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Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia.

ABSTRACT: [(18)F]FDG-PET imaging has been recognized as a crucial diagnostic marker in Mild Cognitive Impairment (MCI), supporting the presence or the exclusion of Alzheimer's Disease (AD) pathology. A clinical heterogeneity, however, underlies MCI definition. In this study, we aimed to evaluate the predictive role of single-subject voxel-based maps of [(18)F]FDG distribution generated through statistical parametric mapping (SPM) in the progression to different dementia subtypes in a sample of 45 MCI. Their scans were compared to a large normal reference dataset developed and validated for comparison at single-subject level. Additionally, A?42 and Tau CSF values were available in 34 MCI subjects. Clinical follow-up (mean 28.5 ± 7.8 months) assessed subsequent progression to AD or non-AD dementias. The SPM analysis showed: 1) normal brain metabolism in 14 MCI cases, none of them progressing to dementia; 2) the typical temporo-parietal pattern suggestive for prodromal AD in 15 cases, 11 of them progressing to AD; 3) brain hypometabolism suggestive of frontotemporal lobar degeneration (FTLD) subtypes in 7 and dementia with Lewy bodies (DLB) in 2 subjects (all fulfilled FTLD or DLB clinical criteria at follow-up); and 4) 7 MCI cases showed a selective unilateral or bilateral temporo-medial hypometabolism without the typical AD pattern, and they all remained stable. In our sample, objective voxel-based analysis of [(18)F]FDG-PET scans showed high predictive prognostic value, by identifying either normal brain metabolism or hypometabolic patterns suggestive of different underlying pathologies, as confirmed by progression at follow-up. These data support the potential usefulness of this SPM [(18)F]FDG PET analysis in the early dementia diagnosis and for improving subject selection in clinical trials based on MCI definition.

SUBMITTER: Cerami C

PROVIDER: S-EPMC4300010 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia.

Cerami Chiara C Della Rosa Pasquale Anthony PA Magnani Giuseppe G Santangelo Roberto R Marcone Alessandra A Cappa Stefano F SF Perani Daniela D

NeuroImage. Clinical 20141205

[(18)F]FDG-PET imaging has been recognized as a crucial diagnostic marker in Mild Cognitive Impairment (MCI), supporting the presence or the exclusion of Alzheimer's Disease (AD) pathology. A clinical heterogeneity, however, underlies MCI definition. In this study, we aimed to evaluate the predictive role of single-subject voxel-based maps of [(18)F]FDG distribution generated through statistical parametric mapping (SPM) in the progression to different dementia subtypes in a sample of 45 MCI. The ...[more]

PMID: 25610780

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Project description:BackgroundAmyloid pathology in subjects with mild cognitive impairment (MCI) is an important risk factor for progression to dementia due to Alzheimer's disease. Predicting the onset of dementia is challenging even in the presence of amyloid, as time to progression varies considerably among patients and depends on the onset of neurodegeneration. Survival analysis can account for variability in time to event, but has not often been applied to MRI measurements beyond singular predefined brain regions such as the hippocampus. Here we used a voxel-wise survival analysis to identify in an unbiased fashion brain regions where decreased gray matter volume is associated with time to dementia, and assessed the effects of amyloid on these associations.MethodsWe included 276 subjects with MCI (mean age 67?±?8, 41% female, mean Mini-Mental State Examination 26.6?±?2.4), baseline 3D T1-weighted structural MRI, baseline cerebrospinal fluid (CSF) biomarkers, and prospective clinical follow-up. We fitted for each voxel a proportional Cox hazards regression model to study whether decreased gray matter volume predicted progression to dementia in the total sample, and stratified for baseline amyloid status.ResultsDementia at follow-up occurred in 122 (44%) subjects over an average follow-up period of 2.5?±?1.5 years. Baseline amyloid positivity was associated with progression to dementia (hazard ratio 2.4, p?<?0.001). Within amyloid-positive subjects, decreased gray matter volume in the hippocampal, temporal, parietal, and frontal regions was associated with more rapid progression to dementia (median (interquartile range) hazard ratio across significant voxels 1.35 (1.32-1.40)). Repeating the analysis in amyloid-negative subjects revealed similar patterns (median (interquartile range) hazard ratio 1.76 (1.66-1.91)).ConclusionsIn subjects with MCI, both abnormal amyloid CSF and decreased gray matter volume were associated with future progression to dementia. The spatial pattern of decreased gray matter volume associated with progression to dementia was consistent for amyloid-positive and amyloid-negative subjects.

