Adolescence is a unique period of physical and cognitive development that includes concurrent pubertal changes and sex-based vulnerabilities. While diffusion tensor imaging (DTI) studies show white matter maturation throughout the lifespan, the state of white matter integrity specific to adolescence is not well understood as are the contributions of puberty and sex. We performed whole-brain DTI studies of 114 children, adolescents, and adults to identify age-related changes in white matter integrity that characterize adolescence. A distinct set of regions across the brain were found to have decreasing radial diffusivity across age groups. Region of interest analyses revealed that maturation was attained by adolescence in broadly distributed association and projection fibers, including those supporting cortical and brain stem integration that may underlie known enhancements in reaction time during this period. Maturation after adolescence included association and projection tracts, including prefrontal-striatal connections, known to support top-down executive control of behavior and interhemispheric connectivity. Maturation proceeded in parallel with pubertal changes to the postpubertal stage, suggesting hormonal influences on white matter development. Females showed earlier maturation of white matter integrity compared with males. Together, these findings suggest that white matter connectivity supporting executive control of behavior is still immature in adolescence.

Isocitrate dehydrogenase () mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data. Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming. With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively. We developed a deep learning technique to noninvasively predict genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set. .

Spatial normalization of diffusion tensor images plays a key role in voxel-based analysis of white matter (WM) group differences. Currently, it has been achieved using low-dimensional registration methods in the large majority of clinical studies. This paper aims to motivate the use of high-dimensional normalization approaches by generating evidence of their impact on the findings of such studies. Using an ongoing amyotrophic lateral sclerosis (ALS) study, we evaluated three normalization methods representing the current range of available approaches: low-dimensional normalization using the fractional anisotropy (FA), high-dimensional normalization using the FA, and high-dimensional normalization using full tensor information. Each method was assessed in terms of its ability to detect significant differences between ALS patients and controls. Our findings suggest that inadequate normalization with low-dimensional approaches can result in insufficient removal of shape differences which in turn can confound FA differences in a complex manner, and that utilizing high-dimensional normalization can both significantly minimize the confounding effect of shape differences to FA differences and provide a more complete description of WM differences in terms of both size and tissue architecture differences. We also found that high-dimensional approaches, by leveraging full tensor features instead of tensor-derived indices, can further improve the alignment of WM tracts.

Intraaxial brain masses are a significant health problem and present several imaging challenges. The role of imaging is no longer limited to merely providing anatomic details. Sophisticated magnetic resonance (MR) imaging techniques allow insight into such processes as the freedom of water molecule movement, the microvascular integrity and hemodynamic characteristics, and the chemical makeup of certain compounds of masses. The role of the most commonly used advanced MR imaging techniques-perfusion imaging, diffusion-weighted imaging, and MR spectroscopy-in the diagnosis and classification of the most common intraaxial brain tumors in adults is explored. These lesions include primary neoplasms (high- and low-grade), secondary (meta-static) neoplasms, lymphoma, tumefactive demyelinating lesions, abscesses, and encephalitis. Application of a diagnostic algorithm that integrates advanced MR imaging features with conventional MR imaging findings may help the practicing radiologist make a more specific diagnosis for an intraaxial tumor.

Background Significant heterogeneity in clinical features of frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS) cases with the pathogenic C9orf72 expansion (C9P) have been described. To clarify this issue, we compared a large C9P cohort with carefully matched non-expansion (C9N) cases with a known or highly-suspected underlying TDP-43 proteinopathy. Methods A retrospective-cohort study using available cross-sectional and longitudinal clinical and neuropsychological data, MRI voxel-based morphometry (VBM) and neuropathological assessment from 64 C9P cases (ALS=31, FTLD=33) and 79 C9N cases (ALS=36, FTLD=43). Results C9P cases had an earlier age of onset (p=0.047), and in the subset of deceased patients, an earlier age of death (p=0.014) than C9N. C9P had more rapid progression than C9N: C9P ALS cases had a shortened survival (2.6±0.3 years) compared to C9N ALS (3.8±0.4 years; log-rankλ2=4.183,p=0.041), and C9P FTLD showed a significantly greater annualized rate of decline in letter fluency (4.5±1.3words/year) than C9N FTLD (1.4±0.8words/year, p=0.023). VBM revealed greater atrophy in the right fronto-insular, thalamus, cerebellum and bilateral parietal regions for C9P FTLD relative to C9N FTLD, and regression analysis related verbal fluency scores to atrophy in frontal and parietal regions. Neuropathologic analysis found greater neuronal loss in the mid-frontal cortex in C9P FTLD, and mid-frontal cortex TDP-43 inclusion severity correlated with poor letter fluency performance. Conclusions C9P cases may have a shorter survival in ALS and more rapid rate of cognitive decline related to frontal and parietal disease in FTLD. C9orf72 genotyping may provide useful prognostic and diagnostic clinical information for ALS and FTLD patients.