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An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients.

ABSTRACT: Alzheimer's disease (AD) and mild cognitive impairment (MCI) are of great current research interest. While there is no consensus on whether MCIs actually "convert" to AD, this concept is widely applied. Thus, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline brain image, of whether an MCI will convert within a multi-year period following the initial clinical visit. This is not a traditional supervised learning problem since, in ADNI, there are no definitive labeled conversion examples. It is not unsupervised, either, since there are (labeled) ADs and Controls, as well as cognitive scores for MCIs. Prior works have defined MCI subclasses based on whether or not clinical scores significantly change from baseline. There are concerns with these definitions, however, since, e.g., most MCIs (and ADs) do not change from a baseline CDR?=?0.5 at any subsequent visit in ADNI, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative definition, wherein an MCI is a converter if any of the patient's brain scans are classified "AD" by a Control-AD classifier. This definition bootstraps design of a second classifier, specifically trained to predict whether or not MCIs will convert. We thus predict whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate that this definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations more consistent with known AD biomarkers (including CSF markers). We also identify key prognostic brain region biomarkers.

SUBMITTER: Aksu Y

PROVIDER: S-EPMC3192038 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients.

Aksu Yaman Y Miller David J DJ Kesidis George G Bigler Don C DC Yang Qing X QX

PloS one 20111012 10

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are of great current research interest. While there is no consensus on whether MCIs actually "convert" to AD, this concept is widely applied. Thus, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline brain image, of whether an MCI will convert within a multi-year period following the initial clinical visit. This is not a tr ...[more]

PMID: 22022375

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Project description:Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (? 2?years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and 18F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-? (A?) burden was quantified using the 18F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on 18F-FDG SUVR. CSF measures included A?1-42, A?1-40, T-tau, P-tau181, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ?4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., ?story recall: ??=?+0.43 [p?<?0.001] and +?0.37 [p?=?0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline A? burden only if SUVR was referenced to the subcortical white matter (e.g., ?story recall: ??=?-0.28 [p?=?0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters.

Project description:BackgroundPatients with Mild Cognitive Impairment (MCI) are at high risk of progression to Alzheimer's dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research. Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD.MethodsWe have conducted a comprehensive study using a large number of samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion. We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors. Our study differs from many of the other ones in three important respects: (1) we use a large cohort of MCI samples that are unbiased with respect to age or education status between case and controls (2) we integrate and test various types of baseline data available in ADNI including MRI, demographic, genetic and cognitive measures and (3) we apply sparse logistic regression with stability selection to ADNI data for robust feature selection.ResultsWe have used 319 MCI subjects from ADNI that had MRI measurements at the baseline and passed quality control, including 177 MCI Non-converters and 142 MCI Converters. Conversion was considered over the course of a 4-year follow-up period. A combination of 15 features (predictors) including those from MRI scans, APOE genotyping, and cognitive measures achieves the best prediction with an AUC score of 0.8587.ConclusionsOur results demonstrate the power of integrating various baseline data for prediction of the conversion from MCI to probable AD. Our results also demonstrate the effectiveness of stability selection for feature selection in the context of sparse logistic regression.

Project description:Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.

Project description:Dementia affects 47 million individuals worldwide, and assuming the status quo is projected to rise to 150 million by 2050. Prevention of age-related cognitive impairment in older persons with lifestyle interventions continues to garner evidence but whether this can combat underlying neurodegeneration is unknown. The Study of Mental Activity and Resistance Training (SMART) trial has previously reported within-training findings; the aim of this study was to investigate the long-term neurostructural and cognitive impact of resistance exercise in Mild Cognitive Impairment (MCI). For the first time we show that hippocampal subareas particularly susceptible to volume loss in Alzheimer's disease (AD) are protected by resistance exercise for up to one year after training. One hundred MCI participants were randomised to one of four training groups: (1) Combined high intensity progressive resistance and computerised cognitive training (PRT+CCT), (2) PRT+Sham CCT, (3) CCT+Sham PRT, (4) Sham physical+sham cognitive training (SHAM+SHAM). Physical, neuropsychological and MRI assessments were carried out at baseline, 6 months (directly after training) and 18 months from baseline (12 months after intervention cessation). Here we report neuro-structural and functional changes over the 18-month trial period and the association with global cognitive and executive function measures. PRT but not CCT or PRT+CCT led to global long-term cognitive improvements above SHAM intervention at 18-month follow-up. Furthermore, hippocampal subfields susceptible to atrophy in AD were protected by PRT revealing an elimination of long-term atrophy in the left subiculum, and attenuation of atrophy in left CA1 and dentate gyrus when compared to SHAM+SHAM (p = 0.023, p = 0.020 and p = 0.027). These neuroprotective effects mediated a significant portion of long-term cognitive benefits. By contrast, within-training posterior cingulate plasticity decayed after training cessation and was unrelated to long term cognitive benefits. Neither general physical activity levels nor fitness change over the 18-month period mediated hippocampal trajectory, demonstrating that enduring hippocampal subfield plasticity is not a simple reflection of post-training changes in fitness or physical activity participation. Notably, resting-state fMRI analysis revealed that both the hippocampus and posterior cingulate participate in a functional network that continued to be upregulated following intervention cessation. Multiple structural mechanisms may contribute to the long-term global cognitive benefit of resistance exercise, developing along different time courses but functionally linked. For the first time we show that 6 months of high intensity resistance exercise is capable of not only promoting better cognition in those with MCI, but also protecting AD-vulnerable hippocampal subfields from degeneration for at least 12 months post-intervention. These findings emphasise the therapeutic potential of resistance exercise; however, future work will need to establish just how long-lived these outcomes are and whether they are sufficient to delay dementia.

Dataset Information

An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients.

Publications

An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients.

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