Recommendations for the diagnosis of preclinical Alzheimer disease (AD) have been formulated by a workgroup of the National Institute on Aging and Alzheimer's Association. Three stages of preclinical ...AD were described. Stage 1 is characterized by abnormal levels of β-amyloid. Stage 2 represents abnormal levels of β-amyloid and evidence of brain neurodegeneration. Stage 3 includes the features of stage 2 plus subtle cognitive changes. Stage 0, not explicitly defined in the criteria, represents subjects with normal biomarkers and normal cognition. The ability of the recommended criteria to predict progression to cognitive impairment is the crux of their validity.
Using previously developed operational definitions of the 3 stages of preclinical AD, we examined the outcomes of subjects from the Mayo Clinic Study of Aging diagnosed as cognitively normal who underwent brain MRI or (18)Ffluorodeoxyglucose and Pittsburgh compound B PET, had global cognitive test scores, and were followed for at least 1 year.
Of the 296 initially normal subjects, 31 (10%) progressed to a diagnosis of mild cognitive impairment (MCI) or dementia (27 amnestic MCI, 2 nonamnestic MCI, and 2 non-AD dementias) within 1 year. The proportion of subjects who progressed to MCI or dementia increased with advancing stage (stage 0, 5%; stage 1, 11%; stage 2, 21%; stage 3, 43%; test for trend, p < 0.001).
Despite the short follow-up period, our operationalization of the new preclinical AD recommendations confirmed that advancing preclinical stage led to higher proportions of subjects who progressed to MCI or dementia.
To investigate the relationship between baseline MRI and CSF biomarkers and subsequent change in continuous measures of cognitive and functional abilities in cognitively normal (CN) subjects and ...patients with amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD) and to examine the ability of these biomarkers to predict time to conversion from aMCI to AD.
Data from the Alzheimer's Disease Neuroimaging Initiative, which consists of CN, aMCI, and AD cohorts with both CSF and MRI, were used. Baseline CSF (t-tau, Abeta(1-42), and p-tau(181P)) and MRI scans were obtained in 399 subjects (109 CN, 192 aMCI, 98 AD). Structural Abnormality Index (STAND) scores, which reflect the degree of AD-like features in MRI, were computed for each subject.
Change on continuous measures of cognitive and functional performance was modeled as average Clinical Dementia Rating-sum of boxes and Mini-Mental State Examination scores over a 2-year period. STAND was a better predictor of subsequent cognitive/functional change than CSF biomarkers. Single-predictor Cox proportional hazard models for time to conversion from aMCI to AD showed that STAND and log (t-tau/Abeta(1-42)) were both predictive of future conversion. The age-adjusted hazard ratio for an interquartile change (95% confidence interval) of STAND was 2.6 (1.7, 4.2) and log (t-tau/Abeta(1-42)) was 2.0 (1.1, 3.4). Both MRI and CSF provided information about future cognitive change even after adjusting for baseline cognitive performance.
MRI and CSF provide complimentary predictive information about time to conversion from amnestic mild cognitive impairment to Alzheimer disease and combination of the 2 provides better prediction than either source alone. However, we found that MRI was a slightly better predictor of future clinical/functional decline than the CSF biomarkers tested.
To test the hypothesis that the atrophy rate measured from serial MRI studies is associated with time to subsequent clinical conversion to a more impaired state in both cognitively healthy elderly ...subjects and in subjects with amnestic mild cognitive impairment (MCI).
Ninety-one healthy elderly patients and 72 patients with amnestic MCI who met inclusion criteria were identified from the Mayo Alzheimer's Disease Research Center and Alzheimer's Disease Patient Registry. Atrophy rates of four different brain structures--hippocampus, entorhinal cortex, whole brain, and ventricle--were measured from a pair of MRI studies separated by 1 to 2 years. The time of the second scan marked the beginning of the clinical observation period.
During follow-up, 13 healthy patients converted to MCI or Alzheimer disease (AD), whereas 39 MCI subjects converted to AD. Among those healthy at baseline, only larger ventricular annual percent volume change (APC) was associated with a higher risk of conversion (hazard ratio for a 1-SD increase 1.9, p = 0.03). Among MCI subjects, both greater ventricular volume APC (hazard ratio for a 1-SD increase 1.7, p < 0.001) and greater whole brain APC (hazard ratio for a 1-SD increase 1.4, p = 0.007) increased the risk of conversion to AD. Both ventricular APC (hazard ratio for a 1-SD increase 1.59, p = 0.001) and whole brain APC (hazard ratio for a 1-SD increase 1.32, p = 0.009) provided additional predictive information to covariate-adjusted cross-sectional hippocampal volume at baseline about the risk of converting from MCI to AD.
