Faculty Bibliography

OBJECTIVE/UNASSIGNED:Autism spectrum disorder (ASD) is heritable, and younger siblings of ASD probands are at higher likelihood of developing ASD themselves. Prospective MRI studies of siblings report that atypical brain development precedes ASD diagnosis, although the link between brain maturation and genetic factors is unclear. Given that familial recurrence of ASD is predicted by higher levels of ASD traits in the proband, the authors investigated associations between proband ASD traits and brain development among younger siblings. METHODS/UNASSIGNED:In a sample of 384 proband-sibling pairs (89 pairs concordant for ASD), the authors examined associations between proband ASD traits and sibling brain development at 6, 12, and 24 months in key MRI phenotypes: total cerebral volume, cortical surface area, extra-axial cerebrospinal fluid, occipital cortical surface area, and splenium white matter microstructure. Results from primary analyses led the authors to implement a data-driven approach using functional connectivity MRI at 6 months. RESULTS/UNASSIGNED:Greater levels of proband ASD traits were associated with larger total cerebral volume and surface area and larger surface area and reduced white matter integrity in components of the visual system in siblings who developed ASD. This aligned with weaker functional connectivity between several networks and the visual system among all siblings during infancy. CONCLUSIONS/UNASSIGNED:The findings provide evidence that specific early brain MRI phenotypes of ASD reflect quantitative variation in familial ASD traits. Multimodal anatomical and functional convergence on cortical regions, fiber pathways, and functional networks involved in visual processing suggest that inherited liability has a role in shaping the prodromal development of visual circuitry in ASD.

Deep networks are now ubiquitous in large-scale multi-center imaging studies. However, the direct aggregation of images across sites is contraindicated for downstream statistical and deep learning-based image analysis due to inconsistent contrast, resolution, and noise. To this end, in the absence of paired data, variations of Cycle-consistent Generative Adversarial Networks have been used to harmonize image sets between a source and target domain. Importantly, these methods are prone to instability, contrast inversion, intractable manipulation of pathology, and steganographic mappings which limit their reliable adoption in real-world medical imaging. In this work, based on an underlying assumption that morphological shape is consistent across imaging sites, we propose a segmentation-renormalized image translation framework to reduce inter-scanner heterogeneity while preserving anatomical layout. We replace the affine transformations used in the normalization layers within generative networks with trainable scale and shift parameters conditioned on jointly learned anatomical segmentation embeddings to modulate features at every level of translation. We evaluate our methodologies against recent baselines across several imaging modalities (T1w MRI, FLAIR MRI, and OCT) on datasets with and without lesions. Segmentation-renormalization for translation GANs yields superior image harmonization as quantified by Inception distances, demonstrates improved downstream utility via post-hoc segmentation accuracy, and improved robustness to translation perturbation and self-adversarial attacks.

Autism Spectrum Disorder (ASD) is a phenotypically and etiologically heterogeneous developmental disorder typically diagnosed around 4 years of age. The development of biomarkers to help in earlier, presymptomatic diagnosis could facilitate earlier identification and therefore earlier intervention and may lead to better outcomes, as well as providing information to help better understand the underlying mechanisms of ASD. In this study, magnetic resonance imaging (MRI) scans of infants at high familial risk, from the Infant Brain Imaging Study (IBIS), at 6, 12 and 24 months of age were included in a morphological analysis, fitting a mixed-effects model to Tensor Based Morphometry (TBM) results to obtain voxel-wise growth trajectories. Subjects were grouped by familial risk and clinical diagnosis at 2 years of age. Several regions, including the posterior cingulate gyrus, the cingulum, the fusiform gyrus, and the precentral gyrus, showed a significant effect for the interaction of group and age associated with ASD, either as an increased or a decreased growth rate of the cerebrum. In general, our results showed increased growth rate within white matter with decreased growth rate found mostly in grey matter. Overall, the regions showing increased growth rate were larger and more numerous than those with decreased growth rate. These results detail, at the voxel level, differences in brain growth trajectories in ASD during the first years of life, previously reported in terms of overall brain volume and surface area.

