Baxter, Leslie C.

AbstractThe male preponderance in autism spectrum disorder (ASD) led to the hypothesis that aspects of female biology are protective against ASD. Females with ASD (ASD-F) report more compensatory behaviors (i.e. “camouflaging”) to overcome ASD-related social differences, which may be a mechanism of protection. No studies have examined sex-related brain pathways supporting camouflaging in ASD-F, despite its potential to inform mechanisms underlying the ASD sex bias. We used functional connectivity (FC) to investigate “sex-atypical” and “sex-typical” FC patterns linked to camouflaging in adults with ASD and examined multimodal coherence of findings via structural connectometry. Exploratory associations with cognitive/emotional functioning examined the adaptive nature of FC patterns. We found (i) “sex-atypical” FC patterns linked to camouflaging in the hypothalamus and precuneus and (ii) “sex-typical” patterns in the right anterior cingulate and anterior parahippocampus. Higher hypothalamic FC with a limbic reward cluster also correlated with better cognitive control/emotion recognition. Structural connectometry validated FC results with consistent brain pathways/effect patterns implicated in ASD-F. In summary, “male-typical” and “female-typical” brain connectivity patterns support camouflaging in ASD-F in circuits implicated in reward, emotion, and memory retrieval. “Sex-atypical” results are consistent with fetal steroidogenic/neuroinflammatory hypotheses. However, female genetics/biology may contribute to “female-typical” patterns implicated in camouflaging.

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disease that affects aging populations. Current MRI techniques are often limited in their sensitivity to underlying neuropathological changes.PurposeTo characterize differences in voxel‐based morphometry (VBM), apparent diffusion coefficient (ADC), and intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) metrics in aging populations. Additionally, to investigate the connection between cognitive assessments and neuroimaging metrics.Study TypeProspective/cross‐sectional.PopulationIn all, 49 subjects, including 13 with AD dementia, 12 with mild cognitive impairment (MCI), and 24 healthy controls (HC).Field Strength/Sequence3T/magnetization‐prepared rapid acquisition gradient echo (MP‐RAGE) and IVIM‐DWIAssessmentAll participants completed a cognitive screening battery prior to MRI. IVIM‐DWI maps (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) were generated from a biexponential fit of diffusion MRI data. VBM was performed on the standard T1‐weighted MP‐RAGE structural images. Group‐wise templates were used to compare across groups.Statistical TestsAnalysis of covariance (ANCOVA) with gender and age as covariates (familywise error [FWE] corrected, post‐hoc comparisons using Bonferroni correction) for group comparisons. Partial‐η2 and Hedges' g were used for effect‐size analysis. Spearman's correlations (false discovery rate [FDR]‐corrected) for the relationship between cognitive scores and imaging.ResultsClusters of significant group‐wise differences were found mainly in the temporal lobe, hippocampus, and amygdala using all VBM and IVIM methods (P < 0.05 FWE). While VBM showed significant changes between MCI and AD groups and between HC and AD groups, no significant clusters were observed between HC and MCI using VBM. ADC and IVIM‐D demonstrated significant changes, at P < 0.05 FWE, between HC and MCI, notably in the amygdala and hippocampus. Several voxel‐based correlations were observed between neuroimaging metrics and cognitive tests within the cognitively impaired groups (P < 0.05 FDR).Data ConclusionThese findings suggest that IVIM‐DWI metrics may be earlier biomarkers for AD‐related changes than VBM. The use of these techniques may provide novel insight into subvoxel neurodegenerative processes.Level of Evidence2Technical Efficacy Stage2 J. MAGN. RESON. IMAGING 2020;52:1811–1826.

