Validation of in vitro probabilistic tractography

Diffusion weighted imaging (DWI) and tractography allow the non-invasive study of anatomical brain connectivity. However, a gold standard for validating tractography of complex connections is lacking. Using the porcine brain as a highly gyrated brain model, we quantitatively and qualitatively assessed the anatomical validity and reproducibility of in vitro multi-fiber probabilistic tractography against two invasive tracers: the histochemically detectable biotinylated dextran amine and manganese enhanced magnetic resonance imaging. Post mortem DWI was used to ensure that most of the sources known to degrade the anatomical accuracy of in vivo DWI did not influence the tracking results. We demonstrate that probabilistic tractography reliably detected specific pathways. Moreover, the applied model allowed identification of the limitations that are likely to appear in many of the current tractography methods. Nevertheless, we conclude that DWI tractography can be a precise tool in studying anatomical brain connectivity.

[1]  Daniel C. Alexander,et al.  Probabilistic Monte Carlo Based Mapping of Cerebral Connections Utilising Whole-Brain Crossing Fibre Information , 2003, IPMI.

[2]  Andrew L. Alexander,et al.  An error analysis of white matter tractography methods: synthetic diffusion tensor field simulations , 2003, NeuroImage.

[3]  Kalvis M. Jansons,et al.  Persistent angular structure: new insights from diffusion magnetic resonance imaging data , 2003 .

[4]  P. Goldman-Rakic,et al.  Longitudinal topography and interdigitation of corticostriatal projections in the rhesus monkey , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[5]  G. E. Alexander,et al.  Basal ganglia-thalamocortical circuits: parallel substrates for motor, oculomotor, "prefrontal" and "limbic" functions. , 1990, Progress in brain research.

[6]  J Sijbers,et al.  Mathematical framework for simulating diffusion tensor MR neural fiber bundles , 2005, Magnetic resonance in medicine.

[7]  S. Skare,et al.  On the effects of gating in diffusion imaging of the brain using single shot EPI. , 2001, Magnetic resonance imaging.

[8]  P. Basser,et al.  New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter , 2004, Magnetic resonance in medicine.

[9]  Alan Connelly,et al.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.

[10]  Koen L. Vincken,et al.  Probabilistic segmentation of white matter lesions in MR imaging , 2004, NeuroImage.

[11]  N. Logothetis,et al.  Magnetic Resonance Imaging of Neuronal Connections in the Macaque Monkey , 2001, Neuron.

[12]  Hangyi Jiang,et al.  DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking , 2006, Comput. Methods Programs Biomed..

[13]  A. K. Hansen,et al.  The use of pigs in neuroscience: Modeling brain disorders , 2007, Neuroscience & Biobehavioral Reviews.

[14]  J. Lanciego,et al.  Current concepts in neuroanatomical tracing , 2000, Progress in Neurobiology.

[15]  Ching-Po Lin,et al.  Validation of Diffusion Tensor Magnetic Resonance Axonal Fiber Imaging with Registered Manganese-Enhanced Optic Tracts , 2001, NeuroImage.

[16]  Philip Anthony Cook,et al.  Modelling uncertainty in brain fibre orientation from diffusion-weighted magnetic resonance imaging. , 2006 .

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

[18]  Afonso C. Silva,et al.  In vivo neuronal tract tracing using manganese‐enhanced magnetic resonance imaging , 1998, Magnetic resonance in medicine.

[19]  Gareth J. Barker,et al.  Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging , 2002, IEEE Transactions on Medical Imaging.

[20]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[21]  Timothy Edward John Behrens,et al.  Between session reproducibility and between subject variability of diffusion MR and tractography measures , 2006, NeuroImage.

[22]  T E Conturo,et al.  Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results , 2002, NMR in biomedicine.

[23]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[24]  D. Tuch Q‐ball imaging , 2004, Magnetic resonance in medicine.

[25]  D. Le Bihan,et al.  Artifacts and pitfalls in diffusion MRI , 2006, Journal of magnetic resonance imaging : JMRI.

[26]  R. Pautler In vivo, trans‐synaptic tract‐tracing utilizing manganese‐enhanced magnetic resonance imaging (MEMRI) , 2004, NMR in biomedicine.

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

[28]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[29]  Anna Devor,et al.  In vivo tracing of major rat brain pathways using manganese-enhanced magnetic resonance imaging and three-dimensional digital atlasing , 2003, NeuroImage.

[30]  Daniel C Alexander,et al.  Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  Geoffrey J M Parker,et al.  A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements , 2003, Journal of magnetic resonance imaging : JMRI.

[32]  Jacob Jelsing,et al.  The prefrontal cortex in the Göttingen minipig brain defined by neural projection criteria and cytoarchitecture , 2006, Brain Research Bulletin.

[33]  L. Frank Characterization of anisotropy in high angular resolution diffusion‐weighted MRI , 2002, Magnetic resonance in medicine.

[34]  G. E. Alexander,et al.  Parallel organization of functionally segregated circuits linking basal ganglia and cortex. , 1986, Annual review of neuroscience.

[35]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[36]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[37]  Floris G. Wouterlood,et al.  Neuroanatomical Tract-Tracing 3 , 2006 .

[38]  Gareth J. Barker,et al.  From diffusion tractography to quantitative white matter tract measures: a reproducibility study , 2003, NeuroImage.

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

[40]  H. Groenewegen,et al.  The anatomical relationships of the prefrontal cortex with limbic structures and the basal ganglia , 1997, Journal of psychopharmacology.

[41]  Carlo Pierpaoli,et al.  PASTA: Pointwise assessment of streamline tractography attributes , 2005, Magnetic resonance in medicine.

[42]  E. Oztaş Neuronal tracing , 2003 .

[43]  Yusuke Murayama,et al.  Tracing neural circuits in vivo with Mn-enhanced MRI. , 2006, Magnetic resonance imaging.