The corpus callosum (CC) is the largest connective pathway in the human brain, linking cerebral hemispheres. There is longstanding debate in the scientific literature whether sex differences are evident in this structure, with many studies indicating the structure is larger in females. However, there are few data pertaining to this issue in infancy, during which time the most rapid developmental changes to the CC occur. In this study, we examined longitudinal brain imaging data collected from 104 infants at ages 6, 12, and 24 months. We identified sex differences in brain-size adjusted CC area and thickness characterized by a steeper rate of growth in males versus females from ages 6-24 months. In contrast to studies of older children and adults, CC size was larger for male compared to female infants. Based on diffusion tensor imaging data, we found that CC thickness is significantly associated with underlying microstructural organization. However, we observed no sex differences in the association between microstructure and thickness, suggesting that the role of factors such as axon density and/or myelination in determining CC size is generally equivalent between sexes. Finally, we found that CC length was negatively associated with nonverbal ability among females.

Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development.

BACKGROUND:Quantitative MRI can detect early changes in cartilage biochemical components, but its routine clinical implementation is challenging. PURPOSE/OBJECTIVE:along radial sections of the hip for accurate and reproducible multiparametric quantitative cartilage assessment in a clinically feasible scan time. STUDY TYPE/METHODS:Reproducibility, technical validation. SUBJECTS/PHANTOM/UNASSIGNED:A seven-compartment phantom and three healthy volunteers. FIELD STRENGTH/SEQUENCE/UNASSIGNED:at 3 T was developed. Automatic positioning and semiautomatic cartilage segmentation were implemented to improve consistency and simplify workflow. ASSESSMENT/RESULTS:Intra- and interscanner variability of our technique was assessed over multiple scans on three different MR scanners. STATISTICAL TESTS/UNASSIGNED:over six radial slices was calculated. Restricted maximum likelihood estimation of variance components was used to estimate intrasubject variances reflecting variation between results from the two scans using the same scanner (intrascanner variance) and variation among results from the three scanners (interscanner variance). RESULTS:. DATA CONCLUSION/UNASSIGNED:Our method, which includes slice positioning, model-based parameter estimation, and cartilage segmentation, is highly reproducible. It could enable employing quantitative hip cartilage evaluation for longitudinal and multicenter studies. LEVEL OF EVIDENCE/METHODS:1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Autofluorescence is the emission of light by naturally occurring tissue components on the absorption of incident light. Autofluorescence within the eye is associated with several disorders, such as Age-related Macular Degeneration (AMD) which is a leading cause of central vision loss. Its pathogenesis is incompletely understood, but endogenous fluorophores in retinal tissue might play a role. Hyperspectral fluorescence microscopy of ex-vivo retinal tissue can be used to determine the fluorescence emission spectra of these fluorophores. Comparisons of spectra in healthy and diseased tissues can provide important insights into the pathogenesis of AMD. However, the spectrum from each pixel of the hyperspectral image is a superposition of spectra from multiple overlapping tissue components. As spectra cannot be negative, there is a need for a non-negative blind source separation model to isolate individual spectra. We propose a tensor formulation by leveraging multiple excitation wavelengths to excite the tissue sample. Arranging images from different excitation wavelengths as a tensor, a non-negative tensor decomposition can be performed to recover a provably unique low-rank model with factors representing emission and excitation spectra of these materials and corresponding abundance maps of autofluorescent substances in the tissue sample. We iteratively impute missing values common in fluorescence measurements using Expectation-Maximization and use L₂ regularization to reduce ill-posedness. Further, we present a framework for performing group hypothesis testing on hyperspectral images, finding significant differences in spectra between AMD and control groups in the peripheral macula. In the absence of ground truth, i.e. molecular identification of fluorophores, we provide a rigorous validation of chosen methods on both synthetic and real images where fluorescence spectra are known. These methodologies can be applied to the study of other pathologies presenting autofluorescence that can be captured by hyperspectral imaging.