Early detection of Alzheimer's disease using MRI hippocampal texture

Abstract

Cognitive impairment in patients with Alzheimer's disease (AD) is associated with reduction in hippocampal volume in magnetic resonance imaging (MRI). However, it is unknown whether hippocampal texture changes in persons with mild cognitive impairment (MCI) that does not have a change in hippocampal volume. We tested the hypothesis that hippocampal texture has association to early cognitive loss beyond that of volumetric changes. The texture marker was trained and evaluated using T1-weighted MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and subsequently applied to score independent data sets from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) and the Metropolit 1953 Danish Male Birth Cohort (Metropolit). Hippocampal texture was superior to volume reduction as predictor of MCI-to-AD conversion in ADNI (area under the receiver operating characteristic curve [AUC] 0.74 vs. 0.67; DeLong test, p = 0.005), and provided even better prognostic results in AIBL (AUC 0.83). Hippocampal texture, but not volume, correlated with Addenbrooke's cognitive examination score (Pearson correlation, r = -0.25, p < 0.001) in the Metropolit cohort. The hippocampal texture marker correlated with hippocampal glucose metabolism as indicated by fluorodeoxyglucose-positron emission tomography (Pearson correlation, r = -0.57, p < 0.001). Texture statistics remained significant after adjustment for volume in all cases, and the combination of texture and volume did not improve diagnostic or prognostic AUCs significantly. Our study highlights the presence of hippocampal texture abnormalities in MCI, and the possibility that texture may serve as a prognostic neuroimaging biomarker of early cognitive impairment.

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

“…Limitations of ANNs include computational load, restricted potential to unmistakably detect possible causal relationships and overfitting suffering [92]. Predict classification in a group based on continues variables [31], [66], [68], [72], [77], [94], [95] Logistic Regression [96] It examines relationships between a categorical Y and a continuous X variable [81], [97] Neural Networks Fit nonlinear models using nodes and layers. Could be very good predictors [78] Support Vector Machines [89], [98] Supervised, multivariate learning methods used for classification, as well as regression [32], [36]- [38], [40], [41], [75], [99] III.…”

“…However, their sample was small (N=30). In the study by Sørensen et al [81], the classification capabilities of hippocampal texture were evaluated using Receiver Operating Characteristic (ROC) curves with the corresponding AUC as performance measure. Texture analysis had an AUC of 0.912 in discriminating NC vs AD and 0.764 between NC vs MCI.…”

“…Sørensen et al [81], tried to detect the accumulated effects caused by NFTs and Aβ plaques, on the hippocampus as changes in the statistical properties of the images intensities. Furthermore, they tested the capability of hippocampal texture in the detection of early cognitive decline and whether texture analysis could reflect changes in hippocampal glucose metabolism in FDG-PET.…”

“…However, some of the best classification accuracies were achieved with textures features [36], [78], [81] and it should be investigated more in the assessment of AD.…”

A Machine Learning Based Approach for Early Detection of Alzheimer’s Disease by Extracting Texture and Shape Features of the Hippocampus Region from MRI Scans

“…In the work proposed by Lauge Sorensen et.al [13], the hypothesis that the hippocampal texture is associated with early cognitive loss was tested. They used three independent datasets from Australian Imaging Biomarkers and Lifestyle flagship study of ageing(AIBL), ADNI and metropolit 1953 for training the classifier.…”