Graph theory is increasingly being used to study brain connectivity across the spectrum of Alzheimer's disease (AD), but prior findings have been inconsistent, likely reflecting methodological differences. We systematically investigated how methods of graph creation (i.e., type of correlation matrix and edge weighting) affect structural network properties and group differences. We estimated the structural connectivity of brain networks based on correlation maps of cortical thickness obtained from MRI. Four groups were compared: 126 cognitively normal older adults, 103 individuals with Mild Cognitive Impairment (MCI) who retained MCI status for at least 3 years (stable MCI), 108 individuals with MCI who progressed to AD-dementia within 3 years (progressive MCI), and 105 individuals with AD-dementia. Small-world measures of connectivity (characteristic path length and clustering coefficient) differed across groups, consistent with prior studies. Groups were best discriminated by the Randić index, which measures the degree to which highly connected nodes connect to other highly connected nodes. The Randić index differentiated the stable and progressive MCI groups, suggesting that it might be useful for tracking and predicting the progression of AD. Notably, however, the magnitude and direction of group differences in all three measures were dependent on the method of graph creation, indicating that it is crucial to take into account how graphs are constructed when interpreting differences across diagnostic groups and studies. The algebraic connectivity measures showed few group differences, independent of the method of graph construction, suggesting that global connectivity as it relates to node degree is not altered in early AD.
Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information SPONSOR/MONITOR'S REPORT NUMBER(S)Trident Scholar Report no. 430 (2014) DISTRIBUTION / AVAILABILITY STATEMENTThis document has been approved for public release; its distribution is UNLIMITED. SUPPLEMENTARY NOTES ABSTRACTRecent 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. 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. SUBJECT TERMS U.S.N.A. ---Trident Scholar project report; no. 430 (2014) A N A L Y Z I N G A N D ASSESSI N G B R A I N ST R U C T U R E W I T H G R A PH C O N N E C T I V I T Y M E ASU R ESThis 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 new...
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