An electrodynamic wave model for the action potential

An alternative to the standard Hodgkin-Huxley model for axon action potential is presented. It is based on our recently developed theory of electric field wave propagation in anisotropic and inhomogeneous brain tissues, which has been shown to explain a broad range of observed coherent synchronous brain electrical processes. We demonstrate that this theory also explains the spiking behavior of single neurons, thus bridging the gap between the fundamental element of brain electrical activity (the neuron) and large-scale coherent synchronous electrical activity. Our analysis indicates that the membrane interface of the axonal cellular system can be mathematically described by a nonlinear system with several small parameters. This allows for the rigorous derivation of an accurate yet simpler nonlinear model following the formal small parameter expansion. The resulting action potential model exhibits a smooth, continuous transition from the linear oscillatory regime to the nonlinear spiking regime, as well as a critical transition to a non-oscillatory regime. These transitions occur with changes in the criticality parameter and include several different bifurcation types, representative of the various experimentally detected neuron types. This new theory overcomes the limitations of the Hodgkin-Huxley model, such as the inability to explain extracellular spiking, efficient brain synchronization, saltatory conduction along myelinated axons, and a variety of other observed coherent macroscopic brain electrical phenomena. We also show that the standard cable axon theory can be recovered by our approach, using the very crude assumptions of piece-wise homogeneity and isotropy. However, the diffusion process described by the cable equation is not capable of supporting action potential propagation across a wide range of experimentally reported axon parameters.

Double pulsed field gradient diffusion MRI to assess skeletal muscle microstructure

PURPOSE

Preliminary study to determine whether double pulsed field gradient (PFG) diffusion MRI is sensitive to key features of muscle microstructure related to function.

METHODS

The restricted diffusion profile of molecules in models of muscle microstructure derived from histology were systematically simulated using a numerical simulation approach. Diffusion tensor subspace imaging analysis of the diffusion signal was performed, and spherical anisotropy (SA) was calculated for each model. Linear regression was used to determine the predictive capacity of SA on the fiber area, fiber diameter, and surface area to volume ratio of the models. Additionally, a rat model of muscle hypertrophy was scanned using a single PFG and a double PFG pulse sequence, and the restricted diffusion measurements were compared with histological measurements of microstructure.

RESULTS

Excellent agreement between SA and muscle fiber area (r2  = 0.71; p < 0.0001), fiber diameter (r2  = 0.83; p < 0.0001), and surface area to volume ratio (r2  = 0.97; p < 0.0001) in simulated models was found. In a scanned rat leg, the distribution of these microstructural features measured from histology was broad and demonstrated that there is a wide variance in the microstructural features observed, similar to the SA distributions. However, the distribution of fractional anisotropy measurements in the same tissue was narrow.

CONCLUSIONS

This study demonstrates that SA-a scalar value from diffusion tensor subspace imaging analysis-is highly sensitive to muscle microstructural features predictive of function. Furthermore, these techniques and analysis tools can be translated to real experiments in skeletal muscle. The increased dynamic range of SA compared with fractional anisotropy in the same tissue suggests increased sensitivity to detecting changes in tissue microstructure.

Neuronal avalanches: Sandpiles of self-organized criticality or critical dynamics of brain waves?

Analytical expressions for scaling of brain wave spectra derived from the general nonlinear wave Hamiltonian form show excellent agreement with experimental "neuronal avalanche" data. The theory of the weakly evanescent nonlinear brain wave dynamics [Phys. Rev. Research 2, 023061 (2020); J. Cognitive Neurosci. 32, 2178 (2020)] reveals the underlying collective processes hidden behind the phenomenological statistical description of the neuronal avalanches and connects together the whole range of brain activity states, from oscillatory wave-like modes, to neuronal avalanches, to incoherent spiking, showing that the neuronal avalanches are just the manifestation of the different nonlinear side of wave processes abundant in cortical tissue. In a more broad way these results show that a system of wave modes interacting through all possible combinations of the third order nonlinear terms described by a general wave Hamiltonian necessarily produces anharmonic wave modes with temporal and spatial scaling properties that follow scale free power laws. To the best of our knowledge this has never been reported in the physical literature and may be applicable to many physical systems that involve wave processes and not just to neuronal avalanches.

An enigmatic pelagic fish with internalized red muscle: A future regional endotherm or forever an ectotherm?

Ectothermy and endothermy in extant fishes are defined by distinct integrated suites of characters. Although only ⁓0.1% of fishes are known to have endothermic capacity, recent discoveries suggest that there may still be uncommon pelagic fish species with yet to be discovered endothermic traits. Among the most rarely encountered marine fishes, the louvar Luvarus imperialis is a remarkable example of adaptive evolution as the only extant pelagic species in the order Acanthuriformes (including surgeonfishes, tangs, unicornfishes and Moorish idol). Magnetic resonance imaging and gross necropsy did not yield evidence of cranial or visceral endothermy but revealed a central-posterior distribution of myotomal red muscle that is a mixture of the character states typifying ectotherms (lateral-posterior) and red muscle endotherms (central-anterior). Dissection of a specimen confirmed, and an osteological proxy supported, that L. imperialis has not evolved the vascular rete that is vital to retaining heat in the red muscle. The combination of presumably relying on caudal propulsion while exhibiting internal red muscle without associated retia is unique to L. imperialis among all extant fishes, raising the macroevolutionary question of whether this species - in geologic timescales - will remain an ectotherm or evolve red muscle endothermy.

Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning

The effectiveness, robustness, and flexibility of memory and learning constitute the very essence of human natural intelligence, cognition, and consciousness. However, currently accepted views on these subjects have, to date, been put forth without any basis on a true physical theory of how the brain communicates internally via its electrical signals. This lack of a solid theoretical framework has implications not only for our understanding of how the brain works, but also for wide range of computational models developed from the standard orthodox view of brain neuronal organization and brain network derived functioning based on the Hodgkin-Huxley ad-hoc circuit analogies that have produced a multitude of Artificial, Recurrent, Convolution, Spiking, etc., Neural Networks (ARCSe NNs) that have in turn led to the standard algorithms that form the basis of artificial intelligence (AI) and machine learning (ML) methods. Our hypothesis, based upon our recently developed physical model of weakly evanescent brain wave propagation (WETCOW) is that, contrary to the current orthodox model that brain neurons just integrate and fire under accompaniment of slow leaking, they can instead perform much more sophisticated tasks of efficient coherent synchronization/desynchronization guided by the collective influence of propagating nonlinear near critical brain waves, the waves that currently assumed to be nothing but inconsequential subthreshold noise. In this paper we highlight the learning and memory capabilities of our WETCOW framework and then apply it to the specific application of AI/ML and Neural Networks. We demonstrate that the learning inspired by these critically synchronized brain waves is shallow, yet its timing and accuracy outperforms deep ARCSe counterparts on standard test datasets. These results have implications for both our understanding of brain function and for the wide range of AI/ML applications.

