Faculty Bibliography

Resting state functional magnetic resonance imaging (rs-fMRI) in infants enables important studies of functional brain organization early in human development. However, rs-fMRI in infants has universally been obtained during sleep to reduce participant motion artifact, raising the question of whether differences in functional organization between awake adults and sleeping infants that are commonly attributed to development may instead derive, at least in part, from sleep. This question is especially important as rs-fMRI differences in adult wake vs. sleep are well documented. To investigate this question, we compared functional connectivity and BOLD signal propagation patterns in 6, 12, and 24 month old sleeping infants with patterns in adult wakefulness and non-REM sleep. We find that important functional connectivity features seen during infant sleep closely resemble those seen during adult sleep, including reduced default mode network functional connectivity. However, we also find differences between infant and adult sleep, especially in thalamic BOLD signal propagation patterns. These findings highlight the importance of considering sleep state when drawing developmental inferences in infant rs-fMRI.

Purpose: To image and analyze individual RPE melanosomes (M), lipofuscin (LF), and melanolipofuscin (MLF) granules using high-resolution structured illumination microscopy (hrSIM) and confocal multispectral laser scanning microscopy (cmLSM). Methods: Human donor RPE-flatmounts (n=35; normal macular status: 9<51yrs, 9>80yrs; age-related macular degeneration (AMD): 17) were scanned apical to basal through RPE cells at the fovea, perifovea, and near periphery using hrSIM (Zeiss Elyra.S1; ex488 nm; em>510 nm; 100 nm step size) and cmLSM (Zeiss LSM780; ex488 nm; em 490-695 nm; 390 nm step size; 8.9 nm spectral channel width). The hrSIM and lower-resolution cmLSM images were co-registered by linear 3D registration and choice of mutual information as the image match criterion (PMID16545965). This results in a 1:1 mapping between the single channel hrSIM and multichannel cmLSM data. Individual granules were segmented from the hrSIM data by expert-guided 3D level-set segmentation. Via the hrSIM-cmLSM mapping, the spectra of individual granules can be extracted for quantitative analysis. M, LF, MLF granules/cell were also counted using a custom FIJI plugin. Results: HrSIM imaging and segmentation enables clear delineation and identification of M, LF, and MLF granules (Fig. A,B). Individual granules can be tracked in the z-direction, and size, shape, dimensions, and intracellular position can be monitored. Each cell contains several hundred granules. A cushion of M localizes apically, while LF/MLF prefer basolateral accumulation. Software-assisted mapping of corresponding z-sections (hrSIM/cmLSM) for spectral characterization (Fig. C,D) demonstrates spectral variability among granules. Conclusions: With the combination of hrSIM and cmLSM imaging, individual autofluorescent RPE granules can be identified, localized in three-dimensions, and spectrally analyzed. The examination of spectral characteristics and changes related to intracellular and tissue localization, age, and disease status, on an individual granule basis, might reveal LF/MLF metabolism, and help elucidate LF's role in human RPE physiology. Ongoing studies are examining RPE granule properties in aging and AMD

Glaucoma, a progressive and degenerative disease of the optic nerve, is the second leading cause of blindness worldwide. Mechanical deformation of the lamina cribrosa (LC) under high intraocular pressure (IOP) can lead to axonal death of optic nerve fibers. To explore the effect of pressure on the LC, we utilize an experimental setup where longitudinal 3D optical coherence tomography (OCT) images are acquired at different levels of IOP administered via a well-controlled external force. Structural changes are measured via image deformations which map all observed images simultaneously into a common coordinate space. These deformations encode local patterns of structural and volume change across the image sequence, resulting in quantification of the spatiotemporal deformation pattern of the LC due to variation of pressure. We also describe a 3D segmentation algorithm to restrict our deformation analysis separately to the beams or pores of the LC. A single case study demonstrates the potential of the proposed methodology for non-invasive in-vivo analysis of LC dynamics in individual subjects

The increased number of challenges for comparative evaluation of biomedical image analysis procedures clearly reflects a need for unbiased assessment of the state-of-the-art methodological advances. Moreover, the ultimate translation of novel image analysis procedures to the clinic requires rigorous validation and evaluation of alternative schemes, a task that is best outsourced to the international research community. We commonly see an increase of the number of metrics to be used in parallel, reflecting alternative ways to measure similarity. Since different measures come with different scales and distributions, these are often normalized or converted into an individual rank ordering, leaving the problem of combining the set of multiple rankings into a final score. Proposed solutions are averaging or accumulation of rankings, raising the question if different metrics are to be treated the same or if all metrics would be needed to assess closeness to truth. We address this issue with a data-driven method for automatic estimation of weights for a set of metrics based on unsupervised rank aggregation. Our method requires no normalization procedures and makes no assumptions about metric distributions. We explore the sensitivity of metrics to small changes in input data with an iterative perturbation scheme, to prioritize the contribution of the most robust metrics in the overall ranking. We show on real anatomical data that our weighting scheme can dramatically change the ranking

