Longitudinal Functional Magnetic Resonance Spectroscopy Study in Subjects with Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND: Longitudinal changes of brain metabolites during a functional stimulation are unknown in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) subjects. OBJECTIVE: This study was to evaluate the longitudinal changes of brain metabolites using proton magnetic resonance spectroscopy (1H MRS) in response to treatment during a memory task in the subjects of cognitive normal (CN), aMCI, and AD. METHODS: We acquired functional magnetic resonance spectroscopy (fMRS) data from 28 CN elderly, 16 aMCI and 12 AD subjects during a face-name association task. We measured fMRS metabolite ratios over 24 months in the 8-month apart, determined the temporal changes of the metabolites, and evaluated the differences among the three groups under the three different conditions (base, novel, repeat). RESULTS: The results of comparisons for the three subject groups and the three-time points showed that tNAA/tCho and tCr/tCho were statistically significant among the three subject groups in any of the three conditions. The dynamic temporal change measurements for the metabolites for each condition showed that Glx/tCho and Glu/tCho levels at the third visit increased significantly compared with in the first visit in the novel condition in the AD group. CONCLUSION: We found declines in tNAA/tCho and tCr/tCho in the aMCI and AD subjects with increasing disease severity, being highest in CN and lowest in AD. The Glx/tCho level increased temporally in the AD subjects after they took an acetylcholine esterase inhibitor. Therefore, Glx may be suitable to demonstrate functional recovery after treatment.

Evaluation and Prediction of Early Alzheimer's Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping.

BACKGROUND: Because Alzheimer's Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations. OBJECTIVE: The objective of this study was to investigate the approach of classification and prediction methods using the Machine Learning (ML)-based Optimized Combination-Feature (OCF) set on Gray Matter Volume (GMV) and Quantitative Susceptibility Mapping (QSM) in the subjects of Cognitive Normal (CN) elderly, Amnestic Mild Cognitive Impairment (aMCI), and mild and moderate AD. MATERIALS AND METHODS: 57 subjects were included: 19 CN, 19 aMCI, and 19 AD with GMV and QSM. Regions-of-Interest (ROIs) were defined at the well-known regions for rich iron contents and amyloid accumulation areas in the AD brain. To differentiate the three subject groups, the Support Vector Machine (SVM) with the three different kernels and with the OCF set was conducted with GMV and QSM values. To predict the aMCI stage, regression-based ML models were performed with the OCF set. The result of prediction was compared with the accuracy of clinical data. RESULTS: In the group classification between CN and aMCI, the highest accuracy was shown using the combination of GMVs (hippocampus and entorhinal cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.94). In the group classification between aMCI and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.93). In the group classification between CN and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.99). To predict aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data was shown the most similar result (RMSE = 0.371) to clinical data (RMSE = 0.319). CONCLUSION: The proposed OCF based ML approach with GMV and QSM was shown the effective performance of the subject group classification and prediction for aMCI stage. Therefore, it can be used as personalized analysis or diagnostic aid program for diagnosis.

Mapping Exchangeable Protons to Monitor Protein Alterations in the Brain of an Alzheimer's Disease Mouse Model by Using MRI.

OBJECTIVE: The study aimed to investigate exchangeable proton signals of Aß proteins of the brains of Alzheimer's disease (AD) model mice by using a chemical exchange-sensitive spin-lock (CESL) MR imaging technique. METHOD: Eight non-transgenic (Tg) mice (5 young and 3 old) and twelve Tg-APPswe/PSdE9 mice (5 young and 7 old) were used in this study. CESL Z-spectra were obtained by using two saturation powers, which were ω1 = 25 Hz with TSL = 3.0 s and ω1 = 500 Hz with TSL = 150 ms, at 71 offsets with uneven intervals between the offset frequencies at Ω = ±7.0 ppm at a 9.4-T animal MRI system. For Zspectrum analyses, regions of interest (ROIs) were drawn in the cortex, hippocampus, and thalamus of both hemispheres. Magnetization transfer ratio asymmetry (MTRasym) curves were obtained from the Zspectra. The Mann-Whitney test was used to compare the MTRasym values between the Tg and non-Tg mice for each offset frequency and for each ROI. RESULTS: The water saturation width of the full Z-spectrum was narrow with the 25-Hz saturation power, but relatively broad with the 500-Hz saturation power. With the 25-Hz CESL saturation power, most of the MTRasym values were negative for 3.5-, 3.0-, 2.0-, and 0.9-ppm offset frequencies and the MTRasym values were significantly different between the control and Tg groups only in the left thalamus region at 3.5 ppm offset (p=0.0487). The MTRasym values were -6% to -7% for both 3.5 and 3.0 ppm, but less than -2% for both 2.0 and 0.9 ppm. With 500-Hz CESL saturation power, all the MTRasym values were positive for the 3.5-, 3.0-, 2.0-, and 0.9-ppm offset frequencies and the MTRasym values were not significantly different between the control and Tg groups at all ROIs and at all offset frequencies. However, a trend towards a significant difference was observed between the control and Tg groups in the right cortex at 3.5 ppm (p=0.0578). The MTRasym values were 6% to 9% for 3.5, 3.0, and 2.0 ppm, but less than 2% for 0.9 ppm. CONCLUSION: In an in-vivo AD model experiment, MTRasym values increased with the high saturation power than with the low saturation power. The MTRasym values were not significantly different, except in the left thalamus region at 3.5 ppm offset. The CESL technique should be further developed to enable its application in the brain of patients with neurodegenerative diseases.