000223027 001__ 223027
000223027 005__ 20181120221951.0
000223027 0247_ $$2doi$$a10.1016/j.nicl.2016.10.004
000223027 022__ $$a2213-1582
000223027 02470 $$2ISI$$a000390196400089
000223027 037__ $$aARTICLE
000223027 245__ $$aPrediction of long-term memory scores in MCI based on resting-state fMRI
000223027 260__ $$aOxford$$bElsevier Sci Ltd$$c2016
000223027 269__ $$a2016
000223027 300__ $$a11
000223027 336__ $$aJournal Articles
000223027 520__ $$aResting-state functional MRI (rs-fMRI) opens a window on large-scale organization of brain function. However, establishing relationships between resting-state brain activity and cognitive or clinical scores is still a difficult task, in particular in terms of prediction as would be meaningful for clinical applications such as early diagnosis of Alzheimer's disease. In this work, we employed partial least square regression under cross-validation scheme to predict episodic memory performance from functional connectivity (FC) patterns in a set of fifty-five MCI subjects for whom rs-fMRI acquisition and neuropsychological evaluation was carried out. We show that a newly introduced FC measure capturing the moments of anti-correlation between brain areas, discordance, contains key information to predict long-term memory scores in MCI patients, and performs better than standard measures of correlation to do so. Our results highlighted that stronger discordance within default mode network (DMN) areas, as well as across DMN, attentional and limbic networks, favor episodic memory performance in MCI. (C) 2016 The Authors. Published by Elsevier B.V.
000223027 6531_ $$aFunctional brain connectivity
000223027 6531_ $$aCross-validation partial least square regression
000223027 6531_ $$aExtreme value modeling
000223027 6531_ $$aLong term memory
000223027 6531_ $$aMild cognitive impairment
000223027 6531_ $$aMedial temporal lobe
000223027 6531_ $$aCIBM-SPC
000223027 700__ $$0242939$$aMeskaldji, Djalel-Eddine$$g120480
000223027 700__ $$0248015$$aPreti, Maria Giulia$$g239040
000223027 700__ $$0247493$$aBolton, Thomas Aw$$g187669
000223027 700__ $$aMontandon, Marie-Louise
000223027 700__ $$aRodriguez, Cristelle
000223027 700__ $$0241889$$aMorgenthaler, Stephan$$g105911
000223027 700__ $$aGiannakopoulos, Panteleimon
000223027 700__ $$aHaller, Sven
000223027 700__ $$0240173$$aVan De Ville, Dimitri$$g152027
000223027 773__ $$j12$$q785-795$$tNeuroImage: Clinical
000223027 8564_ $$s2382773$$uhttps://infoscience.epfl.ch/record/223027/files/meskaldji1601.pdf$$yPublisher's version$$zPublisher's version
000223027 909C0 $$0252209$$pSTAP$$xU10127
000223027 909C0 $$0252169$$pMIPLAB$$xU12143
000223027 909CO $$ooai:infoscience.tind.io:223027$$pSB$$pSTI$$particle$$qGLOBAL_SET
000223027 917Z8 $$x152027
000223027 917Z8 $$x152027
000223027 937__ $$aEPFL-ARTICLE-223027
000223027 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000223027 980__ $$aARTICLE