Critical brain wave dynamics of neuronal avalanches

Analytical expressions for scaling of brain wave spectra derived from the general non-linear wave Hamiltonian form show excellent agreement with experimental "neuronal avalanche" data. The theory of the weakly evanescent non-linear brain wave dynamics reveals the underlying collective processes hidden behind the phenomenological statistical description of the neuronal avalanches and connects together the whole range of brain activity states, from oscillatory wave-like modes, to neuronal avalanches, to incoherent spiking, showing that the neuronal avalanches are just the manifestation of the different non-linear side of wave processes abundant in cortical tissue. In a more broad way these results show that a system of wave modes interacting through all possible combinations of the third order non-linear terms described by a general wave Hamiltonian necessarily produces anharmonic wave modes with temporal and spatial scaling properties that follow scale free power laws. To the best of our knowledge this has never been reported in the physical literature and may be applicable to many physical systems that involve wave processes and not just to neuronal avalanches.

IVIM Imaging of Paraspinal Muscles Following Moderate and High-Intensity Exercise in Healthy Individuals

Background

Quantification of the magnitude and spatial distribution of muscle blood flow changes following exercise may improve our understanding of the effectiveness of various exercise prescriptions. Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) is a technique that quantifies molecular diffusion and microvascular blood flow, and has recently gained momentum as a method to evaluate a muscle's response to exercise. It has also been shown to predict responses to exercise-based physical therapy in individuals with low back pain. However, no study has evaluated the sensitivity of IVIM-MRI to exercise of varying intensity in humans. Here, we aimed to evaluate IVIM signal changes of the paraspinal muscles in response to moderate and high intensity lumbar extension exercise in healthy individuals.

Methods

IVIM data were collected in 11 healthy volunteers before and immediately after a 3-min bout of moderate and high-intensity resisted lumbar extension. IVIM data were analyzed to determine the average perfusion fraction (f), pseudo-diffusion coefficient (D^*), and diffusion coefficient (D) in the bilateral paraspinal muscles. Changes in IVIM parameters were compared between the moderate and high intensity exercise bouts.

Results

Exercise increased all IVIM parameters, regardless of intensity (p < 0.003). Moderate intensity exercise resulted in a 11.2, 19.6, and 3.5% increase in f, D^* and D, respectively. High intensity exercise led to a similar increase in f (12.2%), but much greater changes in D^* (48.6%) and D (7.9%).

Conclusion

IVIM parameter increases suggest that both the moderate and high-intensity exercise conditions elicited measurable changes in blood flow (increased f and D^*) and extravascular molecular diffusion rates (increased D), and that there was a dose-dependence of exercise intensity on D^* and D.

Diffusion MRI tractography of the locus coeruleus-transentorhinal cortex connections using GO-ESP

PURPOSE

The locus coeruleus (LC) is implicated as an early site of protein pathogenesis in Alzheimer's disease (AD). Tau pathology is hypothesized to propagate in a prion-like manner along the LC-transentorhinal cortex (TEC) white matter (WM) pathway, leading to atrophy of the entorhinal cortex and adjacent cortical regions in a progressive and stereotypical manner. However, WM damage along the LC-TEC pathway may be an earlier observable change that can improve detection of preclinical AD.

THEORY AND METHODS

Diffusion-weighted MRI (dMRI) allows reconstruction of WM pathways in vivo, offering promising potential to examine this pathway and enhance our understanding of neural mechanisms underlying the preclinical phase of AD. However, standard dMRI analysis tools have generally been unable to reliably reconstruct this pathway. We apply a novel method, geometric-optics based entropy spectrum pathways (GO-ESP) and produce a new measure of connectivity: the equilibrium probability (EP).

RESULTS

We demonstrated reliable reconstruction of LC-TEC pathways in 50 cognitively normal older adults and showed a negative association between LC-TEC EP and cerebrospinal fluid tau. Using Human Connectome Project data, we demonstrated replicability of the method across acquisition schemes and scanners. Finally, we compared our findings with the only other existing LC-TEC tractography template, and replicated their pathway as well as investigated the source of these discrepant findings.

CONCLUSIONS

AD-related tau pathology may be detectable within GO-ESP-identified LC-TEC pathways. Furthermore, there may be multiple possible routes from LC to TEC, raising important questions for future research on the LC-TEC connectome and its role in AD pathogenesis.

Unveiling the third dimension in morphometry with automated quantitative volumetric computations

As computed tomography and related technologies have become mainstream tools across a broad range of scientific applications, each new generation of instrumentation produces larger volumes of more-complex 3D data. Lagging behind are step-wise improvements in computational methods to rapidly analyze these new large, complex datasets. Here we describe novel computational methods to capture and quantify volumetric information, and to efficiently characterize and compare shape volumes. It is based on innovative theoretical and computational reformulation of volumetric computing. It consists of two theoretical constructs and their numerical implementation: the spherical wave decomposition (SWD), that provides fast, accurate automated characterization of shapes embedded within complex 3D datasets; and symplectomorphic registration with phase space regularization by entropy spectrum pathways (SYMREG), that is a non-linear volumetric registration method that allows homologous structures to be correctly warped to each other or a common template for comparison. Together, these constitute the Shape Analysis for Phenomics from Imaging Data (SAPID) method. We demonstrate its ability to automatically provide rapid quantitative segmentation and characterization of single unique datasets, and both inter-and intra-specific comparative analyses. We go beyond pairwise comparisons and analyze collections of samples from 3D data repositories, highlighting the magnified potential our method has when applied to data collections. We discuss the potential of SAPID in the broader context of generating normative morphologies required for meaningfully quantifying and comparing variations in complex 3D anatomical structures and systems.

Varying diffusion time to discriminate between simulated skeletal muscle injury models using stimulated echo diffusion tensor imaging

PURPOSE

Evaluate the relationship between muscle microstructure, diffusion time (Δ), and the diffusion tensor (DT) to identify the optimal Δ where changes in muscle fiber size may be detected.

METHODS

The DT was simulated in models with histology informed geometry over a range of Δ with a stimulated echo DT imaging (DTI) sequence using the numerical simulation application DifSim. The difference in the DT at each Δ between healthy and injured skeletal muscle models was calculated, to identify the optimal Δ at which changes in muscle fiber size may be detected. The random permeable barrier model (RPBM) was used to estimate muscle microstructure from the simulated DT measurements, which were compared to the ground truth.

