Background: Resting-state functional magnetic resonance imaging (RS-fMRI) appears as a
promising imaging technique to identify early biomarkers of Alzheimer type neurodegeneration,
which can be more sensitive to detect the earliest stages of this disease than structural alterations. Recent
findings have highlighted interesting patterns of alteration in resting-state activity at the mild cognitive impairment
(MCI) prodromal stage of Alzheimer’s disease. However, it has not been established whether RS-fMRI alterations may be
of any diagnostic use at the individual patient level and whether parameters derived from RS-fMRI images add any quantitative
predictive/classificatory value to standard cognitive tests (CTs). Methods: We computed a set of 444 features
based on RS-fMRI and used 21 variables obtained from a neuropsychological assessment battery of tests in 29 MCI patients
and 21 healthy controls. We used these indices to evaluate their impact on MCI/healthy control classification using
machine learning algorithms and a 10-fold cross validation analysis. Results: A classification accuracy (sensitivity/
specificity/area under curve/positive predictive value/negative predictive value) of 0.9559 (0.9620/0.9470/
0.9517/0.9720/0.9628) was achieved when using both sets of indices. There was a statistically significant improvement
over the use of CTs only, highlighting the superior classificatory role of RS-fMRI. Conclusions: RS-fMRI provides complementary
information to CTs for MCI-patient/healthy control individual classification.
Keywords: Accuracy, AD, biomarker, classification, mild cognitive impairment, MCI, neurodegeneration.
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