A robust biomarker of large‐scale network failure in Alzheimer's disease. Issue 1 (24th January 2017)

• Record Type: Journal Article
• Authors: Wiepert, Daniela A.; Lowe, Val J.; Knopman, David S.; Boeve, Bradley F.; Graff‐Radford, Jonathan; Petersen, Ronald C.; Jack, Clifford R.; Jones, David T.
• Is Part Of: Alzheimer's & dementia. Volume 6:Issue 1(2017)
• Page Start: 152
• Page End: 161
• Publication Date: 2017-01-24
• Subjects: Alzheimer's disease -- Cascading network failure -- Biomarker -- Default mode network -- Connectivity; Alzheimer's disease -- Periodicals; Alzheimer's disease -- Diagnosis -- Periodicals; Dementia -- Periodicals; Dementia -- Diagnosis -- Periodicals; 616.831
• DOI: 10.1016/j.dadm.2017.01.004
• Languages: English
• ISSNs: 2352-8729

Abstract

Abstract: Introduction: Biomarkers for Alzheimer's disease (AD) pathophysiology have been developed that focus on various levels of brain organization. However, no robust biomarker of large‐scale network failure has been developed. Using the recently introduced cascading network failure model of AD, we developed the network failure quotient (NFQ) as a biomarker of this process. Methods: We developed and optimized the NFQ using our recently published analyses of task‐free functional magnetic resonance imaging data in clinically normal (n = 43) and AD dementia participants (n = 28) from the Alzheimer's Disease Neuroimaging Initiative. The optimized NFQ (oNFQ) was then validated in a cohort spanning the AD spectrum from the Mayo Clinic (n = 218). Results: The oNFQ ( d = 1.25, 95% confidence interval [1.25, 1.26]) had the highest effect size for differentiating persons with AD dementia from clinically normal participants. The oNFQ measure performed similarly well on the validation Mayo Clinic sample ( d = 1.44, 95% confidence interval [1.43, 1.44]). The oNFQ was also associated with other available key biomarkers in the Mayo cohort. Discussion: This study demonstrates a measure of functional connectivity, based on a cascading network failure model of AD, and was highly successful in identifying AD dementia. A robust biomarker of the large‐scale effects of AD pathophysiology will allow for richer descriptions of the disease process and its modifiers, but is not currently suitable for discriminating clinical diagnostic categories. The large‐scale network level may be one of the earliest manifestations of AD, making this an attractive target for continued biomarker development to be used in prevention trials.