RESULTS

Across all models, fractional anisotropy provided greater contrast between injured and control models than diffusivity measurements. Compared to control models, in atrophic injury models, the greatest difference in the DT was found between 90 ms and 250 ms. In models with acute edema, the contrast between injured and control muscle increased with increasing diffusion time, although these models had smaller mean fiber areas. RPBM systematically underestimated fiber size but accurately estimated surface area-to-volume ratio of simulated models.

CONCLUSION

These findings may better inform pulse sequence parameter selection when performing DTI experiments in vivo. If only a single diffusion experiment can be performed, the selected Δ should be ~170 ms to maximize the ability to discriminate between different injury models. Ideally several diffusion times between 90 ms and 500 ms should be sampled in order to maximize diffusion contrast, particularly when the disease process is unknown.

Collective Synchronous Spiking in a Brain Network of Coupled Nonlinear Oscillators

A network of propagating nonlinear oscillatory modes (waves) in the human brain is shown to generate collectively synchronized spiking activity (hypersynchronous spiking) when both amplitude and phase coupling between modes are taken into account. The nonlinear behavior of the modes participating in the network are the result of the nonresonant dynamics of weakly evanescent cortical waves that, as shown recently, adhere to an inverse frequency-wave number dispersion relation when propagating through an inhomogeneous anisotropic media characteristic of the brain cortex. This description provides a missing link between simplistic models of synchronization in networks of small amplitude phase coupled oscillators and in networks built with various empirically fitted models of pulse or amplitude coupled spiking neurons. Overall the phase-amplitude coupling mechanism presented in the Letter shows significantly more efficient synchronization compared to current standard approaches and demonstrates an emergence of collective synchronized spiking from subthreshold oscillations that neither phase nor amplitude coupling alone are capable of explaining.

Medical imaging of tissue engineering and regenerative medicine constructs

Advancement of tissue engineering and regenerative medicine (TERM) strategies to replicate tissue structure and function has led to the need for noninvasive assessment of key outcome measures of a construct's state, biocompatibility, and function. Histology based approaches are traditionally used in pre-clinical animal experiments, but are not always feasible or practical if a TERM construct is going to be tested for human use. In order to transition these therapies from benchtop to bedside, rigorously validated imaging techniques must be utilized that are sensitive to key outcome measures that fulfill the FDA standards for TERM construct evaluation. This review discusses key outcome measures for TERM constructs and various clinical- and research-based imaging techniques that can be used to assess them. Potential applications and limitations of these techniques are discussed, as well as resources for the processing, analysis, and interpretation of biomedical images.

Brain Waves: Emergence of Localized, Persistent, Weakly Evanescent Cortical Loops

An inhomogeneous anisotropic physical model of the brain cortex is presented that predicts the emergence of nonevanescent (weakly damped) wave-like modes propagating in the thin cortex layers transverse to both the mean neural fiber direction and the cortex spatial gradient. Although the amplitude of these modes stays below the typically observed axon spiking potential, the lifetime of these modes may significantly exceed the spiking potential inverse decay constant. Full-brain numerical simulations based on parameters extracted from diffusion and structural MRI confirm the existence and extended duration of these wave modes. Contrary to the commonly agreed paradigm that the neural fibers determine the pathways for signal propagation in the brain, the signal propagation because of the cortex wave modes in the highly folded areas will exhibit no apparent correlation with the fiber directions. Nonlinear coupling of those linear weakly evanescent wave modes then provides a universal mechanism for the emergence of synchronized brain wave field activity. The resonant and nonresonant terms of nonlinear coupling between multiple modes produce both synchronous spiking-like high-frequency wave activity as well as low-frequency wave rhythms. Numerical simulation of forced multiple-mode dynamics shows that, as forcing increases, there is a transition from damped to oscillatory regime that can then transition quickly to a nonoscillatory state when a critical excitation threshold is reached. The resonant nonlinear coupling results in the emergence of low-frequency rhythms with frequencies that are several orders of magnitude below the linear frequencies of modes taking part in the coupling. The localization and persistence of these weakly evanescent cortical wave modes have significant implications in particular for neuroimaging methods that detect electromagnetic physiological activity, such as EEG and magnetoencephalography, and for the understanding of brain activity in general, including mechanisms of memory.

Multiparametric MRI characterization of level dependent differences in lumbar muscle size, quality, and microstructure

Magnetic resonance imaging (MRI) is a diagnostic tool that can be used to noninvasively assess lumbar muscle size and fatty infiltration, important biomarkers of muscle health. Diffusion tensor imaging (DTI) is an MRI technique that is sensitive to muscle microstructural features such as fiber size (an important biomarker of muscle health), which is typically only assessed using invasive biopsy techniques. The goal of this study was to establish normative values of level-dependent lumbar muscle size, fat signal fraction, and restricted diffusion assessed by MRI in a highly active population. Forty-two active-duty Marines were imaged using a (a) high-resolution anatomical, (b) fat-water separation, and (c) DT-MRI scan. The multifidus and erector spinae muscles were compared at each level using two-way repeated measures ANOVA. Secondary analysis included Three dimensional (3D) reconstructions to qualitatively assess lumbar muscle size, fatty infiltration, and fiber orientation via tractography. The erector spinae was found to be larger than the multifidus above L5, with lower fat signal fraction above L3, and a less restricted diffusion profile than the multifidus above L4, with this pattern reversed in the lower lumbar spine. 3D reconstructions demonstrated accumulations of epimuscular fat in the anterior and posterior regions of the lumbar musculature, with minimal intramuscular fatty infiltration. Tractography images demonstrated different orientations of adjacent lumbar musculature, which cannot be visualized with standard MRI pulse sequences. The level dependent differences found in this study provide a normative baseline, for which to better understand whole muscle and microstructural changes associated with aging, low back pain, and pathology.

Regional variations in ex-vivo diffusion tensor anisotropy are associated with cardiomyocyte remodeling in rats after left ventricular pressure overload

BACKGROUND

Pressure overload left ventricular (LV) hypertrophy is characterized by increased cardiomyocyte width and ventricle wall thickness, however the regional variation of this remodeling is unclear. Cardiovascular magnetic resonance (CMR) diffusion tensor imaging (DTI) may provide a non-invasive, comprehensive, and geometrically accurate method to detect regional differences in structural remodeling in hypertrophy. We hypothesized that DTI parameters, such as fractional and planar anisotropy, would reflect myocyte remodeling due to pressure overload in a regionally-dependent manner.

