Baxter, Leslie C.

AbstractBackgroundGlioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.MethodsWe collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).ResultsWe collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).ConclusionMRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Objective The objective of this study was to identify brain regions having aberrant pain-induced activation in migraineurs, thereby gaining insight into particular aspects of pain processing that are atypical in migraineurs. Methods Functional magnetic resonance imaging assessed whole brain responses to painful heat in 24 adult episodic migraineurs who were at least 48 hours pain free and 27 healthy controls. Regions differentially activated in migraineurs compared to controls were identified. Activation intensities in these regions were correlated with headache frequency, number of migraine years, and time to next migraine attack. Results Migraineurs had greater pain-induced activation of lentiform nucleus, fusiform gyrus, subthalamic nucleus, hippocampus, middle cingulate cortex, premotor cortex, somatosensory cortex, and dorsolateral prefrontal cortex, and less activation in precentral gyrus and superior temporal gyrus. There were significant correlations between activation strength and headache frequency for middle cingulate ( r = 0.627, p = 0.001), right dorsolateral prefrontal cortex ( r = 0.568, p = 0.004), left fusiform gyrus ( r = 0.487, p = 0.016), left precentral gyrus ( r = 0.415, p = 0.044), and left hippocampus ( r = 0.404, p = 0.050) and with number of migraine years for left fusiform gyrus ( r = 0.425, p = 0.038). There were no significant correlations between activation strength and time to next migraine attack. Conclusions The majority of regions with enhanced pain-induced activation in headache-free migraineurs participate in cognitive aspects of pain perception such as attending to pain and pain memory. Enhanced cognitive pain processing by migraineurs might reflect cerebral hypersensitivity related to high expectations and hypervigilance for pain.

AbstractThe apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database—the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High‐order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor‐based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD. Hum Brain Mapp 35:3903–3918, 2014. © 2014 Wiley Periodicals, Inc.