The cortical signature of prodromal AD Regional thinning predicts mild AD dementia

Harvard University, Cambridge, Massachusetts, United States
Neurology (Impact Factor: 8.29). 03/2009; 72(12):1048-55. DOI: 10.1212/01.wnl.0000340981.97664.2f
Source: PubMed

ABSTRACT We previously used exploratory analyses across the entire cortex to determine that mild Alzheimer disease (AD) is reliably associated with a cortical signature of thinning in specific limbic and association regions. Here we investigated whether the cortical signature of AD-related thinning is present in individuals with questionable AD dementia (QAD) and whether a greater degree of regional cortical thinning predicts mild AD dementia.
Participants included 49 older adults with mild impairment consistent with mild cognitive impairment (Clinical Dementia Rating [CDR] = 0.5) at the time of structural MRI scanning. Cortical thickness was measured in nine regions of interest (ROIs) identified previously from a comparison of patients with mild AD and controls.
Longitudinal clinical follow-up revealed that 20 participants converted to mild AD dementia (progressors) while 29 remained stable (nonprogressors) approximately 2.5 years after scanning. At baseline, QAD participants showed a milder degree of cortical thinning than typically seen in mild AD, and CDR Sum-of-Boxes correlated with thickness in temporal and parietal ROIs. Compared to nonprogressors, progressors showed temporal and parietal thinning. Using receiver operating characteristic curves, the thickness of an aggregate measure of these regions predicted progression to mild AD with 83% sensitivity and 65% specificity.
Thinning in specific cortical areas known to be affected by Alzheimer disease (AD) is detectable in individuals with questionable AD dementia (QAD) and predicts conversion to mild AD dementia. This method could be useful for identifying individuals at relatively high risk for imminent progression from QAD to mild AD dementia, which may be of value in clinical trials.

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    • "The typical diagnostic progression from cognitively normal (CN), to MCI ( Petersen et al., 1999; Gauthier et al., 2006), and finally AD reflects a systematic pattern of progressive atrophy ( Whitwell et al., 2007; McDonald et al., 2009; Risacher et al., 2010), which corresponds to the spread of amyloid and tau neuropathology (Arnold et al., 1991; Braak and Braak, 1991; Jack et al., 2010). Measures of cognitive function (Gomar et al., 2011), regional atrophy (Bakkour et al., 2009; Davatzikos et al., 2011), and cerebrospinal fluid (CSF) biomarkers have been used to predict MCI or AD diagnosis. Prognostic classification accuracy based solely on radiologically detected atrophy (Cuingnet et al., 2011) or CSF biomarkers, such as amyloid-β 1–42 (Aβ 1-42 ), tau, and phosphorylated tau at Thr181 (p-tau 181p ), varies widely, but is generally sub-optimal (Arnold et al., 1991; Braak and Braak, 1991; Shaw et al., 2007; Visser et al., 2009; Liu et al., 2013a). "
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    ABSTRACT: Identifying predictors of mild cognitive impairment (MCI) and Alzheimer׳s disease (AD) can lead to more accurate diagnosis and facilitate clinical trial participation. We identified 320 participants (93 cognitively normal or CN, 162 MCI, 65 AD) with baseline magnetic resonance imaging (MRI) data, cerebrospinal fluid biomarkers, and cognition data in the Alzheimer׳s Disease Neuroimaging Initiative database. We used independent component analysis (ICA) on structural MR images to derive 30 matter covariance patterns (ICs) across all participants. These ICs were used in iterative and stepwise discriminant classifier analyses to predict diagnostic classification at 24 months for CN vs. MCI, CN vs. AD, MCI vs. AD, and stable MCI (MCI-S) vs. MCI progression to AD (MCI-P). Models were cross-validated with a "leave-10-out" procedure. For CN vs. MCI, 84.7% accuracy was achieved based on cognitive performance measures, ICs, p-tau181p, and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau181p. For MCI vs. AD and MCI-S vs. MCI-P, models achieved 83.1% and 80.3% accuracy, respectively, based on cognitive performance measures, ICs, and p-tau181p. ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.
    Psychiatry Research: Neuroimaging 08/2014; 224(2). DOI:10.1016/j.pscychresns.2014.08.005 · 2.42 Impact Factor
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    • "Furthermore, the increase in alpha3/alpha2 power ratio has been demonstrated predictive of conversion of patients with MCI in AD, but not in non-AD dementia (Moretti et al., 2012a). The same increase of alpha3/alpha2 power ratio was found to be correlated with hippocampal atrophy in subjects with AD (Bakkour et al., 2009). Finally, a recent study have shown that MCI subjects with highest alpha3/alpha2 power ratio present a peculiar pattern of basal ganglia and thalamic atrophy, detected with voxel-based-morphometry (VBM) technique, as compared to MCI groups with middle and low alpha3/alpha2 power ratio (Klimesch et al., 2006, 2007). "
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    ABSTRACT: Objective: Temporo-parietal cortex thinning is associated to mild cognitive impairment (MCI) due to Alzheimer disease (AD). The increase of EEG upper/low alpha power ratio has been associated with AD-converter MCI subjects. We investigated the association of alpha3/alpha2 ratio with patterns of cortical thickness in MCI. Materials and Methods: Seventy-four adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG) recording and high resolution 3D magnetic resonance imaging. Alpha3/alpha2 power ratio as well as cortical thickness was computed for each subject. Three MCI groups were detected according to increasing tertile values of upper/low alpha power ratio. Difference of cortical thickness among the groups was estimated. Pearson’s r was used to assess the topography of the correlation between cortical thinning and memory impairment. Results: High upper/low alpha power ratio group had total cortical gray matter volume reduction of 471 mm2 than low upper/low alpha power ratio group (p < 0.001). Upper/low alpha group showed a similar but less marked pattern (160 mm2) of cortical thinning when compared to middle upper/low alpha power ratio group (p < 0.001). Moreover, high upper/low alpha group had wider cortical thinning than other groups, mapped to the Supramarginal and Precuneus bilaterally. Finally, in high upper/low alpha group temporo-parietal cortical thickness was correlated to memory performance. No significant cortical thickness differences was found between middle and low alpha3/alpha2 power ratio groups. Conclusion: High EEG upper/low alpha power ratio was associated with temporo-parietal cortical thinning and memory impairment in MCI subjects. The combination of EEG upper/low alpha ratio and cortical thickness measure could be useful for identifying individuals at risk for progression to AD dementia and may be of value in clinical context.
    Frontiers in Aging Neuroscience 10/2013; 5:63. DOI:10.3389/fnagi.2013.00063 · 4.00 Impact Factor
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    • "MRI-based image analysis methods have long been used to track structural atrophy of the aging brain. MRI studies of AD reveal widespread neuronal loss and atrophy in the brain's gray matter, especially in medial temporal and hippocampal regions (Atiya et al., 2003; Chetelat and Baron, 2003; Thompson et al., 2003; Anderson et al., 2005; Apostolova and Thompson, 2008; Bakkour et al., 2009; Risacher et al., 2009; Apostolova et al., 2010; Desikan et al., 2010a; Desikan et al., 2010b; Chiang et al., 2011; Weiner et al., 2012; Leung et al., 2013). Beta-amyloid and tau proteins accumulate in the brain, leading to inflammation, neuronal atrophy and cell death (Braak and Braak, 1991; Braak and Braak, 1995). "
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    ABSTRACT: The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.
    Clinical neuroimaging 07/2013; 3:180–195. DOI:10.1016/j.nicl.2013.07.006 · 2.53 Impact Factor
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