Allen Q. Ye

Analysis of YFP(J16)-R6/2 reporter mice and postmortem brains reveals early pathology and increased vulnerability of callosal axons in Huntington's disease

Cumulative evidence indicates that the onset and severity of Huntingtons disease (HD) symptoms correlate with connectivity deficits involving specific neuronal populations within cortical and basal ganglia circuits. Brain imaging studies and pathological reports further associated these deficits with alterations in cerebral white matter structure and axonal pathology. However, whether axonopathy represents an early pathogenic event or an epiphenomenon in HD remains unknown, nor is clear the identity of specific neuronal populations affected. To directly evaluate early axonal abnormalities in the context of HD in vivo, we bred transgenic YFP(J16) with R6/2 mice, a widely used HD model. Diffusion tensor imaging and fluorescence microscopy studies revealed a marked degeneration of callosal axons long before the onset of motor symptoms. Accordingly, a significant fraction of YFP-positive cortical neurons in YFP(J16) mice cortex were identified as callosal projection neurons. Callosal axon pathology progressively worsened with age and was influenced by polyglutamine tract length in mutant huntingtin (mhtt). Degenerating axons were dissociated from microscopically visible mhtt aggregates and did not result from loss of cortical neurons. Interestingly, other axonal populations were mildly or not affected, suggesting differential vulnerability to mhtt toxicity. Validating these results, increased vulnerability of callosal axons was documented in the brains of HD patients. Observations here provide a structural basis for the alterations in cerebral white matter structure widely reported in HD patients. Collectively, our data demonstrate a dying-back pattern of degeneration for cortical projection neurons affected in HD, suggesting that axons represent an early and potentially critical target for mhtt toxicity.

Anisotropic fractional diffusion tensor imaging

Traditional diffusion tensor imaging (DTI) maps brain structure by fitting a diffusion model to the magnitude of the electrical signal acquired in magnetic resonance imaging (MRI). Fractional DTI employs anomalous diffusion models to obtain a better fit to real MRI data, which can exhibit anomalous diffusion in both time and space. In this paper, we describe the challenge of developing and employing anisotropic fractional diffusion models for DTI. Since anisotropy is clearly present in the three-dimensional MRI signal response, such models hold great promise for improving brain imaging. We then propose some candidate models, based on stochastic theory.

Ex vivo diffusion tensor MRI reflects microscopic structural remodeling associated with aging and disease progression in normal and cardiomyopathic Syrian hamsters

Dilated cardiomyopathy (DCM) is a major cause of mortality and morbidity in cardiac patients. Aging is often an ignored etiology of pathological conditions. Quantification of DCM and aging associated cardiac structural remodeling is important in guiding and evaluating therapeutic interventions. Diffusion tensor magnetic resonance imaging (DTMRI) has recently been used for nondestructive characterization of three‐dimensional myofiber structure. In this study, we explored the potential of DTMRI in delineating microscopic structural remodeling in aging and DCM hearts. Six month (n = 10) and nine month old (n = 11) DCM (TO‐2) hamsters and their age‐matched controls (F1β) were characterized. Both aging and DCM hearts showed increased diffusivity and decreased diffusion anisotropy. DTMRI images of DCM hearts also revealed a subgroup of imaging pixels characterized by decreased radial diffusivity and increased FA. The location of these pixels showed qualitative agreement with regions of calcium deposition determined by X‐ray CT imaging. Histological analysis confirmed expanded extracellular space in aging and DCM hearts as well as substantial calcium deposition in DCM hearts. These results suggest that DTMRI may provide a noninvasive technique to delineate structural remodeling associated with aging and DCM progression at the tissue and cellular level without the use of an exogenous contrast agent. Copyright

Measuring embeddedness: Hierarchical scale-dependent information exchange efficiency of the human brain connectome

This article presents a novel approach for understanding information exchange efficiency and its decay across hierarchies of modularity, from local to global, of the structural human brain connectome. Magnetic resonance imaging techniques have allowed us to study the human brain connectivity as a graph, which can then be analyzed using a graph‐theoretical approach. Collectively termed brain connectomics, these sophisticated mathematical techniques have revealed that the brain connectome, like many networks, is highly modular and brain regions can thus be organized into communities or modules. Here, using tractography‐informed structural connectomes from 46 normal healthy human subjects, we constructed the hierarchical modularity of the structural connectome using bifurcating dendrograms. Moving from fine to coarse (i.e., local to global) up the connectomes hierarchy, we computed the rate of decay of a new metric that hierarchically preferentially weighs the information exchange between two nodes in the same module. By computing “embeddedness”‐the ratio between nodal efficiency and this decay rate, one could thus probe the relative scale‐invariant information exchange efficiency of the human brain. Results suggest that regions that exhibit high embeddedness are those that comprise the limbic system, the default mode network, and the subcortical nuclei. This supports the presence of near‐decomposability overall yet relative embeddedness in select areas of the brain. The areas we identified as highly embedded are varied in function but are arguably linked in the evolutionary role they play in memory, emotion and behavior. Hum Brain Mapp 36:3653–3665, 2015.

The intrinsic geometry of the human brain connectome

Abstract This paper describes novel methods for constructing the intrinsic geometry of the human brain connectome using dimensionality-reduction techniques. We posit that the high-dimensional, complex geometry that represents this intrinsic topology can be mathematically embedded into lower dimensions using coupling patterns encoded in the corresponding brain connectivity graphs. We tested both linear and nonlinear dimensionality-reduction techniques using the diffusion-weighted structural connectome data acquired from a sample of healthy subjects. Results supported the nonlinearity of brain connectivity data, as linear reduction techniques such as the multidimensional scaling yielded inferior lower-dimensional embeddings. To further validate our results, we demonstrated that for tractography-derived structural connectome more influential regions such as rich-club members of the brain are more centrally mapped or embedded. Further, abnormal brain connectivity can be visually understood by inspecting the altered geometry of these three-dimensional (3D) embeddings that represent the topology of the human brain, as illustrated using simulated lesion studies of both targeted and random removal. Last, in order to visualize brain’s intrinsic topology we have developed software that is compatible with virtual reality technologies, thus allowing researchers to collaboratively and interactively explore and manipulate brain connectome data.