Higher whole brain and ventricle atrophy rates 1 to 2 years before baseline are associated with an increased hazard of conversion to a more impaired state. Combining a measure of hippocampal volume at baseline with a measure of either whole brain or ventricle atrophy rates from serial MRI scans provides complimentary predictive information about the hazard of subsequent conversion from mild cognitive impairment to Alzheimer disease. However, overlap among those who did vs those who did not convert indicate that these measures are unlikely to provide absolute prognostic information for individual patients.
To assess the correlations of both MRI and CSF biomarkers with clinical diagnosis and with cognitive performance in cognitively normal (CN) subjects and patients with amnestic mild cognitive ...impairment (aMCI) and Alzheimer disease (AD).
This is a cross-sectional study with data from the Alzheimer's Disease Neuroimaging Initiative, which consists of CN subjects, subjects with aMCI, and subjects with AD with both CSF and MRI. Baseline CSF (t-tau, Abeta(1-42), and p-tau(181P)) and MRI scans were obtained in 399 subjects (109 CN, 192 aMCI, 98 AD). Structural Abnormality Index (STAND) scores, which reflect the degree of AD-like anatomic features on MRI, were computed for each subject.
We found no significant correlation between CSF biomarkers and cognitive scores in any of the 3 clinical groups individually. Conversely, STAND scores correlated with both Clinical Dementia Rating-sum of boxes and Mini-Mental State Examination in aMCI and AD (p < or = 0.01). While STAND and all CSF biomarkers were predictors of clinical group membership (CN, aMCI, or AD) univariately (p < 0.001), STAND was more predictive than CSF both univariately and in combined models.
CSF and MRI biomarkers independently contribute to intergroup diagnostic discrimination and the combination of CSF and MRI provides better prediction than either source of data alone. However, MRI provides greater power to effect cross-sectional groupwise discrimination and better correlation with general cognition and functional status cross-sectionally. We therefore conclude that although MRI and CSF provide complementary information, MRI reflects clinically defined disease stage better than the CSF biomarkers tested.
To use diffusion tensor imaging (DTI) to assess gray matter and white matter tract diffusion in behavioral variant frontotemporal dementia (bvFTD), semantic dementia (SMD), and progressive nonfluent ...aphasia (PNFA).
This was a case-control study where 16 subjects with bvFTD, 7 with PNFA, and 4 with SMD were identified and matched by age and gender to 19 controls. All subjects had 3-T head MRI with a DTI sequence with diffusion encoding in 21 directions. Gray matter mean diffusivity (MD) was assessed using a region-of-interest (ROI) and voxel-level approach, and voxel-based morphometry was used to assess patterns of gray matter loss. White matter tract diffusivity (fractional anisotropy and radial diffusivity) was assessed by placing ROIs on tracts of interest.
In bvFTD, increased gray matter MD and gray matter loss were identified bilaterally throughout frontal and temporal lobes, with abnormal diffusivity observed in white matter tracts that connect to these regions. In SMD, gray matter loss and increased MD were identified predominantly in the left temporal lobe, with tract abnormalities observed in the inferior longitudinal fasciculus and uncinate fasciculus. In PNFA, gray matter loss and increased MD were observed in left inferior frontal lobe, insula, and supplemental motor area, with tract abnormalities observed in the superior longitudinal fasciculus.
The diffusivity of gray matter is increased in regions that are atrophic in frontotemporal dementia, suggesting disruption of the cytoarchitecture of remaining tissue. Furthermore, damage was identified in white matter tracts that interconnect these regions, supporting the hypothesis that these diseases involve different and specific brain networks.
Neurofibrillary tangles (NFTs), composed of hyperphosphorylated tau proteins, are one of the pathologic hallmarks of Alzheimer disease (AD). We aimed to determine whether patterns of gray matter ...atrophy from antemortem MRI correlate with Braak staging of NFT pathology.
Eighty-three subjects with Braak stage III through VI, a pathologic diagnosis of low- to high-probability AD, and MRI within 4 years of death were identified. Voxel-based morphometry assessed gray matter atrophy in each Braak stage compared with 20 pathologic control subjects (Braak stages 0 through II).