Project description:BackgroundMild cognitive impairment is often a precursor to dementia due to Alzheimer's disease, but many patients with mild cognitive impairment never develop dementia. New diagnostic criteria may lead to more patients receiving a diagnosis of mild cognitive impairment.ObjectiveTo develop a prediction index for the 3-year risk of progression from mild cognitive impairment to dementia relying only on information that can be readily obtained in most clinical settings.Design and participants382 participants diagnosed with amnestic mild cognitive impairment enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI), a multi-site, longitudinal, observational study.Main predictors measuresDemographics, comorbid conditions, caregiver report of participant symptoms and function, and participant performance on individual items from basic neuropsychological scales.Main outcome measureProgression to probable Alzheimer's disease.Key resultsSubjects had a mean (SD) age of 75 (7) years and 43% progressed to probable Alzheimer's disease within 3 years. Important independent predictors of progression included being female, resisting help, becoming upset when separated from caregiver, difficulty shopping alone, forgetting appointments, number of words recalled from a 10-word list, orientation and difficulty drawing a clock. The final point score could range from 0 to 16 (mean [SD]: 4.2 [2.9]). The optimism-corrected Harrell's c-statistic was 0.71(95% CI: 0.68-0.75). Fourteen percent of subjects with low risk scores (0-2 points, n = 124) converted to probable Alzheimer's disease over 3 years, compared to 51% of those with moderate risk scores (3-8 points, n = 223) and 91% of those with high risk scores (9-16 points, n = 35).ConclusionsAn index using factors that can be obtained in most clinical settings can predict progression from amnestic mild cognitive impairment to probable Alzheimer's disease and may help clinicians differentiate between mild cognitive impairment patients at low vs. high risk of progression.

Project description:PurposePositron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual's risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual's risk of conversion from MCI to AD.MethodsFDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual's metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell's concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics.ResultsThe KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77-4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model).ConclusionThe KLSE indicator identifies abnormal brain networks predicting an individual's risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.

Project description:BACKGROUND:The previously described and validated Erlangen Score (ES) algorithm enables interpretation of the cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD), ordering them on an ordinal scale: from neurochemically normal (ES?=?0) through improbable AD (ES?=?1), possible AD (ES?=?2 or 3), to probable AD (ES?=?4). Here we assess the accuracy of the ES in predicting hazards of progression from the mild cognitive impairment (MCI) stage of AD to the dementia stage of the disease (Alzheimer's disease dementia (ADD)) in a novel, single-center cohort. METHODS:Baseline CSF biomarkers (amyloid beta (A?) 1-42, A?42/40, Tau, and pTau181), interpreted according to the ES, were used to estimate time to progression from the MCI stage of AD to ADD, conditional on age, gender, APOE ?4 genotype, and Mini Mental State Examination score in 144 MCI subjects, using the Extended Cox Model; the subjects were followed-up until they developed dementia or until they had been cognitively stable for at least 2?years. In addition, ES distributions were studied in 168 ADD cases and 66 neurologic controls. Further, we stratified MCI patients into those who progressed to ADD faster (within 3 years, n?=?47) and those who progressed slower (n?=?74). RESULTS:The distributions of the ES categories across the four diagnostic groups (Controls, MCI-Stable, MCI-AD, and ADD) were highly significantly different (Kruskal-Wallis ?2(df?=?3)?=?151.4, p?<?0.001), with significant contrasts between each pair (p?<?0.005), except between the ADD and the MCI-AD groups (p?=?1.0). MCI patients with ES?=?2 or 3 had 6-8 times higher hazards to progress to ADD compared to patients with ES?=?0 or 1 in the first 3 follow-up years, and then their hazards decreased to those of the group with ES?=?0 or 1. Patients with ES?=?4 had hazards 8-12 times higher compared to the ES?=?0 or 1 group. Faster progressors with ES?=?2 or 3 had, in comparison to slower progressors, significantly lower A?1-42, A?1-40, and A?42/40, but comparable Tau and pTau181. A highly significant difference of the ES distributions between these two groups was observed (p?<?0.001). CONCLUSIONS:Our current results reconfirm and extend the conclusions of the previously published report that the Erlangen Score is a useful tool facilitating interpretation of a complex pattern of the CSF AD biomarkers.

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Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia.

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Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia.

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