Many brain diseases such as stroke, multiple sclerosis and traumatic brain injury result in brain damage and physical or mental disability from cell death. The location and size of the affected area greatly influences the amount and type of disability that the patient incurs. Current MRI-based diagnosis of brain injury is inadequately qualitative, involving subjective assessment of severity and prediction of impairment by the physician. Even when augmented by 3D processing tools that allow for accurate measurement of lesion or tumor volume, this approach is insufficient in characterizing the effect on brain function. This is because functional impairment is determined by both the extent and location of damage, and can be properly assessed only by looking at the connectivity of the affected region to the rest of the brain via its fiber architecture. A new computational methodology is proposed that utilizes DTI and structural MR images of the brain as well as graph theory to methodically assign an overall brain connectivity importance to small sections of white and gray matter regions. This quantitative importance map of the brain will be created by first investigating the brain connectivity of a large group of normal patients and representing it with an object called a graph. Small sections of tissue will be computationally removed and the resulting change in overall brain connectivity will be measured using graph distance metrics. This map will be validated thoroughly with quantitative comparison to existing knowledge of experienced neurologists and by checking the correlation of the map's prediction of injury severity with disability measures in patients with traumatic brain injury. Presented in the form of a quantitative importance map of the brain, this information could greatly enhance a physician's knowledge and have far-reaching and significant impact. Some of the developments could lead to improved surgical planning, assessment of disease severity and possibly the development of a rehabilitation program in pathological states such as stroke, multiple sclerosis, and traumatic brain injury. In the future, the map could be extended to provide not only a score of severity, but also a specific type of disability for a localized region of damage or a predicted probability of recovery. The currently proposed research is expected to be the beginning of a much larger and long-term project that will analyze brain connectivity in many different pathological states affecting the brain.

Public Health Relevance

This project will result in a 3-dimensional map of the brain that indicates in a quantitative manner the importance of small sections of brain tissue in overall brain connectivity, which could greatly bolster a physician's resources when treating diseases that result in brain injury, including stroke, multiple sclerosis, and trauma. In particular, this map could help physicians to improve surgical planning, assess disease severity and possibly improve the development of rehabilitation programs. In the future, the map could be extended to provide not only a score of severity, but also a specific type of disability for a localized region of damage or a predicted probability of recovery.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Postdoctoral Individual National Research Service Award (F32)
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Special Emphasis Panel (ZRG1-F15-D (20))
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Erim, Zeynep
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Weill Medical College of Cornell University
Schools of Medicine
New York
United States
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LoCastro, E; Kuceyeski, A; Raj, A (2014) Brainography: an atlas-independent surface and network rendering tool for neural connectivity visualization. Neuroinformatics 12:355-9
Goel, Pragya; Kuceyeski, Amy; LoCastro, Eve et al. (2014) Spatial patterns of genome-wide expression profiles reflect anatomic and fiber connectivity architecture of healthy human brain. Hum Brain Mapp 35:4204-18
Kuceyeski, Amy; Meyerhoff, Dieter J; Durazzo, Timothy C et al. (2013) Loss in connectivity among regions of the brain reward system in alcohol dependence. Hum Brain Mapp 34:3129-42
Kuceyeski, Amy; Maruta, Jun; Relkin, Norman et al. (2013) The Network Modification (NeMo) Tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity. Brain Connect 3:451-63
Raj, Ashish; Kuceyeski, Amy; Weiner, Michael (2012) A network diffusion model of disease progression in dementia. Neuron 73:1204-15
Kuceyeski, Amy; Zhang, Yu; Raj, Ashish (2012) 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. Neuroimage 61:1311-23
Kuceyeski, Amy; Maruta, Jun; Niogi, Sumit N et al. (2011) The generation and validation of white matter connectivity importance maps. Neuroimage 58:109-21