexander,et al.  Multiple‐Fiber Reconstruction Algorithms for Diffusion MRI , 2005, Annals of the New York Academy of Sciences.