Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease

By G. Karas, Philip Scheltens, Serge A. R. B. Rombouts, Pieter Jelle Visser, Ronald A. van Schijndel, Nick C Fox, Frederik Barkhof. NeuroImage, 2004

614 citations recorded by the source corpus.

PURPOSE Mild cognitive impairment (MCI) is thought to be the prodromal phase to Alzheimer's disease (AD). We analyzed patterns of gray matter (GM) loss to examine what characterizes MCI and what determines the difference with AD. MATERIALS AND METHODS Thirty-three subjects with AD, 14 normal elderly controls (NCLR), and 22 amnestic MCI subjects were included and underwent brain MR imaging. Global GM volume was assessed using segmentation and local GM volume was assessed using voxel-based morphometry (VBM); VBM was optimized for template mismatch and statistical mass. RESULTS AD subjects had significantly (12.3%) lower mean global GM volume when compared to controls (517 +/- 58 vs. 590 +/- 52 ml; P < 0.001). Global GM volume in the MCI group (552 +/- 52) was intermediate between these two: 6.2% lower than AD and 6.5% higher than the controls but not significantly different from either group. VBM showed that subjects with MCI had significant local reductions in gray matter in the medial temporal lobe (MTL), the insula, and thalamus compared to NCLR subjects. By contrast, when compared to subjects with AD, MCI subjects had more GM in the parietal association areas and the anterior and the posterior cingulate. CONCLUSION GM loss in the MTL characterizes MCI, while GM loss in the parietal and cingulate cortices might be a feature of AD.

Subfields: Cognitive Neuroscience, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/4c93e931ea06395476d6448abd354fd8e0c3e686

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Citations in the corpus

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008, background): “In particular, the study in Karas et al. (2004) was retrospective, whereas our study was prospective.”
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging (2009, neutral): “Although lesions start in the medial temporal lobe, 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; Ch”
The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals (2008, background): “There was no signiﬁcant thinning in the primary visual cortex (0.03 mm [2.2%], P = 0.19).”
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).”
Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment (2018, background): “It was reported that MCI and AD were accompanied by losing gray matter in brain (Karas et al., 2004), thus neuropathology changes could be found several years before AD was diagnosed.”
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, neutral): “volumes in the hippocampus, parahippocampal gyrus, cingulate, and other brain regions in both MCI and AD patients (Jack et al., 1999, 2008; Karas et al., 2004; Fox and Schott, 2004; Chetelat et al., 2002; Convit et al., 2000; Dickerson et al., 2001; Kaye et al., 1997; Killiany et al., 2000; Stoub et”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, background): “, 2008), the posterior cingulate gyrus (Karas et al., 2004; Chételat et al., 2005; Laakso et al., 1998) and the posterior”
Ways toward an early diagnosis in Alzheimer's disease: The Alzheimer's Disease Neuroimaging Initiative (ADNI) (2005, neutral): “[39] Karas GB, Scheltens P, Rombouts SA, et al.”
RABBIT: Rapid Alignment of Brains by Building Intermediate Templates (2009, neutral): “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”
Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease (2010, methodology): “Using the VBM method, previous studies showed atrophy of the parahippocampal gyrus, medial temporal lobe [12], entorhinal cortex, cingulum [13], insula and thalamus [14] in subjects with MCI and atrophy of the entire hippocampus and some localized regions in the temporal lobe, cingulum, precuneus, i”
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, pattern classification (2010, neutral): “…patients (Chetelat et al., 2002; Convit et al., 2000; De Leon et al., 2006; Dickerson et al., 2001; Fox and Schott, 2004; Jack et al., 1999, 2008; Karas et al., 2004; Kaye et al., 1997; Killiany et al., 2000; Pen-nanen et al., 2005; Risacher et al., 2009; Stoub et al., 2005; Thompson et al.,…”
DTI measures in crossing-fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease (2011, background): “, 2009), is likely reflected by the clinical preservation of the motor functions until later stages of the disease (95% of the AD patients included in this study had mild AD) and is consistent with the preservation of the sensorimotor cortex (Karas et al., 2004; Chetelat et al., 2008; Dickerson et a”
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline (2008, methodology): “During the past decade, methods of computational neuroanatomy, such as voxel-based and deformation-based analysis, have gained attention in the neuroimaging community (Davatzikos et al., 2001; Thompson et al., 2001; Chetelat et al., 2002; Ashburner et al., 2003; Karas et al., 2004; Pennanen et al., ”
Voxel-based morphometry of the human brain: Methods and applications (2005, background): “In recent years, VBM studies have been successful in characterizing structural brain differences in a variety of diseases including schizophrenia [10], developmental and congenital disorders [11], autism [12], Kallman’s syndrome [13], Klinefelter’s syndrome [14], bipolar disorders [15], cluster head”