PURPOSE: To investigate the effects of stochastic resonance on lateralization of auditory working memory regions, and also to examine the brain-behavior relationship during stochastic resonance.
STUDY TYPE: Cross-sectional.
POPULATION/SUBJECTS: Forty healthy young adults (18-24 years old).
FIELD STRENGTH/SEQUENCE: 3.0T, T1 , and T2 *-weighted imaging.
ASSESSMENT: The auditory working memory performance was assessed using a backward recall task. Functional magnetic resonance imaging (fMRI) was used to measure brain activity during task performance. Functional MRI data were analyzed using SPM12 and WFU PickAtlas.
STATISTICAL TESTS: One-way independent analyses of variance (ANOVA) were conducted on the behavioral and functional data to examine the main effect of noise level on performance (P
METHODS: PubMed, Web of Science, and Scopus databases were searched for studies assessing the diagnostic value of 2D-SWE for thyroid malignancy risk stratification published until December 2016. The retrieved titles and abstracts were screened and evaluated according to the predefined inclusion and exclusion criteria. Methodological quality of the studies was assessed using the Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool. Extracted 2D-SWE diagnostic performance data were meta-analyzed to assess the summary sensitivity, specificity, and area under the receiver operating characteristic curve.
RESULTS: After stepwise review, 14 studies in which 2D-SWE was used to evaluate 2851 thyroid nodules (1092 malignant, 1759 benign) from 2139 patients were selected for the current study. Study quality on QUADAS-2 assessment was moderate to high. The summary sensitivity, specificity and area under the receiver operating characteristic curve of 2D-SWE for differential diagnosis of benign and malignant thyroid nodules were 0.66 (95% confidence interval [CI]: 0.64-0.69), 0.78 (CI: 0.76-0.80), and 0.851 (Q* = 0.85), respectively. The pooled diagnostic odds ratio, negative likelihood ratio, and positive likelihood ratio were 12.73 (CI: 8.80-18.43), 0.31 (CI: 0.22-0.44), and 3.87 (CI: 2.83-5.29), respectively.
CONCLUSION: Diagnostic performance of quantitative 2D-SWE for malignancy risk stratification of thyroid nodules is suboptimal with mediocre sensitivity and specificity, contrary to earlier reports of excellence.
METHODS: This protocol describes a non-interventional case control study. The AD and MCI patients and the healthy elderly controls will undergo multi-parametric MRI. The protocol consists of sMRI, fMRI, DTI, and single-voxel proton MRS sequences. An eco-planar imaging (EPI) will be used to perform resting-state fMRI sequence. The structural images will be analysed using Computational Anatomy Toolbox-12, functional images will be analysed using Statistical Parametric Mapping-12, DPABI (Data Processing & Analysis for Brain Imaging), and Conn software, while DTI and 1H-MRS will be analysed using the FSL (FMRIB's Software Library) and Tarquin respectively. Correlation of the MRI results and the data acquired from the APOE genotyping, neuropsychological evaluations (i.e. Montreal Cognitive Assessment [MoCA], and Mini-Mental State Examination [MMSE] scores) will be performed. The imaging results will also be correlated with the sociodemographic factors. The diagnosis of AD and MCI will be standardized and based on the DSM-5 criteria and the neuropsychological scores.
DISCUSSION: The combination of sMRI, fMRI, DTI, and MRS sequences can provide information on the anatomical and functional changes in the brain such as regional grey matter volume atrophy, impaired functional connectivity among brain regions, and decreased metabolite levels specifically at the posterior cingulate cortex/precuneus. The combination of multiparametric MRI sequences can be used to stratify the management of MCI and AD patients. Accurate imaging can decide on the frequency of follow-up at memory clinics and select classifiers for machine learning that may aid in the disease identification and prognostication. Reliable and consistent quantification, using standardised protocols, are crucial to establish an optimal diagnostic capability in the early detection of Alzheimer's disease.