The Network Modification (NeMo) Tool: Elucidating the Effect of White Matter Integrity Changes on Cortical and Subcortical Structural Connectivity

Accurate prediction of brain dysfunction caused by disease or injury requires the quantification of resultant neural connectivity changes compared with the normal state. There are many methods with which to assess anatomical changes in structural or diffusion magnetic resonance imaging, but most overlook the topology of white matter (WM) connections that make up the healthy brain network. Here, a new neuroimaging software pipeline called the Network Modification (NeMo) Tool is presented that associates alterations in WM integrity with expected changes in neural connectivity between gray matter regions. The NeMo Tool uses a large reference set of healthy tractograms to assess implied network changes arising from a particular pattern of WM alteration on a region- and network-wise level. In this way, WM integrity changes can be extrapolated to the cortices and deep brain nuclei, enabling assessment of functional and cognitive alterations. Unlike current techniques that assess network dysfunction, the NeMo tool does not require tractography in pathological brains for which the algorithms may be unreliable or diffusion data are unavailable. The versatility of the NeMo Tool is demonstrated by applying it to data from patients with Alzheimer's disease, fronto-temporal dementia, normal pressure hydrocephalus, and mild traumatic brain injury. This tool fills a gap in the quantitative neuroimaging field by enabling an investigation of morphological and functional implications of changes in structural WM integrity.

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

[2]  M. Fontes,et al.  Structural and Functional Studies of a Bothropic Myotoxin Complexed to Rosmarinic Acid: New Insights into Lys49-PLA2 Inhibition , 2011, PloS one.

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

[4]  P. Basser,et al.  Water Diffusion Changes in Wallerian Degeneration and Their Dependence on White Matter Architecture , 2000 .

[5]  Robert Leech,et al.  White matter damage and cognitive impairment after traumatic brain injury , 2010, Brain : a journal of neurology.

[6]  H. Johansen-Berg,et al.  Relevance of Structural Brain Connectivity to Learning and Recovery from Stroke , 2010, Front. Syst. Neurosci..

[7]  Greg Brown,et al.  The average pathlength map: A diffusion MRI tractography-derived index for studying brain pathology , 2011, NeuroImage.

[8]  S. Aoki,et al.  White Matter Alteration in Idiopathic Normal Pressure Hydrocephalus: Tract-Based Spatial Statistics Study , 2012, American Journal of Neuroradiology.

[9]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

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

[11]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[12]  Michael L. Lipton,et al.  Robust detection of traumatic axonal injury in individual mild traumatic brain injury patients: Intersubject variation, change over time and bidirectional changes in anisotropy , 2012, Brain Imaging and Behavior.

[13]  Desmond J. Higham,et al.  Network analysis detects changes in the contralesional hemisphere following stroke , 2011, NeuroImage.

[14]  M. Weiner,et al.  A Network Diffusion Model of Disease Progression in Dementia , 2012, Neuron.

[15]  Hans Lassmann,et al.  Cortical demyelination and diffuse white matter injury in multiple sclerosis. , 2005, Brain : a journal of neurology.

[16]  Norbert Schuff,et al.  White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI , 2009, Brain : a journal of neurology.

[17]  Pratik Mukherjee,et al.  Diffusion tensor imaging and fiber tractography in acute stroke. , 2005, Neuroimaging clinics of North America.

[18]  Guido Gerig,et al.  Patient-Tailored Connectomics Visualization for the Assessment of White Matter Atrophy in Traumatic Brain Injury , 2011, Front. Neur..

[19]  Olaf Sporns,et al.  The small world of the cerebral cortex , 2007, Neuroinformatics.

[20]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[21]  M. Horsfield,et al.  Diffusion MR imaging in multiple sclerosis: technical aspects and challenges. , 2007, AJNR. American journal of neuroradiology.

[22]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[23]  Shinji Fukui,et al.  Normal pressure hydrocephalus manifesting as transient prosopagnosia, topographical disorientation, and visual objective agnosia , 2004, Journal of Clinical Neuroscience.

[24]  Edith V. Sullivan,et al.  Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging , 2005, NeuroImage.

[25]  Jin Fan,et al.  The activation of attentional networks , 2005, NeuroImage.

[26]  Y. Watanabe,et al.  Shunt-responsive parkinsonism and reversible white matter lesions in patients with idiopathic NPH , 2008, Journal of Neurology.

[27]  M. Wong,et al.  Brain lesion size and location: effects on motor recovery and functional outcome in stroke patients. , 2000, Archives of physical medicine and rehabilitation.

[28]  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.

[29]  Ashish Raj,et al.  The generation and validation of white matter connectivity importance maps , 2011, NeuroImage.

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

[31]  E. Bullmore,et al.  Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia , 2008, The Journal of Neuroscience.

[32]  Facundo Manes,et al.  Executive Function Improvement in Normal Pressure Hydrocephalus Following Shunt Surgery , 2009, Behavioural neurology.

[33]  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.

[34]  Yu Zhang,et al.  Linking white matter integrity loss to associated cortical regions using structural connectivity information in Alzheimer's disease and fronto-temporal dementia: The Loss in Connectivity (LoCo) score , 2012, NeuroImage.

[35]  Olaf Sporns,et al.  Modeling the Impact of Lesions in the Human Brain , 2009, PLoS Comput. Biol..

[36]  C. Wheeler-Kingshott,et al.  About “axial” and “radial” diffusivities , 2009, Magnetic resonance in medicine.

[37]  Brandon G. Oberlin,et al.  How acute and chronic alcohol consumption affects brain networks: insights from multimodal neuroimaging. , 2012, Alcoholism, clinical and experimental research.

[38]  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.

[39]  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.

[40]  Peter A. Calabresi,et al.  Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification , 2008, NeuroImage.

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

[42]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[43]  E. Bautz-Holter,et al.  Post-concussion symptoms after mild traumatic brain injury: influence of demographic factors and injury severity in a 1-year cohort study , 2009, Disability and rehabilitation.

[44]  P. Yen,et al.  White Matter tract involvement in brain tumors: a diffusion tensor imaging analysis. , 2009, Surgical neurology.

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

[46]  Alan C. Evans,et al.  Neuronal Networks in Alzheimer's Disease , 2009, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[47]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.

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

[49]  Timothy Edward John Behrens,et al.  Training induces changes in white matter architecture , 2009, Nature Neuroscience.

[50]  Makoto Uchiyama,et al.  Cognitive Profile of Idiopathic Normal Pressure Hydrocephalus , 2011, Dementia and Geriatric Cognitive Disorders Extra.

[51]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[52]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[53]  Heidi Johansen-Berg,et al.  Behavioural relevance of variation in white matter microstructure. , 2010, Current opinion in neurology.

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

[55]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

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

[57]  R. Fields,et al.  White matter in learning, cognition and psychiatric disorders , 2008, Trends in Neurosciences.

[58]  Alan C. Evans,et al.  A Unified Statistical Approach to Deformation-Based Morphometry , 2001, NeuroImage.

[59]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[60]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[61]  Ashish Raj,et al.  Loss in connectivity among regions of the brain reward system in alcohol dependence , 2012, Human brain mapping.

[62]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

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

[64]  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.