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IC‐P‐041: Prediction of disease progression in mild cognitive impairment from vMRI and concordance with CSF biomarkers
Author(s) -
Yu Peng,
Schwarz Adam,
Sun Jia,
Beckett Laurel,
Kelleher Thomas,
Wang Huanli,
Davidson Chris,
Frank Denise,
Jack Clifford,
Cole Patricia,
Hill Derek
Publication year - 2012
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2012.05.073
Subject(s) - concordance , biomarker , oncology , medicine , dementia , frontotemporal dementia , population , alzheimer's disease neuroimaging initiative , disease , biology , biochemistry , environmental health
ance in cortical gray matter changes in 313 Alzheimer’s Disease Neuroimaging Initiative participants who were clinically diagnosed with amnestic mild cognitive impairment at baseline and underwent serial MRI at 6-month intervals over the course of 2 years. A set of 35 bilateral cortical gray matter region volumes were estimated for each MRI using FreeSurfer. Baseline region volumes and rates of change in volume over 2 years were derived from region specific growth curve models. The covariance of the rates of change between regions was analyzed with exploratory structural equation modeling (ESEM). The ESEMmodel was used to estimate a factor analysis model with pre-specified residual covariance structure to identify factors (i.e. groupings of regions) that exhibited highly correlated rates of change. Results: A four-factor model provided the best account of regional changes: this model exhibited adequate fit (CFI 1⁄4 0.965, RMSEA 1⁄4 0.06) and minimized the Bayesian Information Criterion over all models between 1 and 5 factors (see Table and Figure). The four factors approximately corresponded to co-occurring change within the prefrontal cortex; medial temporal lobe; posterior default mode network (i.e., posterior cingulate, precuneus, and inferior parietal regions); and regions largely spared by the early pathological course of AD (i.e., sensorimotor and occipital cortex). Conclusions: The data-driven observation of coordinated “frontal aging” superimposed upon traditional early-AD atrophy and default mode network changes supports the view that in individuals at high risk of eventual clinical AD, multiple co-occurring patterns of distributed neuronal death may be detectable. These coordinated changes may correspond to differing biological substrates and differing cognitive consequences. Brain structural changes in AD may be best modeled in terms of co-occurring damage to multiple distributed cognitive networks.