In pairwise comparisons with Braak stages 0 through II, a graded response was observed across Braak stages V and VI, with more severe and widespread loss identified at Braak stage VI. No regions of loss were identified in Braak stage III or IV compared with Braak stages 0 through II. The lack of findings in Braak stages III and IV could be because Braak stage is based on the presence of any NFT pathology regardless of severity. Actual NFT burden may vary by Braak stage. Therefore, tau burden was assessed in subjects with Braak stages 0 through IV. Those with high tau burden showed greater gray matter loss in medial and lateral temporal lobes than those with low tau burden.
Patterns of gray matter loss are associated with neurofibrillary tangle (NFT) pathology, specifically with NFT burden at Braak stages III and IV and with Braak stage itself at higher stages. This validates three-dimensional patterns of atrophy on MRI as an approximate in vivo surrogate indicator of the full brain topographic representation of the neurodegenerative aspect of Alzheimer disease pathology.
A major recent discovery was the identification of an expansion of a non-coding GGGGCC hexanucleotide repeat in the C9ORF72 gene in patients with frontotemporal dementia and amyotrophic lateral ...sclerosis. Mutations in two other genes are known to account for familial frontotemporal dementia: microtubule-associated protein tau and progranulin. Although imaging features have been previously reported in subjects with mutations in tau and progranulin, no imaging features have been published in C9ORF72. Furthermore, it remains unknown whether there are differences in atrophy patterns across these mutations, and whether regional differences could help differentiate C9ORF72 from the other two mutations at the single-subject level. We aimed to determine the regional pattern of brain atrophy associated with the C9ORF72 gene mutation, and to determine which regions best differentiate C9ORF72 from subjects with mutations in tau and progranulin, and from sporadic frontotemporal dementia. A total of 76 subjects, including 56 with a clinical diagnosis of behavioural variant frontotemporal dementia and a mutation in one of these genes (19 with C9ORF72 mutations, 25 with tau mutations and 12 with progranulin mutations) and 20 sporadic subjects with behavioural variant frontotemporal dementia (including 50% with amyotrophic lateral sclerosis), with magnetic resonance imaging were included in this study. Voxel-based morphometry was used to assess and compare patterns of grey matter atrophy. Atlas-based parcellation was performed utilizing the automated anatomical labelling atlas and Statistical Parametric Mapping software to compute volumes of 37 regions of interest. Hemispheric asymmetry was calculated. Penalized multinomial logistic regression was utilized to create a prediction model to discriminate among groups using regional volumes and asymmetry score. Principal component analysis assessed for variance within groups. C9ORF72 was associated with symmetric atrophy predominantly involving dorsolateral, medial and orbitofrontal lobes, with additional loss in anterior temporal lobes, parietal lobes, occipital lobes and cerebellum. In contrast, striking anteromedial temporal atrophy was associated with tau mutations and temporoparietal atrophy was associated with progranulin mutations. The sporadic group was associated with frontal and anterior temporal atrophy. A conservative penalized multinomial logistic regression model identified 14 variables that could accurately classify subjects, including frontal, temporal, parietal, occipital and cerebellum volume. The principal component analysis revealed similar degrees of heterogeneity within all disease groups. Patterns of atrophy therefore differed across subjects with C9ORF72, tau and progranulin mutations and sporadic frontotemporal dementia. Our analysis suggested that imaging has the potential to be useful to help differentiate C9ORF72 from these other groups at the single-subject level.
To correlate different methods of measuring rates of brain atrophy from serial MRI with corresponding clinical change in normal elderly subjects, patients with mild cognitive impairment (MCI), and ...patients with probable Alzheimer disease (AD).
One hundred sixty subjects were recruited from the Mayo Clinic Alzheimer's Disease Research Center and Alzheimer's Disease Patient Registry Studies. At baseline, 55 subjects were cognitively normal, 41 met criteria for MCI, and 64 met criteria for AD. Each subject underwent an MRI examination of the brain at the time of the baseline clinical assessment and then again at the time of a follow-up clinical assessment, 1 to 5 years later. The annualized changes in volume of four structures were measured from the serial MRI studies: hippocampus, entorhinal cortex, whole brain, and ventricle. Rates of change on several cognitive tests/rating scales were also assessed. Subjects who were classified as normal or MCI at baseline could either remain stable or convert to a lower-functioning group. AD subjects were dichotomized into slow vs fast progressors.
All four atrophy rates were greater among normal subjects who converted to MCI or AD than among those who remained stable, greater among MCI subjects who converted to AD than among those who remained stable, and greater among fast than slow AD progressors. In general, atrophy on MRI was detected more consistently than decline on specific cognitive tests/rating scales. With one exception, no differences were found among the four MRI rate measures in the strength of the correlation with clinical deterioration at different stages of the disease.
