Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD

Xu, Yue; Jack, Clifford R.; O’Brien, Peter C.; Kokmen, Emre; Smith, Glenn E.; Ivnik, Robert J.; Boeve, Bradley F.; Tangalos, R. G.; Petersen, Ronald C.

doi:10.1212/wnl.54.9.1760

Cited by 329 publications

(222 citation statements)

References 35 publications

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“…Although several studies examining cross-sectional and longitudinal effects in volumes of brain regions have shown significant group differences between MCI patients that convert to AD and MCI patients that don't convert, or between healthy controls and AD or MCI patients [2,8,9,[12][13][14][15][16]18,22,25,26,29,[31][32][33][34]36,44,46,50], the ability to detect structural patterns that enable accurate prediction for specific individuals is ultimately what determines the clinical value of MRI and measurements obtained from it. Our results further confirmed that evaluation of all these brain regions jointly is necessary to obtain high predictive accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Our results further confirmed that evaluation of all these brain regions jointly is necessary to obtain high predictive accuracy. Measurements restricted to volumes of the hippocampus and ERC, two structures that are known to be affected early in AD [24] and show group differences in neuroimaging studies of AD and MCI [2,8,12,[14][15][16]18,26,29,32,36,44,50], provided considerably lower predictive accuracy. Lower accuracy was observed even when these measures were evaluated jointly, due to the high overlap of the volume distributions of these two structures between MCI and healthy individuals (Fig.…”

Section: Discussionmentioning

confidence: 99%

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Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Davatzikos

Fan

et al. 2008

Neurobiology of Aging

351

237

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We report evidence that computer-based high-dimensional pattern classification of MRI detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's Disease (AD). 90% diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Davatzikos

Fan

et al. 2008

Neurobiology of Aging

351

237

View full text Add to dashboard Cite

show abstract

“…Although several studies examining cross-sectional and longitudinal effects in volumes of brain regions have shown significant group differences between AD or FTD and their respective healthy controls (DeLeon, George et al 1991;Cuenod, Denys et al 1993;Golomb, deLeon et al 1993;Killiany, Moss et al 1993;Lehericy, Baulac et al 1994;Laakso, Soininen et al 1995;Frisoni, Beltramello et al 1996;Convit, De Leon et al 1997;deToledo-Morrell, Sullivan et al 1997;Jack, Petersen et al 1997;Krasuski, Alexander et al 1998;Bobinski, de Leon et al 1999;Jack, Petersen et al 1999;Killiany, Gomez-Isla et al 2000;Laakso, Hallikainen et al 2000;Xu, Jack et al 2000;De Santi, de Leon et al 2001;Dickerson, Goncharova et al 2001;Du, Schuff et al 2001;Rosen, Prull et al 2003;Stoub, Bulgakova et al 2005), the ability to detect structural patterns that enable accurate prediction for specific individuals is ultimately what determines the clinical value of MRI and measurements obtained from it. Our results further confirmed that evaluation of an extensive and optimally determined set of brain regions, which collectively form a spatial pattern of brain atrophy, is necessary and sufficient to obtain high diagnostic accuracy.…”

Section: Discussionmentioning

confidence: 99%

Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI

Davatzikos¹,

Resnick²,

Wu³

et al. 2008

NeuroImage

222

177

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The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxelbased analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD-and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting.

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“…Cross-sectional MRI studies have shown that AD patients have significantly greater ERC and hippocampal atrophy than cognitively normal (CN) subjects [9,21,26,48,57]. Longitudinal MRI studies have suggested that the rates of ERC and hippocampal atrophy may be useful in predicting AD before the onset of clinical symptoms and in assessing the potential usefulness of pharmacological interventions in treatment trials [10,22,24,27,43].…”

Section: Introductionmentioning

confidence: 99%

Age effects on atrophy rates of entorhinal cortex and hippocampus

Schuff

Chao

et al. 2006

Neurobiology of Aging

193

150

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The effects of age, subcortical vascular disease, apolipoprotein E (APOE) 4 allele and hypertension on entorhinal cortex (ERC) and hippocampal atrophy rates were explored in a longitudinal MRI study with 42 cognitively normal (CN) elderly subjects from 58 to 87 years old. The volumes of the ERC, hippocampus, and white matter hyperintensities (WMH) and the presence of lacunes were assessed on MR images. Age was significantly associated with increased atrophy rates of 0.04 ± 0.02% per year for ERC and 0.05 ± 0.02% per year for hippocampus. Atrophy rates of hippocampus, but not that of ERC increased with presence of lacunes, in addition to age. WMH, APOE 4 and hypertension had no significant effect on atrophy rates. In conclusion, age and presence of lacunes should be taken into consideration in imaging studies of CN subjects and AD patients to predict AD progression and assess the response to treatment trials.

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Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD

Cited by 329 publications

References 35 publications

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI

Age effects on atrophy rates of entorhinal cortex and hippocampus

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