Emily R Lindemer1, Douglas N Greve2, Bruce Fischl2, David H Salat2, Teresa Gomez-Isla2. 1. From the Division of Health Sciences and Technology (E.R.L.), Massachusetts Institute of Technology/Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (E.R.L., D.N.G., D.H.S.), Massachusetts General Hospital, Charlestown; Department of Neurology (T.G.-I.), Massachusetts General Hospital (B.F.), Boston; Department of Radiology (D.N.G., D.H.S.), Harvard Medical School (B.F.), Charlestown; Health Sciences and Technology/Electrical Engineering and Computer Science (B.F.), Massachusetts Institute of Technology, Boston; and NeRVe Neuroimaging Center for Veterans (D.H.S.), Boston VA Healthcare System, MA. lindemer@alum.mit.edu. 2. From the Division of Health Sciences and Technology (E.R.L.), Massachusetts Institute of Technology/Harvard Medical School, Boston; Athinoula A. Martinos Center for Biomedical Imaging (E.R.L., D.N.G., D.H.S.), Massachusetts General Hospital, Charlestown; Department of Neurology (T.G.-I.), Massachusetts General Hospital (B.F.), Boston; Department of Radiology (D.N.G., D.H.S.), Harvard Medical School (B.F.), Charlestown; Health Sciences and Technology/Electrical Engineering and Computer Science (B.F.), Massachusetts Institute of Technology, Boston; and NeRVe Neuroimaging Center for Veterans (D.H.S.), Boston VA Healthcare System, MA.
Abstract
OBJECTIVE: To determine whether white matter changes influence progression of cognitive decline in individuals with clinically diagnosed Alzheimer disease (AD) and differing biomarker profiles. METHODS: Two hundred thirty-six individuals from the Alzheimer's Disease Neuroimaging Initiative database with clinical diagnoses of cognitively normal older adult (older controls [OCs]), mild cognitive impairment, and AD were studied. Support vector machine experiments were first performed to determine the utility of various biomarkers for classifying individuals by clinical diagnosis. General linear models were implemented to assess the relationships between CSF measures of β-amyloid 1-42, phosphorylated tau181p, and MRI-based white matter signal abnormality (WMSA) volumes and cognitive decline. Analyses were performed across all patients as well as within subgroups of individuals that were defined by clinical cutoff points for both CSF measures. RESULTS: CSF biomarkers alone classified individuals with AD vs OCs with 82% accuracy, and the addition of WMSA did not enhance this. Both CSF biomarkers as well as WMSA volume significantly contributed to predicting cognitive decline in executive and memory domains when assessed across all 236 individuals. In individuals with pathologic levels of both CSF biomarkers, WMSA only significantly contributed to models of future executive function decline. In individuals with subpathologic CSF biomarker levels (levels similar to those in OC individuals), WMSA significantly contributed to prediction of memory decline and were the sole significant predictor of executive function decline. CONCLUSIONS: WMSA hold additional predictive power regarding cognitive progression in older individuals and are most effective as biomarkers in individuals who are cognitively impaired but do not fit the expected CSF biomarker profile of AD.
OBJECTIVE: To determine whether white matter changes influence progression of cognitive decline in individuals with clinically diagnosed Alzheimer disease (AD) and differing biomarker profiles. METHODS: Two hundred thirty-six individuals from the Alzheimer's Disease Neuroimaging Initiative database with clinical diagnoses of cognitively normal older adult (older controls [OCs]), mild cognitive impairment, and AD were studied. Support vector machine experiments were first performed to determine the utility of various biomarkers for classifying individuals by clinical diagnosis. General linear models were implemented to assess the relationships between CSF measures of β-amyloid 1-42, phosphorylated tau181p, and MRI-based white matter signal abnormality (WMSA) volumes and cognitive decline. Analyses were performed across all patients as well as within subgroups of individuals that were defined by clinical cutoff points for both CSF measures. RESULTS: CSF biomarkers alone classified individuals with AD vs OCs with 82% accuracy, and the addition of WMSA did not enhance this. Both CSF biomarkers as well as WMSA volume significantly contributed to predicting cognitive decline in executive and memory domains when assessed across all 236 individuals. In individuals with pathologic levels of both CSF biomarkers, WMSA only significantly contributed to models of future executive function decline. In individuals with subpathologic CSF biomarker levels (levels similar to those in OC individuals), WMSA significantly contributed to prediction of memory decline and were the sole significant predictor of executive function decline. CONCLUSIONS: WMSA hold additional predictive power regarding cognitive progression in older individuals and are most effective as biomarkers in individuals who are cognitively impaired but do not fit the expected CSF biomarker profile of AD.
Authors: Laura E Gibbons; Adam C Carle; R Scott Mackin; Danielle Harvey; Shubhabrata Mukherjee; Philip Insel; S McKay Curtis; Dan Mungas; Paul K Crane Journal: Brain Imaging Behav Date: 2012-12 Impact factor: 3.978
Authors: Leslie M Shaw; Hugo Vanderstichele; Malgorzata Knapik-Czajka; Christopher M Clark; Paul S Aisen; Ronald C Petersen; Kaj Blennow; Holly Soares; Adam Simon; Piotr Lewczuk; Robert Dean; Eric Siemers; William Potter; Virginia M-Y Lee; John Q Trojanowski Journal: Ann Neurol Date: 2009-04 Impact factor: 10.422
Authors: Adam M Brickman; Frank A Provenzano; Jordan Muraskin; Jennifer J Manly; Sonja Blum; Zoltan Apa; Yaakov Stern; Truman R Brown; José A Luchsinger; Richard Mayeux Journal: Arch Neurol Date: 2012-12