The brain undergoes rapid development during early childhood as a series of biophysical and chemical processes occur, which can be observed in magnetic resonance (MR) images as a change over time of white matter intensity relative to gray matter. Such a contrast change manifests in specific patterns in different imaging modalities, suggesting that brain maturation is encoded by appearance changes in multi-modal MRI. In this paper, we explore the patterns of early brain growth encoded by multi-modal contrast changes in a longitudinal study of children. For a given modality, contrast is measured by comparing histograms of intensity distributions between white and gray matter. Multivariate non-linear mixed effects (NLME) modeling provides subject-specific as well as population growth trajectories which accounts for contrast from multiple modalities. The multivariate NLME procedure and resulting non-linear contrast functions enable the study of maturation in various regions of interest. Our analysis of several brain regions in a study of 70 healthy children reveals a posterior to anterior pattern of timing of maturation in the major lobes of the cerebral cortex, with posterior regions maturing earlier than anterior regions. Furthermore, we find significant differences between maturation rates between males and females

Modeling subject-specific shape change is one of the most important challenges in longitudinal shape analysis of disease progression. Whereas anatomical change over time can be a function of normal aging; anatomy can also be impacted by disease related degeneration. Shape changes to anatomy may also be affected by external structural changes from neighboring structures, which may cause non-linear pose variations. In this paper, we propose a framework to analyze disease related shape changes by coupling extrinsic modeling of the ambient anatomical space via spatiotemporal deformations with intrinsic shape properties from medial surface analysis. We compare intrinsic shape properties of a subject-specific shape trajectory to a normative 4D shape atlas representing normal aging to separately quantify shape changes related to disease. The spatiotemporal shape modeling establishes inter/intra subject anatomical correspondence, which in turn enables comparisons between subjects and the 4D shape atlas, and also quantitative analysis of disease related shape change. The medial surface analysis captures intrinsic shape properties related to local patterns of deformation. The proposed framework simultaneously models extrinsic longitudinal shape changes in the ambient anatomical space, as well as intrinsic shape properties to give localized measurements of degeneration. Six high risk subjects and six controls are randomly sampled from a Huntington's disease image database for quantitative and qualitative comparison.

Imaging is a crucial tool in medicine and biomedical research. Magnetic resonance imaging (MRI), computational tomography (CT), proton emission tomography (PET), and ultrasound are routinely used not only to diagnose disease but also to plan and guide surgical interventions, track disease progression, measure the response of the body to treatment, and understand how genetic and environmental factors relate to anatomical and functional phenotypes.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social deficits and repetitive behaviors that typically emerge by 24 months of age. To develop effective early interventions that can potentially ameliorate the defining deficits of ASD and improve long-term outcomes, early detection is essential. Using prospective neuroimaging of 59 6-month-old infants with a high familial risk for ASD, we show that functional connectivity magnetic resonance imaging correctly identified which individual children would receive a research clinical best-estimate diagnosis of ASD at 24 months of age. Functional brain connections were defined in 6-month-old infants that correlated with 24-month scores on measures of social behavior, language, motor development, and repetitive behavior, which are all features common to the diagnosis of ASD. A fully cross-validated machine learning algorithm applied at age 6 months had a positive predictive value of 100% [95% confidence interval (CI), 62.9 to 100], correctly predicting 9 of 11 infants who received a diagnosis of ASD at 24 months (sensitivity, 81.8%; 95% CI, 47.8 to 96.8). All 48 6-month-old infants who were not diagnosed with ASD were correctly classified [specificity, 100% (95% CI, 90.8 to 100); negative predictive value, 96.0% (95% CI, 85.1 to 99.3)]. These findings have clinical implications for early risk assessment and the feasibility of developing early preventative interventions for ASD.

BACKGROUND: We previously reported that infants who developed autism spectrum disorder (ASD) had increased cerebrospinal fluid (CSF) in the subarachnoid space (i.e., extra-axial CSF) from 6 to 24 months of age. We attempted to confirm and extend this finding in a larger independent sample. METHODS: A longitudinal magnetic resonance imaging study of infants at risk for ASD was carried out on 343 infants, who underwent neuroimaging at 6, 12, and 24 months. Of these infants, 221 were at high risk for ASD because of an older sibling with ASD, and 122 were at low risk with no family history of ASD. A total of 47 infants were diagnosed with ASD at 24 months and were compared with 174 high-risk and 122 low-risk infants without ASD. RESULTS: Infants who developed ASD had significantly greater extra-axial CSF volume at 6 months compared with both comparison groups without ASD (18% greater than high-risk infants without ASD; Cohen's d = 0.54). Extra-axial CSF volume remained elevated through 24 months (d = 0.46). Infants with more severe autism symptoms had an even greater volume of extra-axial CSF from 6 to 24 months (24% greater at 6 months, d = 0.70; 15% greater at 24 months, d = 0.70). Extra-axial CSF volume at 6 months predicted which high-risk infants would be diagnosed with ASD at 24 months with an overall accuracy of 69% and corresponding 66% sensitivity and 68% specificity, which was fully cross-validated in a separate sample. CONCLUSIONS: This study confirms and extends previous findings that increased extra-axial CSF is detectable at 6 months in high-risk infants who develop ASD. Future studies will address whether this anomaly is a contributing factor to the etiology of ASD or an early risk marker for ASD.