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. Author manuscript; available in PMC: 2021 Feb 1.
Published in final edited form as: Neurobiol Aging. 2019 Nov 5;86:112–122. doi: 10.1016/j.neurobiolaging.2019.10.013

Table 2.

Connectivity characteristics between CN and AD participants

Measure Group Mean Variance Skewness Kurtosis
Global efficiency CN 0.5009 0.0022 −0.698 3.0281
AD 0.5125 0.0017 −0.9601 4.0758
Local efficiency CN 0.7139 0.0033 −0.3135 3.1841
AD 0.7 0.0023 −0.2049 2.9144
Clustering coefficient CN 0.5214 0.0038   0.7414 3.1858
AD 0.4937 0.0025   0.4945 2.7032
Average path length CN 2.2716 0.0303   0.8843 3.2139
AD 2.2031 0.0195   0.7247 3.0054

Bold indicates significance (p < 0.05). There was a significant difference in all graph theory measures between AD and CN. There was no difference between APOE4 carriers and noncarriers. We averaged metric ROI values across the brain for each subject to obtain a mean value per diagnosis. Additionally, we used variance, skewness and kurtosis to better characterize the difference in distributions between AD and CN for our respective graph theory metrics.

Key: AD, Alzheimer’s disease; APOE4, apolipoprotein E-ε4; CN, cognitively normal.