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Analyzing and Assessing Brain Structure with Graph Connectivity Measures

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Trident Scholar Project rept. no. 430

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Recent studies have shown that graph theory is a useful tool in studying changes in brain connectivity resulting from degenerative conditions such as Alzheimers disease AD. The human brain can be naturally modeled as a network and graph theory measures enable the connectivity properties of these models to be quantified. These measures allow differences in connectivity between brains with and without signs of dementia to be identified. This study is an investigation of methods used to create network models from magnetic resonance imaging MRI data and the impact of these methods on connectivity measures. We tested previous network creation methods and newly developed methods, in combination with connectivity measures to determine which combinations yielded the most reliable identification of dementia severity. We categorized dementia severity using four diagnostic groups healthy older adults who maintained normal cognition for 36 months, individuals with Mild Cognitive impairment MCI who remained MCI for 36 months, individuals who started the study with MCI but developed AD within 36 months MCI-AD, and individuals with AD. We modeled connectivity between brain regions using correlations between regional cortical thickness measurements obtained using MRI. Our results suggest that different graph measures change in an ordered fashion for the structural brain network as an individual develops AD and may be useful as early-diagnosis tools.

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  • Medicine and Medical Research
  • Theoretical Mathematics

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