Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression

Abstract

Abstract. Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different (p-value ¼ 2.04e −11 ). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.

Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review

“…It is also noted that are very few studies [30], [129] that combine volume, thickness, shape, intensity, and texture in multivariate assessment of the disease, which in turn may result to better classification and prediction accuracies. Martinez Torteya et al [80] used images from the ADNI database with their corresponding segmentation masks, provided by [147], to establish ROIs for every image. For each ROI they used 9 texture-related features together with 13 morphometrical features and 28 signal distribution related features.…”

Assessing Mild Cognitive Impairment Progression using a Spherical Brain Mapping of Magnetic Resonance Imaging

“…Its in luence in declarative memory is patent, and many studies have reported atrophy in early AD, along with surrounding structures such as the amygdala and the parahippocampal gyrus. Most studies report early neurodegeneration in MCI in these structures and generally in the temporal lobe [27,8,35,22]. Martinez-Torteya et al [22] also reported the left medial orbital gyrus and the left inferiolateral remainder of the parietal lobe (area between inferior parietal gyrus and the supramarginal gyrus).…”

“…Neuroimaging, with its ability to perform in vivo exploration of brain structure and function, could be a key source of information to enhance MCI diagnosis. Many works have explored the possibility of multivariate analyses of structural and functional imaging using techniques such as Principal Component Analysis [14,28], Support Vector Machines [24,16,10], texture analysis [35,22,19] or volume and shape analysis [5,23,27,26,6,8]. These works used semiautomatic methodology to segment and extract features from images that leaded to higher discriminative Computer Aided Diagnosis (CAD) systems [18], offering performances up to 80% in the diagnosis of MCI.…”

A Generalized Model for Indoor Location Estimation Using Environmental Sound from Human Activity Recognition

“…From our experience [32,34], first-and second-order statistical features can summarize the behavior of different types of natural signals [6] that can be used to develop ILE systems; additionally, other types of problems with several approaches have been tackled with statistical features [35][36][37]. Therefore, the 16 statistical features listed in Table 4 were extracted from each sample of the human activities done in each selected room.…”