Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodologies to identify a T2DM-related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity. PURPOSE: To develop a machine-learning pipeline to investigate the method's discriminative value between T2DM patients and normal controls, the T2DM-related network pattern, and association of the pattern with cognitive performance/disease severity. STUDY TYPE: A cross-sectional study and prospective longitudinal study with a 2-year time interval. POPULATION: Seventy-three subjects (41 T2DM patients and 32 controls) aged 50-85 years old at baseline, and 42 subjects (19 T2DM and 23 controls) aged 53-88 years old at 2-year follow-up. FIELD STRENGTH/SEQUENCE: 3T pseudocontinuous arterial spin-labeling MRI. ASSESSMENT: Machine-learning-based pipeline (principal component analysis, feature selection, and logistic regression classifier) to generate the T2DM-related network pattern and the individual scores associated with the pattern. STATISTICAL TESTS: Linear regression analysis with gray matter volume and education years as covariates. RESULTS: The machine-learning-based method is superior to the widely used univariate group comparison method with increased test accuracy, test area under the curve, test positive predictive value, adjusted McFadden's R square of 4%, 12%, 7%, and 24%, respectively. The pattern-related individual scores are associated with diabetes severity variables, mobility, and cognitive performance at baseline (P < 0.05, |r| > 0.3). More important, the longitudinal change of individual pattern scores is associated with the longitudinal change of HbA1c (P = 0.0053, r = 0.64), and baseline cholesterol (P = 0.037, r = 0.51). DATA CONCLUSION: The individual perfusion diabetes pattern score is a highly promising perfusion imaging biomarker for tracing the disease progression of individual T2DM patients. Further validation is needed from a larger study. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:834-844.

Pattern of Altered Magnetization Transfer Rate in Alzheimer's Disease.

BACKGROUND: Biomarkers for Alzheimer's disease (AD) are crucial for early diagnosis and treatment monitoring once disease modifying therapies become available. OBJECTIVE: This study aims to quantify the forward magnetization transfer rate (kfor) map from brain tissue water to macromolecular protons and use it to identify the brain regions with abnormal kfor in AD and AD progression. METHODS: From the Cardiovascular Health Study (CHS) cognition study, magnetization transfer imaging (MTI) was acquired at baseline from 63 participants, including 20 normal controls (NC), 18 with mild cognitive impairment (MCI), and 25 AD subjects. Of those, 53 participants completed a follow-up MRI scan and were divided into four groups: 15 stable NC, 12 NC-to-MCI, 12 stable MCI, and 14 MCI/AD-to-AD subjects. kfor maps were compared across NC, MCI, and AD groups at baseline for the cross-sectional study and across four longitudinal groups for the longitudinal study. RESULTS: We found a lower kfor in the frontal gray matter (GM), parietal GM, frontal corona radiata (CR) white matter (WM) tracts, frontal and parietal superior longitudinal fasciculus (SLF) WM tracts in AD relative to both NC and MCI. Further, we observed progressive decreases of kfor in the frontal GM, parietal GM, frontal and parietal CR WM tracts, and parietal SLF WM tracts in stable MCI. In the parietal GM, parietal CR WM tracts, and parietal SLF WM tracts, we found trend differences between MCI/AD-to-AD and stable NC. CONCLUSION: Forward magnetization transfer rate is a promising biomarker for AD diagnosis and progression.

Cerebral Blood Flow Is Associated with Diagnostic Class and Cognitive Decline in Alzheimer's Disease.

BACKGROUND: Reliable cerebral blood flow (CBF) biomarkers using a noninvasive imaging technique are sought to facilitate early diagnosis and intervention in early Alzheimer's disease (AD). OBJECTIVE: We aim to identify brain regions in which CBF values are affected and related to cognitive decline in early AD using a large cohort. METHODS: Perfusion MRIs using continuous arterial spin labeling were acquired at 1.5 T in 58 normal controls (NC), 50 mild cognitive impairments (MCI), and 40 AD subjects from the Cardiovascular Health Study Cognition Study. Regional absolute CBF and normalized CBF (nCBF) values, without and with correction of partial volume effects, were compared across three groups. Association between regional CBF values and Modified Mini-Mental State Examination (3MSE) were investigated by multiple linear regression analyses adjusted for cardiovascular risk factors. RESULTS: After correcting for partial volume effects and cardiovascular risk factors, ADs exhibited decreased nCBF with the strongest reduction in the bilateral posterior cingulate & precuneus region (p < 0.001) compared to NCs, and the strongest reduction in the bilateral superior medial frontal region (p < 0.001) compared to MCIs. MCIs exhibited the strongest nCBF decrease in the left hippocampus and nCBF increase in the right inferior frontal and insular region. The 3MSE scores within the symptomatic subjects were significantly associated with nCBF in the bilateral posterior and middle cingulate and parietal (p < 0.001), bilateral superior medial frontal (p < 0.001), bilateral temporoparietal (p < 0.02), and right hippocampus (p = 0.02) regions. CONCLUSION: Noninvasive perfusion MRI can detect functional changes across diagnostic class and serve as a staging biomarker of cognitive status.