BRAINtrinsic: A Virtual Reality-Compatible Tool for Exploring Intrinsic Topologies of the Human Brain Connectome

Thanks to advances in non-invasive technologies such as functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), highly-detailed maps of brain structure and function can now be collected. In this context, brain connectomics have emerged as a fast growing field that aims at understanding these comprehensive maps of brain connectivity using sophisticated computational models. In this paper we present BRAINtrinsic, an innovative web-based 3D visual analytics tool that allows users to intuitively and iteratively interact with connectome data. Moreover, BRAINtrinsic implements a novel visualization platform that reconstructs connectomes’ intrinsic geometry, i.e., the topological space as informed by brain connectivity, via dimensionality reduction. BRAINtrinsic is implemented with virtual reality in mind and is fully compatible with the Oculus Rift technology. Last, we demonstrate its effectiveness through a series of case studies involving both structural and resting-state MR imaging data.

Diffusion tensor MRI phantom exhibits anomalous diffusion

This paper reports diffusion weighted MRI measurements of cyclohexane in a novel diffusion tensor MRI phantom composed of hollow coaxial electrospun fibers (average diameter 10.2 μm). Recent studies of the phantom demonstrated its potential as a calibration standard at low b values (less than 1000 s/mm<;sup>2<;/sup>) for mean diffusivity and fractional anisotropy. In this paper, we extend the characterization of cyclohexane diffusion in this heterogeneous, anisotropic material to high b values (up to 5000 s/mm<;sup>2<;/sup>), where the apparent diffusive motion of the cyclohexane exhibits anomalous behavior (i.e., the molecular mean squared displacement increases with time raised to the fractional power 2α/β). Diffusion tensor MRI was performed at 9.4 T using an Agilent imaging scanner and the data fit to a fractional order Mittag-Leffler (generalized exponential) decay model. Diffusion along the fibers was found to be Gaussian (2α/β=l), while diffusion across the fibers was sub-diffusive (2α/β<;l). Fiber tract reconstruction of the data was consistent with scanning electron micrograph images of the material. These studies suggest that this phantom material may be used to calibrate MR systems in both the normal (Gaussian) and anomalous diffusion regimes.

In vivo assessment of oxygen consumption via Deuterium Magnetic Resonance.

We present a novel approach to simultaneously measure, in vivo, noninvasively, glucose and oxygen consumption via Deuterium Magnetic Resonance (DMR). Mice are administered deuteriated glucose by intravenous injection. The rate of formation of nascent (deuteriated) mitochondrial water is then measured via DMR. The rate of glucose metabolism and oxygen utilization is assessed by tracking their separate peaks in DMR spectra during dynamic scanning. Further studies will aim to validate these results by comparison with in vivo (17)O-MRI (mitochondrial function), (13)C-MRI and (19)FDG-PET (glucose metabolism) and ex vivo 1H- and 2H-MR, as well as mass spectrometry.

Intrinsic Geometry Visualization for the Interactive Analysis of Brain Connectivity Patterns.

Understanding how brain regions are interconnected is an important topic within the domain of neuroimaging. Advances in non-invasive technologies enable larger and more detailed images to be collected more quickly than ever before. These data contribute to create what is usually referred to as a connectome, that is, a comprehensive map of neural connections. The availability of connectome data allows for more interesting questions to be asked and more complex analyses to be conducted. In this paper we present a novel web-based 3D visual analytics tool that allows user to interactively explore the intrinsic geometry of the connectome. That is, brain data that has been transformed through a dimensionality reduction step, such as multidimensional scaling (MDS), Isomap, or t-distributed stochastic neighbor embedding (t-SNE) techniques. We evaluate our tool through a series of real-world case studies, demonstrating its effectiveness in aiding domain experts for a range of neuroimaging

Scattering and Diffraction of Elastodynamic Waves in a Concentric Cylindrical Phantom for MR Elastography

Aim: The focus of this paper is to report on the design and construction of a multiply connected phantom for use in magnetic resonance elastography (MRE)-an imaging technique that allows for the noninvasive visualization of the displacement field throughout an object from externally driven harmonic motion-as well as its inverse modeling with a closed-form analytic solution which is derived herein from first principles. Methods: Mathematically, the phantom is described as two infinite concentric circular cylinders with unequal complex shear moduli, harmonically vibrated at the exterior surface in a direction along their common axis. Each concentric cylinder is made of a hydrocolloid with its own specific solute concentration. They are assembled in a multistep process for which custom scaffolding was designed and built. A customized spin-echo-based MR elastography sequence with a sinusoidal motion-sensitizing gradient was used for data acquisition on a 9.4 T Agilent small-animal MR scanner. Complex moduli obtained from the inverse model are used to solve the forward problem with a finite-element method.Results: Both complex shear moduli show a significant frequency dependence (p <; 0.001) in keeping with previous work. Conclusion: The novel multiply connected phantom and mathematical model are validated as a viable tool for MRE studies. Significance: On a small enough scale much of physiology can be mathematically modeled with basic geometric shapes, e.g., a cylinder representing a blood vessel. This study demonstrates the possibility of elegant mathematical analysis of phantoms specifically designed and carefully constructed for biomedical MRE studies.