These data support the use of rates of change from serial MRI studies in addition to standard clinical/psychometric measures as surrogate markers of disease progression in AD. Estimated sample sizes required to power a therapeutic trial in MCI were an order of magnitude less for MRI than for change measures based on cognitive tests/rating scales.
Biomarkers of brain Aβ amyloid deposition can be measured either by cerebrospinal fluid Aβ42 or Pittsburgh compound B positron emission tomography imaging. Our objective was to evaluate the ability ...of Aβ load and neurodegenerative atrophy on magnetic resonance imaging to predict shorter time-to-progression from mild cognitive impairment to Alzheimer’s dementia and to characterize the effect of these biomarkers on the risk of progression as they become increasingly abnormal. A total of 218 subjects with mild cognitive impairment were identified from the Alzheimer’s Disease Neuroimaging Initiative. The primary outcome was time-to-progression to Alzheimer’s dementia. Hippocampal volumes were measured and adjusted for intracranial volume. We used a new method of pooling cerebrospinal fluid Aβ42 and Pittsburgh compound B positron emission tomography measures to produce equivalent measures of brain Aβ load from either source and analysed the results using multiple imputation methods. We performed our analyses in two phases. First, we grouped our subjects into those who were ‘amyloid positive’ (n = 165, with the assumption that Alzheimer's pathology is dominant in this group) and those who were ‘amyloid negative’ (n = 53). In the second phase, we included all 218 subjects with mild cognitive impairment to evaluate the biomarkers in a sample that we assumed to contain a full spectrum of expected pathologies. In a Kaplan–Meier analysis, amyloid positive subjects with mild cognitive impairment were much more likely to progress to dementia within 2 years than amyloid negative subjects with mild cognitive impairment (50 versus 19%). Among amyloid positive subjects with mild cognitive impairment only, hippocampal atrophy predicted shorter time-to-progression (P < 0.001) while Aβ load did not (P = 0.44). In contrast, when all 218 subjects with mild cognitive impairment were combined (amyloid positive and negative), hippocampal atrophy and Aβ load predicted shorter time-to-progression with comparable power (hazard ratio for an inter-quartile difference of 2.6 for both); however, the risk profile was linear throughout the range of hippocampal atrophy values but reached a ceiling at higher values of brain Aβ load. Our results are consistent with a model of Alzheimer’s disease in which Aβ deposition initiates the pathological cascade but is not the direct cause of cognitive impairment as evidenced by the fact that Aβ load severity is decoupled from risk of progression at high levels. In contrast, hippocampal atrophy indicates how far along the neurodegenerative path one is, and hence how close to progressing to dementia. Possible explanations for our finding that many subjects with mild cognitive impairment have intermediate levels of Aβ load include: (i) individual subjects may reach an Aβ load plateau at varying absolute levels; (ii) some subjects may be more biologically susceptible to Aβ than others; and (iii) subjects with mild cognitive impairment with intermediate levels of Aβ may represent individuals with Alzheimer’s disease co-existent with other pathologies.
To determine the relationship between β-amyloid (Aβ) load as measured by (11)C-Pittsburgh compound B (PiB) PET and cognitive function in cognitively normal older adults.
We studied 408 cognitively ...normal older adults who participated in the population-based Mayo Clinic Study of Aging (MCSA) from January 2009 through March 2011. The participants underwent PiB PET and neuropsychometric testing within 6 months. The association between PiB retention and cognitive function was measured by partial correlation and an interaction with APOE status was tested using linear regression after adjusting for age, sex, and education.
Higher PiB retention was associated with cognitive performance (Spearman partial r = -0.18; p < 0.01), specifically the memory, language, attention/executive, and visual-spatial processing domains in the whole group of participants. The association between PiB retention and cognition was modified by the APOE status on linear regression analysis even after controlling for the differences in the distribution of PiB values among APOE ε4 carriers and noncarriers (p = 0.02). Cognitive performance was associated with the Aβ deposition in the frontal, temporal, and parietal lobe association cortices in APOE ε4 carriers on SPM analysis (p < 0.001).
There is a modest association between PiB retention and cognitive function in cognitively normal older adults and this relationship between Aβ load and cognitive function is modified by APOE status. Whereas Aβ load is associated with greater cognitive impairment in APOE ε4 carriers, the cognitive function in APOE ε4 noncarriers is influenced less by the Aβ load, suggesting that APOE isoforms modulate the harmful effects of Aβ on cognitive function.
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