Early diagnosis of alzheimer's disease: contribution of structural neuroimaging

By Gaël Chételat, J. Baron. NeuroImage, 2003

455 citations recorded by the source corpus.

Subfields: Physiology, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3a3252d6b967d939c1c7d3656adbcc0cb1952379

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In Physiology

In Psychiatry and Mental health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI (2008, unknown): “Dense connections existing between the hippocampus and the posterior cingulate, which coupledwith the early changes that have been reported in the posterior cingulate (Chetelat, 2003; Chetelat et al., 2003), might imply that changes in WM might provide”
Modeling disease progression via multi-task learning (2013, unknown): “…the relationship between cognitive scores and imagingmarkers based onMRI such as graymatter volumes, density and loss (Apostolova et al., 2006; Chetelat and Baron, 2003; Frisoni et al., 2002, 2010; Stonnington et al., 2010), shape of ventricles (Ferrarini et al., 2008; Thompson et al., 2004)…”
Relationship between hippocampal volume and memory ability in healthy individuals across the lifespan: review and meta-analysis. (2004, background): “It is generally accepted that patients with probable Alzheimer's disease as well as those diagnosed with mild cognitive impairment (MCI) have smaller-than-expected hippocampi for their age (Chetelat & Baron, 2003; Kantarci et al., 2002; Wolf et al., 2003, in press).”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, background): “Many group studies based on volumetric measurements of regions of interest (ROI) (Convit et al., 1997, 2000; Jack et al., 1997,1998; Juottonen et al., 1998; Laakso et al., 1998, 2000; Busatto et al., 2003; Xu et al., 2000; Good et al., 2002; Chételat and Baron, 2003; Rusinek et al., 2004; Tapiola et”
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline (2008, background): “…between the hippocampus and the posterior cingulate, which coupled with the early changes that have been reported in the posterior cingulate (Chetelat, 2003; Chetelat et al., 2003), might imply that changes in WM might provide additional markers of disease progression, something that has…”
RABBIT: Rapid Alignment of Brains by Building Intermediate Templates (2009, unknown): “05 (false discover rate (FDR) corrected) (Bozzali et al., 2006; Busatto et al., 2003; Chetelat and Baron, 2003; Convit et al., 1997; Cuenod et al., 1993; De Santi et al., 2001; Du et al., 2001; Fan et al., 2008; Fox et al., 1996; Frisoni et al., 2007; Grundman et al., 2002; Jack et al., 2000,1999; K”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, unknown): “MARK (ROI)-types of analyses to investigate subtle changes associated with AD (Chetelat and Baron, 2003; Lerch et al., 2008).”
Recommendations for the diagnosis and management of Alzheimer's disease and other disorders associated with dementia: EFNS guideline (2007, background): “The overall sensitivity and specificity figures for detection of mild to moderate AD versus controls were 85% and 88% in a meta-analysis [78], and the accuracy of hippocampal atrophy in mild AD ranged from 67% to 100% in a systematic review [79] (I–II).”
A multi-task learning formulation for predicting disease progression (2011, methodology): “imaging markers based on MRI such as gray matter volumes, density and loss [3, 8, 15, 16, 33], shape of ventricles [14, 34] and hippocampal [34] by correlating these features with baseline MMSE scores.”