METHODS

We investigated the regional distributions of myocyte remodeling in rats with or without transverse aortic constriction (TAC) via direct measurement of myocyte dimensions with confocal imaging of thick tissue sections, and correlated myocyte cross-sectional area and other geometric features with parameters of diffusivity from ex-vivo DTI in the same regions of the same hearts.

RESULTS

We observed regional differences in several parameters from DTI between TAC hearts and SHAM controls. Consistent with previous studies, helix angles from DTI correlated strongly with those measured directly from histological sections (p < 0.001, R2 = 0.71). There was a transmural gradient in myocyte cross-sectional area in SHAM hearts that was diminished in the TAC group. We also found several regions of significantly altered DTI parameters in TAC LV compared to SHAM, especially in myocyte sheet angle dispersion and planar anisotropy. Among others, these parameters correlated significantly with directly measured myocyte aspect ratios.

CONCLUSIONS

These results show that structural remodeling in pressure overload LV hypertrophy is regionally heterogeneous, especially transmurally, with a greater degree of remodeling in the sub-endocardium compared to the sub-epicardium. Additionally, several parameters derived from DTI correlated significantly with measurements of myocyte geometry from direct measurement in histological sections. We suggest that DTI may provide a non-invasive, comprehensive method to detect regional structural myocyte LV remodeling during disease.

Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms

An inhomogeneous anisotropic physical model of the brain cortex is presented that predicts the emergence of non-evanescent (weakly damped) wave-like modes propagating in the thin cortex layers transverse to both the mean neural fiber direction and to the cortex spatial gradient. Although the amplitude of these modes stays below the typically observed axon spiking potential, the lifetime of these modes may significantly exceed the spiking potential inverse decay constant. Full brain numerical simulations based on parameters extracted from diffusion and structural MRI confirm the existence and extended duration of these wave modes. Contrary to the standard paradigm that the neural fibers determine the pathways for signal propagation in the brain, the signal propagation due to the cortex wave modes in highly folded areas will exhibit no apparent correlation with the fiber directions. The results are consistent with numerous recent experimental animal and human brain studies demonstrating the existence of electrostatic field activity in the form of traveling waves (including studies where neuronal connections were severed) and with wave loop induced peaks observed in EEG spectra. In addition, we demonstrate that the resonant and non-resonant terms of the nonlinear coupling between multiple modes produce both synchronous spiking-like high frequency wave activity as well as low frequency wave rhythms as a result of their unique dispersion properties. Numerical simulation of forced multiple mode dynamics shows that as forcing increases there is a transition from damped to oscillatory regime that subsequently decays away as over-excitation is reached. The resonant nonlinear coupling results in the emergence of low frequency rhythms with frequencies that are several orders of magnitude below the linear frequencies of modes taking part in the coupling. The localization and persistence of these cortical wave modes, and this new mechanism for understanding the nature of spiking behavior, have significant implications in particular for neuroimaging methods that detect electromagnetic physiological activity, such as EEG and MEG, and in general for the understanding of brain activity, including mechanisms of memory.

JEDI: Joint Estimation Diffusion Imaging of macroscopic and microscopic tissue properties

PURPOSE

A new method for enhancing the sensitivity of diffusion MRI (dMRI) by combining the data from single (sPFG) and double (dPFG) pulsed field gradient experiments is presented.

METHODS

This method uses our JESTER framework to combine microscopic anisotropy information from dFPG experiments using a new method called diffusion tensor subspace imaging (DiTSI) to augment the macroscopic anisotropy information from sPFG data analyzed using our guided by entropy spectrum pathways method. This new method, called joint estimation diffusion imaging (JEDI), combines the sensitivity to macroscopic diffusion anisotropy of sPFG with the sensitivity to microscopic diffusion anisotropy of dPFG methods.

RESULTS

Its ability to produce significantly more detailed anisotropy maps and more complete fiber tracts than existing methods within both brain white matter (WM) and gray matter (GM) is demonstrated on normal human subjects on data collected using a novel fast, robust, and clinically feasible sPFG/dPFG acquisition.

CONCLUSIONS

The potential utility of this method is suggested by an initial demonstration of its ability to mitigate the problem of gyral bias. The capability of more completely characterizing the tissue structure and connectivity throughout the entire brain has broad implications for the utility and scope of dMRI in a wide range of research and clinical applications.

Symplectomorphic registration with phase space regularization by entropy spectrum pathways

PURPOSE

The ability to register image data to a common coordinate system is a critical feature of virtually all imaging studies. However, in spite of the abundance of literature on the subject and the existence of several variants of registration algorithms, their practical utility remains problematic, as commonly acknowledged even by developers of these methods.

METHODS

A new registration method is presented that utilizes a Hamiltonian formalism and constructs registration as a sequence of symplectomorphic maps in conjunction with a novel phase space regularization. For validation of the framework a panel of deformations expressed in analytical form is developed that includes deformations based on known physical processes in MRI and reproduces various distortions and artifacts typically present in images collected using these different MRI modalities.

RESULTS

The method is demonstrated on the three different magnetic resonance imaging (MRI) modalities by mapping between high resolution anatomical (HRA) volumes, medium resolution diffusion weighted MRI (DW-MRI) and HRA volumes, and low resolution functional MRI (fMRI) and HRA volumes.

CONCLUSIONS

The method has shown an excellent performance and the panel of deformations was instrumental to quantify its repeatability and reproducibility in comparison to several available alternative approaches.

Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes

In this letter, we present a new method for integration of sensor-based multifrequency bands of electroencephalography and magnetoencephalography data sets into a voxel-based structural-temporal magnetic resonance imaging analysis by utilizing the general joint estimation using entropy regularization (JESTER) framework. This allows enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities, thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.

Decreasing Compensatory Ability of Concentric Ventricular Hypertrophy in Aortic-Banded Rat Hearts

The cardiac system compensates for variations in physiological and pathophysiological conditions through a dynamic remodeling at the organ, tissue, and intracellular levels in order to maintain function. However, on longer time scales following the onset of ventricular pressure overload, such remodeling may begin to inhibit physiological function and ultimately lead to heart failure. This progression from compensatory to decompensatory behavior is poorly understood, in particular owing to the absence of a unified perspective of the concomitantly remodeling subsystems. To address this issue, the present study investigates the evolution of compensatory mechanisms, in response to overload, by integrating diffusion-tensor MRI, echocardiography, and intracellular and hemodynamic measurements within consistent computational simulations of aortic-banded rat hearts. This approach allows a comparison of the relative leverage of different cardiac properties (geometry, passive mechanical stiffness, fiber configuration, diastolic and peak calcium concentrations, calcium-binding affinity, and aortic impedance) to affect cardiac contraction. Measurements indicate that, following aortic banding, an ejection fraction (EF) of 75% was maintained, relative to control rats, despite significant remodeling of the left-ventricular wall thickness (increasing by ~90% over 4 weeks). Applying our framework, we identified the left-ventricular wall thickness (concentric hypertrophy) and the intracellular calcium dynamics as playing the dominant roles in preserving EF acutely, whereas the significance of hypertrophy decreased subsequently. This trend suggests an increasing reliance on intracellular mechanisms (average increase ~50%), rather than on anatomical features (average decrease ~60%), to achieve compensation of pump function in the early phase of heart failure.

Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle

PURPOSE

To establish a series of relationships defining how muscle microstructure and diffusion tensor imaging (DTI) are related.

METHODS

The relationship among key microstructural features of skeletal muscle (fiber size, fibrosis, edema, and permeability) and the diffusion tensor were systematically simulated over physiologically relevant dimensions individually, and in combination, using a numerical simulation application. Stepwise multiple regression was used to identify which microstructural features of muscle significantly predict the diffusion tensor using single-echo and multi-echo DTI pulse sequences. Simulations were also performed in models with histology-informed geometry to investigate the relationship between fiber size and the diffusion tensor in models with real muscle geometry.

RESULTS

Fiber size is the strongest predictor of λ2, λ3, mean diffusivity, and fractional anisotropy in skeletal muscle, accounting for approximately 40% of the variance in the diffusion model when calculated with single-echo DTI. This increased to approximately 70% when diffusion measures were calculated from the short T₂ component of the multi-echo DTI sequence. This nonlinear relationship begins to plateau in fibers with greater than 60-μm diameter.

CONCLUSIONS

As the normal fiber size of a human muscle fiber is 40 to 60 μm, this suggests that DTI is a sensitive tool to monitor muscle atrophy, but may be limited in measurements of muscle with larger fibers. Magn Reson Med 80:317-329, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A Unified Theory of Neuro-MRI Data Shows Scale-Free Nature of Connectivity Modes

A primary goal of many neuroimaging studies that use magnetic resonance imaging (MRI) is to deduce the structure-function relationships in the human brain using data from the three major neuro-MRI modalities: high-resolution anatomical, diffusion tensor imaging, and functional MRI. To date, the general procedure for analyzing these data is to combine the results derived independently from each of these modalities. In this article, we develop a new theoretical and computational approach for combining these different MRI modalities into a powerful and versatile framework that combines our recently developed methods for morphological shape analysis and segmentation, simultaneous local diffusion estimation and global tractography, and nonlinear and nongaussian spatial-temporal activation pattern classification and ranking, as well as our fast and accurate approach for nonlinear registration between modalities. This joint analysis method is capable of extracting new levels of information that is not achievable from any of those single modalities alone. A theoretical probabilistic framework based on a reformulation of prior information and available interdependencies between modalities through a joint coupling matrix and an efficient computational implementation allows construction of quantitative functional, structural, and effective brain connectivity modes and parcellation. This new method provides an overall increase of resolution, accuracy, level of detail, and information content and has the potential to be instrumental in the clinical adaptation of neuro-MRI modalities, which, when jointly analyzed, provide a more comprehensive view of a subject's structure-function relations, while the current standard, wherein single-modality methods are analyzed separately, leaves a critical gap in an integrated view of a subject's neuorphysiological state. As one example of this increased sensitivity, we demonstrate that the jointly estimated structural and functional dependencies of mode power follow the same power law decay with the same exponent.

* A 3D Tissue-Printing Approach for Validation of Diffusion Tensor Imaging in Skeletal Muscle

The ability to noninvasively assess skeletal muscle microstructure, which predicts function and disease, would be of significant clinical value. One method that holds this promise is diffusion tensor magnetic resonance imaging (DT-MRI), which is sensitive to the microscopic diffusion of water within tissues and has become ubiquitous in neuroimaging as a way of assessing neuronal structure and damage. However, its application to the assessment of changes in muscle microstructure associated with injury, pathology, or age remains poorly defined, because it is difficult to precisely control muscle microstructural features in vivo. However, recent advances in additive manufacturing technologies allow precision-engineered diffusion phantoms with histology informed skeletal muscle geometry to be manufactured. Therefore, the goal of this study was to develop skeletal muscle phantoms at relevant size scales to relate microstructural features to MRI-based diffusion measurements. A digital light projection based rapid 3D printing method was used to fabricate polyethylene glycol diacrylate based diffusion phantoms with (1) idealized muscle geometry (no geometry; fiber sizes of 30, 50, or 70 μm or fiber size of 50 μm with 40% of walls randomly deleted) or (2) histology-based geometry (normal and after 30-days of denervation) containing 20% or 50% phosphate-buffered saline (PBS). Mean absolute percent error (8%) of the printed phantoms indicated high conformity to templates when "fibers" were >50 μm. A multiple spin-echo echo planar imaging diffusion sequence, capable of acquiring diffusion weighted data at several echo times, was used in an attempt to combine relaxometry and diffusion techniques with the goal of separating intracellular and extracellular diffusion signals. When fiber size increased (30-70 μm) in the 20% PBS phantom, fractional anisotropy (FA) decreased (0.32-0.26) and mean diffusivity (MD) increased (0.44 × 10^-3 mm²/s-0.70 × 10^-3 mm²/s). Similarly, when fiber size increased from 30 to 70 μm in the 50% PBS diffusion phantoms, a small change in FA was observed (0.18-0.22), but MD increased from 0.86 × 10^-3 mm²/s to 1.79 × 10^-3 mm²/s. This study demonstrates a novel application of tissue engineering to understand complex diffusion signals in skeletal muscle. Through this work, we have also demonstrated the feasibility of 3D printing for skeletal muscle with relevant matrix geometries and physiologically relevant tissue characteristics.

Detecting Spatio-Temporal Modes in Multivariate Data by Entropy Field Decomposition

A new data analysis method that addresses a general problem of detecting spatio-temporal variations in multivariate data is presented. The method utilizes two recent and complimentary general approaches to data analysis, information field theory (IFT) and entropy spectrum pathways (ESP). Both methods reformulate and incorporate Bayesian theory, thus use prior information to uncover underlying structure of the unknown signal. Unification of ESP and IFT creates an approach that is non-Gaussian and non-linear by construction and is found to produce unique spatio-temporal modes of signal behavior that can be ranked according to their significance, from which space-time trajectories of parameter variations can be constructed and quantified. Two brief examples of real world applications of the theory to the analysis of data bearing completely different, unrelated nature, lacking any underlying similarity, are also presented. The first example provides an analysis of resting state functional magnetic resonance imaging (rsFMRI) data that allowed us to create an efficient and accurate computational method for assessing and categorizing brain activity. The second example demonstrates the potential of the method in the application to the analysis of a strong atmospheric storm circulation system during the complicated stage of tornado development and formation using data recorded by a mobile Doppler radar. Reference implementation of the method will be made available as a part of the QUEST toolkit that is currently under development at the Center for Scientific Computation in Imaging.

