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

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          Abstract

          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” ( psymri.org), 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.

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          Most cited references25

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          Ridge Regression: Biased Estimation for Nonorthogonal Problems

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            Multicollinearity in Regression Analysis: The Problem Revisited

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              Extrastriate body area in human occipital cortex responds to the performance of motor actions.

              A region in human lateral occipital cortex (the 'extrastriate body area' or EBA) has been implicated in the perception of body parts. Here we report functional magnetic resonance imaging (fMRI) evidence that the EBA is strongly modulated by limb (arm, foot) movements to a visual target stimulus, even in the absence of visual feedback from the movement. Therefore, the EBA responds not only during the perception of other people's body parts, but also during goal-directed movements of the observer's body parts. In addition, both limb movements and saccades to a detected stimulus produced stronger signals than stimulus detection without motor movements ('covert detection') in the calcarine sulcus and lingual gyrus. These motor-related modulations cannot be explained by simple visual or attentional factors related to the target stimulus, and suggest a potentially widespread influence of actions on visual cortex.
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                Author and article information

                Contributors
                Journal
                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                1663-4365
                04 January 2017
                2016
                : 8
                : 318
                Affiliations
                [1] 1Department of Psychosomatic Medicine, University of Rostock Rostock, Germany
                [2] 2German Center for Neurodegenerative Diseases, Site Rostock/Greifswald Rostock, Germany
                [3] 3Institute of Cognitive Neurology and Dementia Research and Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany
                [4] 4German Center for Neurodegenerative Diseases, Site Magdeburg Magdeburg, Germany
                [5] 5Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technische Universität München Munich, Germany
                [6] 6Department of Neuroradiology of Klinikum rechts der Isar, Technische Universität München Munich, Germany
                [7] 7Department of Psychiatry of Klinikum rechts der Isar, Technische Universität München Munich, Germany
                [8] 8TUM-Neuroimaging Center, Technische Universität München Munich, Germany
                [9] 9Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität Munich, Germany
                [10] 10Department of Psychiatry, Klinikum der Universität München, Ludwig-Maximilians-Universität Munich, Germany
                [11] 11Department of Psychiatry and Psychotherapy, Section of Gerontopsychiatry and Neuropsychology, Faculty of Medicine, University of Freiburg Freiburg, Germany
                [12] 12University Hospital of Old Age Psychiatry Bern, Switzerland
                [13] 13Leibniz Institute for Neurobiology Magdeburg, Germany
                [14] 14Department of Psychiatry, University of Tübingen Tübingen, Germany
                Author notes

                Edited by: Pedro Rosa-Neto, McGill University, Canada

                Reviewed by: Ramesh Kandimalla, Texas Tech University, USA; Haixian Wang, Southeast University, China

                *Correspondence: Stefan J. Teipel stefan.teipel@ 123456med.uni-rostock.de
                Article
                10.3389/fnagi.2016.00318
                5209379
                28101051
                894ceb20-2f0a-4845-b641-0dd3d15cc44c
                Copyright © 2017 Teipel, Grothe, Metzger, Grimmer, Sorg, Ewers, Franzmeier, Meisenzahl, Klöppel, Borchardt, Walter and Dyrba.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 21 September 2016
                : 09 December 2016
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 57, Pages: 9, Words: 6571
                Funding
                Funded by: Bundesministerium für Bildung und Forschung 10.13039/501100002347
                Award ID: 1GQ1425B
                Categories
                Neuroscience
                Original Research

                Neurosciences
                regularization,diagnostic imaging,feature selection,functional magnetic resonance imaging (fmri),alzheimer's disease

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