Cortical Thickness Analysis to Detect Progressive Mild Cognitive Impairment: A Reference to Alzheimer’s Disease

Abstract

Background/Aims: Mild cognitive impairment (MCI) is associated with an increased risk of Alzheimer’s disease (AD). It would be advantageous to be able to distinguish the characteristics of those MCI patients with a high probability to progress to AD if one wishes to monitor the disease development and treatment. Methods: We assessed the baseline MRI and maximum of 7 years clinical follow-up data of 60 MCI subjects in order to examine differences in cortical thickness (CTH) between the progressive MCI (P-MCI) and stable MCI (S-MCI) subjects. CTH was measured using an automatic computational surface-based method. During the follow-up, 15 MCI subjects converted to AD on average 1.9 ± 1.3 years after the baseline examination, while 45 MCI subjects remained stable. Results: The P-MCI group displayed significantly reduced CTH bilaterally in the superior and middle frontal, superior, middle and inferior temporal, fusiform and parahippocampal regions as well as the cingulate and retrosplenial cortices and also in the right precuneal and paracentral regions compared to S-MCI subjects. Conclusions: Analysis of CTH could be used in conjunction with neuropsychological testing to identify those subjects with imminent conversion from MCI to AD several years before dementia diagnosis.

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

“…The combination of two provide better predictive accuracy [144]- [146]. 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.…”

Automated MR morphometry to predict Alzheimer’s disease in mild cognitive impairment

“…In the area of predicting conversion from MCI to AD, the methodology is related to a number of other neuroimaging techniques for estimation of atrophic changes [6,[9][10][11][12][13][14] and classification [18][19][20][21][22]. Some of the approaches require manual interaction [9,10,14].…”

“…Some of the approaches require manual interaction [9,10,14]. Many studies do not perform individual patient classification, but group analysis or correlation with clinical scores [6,[9][10][11][12][13][14]. The voxel-wise measure that is derived in this study is mainly based on the gray value intensities found in the image, while other studies are based on features like volumetric measures [6,[9][10][11]13,14], measures of cortical thickness [12], shape-or deformation-based features [18,20,21], or tissue classification [19,22].…”

“…However, this is also the most challenging task. Current studies have shown good correlation of morphometric measures in MCI and subsequent cognitive decline [6,[9][10][11][12][13][14]. It is known that the medial temporal lobes, and in particular the hippocampi and entorhinal cortex, are among the first regions of the brain undergoing atrophy in AD [15].…”

Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment

“…Identification of MCI patients who will progress to AD would allow the application of disease-modifying treatments to slow progression at a point where clinical manifestations are limited. A combination of results from neuropsychological testing [5], MRI [6] and cerebrospinal fluid (CSF) biomarkers [7] can aid in the prediction of which patients with MCI will progress to AD. Furthermore, measurements of brain amyloid by PET using the ligand 11 C PIB (Pittsburg compound B) were shown to predict a 3-year conversion to AD in a group of patients with amnestic MCI [8].…”