The Lamellar Structure of the Brain Fiber Pathways

We present a quantitative statistical analysis of pairwise crossings for all fibers obtained from whole brain tractography that confirms with high confidence that the brain grid theory (Wedeen et al., 2012a ) is not supported by the evidence. The overall fiber tracts structure appears to be more consistent with small angle treelike branching of tracts rather than with near-orthogonal gridlike crossing of fiber sheets. The analysis uses our new method for high-resolution whole brain tractography that is capable of resolving fibers crossing of less than 10 degrees and correctly following a continuous angular distribution of fibers even when the individual fiber directions are not resolved. This analysis also allows us to demonstrate that the whole brain fiber pathway system is very well approximated by a lamellar vector field, providing a concise and quantitative mathematical characterization of the structural connectivity of the human brain.

White Matter Microstructural Compromise Is Associated With Cognition But Not Posttraumatic Stress Disorder Symptoms in Military Veterans With Traumatic Brain Injury

OBJECTIVE

To investigate white matter microstructure compromise in Veterans with a history of traumatic brain injury (TBI) and its possible contribution to posttraumatic stress disorder (PTSD) symptomatology and neuropsychological functioning via diffusion tensor imaging.

PARTICIPANTS AND METHODS

Thirty-eight Veterans with mild (n = 33) and moderate (n = 5) TBI and 17 military control participants without TBI completed neuropsychological testing and psychiatric screening and underwent magnetic resonance imaging an average of 4 years following their TBI event(s). Fractional anisotropy (FA) and diffusivity measures were extracted from 9 white matter tracts.

RESULTS

Compared with military control participants, TBI participants reported higher levels of PTSD symptoms and performed worse on measures of memory and psychomotor-processing speed. Traumatic brain injury was associated with lower FA in the genu of the corpus callosum and left cingulum bundle. Fractional anisotropy negatively correlated with processing speed and/or executive functions in 7 of the 8 tracts. Regional FA did not correlate with memory or PTSD symptom ratings.

CONCLUSION

Results suggest that current PTSD symptoms are independent of TBI-related white matter alterations, as measured by diffusion tensor imaging. In addition, white matter microstructural compromise may contribute to reduced processing speed in our sample of participants with history of neurotrauma. Findings of the current study add insight into the factors associated with complicated recovery from mild to moderate TBI.

Quantitative Classification of Cerebellar Foliation in Cartilaginous Fishes (Class: Chondrichthyes) Using Three-Dimensional Shape Analysis and Its Implications for Evolutionary Biology

A true cerebellum appeared at the onset of the chondrichthyan (sharks, batoids, and chimaerids) radiation and is known to be essential for executing fast, accurate, and efficient movement. In addition to a high degree of variation in size, the corpus cerebellum in this group has a high degree of variation in convolution (or foliation) and symmetry, which ranges from a smooth cerebellar surface to deep, branched convexities and folds, although the functional significance of this trait is unclear. As variation in the degree of foliation similarly exists throughout vertebrate evolution, it becomes critical to understand this evolutionary process in a wide variety of species. However, current methods are either qualitative and lack numerical rigor or they are restricted to two dimensions. In this paper, a recently developed method for the characterization of shapes embedded within noisy, three-dimensional data called spherical wave decomposition (SWD) is applied to the problem of characterizing cerebellar foliation in cartilaginous fishes. The SWD method provides a quantitative characterization of shapes in terms of well-defined mathematical functions. An additional feature of the SWD method is the construction of a statistical criterion for the optimal fit, which represents the most parsimonious choice of parameters that fits to the data without overfitting to background noise. We propose that this optimal fit can replace a previously described qualitative visual foliation index (VFI) in cartilaginous fishes with a quantitative analog, i.e. the cerebellar foliation index (CFI). The capability of the SWD method is demonstrated in a series of volumetric images of brains from different chondrichthyan species that span the range of foliation gradings currently described for this group. The CFI is consistent with the qualitative grading provided by the VFI, delivers a robust measure of cerebellar foliation, and can provide a quantitative basis for brain shape characterization across taxa.

Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition

The ability of functional magnetic resonance imaging (FMRI) to noninvasively measure fluctuations in brain activity in the absence of an applied stimulus offers the possibility of discerning functional networks in the resting state of the brain. However, the reconstruction of brain networks from these signal fluctuations poses a significant challenge because they are generally nonlinear and nongaussian and can overlap in both their spatial and temporal extent. Moreover, because there is no explicit input stimulus, there is no signal model with which to compare the brain responses. A variety of techniques have been devised to address this problem, but the predominant approaches are based on the presupposition of statistical properties of complex brain signal parameters, which are unprovable but facilitate the analysis. In this article, we address this problem with a new method, entropy field decomposition, for estimating structure within spatiotemporal data. This method is based on a general information field-theoretic formulation of Bayesian probability theory incorporating prior coupling information that allows the enumeration of the most probable parameter configurations without the need for unjustified statistical assumptions. This approach facilitates the construction of brain activation modes directly from the spatial-temporal correlation structure of the data. These modes and their associated spatial-temporal correlation structure can then be used to generate space-time activity probability trajectories, called functional connectivity pathways, which provide a characterization of functional brain networks.

Simultaneous multi-scale diffusion estimation and tractography guided by entropy spectrum pathways

We have developed a method for the simultaneous estimation of local diffusion and the global fiber tracts based upon the information entropy flow that computes the maximum entropy trajectories between locations and depends upon the global structure of the multi-dimensional and multi-modal diffusion field. Computation of the entropy spectrum pathways requires only solving a simple eigenvector problem for the probability distribution for which efficient numerical routines exist, and a straight forward integration of the probability conservation through ray tracing of the convective modes guided by a global structure of the entropy spectrum coupled with a small scale local diffusion. The intervoxel diffusion is sampled by multi b-shell multi q-angle diffusion weighted imaging data expanded in spherical waves. This novel approach to fiber tracking incorporates global information about multiple fiber crossings in every individual voxel and ranks it in the most scientifically rigorous way. This method has potential significance for a wide range of applications, including studies of brain connectivity.

