Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression.

Teipel, Stefan J; Grothe, Michel J; Metzger, Coraline D; Grimmer, Timo; Sorg, Christian; Ewers, Michael; Franzmeier, Nicolai; Meisenzahl, Eva; Klöppel, Stefan; Borchardt, Viola; Walter, Martin; Dyrba, Martin (2017). Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression. Frontiers in aging neuroscience, 8(318), p. 318. Frontiers Research Foundation 10.3389/fnagi.2016.00318

fnagi-08-00318.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (1MB) | Preview

The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the "German resting-state initiative for diagnostic biomarkers" (, comprising 53 AD patients and 118 age and sex matched healthy controls. Using all possible pairs of correlations between the time series of rs-fMRI signal from 84 functionally defined brain regions as the initial set of predictor variables, we calculated accuracy of group discrimination and consistency of feature selection with bootstrap cross-validation. Mean areas under the receiver operating characteristic curves as measure of diagnostic accuracy were 0.70 in unregularized and 0.80 in regularized regression. Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain regions involved in attention, executive control, and language processing. Stepwise logistic regression found no consistent network of AD related functional connectivity decline. Regularized regression has high potential to increase diagnostic accuracy and consistency of feature selection from multicollinear functional neuroimaging data in AD. Our findings suggest an extended network of functional alterations in AD, but the diagnostic accuracy of rs-fMRI in this multicenter setting did not reach the benchmark defined for a useful biomarker of AD.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > University Psychiatric Services > University Hospital of Geriatric Psychiatry and Psychotherapy

UniBE Contributor:

Klöppel, Stefan


600 Technology > 610 Medicine & health




Frontiers Research Foundation




Katharina Klink

Date Deposited:

13 Sep 2017 15:39

Last Modified:

17 Sep 2017 02:40

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

Alzheimer's disease diagnostic imaging feature selection functional magnetic resonance imaging (fMRI) regularization




Actions (login required)

Edit item Edit item
Provide Feedback