July, 2016

$(A) Significant associations with plasma C22:0 and subsequent FA. (A) Red–yellow indicates higher plasma C22:0 associated with lower FA in white matter regions, while cyan–blue indicates higher plasma C22:0 associated with higher FA. Regional statistics are overlaid onto an FA image from a single subject; results are not corrected for multiple comparisons. (B) ACR FA by mean centered Cer 22:0. (B) Association between FA in ACR and Cer 22:0. The residual FA of ACR was calculated by regressing standardized FA on baseline age, sex, interval between blood draw and DTI, diabetes, BMI, scanner, and ratio of white matter lesions to intracranial volume. This is shown plotted against mean centered Cer 22:0 with a linear fit overlaid. Abbreviations: ACR, anterior corona radiata; ALIC, anterior limb of internal capsule; BMI, body mass index; CG, cingulum of cingulate gyrus; DTI, diffusion tensor imaging; FA, fractional anisotropy; Scc, splenium of corpus callosum; SLF, superior longitudinal fasciculus; SS, sagittal stratum. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)$

Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.

Jul. 31, 2016—Christopher E. Gonzalez, Vijay K. Venkatraman, Yang An, Bennett A. Landman, Christos Davatzikos, Veera Venkata Ratnam Bandaru, Norman J. Haughey, Luigi Ferruci, Michelle M. Mielke, Susan M. Resnick. “Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.” Neurobiology of Aging. July 2016. Volume 43, Pages 156–163 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/27255825 Abstract Sphingolipids...

(A) A standard fully connected neural network where each layer's node is connected to each node from the
previous layer.7 (B) A convolutional neural network connecting a covolutional layer to a pooling layer.

Deep Learning for Brain Tumor Classification

Jul. 1, 2016—Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, Daniel Fabbri. “Deep Learning for Brain Tumor Classification.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Abstract Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is...