White matter integrity in veterans with mild traumatic brain injury: associations with executive function and loss of consciousness

OBJECTIVE

We investigated using diffusion tensor imaging (DTI) and the association between white matter integrity and executive function (EF) performance in postacute mild traumatic brain injury (mTBI). In addition, we examined whether injury severity, as measured by loss of consciousness (LOC) versus alterations in consciousness (AOC), is related to white matter microstructural alterations and neuropsychological outcome.

PARTICIPANTS

Thirty Iraq and Afghanistan War era veterans with a history of mTBI and 15 healthy veteran control participants.

RESULTS

There were no significant overall group differences between control and mTBI participants on DTI measures. However, a subgroup of mTBI participants with EF decrements (n = 13) demonstrated significantly decreased fractional anisotropy of prefrontal white matter, corpus callosum, and cingulum bundle structures compared with mTBI participants without EF decrements (n = 17) and control participants. Participants having mTBI with LOC were more likely to evidence reduced EF performances and disrupted ventral prefrontal white matter integrity when compared with either mTBI participants without LOC or control participants.

CONCLUSIONS

Findings suggest that altered white matter integrity contributes to reduced EF in subgroups of veterans with a history of mTBI and that LOC may be a risk factor for reduced EF as well as associated changes to ventral prefrontal white matter.

Information pathways in a disordered lattice

The maximum entropy random walk in a disordered lattice is obtained as a consequence of the principle of maximum entropy for a particular type of prior information without restriction on the number of steps. This novel result demonstrates that transition probabilities defining the random walk represent a general characterization of information on a defective lattice and does not necessarily reflect a physical process. The localization phenomenon is shown to be a consequence of solution of the Laplacian on the lattice-hence it contradicts the previous interpretation as a spherical Lifshitz state-and naturally generalizes to multiple modes, whose order reflects the significance of information. The dynamics of information flow on the microscale is related to the macroscopic structure of the lattice through a Fokker-Planck formalism. This newly derived theoretical framework is opening doors for a wide range of applications in analysis of (information) flow in disordered systems. That includes potentially breakthrough resolution of the outstanding problem of inferring connectivity from discrete imaging (i.e., neural) data.

Automated segmentation and shape characterization of volumetric data

Characterization of complex shapes embedded within volumetric data is an important step in a wide range of applications. Standard approaches to this problem employ surface-based methods that require inefficient, time consuming, and error prone steps of surface segmentation and inflation to satisfy the uniqueness or stability of subsequent surface fitting algorithms. Here we present a novel method based on a spherical wave decomposition (SWD) of the data that overcomes several of these limitations by directly analyzing the entire data volume, obviating the segmentation, inflation, and surface fitting steps, significantly reducing the computational time and eliminating topological errors while providing a more detailed quantitative description based upon a more complete theoretical framework of volumetric data. The method is demonstrated and compared to the current state-of-the-art neuroimaging methods for segmentation and characterization of volumetric magnetic resonance imaging data of the human brain.

Novel role for vinculin in ventricular myocyte mechanics and dysfunction

Vinculin (Vcl) plays a key structural role in ventricular myocytes that, when disrupted, can lead to contractile dysfunction and dilated cardiomyopathy. To investigate the role of Vcl in myocyte and myocardial function, cardiomyocyte-specific Vcl knockout mice (cVclKO) and littermate control wild-type mice were studied with transmission electron microscopy (TEM) and in vivo magnetic resonance imaging (MRI) tagging before the onset of global ventricular dysfunction. MRI revealed significantly decreased systolic strains transverse to the myofiber axis in vivo, but no changes along the muscle fibers or in fiber tension in papillary muscles from heterozygous global Vcl null mice. Myofilament lattice spacing from TEM was significantly greater in cVclKO versus wild-type hearts fixed in the unloaded state. AFM in Vcl heterozygous null mouse myocytes showed a significant decrease in membrane cortical stiffness. A multiscale computational model of ventricular mechanics incorporating cross-bridge geometry and lattice mechanics showed that increased transverse systolic stiffness due to increased lattice spacing may explain the systolic wall strains associated with Vcl deficiency, before the onset of ventricular dysfunction. Loss of cardiac myocyte Vcl may decrease systolic transverse strains in vivo by decreasing membrane cortical tension, which decreases transverse compression of the lattice thereby increasing interfilament spacing and stress transverse to the myofibers.

Patient-Specific Models of Cardiac Biomechanics

Patient-specific models of cardiac function have the potential to improve diagnosis and management of heart disease by integrating medical images with heterogeneous clinical measurements subject to constraints imposed by physical first principles and prior experimental knowledge. We describe new methods for creating three-dimensional patient-specific models of ventricular biomechanics in the failing heart. Three-dimensional bi-ventricular geometry is segmented from cardiac CT images at end-diastole from patients with heart failure. Human myofiber and sheet architecture is modeled using eigenvectors computed from diffusion tensor MR images from an isolated, fixed human organ-donor heart and transformed to the patient-specific geometric model using large deformation diffeomorphic mapping. Semi-automated methods were developed for optimizing the passive material properties while simultaneously computing the unloaded reference geometry of the ventricles for stress analysis. Material properties of active cardiac muscle contraction were optimized to match ventricular pressures measured by cardiac catheterization, and parameters of a lumped-parameter closed-loop model of the circulation were estimated with a circulatory adaptation algorithm making use of information derived from echocardiography. These components were then integrated to create a multi-scale model of the patient-specific heart. These methods were tested in five heart failure patients from the San Diego Veteran's Affairs Medical Center who gave informed consent. The simulation results showed good agreement with measured echocardiographic and global functional parameters such as ejection fraction and peak cavity pressures.

White matter integrity, substance use, and risk taking in adolescence

White matter development is important for efficient communication between brain regions, higher order cognitive functioning, and complex behaviors. Adolescents have a higher propensity for engaging in risky behaviors, yet few studies have explored associations between white matter integrity and risk taking directly. Altered white matter integrity in mid-adolescence was hypothesized to predict subsequent risk taking behaviors 1.5 years later. Adolescent substance users (predominantly alcohol and marijuana, n = 47) and demographically similar nonusers (n = 49) received diffusion tensor imaging at baseline (ages 16-19), and risk taking measures at both baseline and an 18-month follow-up (i.e., at ages 17-20). Brain regions of interest were the fornix, superior corona radiata, superior longitudinal fasciculus, and superior fronto-occipital fasciculus. In substance-using youth (n = 47), lower white matter integrity at baseline in the fornix and superior corona radiata predicted follow-up substance use (ΔR2 = 10-12%, ps < .01), and baseline fornix integrity predicted follow-up delinquent behaviors (ΔR2 = 10%, p < .01) 1.5 years later. Poorer fronto-limbic white matter integrity was linked to a greater propensity for future risk taking behaviors among youth who initiated heavy substance use by mid-adolescence. Most notable were relationships between projection and limbic-system fibers and future substance-use frequency. Subcortical white matter coherence, along with an imbalance between the maturation levels in cognitive control and reward systems, may disadvantage the resistance to engage in risk taking behaviors during adolescence.

