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IC‐P‐117: Characterization of regional cerebral blood flow in mild cognitive impairment and older adults with cognitive complaints
Author(s) -
Wang Yang,
West John,
Risacher Shan,
McDonald Brenna,
Tallman Eileen,
Ghetti Bernardino,
Farlow Martin,
Gao Sujuan,
O'Neill Darren,
Saykin Andrew
Publication year - 2013
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.2013.05.114
Subject(s) - cerebral blood flow , default mode network , posterior cingulate , cardiology , psychology , neuropsychology , neuroimaging , cognition , medicine , cognitive impairment , magnetic resonance imaging , neuroscience , radiology
differences in cognitive decline (variability in longitudinal slope) measured by neuropsychological tests can be explained by changes/progression of biomarkers as opposed to their baseline values at each cognitive stage (normal, MCI, mild AD). Methods: 526 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with valid data in all of our variables of interest were used in this study. The primary clinical outcome is the cognitive composite score tapping the memory domain. Baseline values and progression in the following biomarkers were examined in their association with trajectory of the cognitive outcome: MRI total brain, hippocampal, ventricular, WMH volumes, ROI cortical thickness (medial and inferior temporal thickness), FDG-PET summary score (n1⁄4260) and CSF p-tau, t-tau and abeta42 (n1⁄4271). First, individual-specific slope (i.e., random component) of the longitudinal trajectory of each biomarker was estimated using mixed effects models, controlling for age, sex, education, practice effects, apoe 4 allele and changes in diagnosis. Then these estimates and observed baseline values were used as predictors of cognitive decline using mixed effects models. Variability in cognitive decline (i.e., individual differences in slopes) explained by the subject-specific baseline biomarker values was compared with that explained by the progression. Results: Even among the normal subjects where cognitive decline is minimal, progression in FDG-PET (but not baseline) explained the variability in memory decline. Also progression explained variability in memory decline more than the baseline values in most biomarkers; the proportion of variability explained ranged from 14.6% (changes in FDT-PET) to 28.3% (changes in inferior temporal thickness) among the MCI subjects. Among AD subjects, an even higher proportion was explained by the progression: 39.4% (changes in FDG-PET), 51.4% (changes in medial temporal thickness), 68.9% (ventricular expansion), 73.0% (changes in inferior temporal thickness). Conclusions: Progression in biomarkers is more important than baseline values in most biomarkers in predicting cognitive decline. This has important implications for clinical trials targeted to modify AD biomarkers, as well as for prognosis and prediction of clinical outcomes.