Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning

Abstract

Predicting Alzheimer’s disease (AD) in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. Structural magnetic resonance imaging (MRI) has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensitivity to cortical gray matter changes. In this study we investigated the possibility for using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). We used a novel technique for identifying cortical regions potentially discriminative for separating individuals with MCI who progress to probable AD, from individuals with MCI who do not progress to probable AD. Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and features were selected as regions of interest within these patterns. The selected regions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. In the validation, the test subjects were excluded from the feature selection to obtain unbiased results. The accuracy of the prediction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. By inclusion of test subjects in the feature selection process, the prediction accuracies were artificially inflated to a range of 73% to 81%. Two important results emerge from this study. First, prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD. Second, the results show that one needs to be careful when designing training, testing and validation schemes to ensure that datasets used to build the predictive models are not used in testing and validation.

An Overview of Quantitative Magnetic Resonance Imaging Analysis Studies in the Assessment of Alzheimer’s Disease

“…Querbes et al, [28] used mean cortical thickness within 22 ROI's and they obtained an accuracy of 73% and a sensitivity of 75% by applying their Normalized Thickness Index (NTI) on subjects from the ADNI database. In a very similar study [13], cortical thickness was measured, and based on the results, it was noticed that atrophy patterns differ with the disease progression, thus by learning these differences, the prediction accuracies can be improved.…”

“…Furthermore, because MRI does not use any ionizing radiation it is a suitable technique for longitudinal studies, which are necessary in the investigation of AD. It has been proved by many volumetric and shape and thickness analysis studies [9]- [13] that structural MRI based software tools allow the visualization of macroscopic tissue changes, and thus can help on the detection of neuronal loss in the initial stages of the disease. Features related to texture may have the potential to detect earlier and more subtle changes in neural tissue than other volumetric or shape analysis techniques [14].…”

Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease

“…The size, the genders, the average ages and the average MMSE (Minimal Mental State Examination) are summarized in Table 1. These groups are similar to the ones used in [4,[7][8][9]. All 814 MRI were first segmented, normalized, modulated (correction of volume changes due to the normalization), and registered into a common space.…”

“…As is done in [7][8][9], the classification process is performed using a leave-one-out cross-validation procedure to avoid bias. To validate the efficiency of our framework, we conducted several experiments.…”

“…Therefore, automatic frameworks using MRI-based features have been developed to achieve computer-aided prognosis [3][4][5]. One part of these works focused on advanced machine learning techniques [6] while another part aimed to enhance the biomarker quality [4,7]. Among them, patch-based methods [8,9] demonstrated competitive AD prediction results.…”