The generation and validation of white matter connectivity importance maps

Both the size and location of injury in the brain influences the type and severity of cognitive or sensorimotor dysfunction. However, even with advances in MR imaging and analysis, the correspondence between lesion location and clinical deficit remains poorly understood. Here, structural and diffusion images from 14 healthy subjects are used to create spatially unbiased white matter connectivity importance maps that quantify the amount of disruption to the overall brain network that would be incurred if that region were compromised. Some regions in the white matter that were identified as highly important by such maps have been implicated in strategic infarct dementia and linked to various attention tasks in previous studies. Validation of the maps is performed by investigating the correlations of the importance maps' predicted cognitive deficits in a group of 15 traumatic brain injury patients with their cognitive test scores measuring attention and memory. While no correlation was found between amount of white matter injury and cognitive test scores, significant correlations (r>0.68, p<0.006) were found when including location information contained in the importance maps. These tools could be used by physicians to improve surgical planning, diagnosis, and assessment of disease severity in a variety of pathologies like multiple sclerosis, trauma, and stroke.

[1]  Carl-Fredrik Westin,et al.  A Bayesian approach for stochastic white matter tractography , 2006, IEEE Transactions on Medical Imaging.

[2]  P. Basser Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images , 1995, NMR in biomedicine.

[3]  Guido Gerig,et al.  Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling , 2009, Medical Image Anal..

[4]  Yonggang Lu,et al.  Improved fiber tractography with Bayesian tensor regularization , 2006, NeuroImage.

[5]  Pratik Mukherjee,et al.  Neuroanatomy Original Research Article , 2022 .

[6]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[7]  Lijun Zhang,et al.  Determining functional connectivity using fMRI data with diffusion-based anatomical weighting , 2009, NeuroImage.

[8]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. , 1996, Journal of magnetic resonance.

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

[10]  Yong He,et al.  Diffusion Tensor Tractography Reveals Abnormal Topological Organization in Structural Cortical Networks in Alzheimer's Disease , 2010, The Journal of Neuroscience.

[11]  P. Basser,et al.  A simplified method to measure the diffusion tensor from seven MR images , 1998, Magnetic resonance in medicine.

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

[13]  Karl Pearson F.R.S. LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[14]  Y. Assaf,et al.  Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review , 2007, Journal of Molecular Neuroscience.

[15]  Arthur W. Toga,et al.  Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants , 2009, NeuroImage.

[16]  Peter Gruen,et al.  Novel diffusion tensor imaging methodology to detect and quantify injured regions and affected brain pathways in traumatic brain injury. , 2010, Magnetic resonance imaging.

[17]  A. G. Osborn,et al.  White Matter Abnormalities in Mild Traumatic Brain Injury: A Diffusion Tensor Imaging Study , 2009 .

[18]  J. Donders,et al.  Criterion validity of the California Verbal Learning Test-Second Edition (CVLT-II) after traumatic brain injury. , 2007, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[19]  Lester Melie-García,et al.  Characterizing brain anatomical connections using diffusion weighted MRI and graph theory , 2007, NeuroImage.

[20]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[21]  M. Saadah,et al.  Acute ischemic stroke: relationship of brain lesion location & functional outcome. , 2009, Disability and rehabilitation.

[22]  P. Morgan,et al.  Pyramidal tract mapping by diffusion tensor magnetic resonance imaging in multiple sclerosis: improving correlations with disability , 2003, Journal of neurology, neurosurgery, and psychiatry.

[23]  T. Tatemichi,et al.  Strategic infarcts in vascular dementia. A clinical and brain imaging experience. , 1995, Arzneimittel-Forschung.

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

[25]  S. Strogatz Exploring complex networks , 2001, Nature.

[26]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[27]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[28]  Jonathan Taylor,et al.  Statistical mapping analysis of lesion location and neurological disability in multiple sclerosis: application to 452 patient data sets , 2003, NeuroImage.

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

[30]  F. Barkhof,et al.  Clinical correlations of brain lesion distribution in multiple sclerosis , 2009, Journal of magnetic resonance imaging : JMRI.

[31]  Guy B. Williams,et al.  Registration accuracy for VBM studies varies according to region and degenerative disease grouping , 2010, NeuroImage.

[32]  M Filippi,et al.  Correlation between brain MRI lesion volume and disability in patients with multiple sclerosis , 1996, Acta neurologica Scandinavica.

[33]  Pratik Mukherjee,et al.  Structural dissociation of attentional control and memory in adults with and without mild traumatic brain injury. , 2008, Brain : a journal of neurology.

[34]  Bruce D. McCandliss,et al.  Testing the Efficiency and Independence of Attentional Networks , 2002, Journal of Cognitive Neuroscience.

[35]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[36]  Nelson J. Trujillo-Barreto,et al.  Realistically Coupled Neural Mass Models Can Generate EEG Rhythms , 2007, Neural Computation.

[37]  F. Bowman,et al.  Evaluating Functional Connectivity using fMRI Data with Diffusion-Based Anatomical Weighting , 2009, NeuroImage.

[38]  A Gregory Sorensen,et al.  The Real Estate Factor: Quantifying the Impact of Infarct Location on Stroke Severity , 2007, Stroke.

[39]  O. Sporns,et al.  Organization, development and function of complex brain networks , 2004, Trends in Cognitive Sciences.

[40]  Lester Melie-García,et al.  Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory , 2008, NeuroImage.

[41]  Yong He,et al.  Discrete Neuroanatomical Networks Are Associated with Specific Cognitive Abilities in Old Age , 2011, The Journal of Neuroscience.

[42]  Bruce D. McCandliss,et al.  Extent of Microstructural White Matter Injury in Postconcussive Syndrome Correlates with Impaired Cognitive Reaction Time: A 3T Diffusion Tensor Imaging Study of Mild Traumatic Brain Injury , 2008, American Journal of Neuroradiology.

[43]  Jacobus F. A. Jansen,et al.  The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures , 2010, NeuroImage.

[44]  C. Mainero,et al.  Correlates of MS disability assessed in vivo using aggregates of MR quantities , 2001, Neurology.

[45]  E. Bullmore,et al.  Disrupted Axonal Fiber Connectivity in Schizophrenia , 2011, Biological Psychiatry.

[46]  D. Pandya,et al.  Fiber Pathways of the Brain , 2006 .

[47]  Ashish Raj,et al.  Statistics of Weighted Brain Networks Reveal Hierarchical Organization and Gaussian Degree Distribution , 2012, PloS one.

[48]  P. Basser,et al.  Toward a quantitative assessment of diffusion anisotropy , 1996, Magnetic resonance in medicine.

[49]  Michel Minoux,et al.  Graphs and Algorithms , 1984 .