Faculty Bibliography

PURPOSE/OBJECTIVE:To evaluate fetal cerebral venous blood oxygenation, Yv, using principles of MR susceptometry. MATERIALS AND METHODS/METHODS:A cohort of 19 pregnant subjects, with a mean gestational age of 31.6 ± 4.7 weeks were imaged using a modified susceptibility-weighted imaging (SWI) sequence. Data quality was first assessed for feasibility of oxygen saturation measurement, and data from five subjects (mean ± std gestational age of 33.7 ± 3.6 weeks) were then chosen for further quantitative analysis. SWI phase in the superior sagittal sinus was used to evaluate oxygen saturation using the principles of MR susceptometry. Systematic error in the measured Y(v) values was studied through simulations. RESULTS:Simulations showed that the systematic error in Yv depended upon the assumed angle of the vessel, θ, relative to the main magnetic field and the error in that vessel angle δθ. For the typical vessel angle of θ = 30° encountered in the fetal data analyzed, a δθ as large as ±20° led to an absolute error, δYv, of less than 11%. The measured mean oxygen saturation across the five fetuses was 66% ± 9.4%. This average cerebral venous blood oxygenation value is in close agreement with values in the published literature. CONCLUSION/CONCLUSIONS:We have reported the first in vivo measurement of human fetal cerebral venous oxygen saturation using MRI.

The target article argues that developmental processes are key to understanding the mirror neuron system, yet neglects several bodies of developmental research that are informative for doing so. Infants' actions and action understanding are structured by goals, and the former lends structure to the latter. Evaluating the origins and functions of mirror neurons depends on integrating investigations of neural, social-cognitive and motor development.

Recent progress in neurobiology and genetics is beginning to revolutionise our thinking about the developmental origins of children's mental health problems. Such advances, for instance in relation to neural plasticity and programming, and epigenetics, are moving us away from reductionist models of development and motivating a new enthusiasm to incorporate social factors within biological models of developmental psychopathology. As Burt (2014)(1) convincingly argues in the current issue of the JC

Medical imaging studies increasingly use longitudinal images of individual subjects in order to follow-up changes due to development, degeneration, disease progression or efficacy of therapeutic intervention. Repeated image data of individuals are highly correlated, and the strong causality of information over time lead to the development of procedures for joint segmentation of the series of scans, called 4D segmentation. A main aim was improved consistency of quantitative analysis, most often solved via patient-specific atlases. Challenging open problems are contrast changes and occurance of subclasses within tissue as observed in multimodal MRI of infant development, neurodegeneration and disease. This paper proposes a new 4D segmentation framework that enforces continuous dynamic changes of tissue contrast patterns over time as observed in such data. Moreover, our model includes the capability to segment different contrast patterns within a specific tissue class, for example as seen in myelinated and unmyelinated white matter regions in early brain development. Proof of concept is shown with validation on synthetic image data and with 4D segmentation of longitudinal, multimodal pediatric MRI taken at 6, 12 and 24 months of age, but the methodology is generic w.r.t. different application domains using serial imaging.

Post-acquisition motion correction is widely performed in diffusion-weighted imaging (DWI) to guarantee voxel-wise correspondence between DWIs. Whereas this is primarily motivated to save as many scans as possible if corrupted by motion, users do not fully understand the consequences of different types of interpolation schemes on the final analysis. Nonetheless, interpolation might increase the partial volume effect while not preserving the volume of the diffusion profile, whereas excluding poor DWIs may affect the ability to resolve crossing fibers especially with small separation angles. In this paper, we investigate the effect of interpolating diffusion measurements as well as the elimination of bad directions on the reconstructed fiber orientation diffusion functions and on the estimated fiber orientations. We demonstrate such an effect on synthetic and real HARDI datasets. Our experiments demonstrate that the effect of interpolation is more significant with small fibers separation angles where the exclusion of motion-corrupted directions decreases the ability to resolve such crossing fibers.

Temporal modeling frameworks often operate on scalar variables by summarizing data at initial stages as statistical summaries of the underlying distributions. For instance, DTI analysis often employs summary statistics, like mean, for regions of interest and properties along fiber tracts for population studies and hypothesis testing. This reduction via discarding of variability information may introduce significant errors which propagate through the procedures. We propose a novel framework which uses distribution-valued variables to retain and utilize the local variability information. Classic linear regression is adapted to employ these variables for model estimation. The increased stability and reliability of our proposed method when compared with regression using single-valued statistical summaries, is demonstrated in a validation experiment with synthetic data. Our driving application is the modeling of age-related changes along DTI white matter tracts. Results are shown for the spatiotemporal population trajectory of genu tract estimated from 45 healthy infants and compared with a Krabbe's patient.

