In Vivo Mapping of Gray Matter Loss with Voxel-Based Morphometry in Mild Alzheimer's Disease

By J. Baron, Gaël Chételat, B. Desgranges, G. Perchey, B. Landeau, V. Sayette, F. Eustache. NeuroImage, 2001

716 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/65296ef689724c1fe7582896e121b7635e1cc3ef

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Citations in the corpus

A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, neutral): “…measured in the CSF (Melah et al., 2016), brain atrophy, particularly in the hippocampus (Frisoni et al., 2002) and posterior cingulate gyrus (Baron et al., 2001), measured through structural MRI, and hypometabolism, associated with AD in the temporal and parietal lobe, as well as in the…”
Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease (2010, background): “…in MCI and mild AD have shown marked volume differences not only within the hippocampus area but also distributed within cortical brain areas such as the precuneus and cingulate gyrus (Baron et al., 2001; Chetelat et al., 2002, 2005; Frisoni et al., 2002; Karas et al., 2004; Pennanen et al., 2005).”
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging (2009, neutral): “…atrophy is also present in a distributed pattern of brain regions, including the temporal neocortex, the cingulate gyrus, the precuneus, the temporo-parietal association and the perisylvian association (Baron et al., 2001; Chételat et al., 2002, 2005; Karas et al., 2003, 2004; Lerch et al., 2005).”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, neutral): “…or hydration levels (Biller et al., 2015) the good agreement of FastSurfer with FreeSurfer and with established ﬁ ndings (Braak and Braak, 1995; Baron et al., 2001; Wenk, 2003; Lerch et al., 2004; de Jong et al., 2008; Henneman et al., 2009; Schuff et al., 2009; Poulin et al., 2011; Aggleton…”
Cortical thickness analysis examined through power analysis and a population simulation (2005, neutral): “Most methods (cf. Ashburner and Friston, 2000; Baron et al., 2001; Paus et al., 1999) employ a standard set of image processing steps involving linear registration, tissue classification, and creation of bvoxel densityQ maps representing tissue concentration in a local neighborhood.”
Discrimination between Alzheimer Dementia and Controls by Automated Analysis of Multicenter FDG PET (2002, background): “Local atrophy due to disease, in this case in particular due to AD, does contribute to a reduction of the FDG PET signal in affected regions (Baron et al., 2001).”
Early diagnosis of alzheimer's disease: contribution of structural neuroimaging (2003, methodology): “Secondly, novel methods to analyze MRI data sets such as voxel-based morphometry (VBM) explore the entire brain without bias (Rombouts et al., 2000; Baron et al., 2001; Matsuda et al., 2002; Chételat et al., 2002) and so would have interesting diagnostic applications, but their reliable use at the s”
Clinical Neurology and Epidemiology of the Major Neurodegenerative Diseases. (2018, background): “…shows disproportionate atrophy of the hippocampus and mesial temporal, lateral temporoparietal, and posterior cingulate/precuneus cortices bilaterally (Baron et al. 2001; Frisoni et al. 2002; Ishii et al. 2005), with the most characteristic finding being mesial temporal atrophy for typical AD (Fig.”
Cerebral atrophy in Parkinson's disease with and without dementia: a comparison with Alzheimer's disease, dementia with Lewy bodies and controls. (2004, methodology): “Voxel-based morphometry (VBM) has been implemented in various studies of dementia including Alzheimer's disease (Rombouts et al., 2000; Baron et al., 2001; Karas et al., 2003), DLB (Burton et al.”
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis (2014, neutral): “A voxelbased approach is themost simple and directway that uses the voxel intensities as features in classification (Baron et al., 2001; Ishii et al., 2005).”
Ensemble Sparse Classification of Alzheimer’s Disease (2012, background): “Voxelwise features, such as probabilities of gray matter (GM), white matter (WM) and cerebrospinalfluid (CSF), play an important role in neuroimaging study, and have been widely used to identify regional GM loss of AD compared to the normal aging controls (Baron et al., 2001; Ishii et al., 2005).”
A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry (2003, methodology): “There have been other cross-sectional studies examining brain atrophy without prior assumptions (Rombouts et al., 2000; Baron et al., 2001; Thompson et al., 2001).”
Dynamics of Gray Matter Loss in Alzheimer's Disease (2003, background): “…in many previous studies to compare the spatial distribution of gray matter across subjects (Wright et al., 1995; Bullmore et al., 1999; Sowell et al., 1999; Ashburner and Friston, 2000; Mummery et al., 2000; Rombouts et al., 2000; Baron et al., 2001; Good et al., 2001; Thompson et al., 2001a,b).”
Regional coherence changes in the early stages of Alzheimer's disease: A combined structural and resting-state functional MRI study (2007, background): “Numerous studies have demonstrated that AD patients have more brain atrophy in the PCC/PCu than normal controls (Buckner et al., 2005; Baron et al., 2001; Chetelat et al., 2002).”
Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease (2010, background): “Since gray matter volume has played an important role in brain research in recent years [14,35– 36], we constructed cortical networks using gray matter volumes to investigate small-world properties in subjects with MCI and AD.”