Fourteen single-sided deaf listeners fit with an MED-EL cochlear implant (CI) judged the similarity of clean signals presented to their CI and modified signals presented to their normal-hearing ear. The signals to the normal-hearing ear were created by (a) filtering, (b) spectral smearing, (c) changing overall fundamental frequency (F0), (d) F0 contour flattening, (e) changing formant frequencies, (f) altering resonances and ring times to create a metallic sound quality, (g) using a noise vocoder, or (h) using a sine vocoder. The operations could be used singly or in any combination. On a scale of 1 to 10 where 10 was a complete match to the sound of the CI, the mean match score was 8.8. Over half of the matches were 9.0 or higher. The most common alterations to a clean signal were band-pass or low-pass filtering, spectral peak smearing, and F0 contour flattening. On average, 3.4 operations were used to create a match. Upshifts in formant frequencies were implemented most often for electrode insertion angles less than approximately 500°. A relatively small set of operations can produce signals that approximate the sound of the MED-EL CI. There are large individual differences in the combination of operations needed. The sound files in Supplemental Material approximate the sound of the MED-EL CI for patients fit with 28-mm electrode arrays.

Abstract INTRODUCTION Neuroimaging dogma for glioblastoma asserts that hyperintensity on T1Gd MRI reveals the bulk of the tumor, while T2/FLAIR signal indicates edema. However, it is unclear whether this edema results from immune response or increased tumor cells. Further, one significant driver of the known sex differences in glioblastoma may be differences in immune response, due to the X-linkage of many immune genes. Based on this, we hypothesized that assumptions regarding tumor cellularity in T2/FLAIR images should be tailored to the biological sex of the patient. METHODS Using a retrospective cohort of 18 primary glioblastoma patients receiving multiple image-localized biopsies (82 total) and standard MRI, we assessed: distance of biopsy from T1Gd and T2 areas; a pathologist’s score of percent tumor cell density; and an imaging-based invasion metric, D/ρ. This metric is derived from the biomathematical Proliferation-Invasion model of glioma growth, which features two parameters, net growth rate (ρ) and net invasion rate (D). Their ratio D/ρ is related to degree of invasion, and is estimated from volumetric measurements of MRI abnormalities. Additionally, 25 patient-derived xenograft models implanted in females were grown until moribund, at which point brains were excised and stained for DAPI (to show all cells) and Lamin (to highlight tumor cells). Image processing of lamin-stained sections defines contours of intensity correlating with cell density. RESULTS Outside both the T1Gd and T2 region, male patient biopsies had higher tumor cell densities than females. Males also tended to have higher invasion metrics. Although each set derived from different patients, preclinical metrics of invasion were positively correlated with clinical invasion in females but negatively correlated in males. CONCLUSION Our preliminary finding that cell distribution patterns correlate with imaging metrics differently between the sexes supports the hypothesis that the degree of tumor cell density represented on certain MRI sequences may be sex-specific.

Abstract BACKGROUND An important challenge in radiomics research is reproducibility. Images are collected on different image scanners and protocols, which introduces significant variability even for the same type of image across institutions. In the present proof-of-concept study, we address the reproducibility issue by using domain adaptation – an algorithm that transforms the radiomic features of each new patient to align with the distribution of features formed by the patient samples in a training set. METHOD Our dataset included 18 patients in training with a total of 82 biopsy sample. The pathological tumor cell density was available for each sample. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. A Gaussian Process (GP) classifier was built to predict tumor cell density using radiomic features. Another 6 patients were used to test the training model. These patients had a total of 31 biopsy samples. The images of each test patient were purposely normalized using a different approach, i.e., using the CSF instead of the whole brain as the reference. This was to mimic the practical scenario of image source discrepancy between different patients. Domain adaptation was applied to each test patient. RESULTS Among the 18 training patients, the leave-one-patient-out cross validation accuracy is 0.81 AUC, 0.78 sensitivity, and 0.83 specificity. When the trained model was applied to the 6 test patients (purposely normalized using a different approach than that of the training data), the accuracy dramatically reduced to 0.39 AUC, 0.08 sensitivity, and 0.61 specificity. After using domain adaption, the accuracy improved to 0.68 AUC, 0.62 sensitivity, and 0.72 specificity. CONCLUSION We provide candidate enabling tools to address reproducibility in radiomics models by using domain adaption algorithms to account for discrepancy of the images between different patients.

Abstract INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.