4D pathological anatomy modeling is key to understanding complex pathological brain images. It is a challenging problem due to the difficulties in detecting multiple appearing and disappearing lesions across time points and estimating dynamic changes and deformations between them. We propose a novel semi-supervised method, called 4D active cut, for lesion recognition and deformation estimation. Existing interactive segmentation methods passively wait for user to refine the segmentations which is a difficult task in 3D images that change over time. 4D active cut instead actively selects candidate regions for querying the user, and obtains the most informative user feedback. A user simply answers 'yes' or 'no' to a candidate object without having to refine the segmentation slice by slice. Compared to single-object detection of the existing methods, our method also detects multiple lesions with spatial coherence using Markov random fields constraints. Results show improvement on the lesion detection, which subsequently improves deformation estimation.

A variety of regression schemes have been proposed on images or shapes, although available methods do not handle them jointly. In this paper, we present a framework for joint image and shape regression which incorporates images as well as anatomical shape information in a consistent manner. Evolution is described by a generative model that is the analog of linear regression, which is fully characterized by baseline images and shapes (intercept) and initial momenta vectors (slope). Further, our framework adopts a control point parameterization of deformations, where the dimensionality of the deformation is determined by the complexity of anatomical changes in time rather than the sampling of the image and/or the geometric data. We derive a gradient descent algorithm which simultaneously estimates baseline images and shapes, location of control points, and momenta. Experiments on real medical data demonstrate that our framework effectively combines image and shape information, resulting in improved modeling of 4D (3D space + time) trajectories.

Touch plays a crucial role in social-emotional development. Slow, gentle touch applied to hairy skin is processed by C-tactile (CT) nerve fibers. Furthermore, 'social brain' regions, such as the posterior superior temporal sulcus (pSTS) have been shown to process CT-targeted touch. Research on the development of these neural mechanisms is scant, yet such knowledge may inform our understanding of the critical role of touch in development and its dysfunction in disorders involving sensory issues, such as autism. The aim of this study was to validate the ability of functional near-infrared spectroscopy (fNIRS), an imaging technique well-suited for use with infants, to measure temporal lobe responses to CT-targeted touch. Healthy adults received brushing to the right forearm (CT) and palm (non-CT) separately, in a block design procedure. We found significant activation in right pSTS and dorsolateral prefrontal cortex to arm > palm touch. In addition, individual differences in autistic traits were related to the magnitude of peak activation within pSTS. These findings demonstrate that fNIRS can detect brain responses to CT-targeted touch and lay the foundation for future work with infant populations that will characterize the development of brain mechanisms for processing CT-targeted touch in typical and atypical populations.

Understanding the growth patterns of the early brain is crucial to the study of neuro-development. In the early stages of brain growth, a rapid sequence of biophysical and chemical processes take place. A crucial component of these processes, known as myelination, consists of the formation of a myelin sheath around a nerve fiber, enabling the effective transmission of neural impulses. As the brain undergoes myelination, there is a subsequent change in the contrast between gray matter and white matter as observed in MR scans. In this work, gray-white matter contrast is proposed as an effective measure of appearance which is relatively invariant to location, scanner type, and scanning conditions. To validate this, contrast is computed over various cortical regions for an adult human phantom. MR (Magnetic Resonance) images of the phantom were repeatedly generated using different scanners, and at different locations. Contrast displays less variability over changing conditions of scan compared to intensity-based measures, demonstrating that it is less dependent than intensity on external factors. Additionally, contrast is used to analyze longitudinal MR scans of the early brain, belonging to healthy controls and Down's Syndrome (DS) patients. Kernel regression is used to model subject-specific trajectories of contrast changing with time. Trajectories of contrast changing with time, as well as time-based biomarkers extracted from contrast modeling, show large differences between groups. The preliminary applications of contrast based analysis indicate its future potential to reveal new information not covered by conventional volumetric or deformation-based analysis, particularly for distinguishing between normal and abnormal growth patterns.