Affiliations: [a] Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| [b] Freiburg Brain Imaging, Departments of Neurology and Psychiatry, University Medical Center Freiburg, Freiburg, Germany
| [c]
Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
Correspondence:
[*]
Correspondence to: Miguel Ángel Araque Caballero, Klinikum der Universität München, Institut für Schlaganfall- und Demenzforschung (ISD), Feodor-Lynen-Straße 17, 81377 München, Germany. Tel.: +49 89 4400 46159; Fax: +49 89 7095 8729; E-mail: [email protected].
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 implementationof ADNI and/or provided data but did not participate in analysisor 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: A substantial proportion of cognitively healthy elders (HC) show abnormally high amyloid-β (Aβ) deposition, a major pathology of Alzheimer’s disease (AD). These subjects are at increased risk of Alzheimer’s disease (AD) dementia, and biomarkers are needed to predict their cognitive deterioration. Here we used relevance vector regression (RVR), a pattern-recognition method, to predict concurrent cognitive decline on the basis of longitudinal gray matter (GM) changes, within two a priori, meta-analytically defined functional networks subserving episodic memory and executive function. Ninety-six HC subjects were assessed annually for three years with structural MRI and cognitive tests within the Alzheimer’s Disease Neuroimaging Initiative. Presence of abnormal biomarker values of Aβ (Aβ+) were determined with cerebrospinal fluid and amyloid-PET (HC-Aβ+, n=30; with n=66 for normal HC-Aβ–). Using leave-one-out cross-validation, we found that in HC-Aβ+ patterns of GM changes within both networks predicted decline in episodic memory (r=0.61, p<0.001; r=0.40, p=0.03), but not executive function. In HC-Aβ–, GM changes within the executive function network predicted decline in executive function (r=0.44, p<0.001). Previously established region-of-interest (ROI)-based predictors such as changes in hippocampal volume, within an AD-signature multi-ROI, or total GM volume were not predictive of cognitive decline in any group or cognitive domain. RVR analyses unrestricted to the a priori networks yielded compatible results with the restricted case. In conclusion, RVR-derived patterns of subtle cortical GM changes are biomarker candidates of concurrent cognitive decline in aging and subjects at risk for AD.
