Affiliations: [a]
The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| [b] Department of Psychiatry, New York University School of Medicine, New York, NY, USA
Correspondence:
[*]
Correspondence to: Babak A. Ardekani, PhD, Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, NY 10962, USA. Tel.: +1 845 398 5490; Fax: +1 845 398 5472; E-mail: ardekani@nki.rfmh.org.
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Abstract: Background: Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer’s disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose. Objective: To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm. Methods: We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD. Results: The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI. Conclusion: The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.
Keywords: Alzheimer’s disease, atrophy, hippocampus, longitudinal analysis, magnetic resonance imaging, mild cognitive impairment, prediction, Random Forest, sex
