Browsing by Author "Newman, Sharlene"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Anterior Cingulate Cortex Metabolites and White Matter Microstructure: A Multimodal Study of Emergent Alcohol Use Disorder
    (Springer, 2021) Grecco, Gregory G.; Chumin, Evgeny J.; Dzemidzic, Mario; Cheng, Hu; Finn, Peter; Newman, Sharlene; Dydak, Ulrike; Yoder, Karmen K.; Radiology and Imaging Sciences, School of Medicine
    Multimodal imaging is increasingly used to address neuropathology associated with alcohol use disorder (AUD). Few studies have investigated relationships between metabolite concentrations and white matter (WM) integrity; currently, there are no such data in AUD. In this preliminary study, we used complementary neuroimaging techniques, magnetic resonance spectroscopy (MRS), and diffusion weighted imaging (DWI), to study AUD neurophysiology. We tested for relationships between metabolites in the dorsal anterior cingulate cortex (dACC) and adjacent WM microstructure in young adult AUD and control (CON) subjects. Sixteen AUD and fourteen CON underwent whole-brain DWI and MRS of the dACC. Outcomes were dACC metabolites, and diffusion tensor metrics of dACC-adjacent WM. Multiple linear regression terms included WM region, group, and region × group for prediction of dACC metabolites. dACC myo-inositol was positively correlated with axial diffusivity in the left anterior corona radiata (p < 0.0001) in CON but not AUD (group effect: p < 0.001; region × group: p < 0.001; Bonferroni-corrected). In the bilateral anterior corona radiata and right genu of the corpus callosum, glutamate was negatively related to mean diffusivity in AUD, but not CON subjects (all model terms: p < 0.05, uncorrected). In AUD subjects, dACC glutamate was negatively correlated with AUD symptom severity. This is likely the first integrative study of cortical metabolites and WM integrity in young individuals with AUD. Differential relationships between dACC metabolites and adjacent WM tract integrity in AUD could represent early consequences of hazardous drinking, and/or novel biomarkers of early-stage AUD. Additional studies are required to replicate these findings, and to determine the behavioral relevance of these results.
  • Thumbnail Image
    Item
    Denoising diffusion weighted imaging data using convolutional neural networks
    (Public Library of Science, 2022-09-15) Cheng, Hu; Vinci-Booher, Sophia; Wang, Jian; Caron, Bradley; Wen, Qiuting; Newman, Sharlene; Pestilli, Franco; Radiology and Imaging Sciences, School of Medicine
    Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.
  • Thumbnail Image
    Item
    Effects of Alcohol Cues on MRS Glutamate Levels in the Anterior Cingulate
    (Oxford University Press, 2018-05-01) Cheng, Hu; Kellar, Derek; Lake, Allison; Finn, Peter; Rebec, George V.; Dharmadhikari, Shalmali; Dydak, Ulrike; Newman, Sharlene; Radiology and Imaging Sciences, School of Medicine
    Growing evidence suggests that glutamate neurotransmission plays a critical role in alcohol addiction. Cue-induced change of glutamate has been observed in animal studies but never been investigated in humans. This work investigates cue-induced change in forebrain glutamate in individuals with alcohol use disorder (AUD). A total of 35 subjects (17 individuals with AUD and 18 healthy controls) participated in this study. The glutamate concentration was measured with single-voxel 1H-MR spectroscopy at the dorsal anterior cingulate. Two MRS sessions were performed in succession, the first to establish basal glutamate levels and the second to measure the change in response to alcohol cues. The changes in glutamate were quantified for both AUD subjects and controls. A mixed model ANOVA and t-tests were performed for statistical analysis. ANOVA revealed a main effect of cue-induced decrease of glutamate level in the anterior cingulate cortex (ACC). A significant interaction revealed that only AUD subjects showed significant decrease of glutamate in the ACC. There were no significant group differences in the level of basal glutamate. However, a negative correlation was found between the basal glutamate level and the number of drinking days in the past 2 weeks for the AUD subjects. Collectively, our results indicate that glutamate in key areas of the forebrain reward circuit is modulated by alcohol cues in early alcohol dependence.