Mapping progressive brain structural changes in early Alzheimer’s disease and mild cognitive impairment

Department of Neurology, David Geffen School of Medicine, UCLA, CA, United States.
Neuropsychologia (Impact Factor: 3.3). 02/2008; 46(6):1597-612. DOI: 10.1016/j.neuropsychologia.2007.10.026
Source: PubMed


Alzheimer's disease (AD), the most common neurodegenerative disorder of the elderly, ranks third in health care cost after heart disease and cancer. Given the disproportionate aging of the population in all developed countries, the socio-economic impact of AD will continue to rise. Mild cognitive impairment (MCI), a transitional state between normal aging and dementia, carries a four- to sixfold increased risk of future diagnosis of dementia. As complete drug-induced reversal of AD symptoms seems unlikely, researchers are now focusing on the earliest stages of AD where a therapeutic intervention is likely to realize the greatest impact. Recently neuroimaging has received significant scientific consideration as a promising in vivo disease-tracking modality that can also provide potential surrogate biomarkers for therapeutic trials. While several volumetric techniques laid the foundation of the neuroimaging research in AD and MCI, more precise computational anatomy techniques have recently become available. This new technology detects and visualizes discrete changes in cortical and hippocampal integrity and tracks the spread of AD pathology throughout the living brain. Related methods can visualize regionally specific correlations between brain atrophy and important proxy measures of disease such as neuropsychological tests, age of onset or factors that may influence disease progression. We describe extensively validated cortical and hippocampal mapping techniques that are sensitive to clinically relevant changes even in the single individual, and can identify group differences in epidemiological studies or clinical treatment trials. We give an overview of some recent neuroimaging advances in AD and MCI and discuss strengths and weaknesses of the various analytic approaches.

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    • "The MCI-converter examples (Figure 4) recapitulate almost perfectly the progression described in Whitwell et al. (2007). Similarly , the AD cases (Figure 3) are in good agreement with the topographic evolution shown in Apostolova and Thompson (2008) and Whitwell et al. (2007). Of note, the ND model gives more accurate prediction than linear local growth reflected by baseline correlations. "
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    ABSTRACT: Alzheimer’s disease (AD) is an amyloid-facilitated tauopathy (Braak et al., 2000) whose origin and subsequent advance within the brain is well characterized: the disease begins in the mesial temporal lobe, an event accompanied by the accumulation of misfolded β-amyloid and tau proteins, and thence progresses along fiber pathways. Histopathological evidence of this highly stereotyped progression has come to be known as the Braak model (Braak and Braak, 1996): neurofibrillary tau tangles are first found in entorhinal cortex and hippocampus (stages I–II), then spread into the amygdala and basolateral temporal lobe (stages III–IV), followed by isocortical association areas (stages V–VI). Morphological changes accompanying this pathological progression are clearly visible on MRI, especially from cross-sectional and longitudinal morphometric mapping (Fischl et al., 2002, Klauschen et al., 2009, Smith et al., 2004 and Wu et al., 2007). Longitudinal studies (Apostolova and Thompson, 2008, Apostolova et al., 2007, Thompson et al., 2003 and Whitwell et al., 2007) confirm that progression follows vulnerable fiber pathways rather than spatial proximity (Englund et al., 1988, Kuczynski et al., 2010 and Villain et al., 2008), closely mirroring Braak pathological stages (Whitwell et al., 2007).
    Full-text · Article · Jan 2015 · Cell Reports
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    • "Despite decades of research, there is still no cure for AD. While a number of lifestyle factors have been linked to the subsequent development of this devastating disorder, effective therapies for preventing or slowing progression of AD in at-risk populations remain elusive (80), and there are no approved treatments for MCI or age-associated cognitive decline (22, 81). Given the high prevalence of chronic stress, sleep disturbance, and mood impairment in those with or at risk for cognitive impairment, the deleterious impact of these and related factors on health and cognitive function, interventions that specifically address these risk factors may hold promise not only for enhancing health and well-being, but also for slowing and possibly preventing cognitive decline in those at risk for AD. "
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    ABSTRACT: Alzheimer's disease (AD) is a chronic, progressive, brain disorder that affects at least 5.3 million Americans at an estimated cost of $148 billion, figures that are expected to rise steeply in coming years. Despite decades of research, there is still no cure for AD, and effective therapies for preventing or slowing progression of cognitive decline in at-risk populations remain elusive. Although the etiology of AD remains uncertain, chronic stress, sleep deficits, and mood disturbance, conditions common in those with cognitive impairment, have been prospectively linked to the development and progression of both chronic illness and memory loss and are significant predictors of AD. Therapies such as meditation that specifically target these risk factors may thus hold promise for slowing and possibly preventing cognitive decline in those at risk. In this study, we briefly review the existing evidence regarding the potential utility of meditation as a therapeutic intervention for those with and at risk for AD, discuss possible mechanisms underlying the observed benefits of meditation, and outline directions for future research.
    Full-text · Article · Apr 2014 · Frontiers in Psychiatry
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    • "The CSF outperformed the hippocampal metrics in discriminating NC from MCI and AD. Low CSF Aβ 42 is tightly linked to the presence of amyloid pathology in the brain while hippocampal atrophy is criticized for being a nonspecific measure observed in many disease states and in normal aging (Apostolova and Thompson, 2008). Techniques capable of detecting hippocampal atrophy in selected subfields are being developed (Apostolova et al., 2010a,c; Csernansky et al., 2000; Mueller and Weiner, 2009) and some have even demonstrated ability to detect hippocampal atrophy in the presymptomatic disease stages (Apostolova et al., 2010c). "
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    ABSTRACT: Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity.
    Full-text · Article · Jan 2014 · Clinical neuroimaging
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