Radiology. 2011 Jun;259(3):844-51. doi: 10.1148/radiol.11101637. Epub 2011 Apr 5.
Identifying cognitively healthy elderly individuals with subsequent memory decline by using automated MR temporoparietal volumes.
Weiner M, Aisen P, Weiner M, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Liu E, Green RC, Montine T, Petersen R, Aisen P, Gamst A, Thomas RG, Donohue M, Walter S, Gessert D, Sather T, Beckett L, Harvey D, Gamst A, Donohue M, Kornak J, Jack CR Jr, Dale A, Bernstein M, Felmlee J, Fox N, Thompson P, Schuff N, Alexander G, DeCarli C, Jagust W, Bandy D, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Morris J, Cairns NJ, Taylor-Reinwald L, Trojanowki JQ, Shaw L, Lee VM, Korecka M, Toga AW, Crawford K, Neu S, Saykin AJ, Foroud TM, Potkin S, Shen L, Kachaturian Z, Frank R, Snyder PJ, Molchan S, Kaye J, Dolen S, Quinn J, Schneider LS, Pawluczyk S, Spann BM, Brewer J, Vanderswag H, Heidebrink JL, Lord JL, Petersen R, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Stern Y, Honig LS, Bell KL, Morris JC, Mintun MA, Schneider S, Marson D, Griffith R, Clark D, Grossman H, Mitsis E, Romirowsky A, deToledo-Morrell L, Shah RC, Duara R, Varon D, Roberts P, Albert M, Onyike C, Kielb S, Rusinek H, de Leon MJ, Glodzik L, Doraiswamy PM, Petrella JR, Arnold SE, Karlawish JH, Wolk D, Smith CD, Jicha G, Hardy P, Lopez OL, Oakley M, Simpson DM, Ismail MS, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Diaz-Arrastia R, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Swerdlow RH, Bartzokis G, Silverman DH, Lu PH, Apostolova L, Graff-Radford NR, Parfitt F, Johnson H, Farlow M, Herring S, Hake AM, van Dyck CH, Carson RE, MacAvoy MG, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung GY, Feldman H, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Wu CK, Johnson N, Mesulam M, Sadowsky C, Martinez W, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Frey M, Johnson KA, Rosen A, Tinklenberg J, Sabbagh M, Belden C, Jacobson S, Killiany R, Norbash A, Obisesan TO, Wolday S, Bwayo SK, Lerner A, Hudson L, Ogrocki P, DeCarli C, Fletcher E, Carmichael O, Kittur S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Hendin BA, Scharre DW, Kataki M, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Saykin AJ, Santulli RB, Schwartz ES, Williamson JD, Sink KM, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Longmire CF, Spicer K.
Source
Department of Radiology, University of California, San Francisco, 505 Parnassus Ave, Room M391, San Francisco, CA, 94143, USA. gloria.chiang@ucsf.edu
Abstract
PURPOSE:
To determine whether automated temporoparietal brain volumes can be used to accurately predict future memory decline among a multicenter cohort of cognitively healthy elderly individuals.
MATERIALS AND METHODS:
The study was approved by the institutional review board at each site and was HIPAA compliant, with written consent obtained from all participants. One hundred forty-nine cognitively healthy study participants were recruited through the Alzheimer's Disease Neuroimaging Initiative and underwent a standardized baseline 1.5-T magnetic resonance (MR) imaging examination, as well as neuropsychological assessment at baseline and after 2 years of follow-up. A composite memory score for the 2-year change in the results of two delayed-recall tests was calculated, and memory decline was defined as a composite score that was at least 1 standard deviation below the group mean score. The predictive accuracy of the brain volumes was estimated by using areas under receiver operating characteristic curves and was further assessed by using leave-one-out cross validation.
RESULTS:
Use of the most accurate region model, which included the hippocampus; parahippocampal gyrus; amygdala; superior, middle, and inferior temporal gyri; superior parietal lobe; and posterior cingulate gyrus, resulted in a fitted accuracy of 94% and a cross-validated accuracy of 81%.
CONCLUSION:
Study results indicate that automated temporal and parietal volumes can be used to identify with high accuracy cognitively healthy individuals who are at risk for future memory decline. Further validation of this predictive model in a new cohort is required.
RSNA, 2011
- PMID:
- 21467255
- [PubMed - indexed for MEDLINE]
- PMCID:
- PMC3099048
Free PMC ArticleFigure 1a:
Automated segmentation of 15 temporal and parietal volumes of interest on (a) axial, (b) coronal, and (c) sagittal MR images, performed by Freesurfer software.
Radiology. 2011 June;259(3):844-851.
Figure 1b:
Automated segmentation of 15 temporal and parietal volumes of interest on (a) axial, (b) coronal, and (c) sagittal MR images, performed by Freesurfer software.
Radiology. 2011 June;259(3):844-851.
Figure 1c:
Automated segmentation of 15 temporal and parietal volumes of interest on (a) axial, (b) coronal, and (c) sagittal MR images, performed by Freesurfer software.
Radiology. 2011 June;259(3):844-851.
Figure 2:
Receiver operating characteristic curves for classification models show greater accuracy with inclusion of more brain regions.
Radiology. 2011 June;259(3):844-851.
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