A computational model for diffusion weighted imaging of myelinated white matter

The signal for diffusion weighted magnetic resonance imaging has previously been represented analytically and simulated numerically for a variety of model problems with idealized geometries. Numerical simulations hold the promise of computing the diffusion weighted MR signal for more complex realistic tissue architectures and physiological models. This paper investigates a white matter model consisting of a matrix of coated cylinders with distinct diffusion coefficients and spin concentrations for each of the cylinder core, the coating, and the surrounding bath and compares results with an the analytical solution developed by Sen and Basser for the long diffusion time limit. Numerical simulations of diffusion weighted imaging experiments are performed for the three-medium model using a Monte Carlo diffusion simulation. Experiments are carried out for model parameters representing normal white matter. Pulse sequence parameters range from a low b value, long time limit, short pulse approximation to realistic clinical values. For simulations in the short pulse width, long diffusion time limit, numerical simulations agree with the Sen-Basser analytical result. When tested with realistic pulse sequence parameters, numerical simulations show lower anisotropy than the analytical model predicts.

Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment

Medial temporal lobe and temporoparietal brain regions are among the earliest neocortical sites to undergo pathophysiologic alterations in Alzheimer's disease (AD), although the underlying white matter changes in these regions is less well known. We employed diffusion tensor imaging to evaluate early alterations in regional white matter integrity in participants diagnosed with mild cognitive impairment (MCI). The following regions of interests (ROIs) were examined: 1) anterior cingulum (AC); 2) posterior cingulum (PC); 3) genu of the corpus callosum; 4) splenium of the corpus callosum; and 5) as a control site for comparison, posterior limb of the internal capsule. Forty nondemented participants were divided into demographically-similar groups based on cognitive status (MCI: n = 20; normal control: n = 20), and fractional anisotropy (FA) estimates of each ROI were obtained. MCI participants showed greater posterior white matter (i.e., PC, splenium) but not anterior white matter (i.e., AC, genu) changes, after adjusting for age, stroke risk, and whole brain volume. FA differences of the posterior white matter were best accounted for by changes in radial but not axial diffusivity. PC FA was also significantly positively correlated with hippocampal volume as well as with performance on tests of verbal memory, whereas stroke risk was significantly correlated with genu FA and was unrelated to PC FA. When investigating subtypes of our MCI population, amnestic MCI participants showed lower PC white matter integrity relative to those with non-amnestic MCI. Findings implicate involvement of posterior microstructural white matter degeneration in the development of MCI-related cognitive changes and suggest that reduced FA of the PC may be a candidate neuroimaging marker of AD risk.

An atlas-based geometry pipeline for cardiac Hermite model construction and diffusion tensor reorientation

Here we present a novel atlas-based geometry pipeline for constructing three-dimensional cubic Hermite finite element meshes of the whole human heart from tomographic patient image data. To build the cardiac atlas, two superior atria, two inferior ventricles as well as the aorta and the pulmonary trunk are first segmented, and epicardial and endocardial boundary surfaces are extracted and smoothed. Critical points and skeletons (or central-line paths) are identified, following the cardiac topology. The surface model and the path tree are used to construct a hexahedral control mesh via a skeleton-based sweeping method. Derivative parameters are computed from the control mesh, defining cubic Hermite finite elements. The thickness of the atria and the ventricles is obtained using segmented epicardial boundaries or via offsetting from the endocardial surfaces in regions where the image resolution is insufficient. We also develop a robust optical flow approach to deform the constructed atlas and align it with the image from a second patient. This registration method is fully-automatic, and avoids manual operations required by segmentation and path extraction. Moreover, we demonstrate that this method can also be used to deformably map diffusion tensor MRI data with patient geometries to include fiber and sheet orientations in the finite element model.

The Digital Fish Library: using MRI to digitize, database, and document the morphological diversity of fish

Museum fish collections possess a wealth of anatomical and morphological data that are essential for documenting and understanding biodiversity. Obtaining access to specimens for research, however, is not always practical and frequently conflicts with the need to maintain the physical integrity of specimens and the collection as a whole. Non-invasive three-dimensional (3D) digital imaging therefore serves a critical role in facilitating the digitization of these specimens for anatomical and morphological analysis as well as facilitating an efficient method for online storage and sharing of this imaging data. Here we describe the development of the Digital Fish Library (DFL, http://www.digitalfishlibrary.org), an online digital archive of high-resolution, high-contrast, magnetic resonance imaging (MRI) scans of the soft tissue anatomy of an array of fishes preserved in the Marine Vertebrate Collection of Scripps Institution of Oceanography. We have imaged and uploaded MRI data for over 300 marine and freshwater species, developed a data archival and retrieval system with a web-based image analysis and visualization tool, and integrated these into the public DFL website to disseminate data and associated metadata freely over the web. We show that MRI is a rapid and powerful method for accurately depicting the in-situ soft-tissue anatomy of preserved fishes in sufficient detail for large-scale comparative digital morphology. However these 3D volumetric data require a sophisticated computational and archival infrastructure in order to be broadly accessible to researchers and educators.

Frontal white matter integrity predictors of adult alcohol treatment outcome

BACKGROUND

Previous research has associated abnormalities in frontal lobe functioning with alcohol relapse. In this study, we used diffusion tensor imaging to investigate whether frontal white matter integrity measured at the start of treatment differs between persons with alcohol use disorders (AUD) who sustain treatment gains and those who return to heavy use after treatment.

METHODS

Forty-five treatment-seeking AUD inpatients and 30 healthy control subjects were included in the study. Six months after completing treatment, 16 of the AUD participants had resumed heavy use (RHU) and 29 others remained abstinent or drank minimally (treatment sustainers [TS]). Voxel-wise group comparisons (TS vs. RHU) were performed on fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity maps generated from each subject's diffusion tensor imaging scan at the start of treatment.

RESULTS

We found significantly lower FA and significantly higher RD in the frontal lobes of the RHU group, relative to the TS group. The RHU group data are consistent with previous reports of abnormal frontal white matter tract abnormalities in persons with AUD.

CONCLUSIONS

It is possible that the lower FA and higher RD in the RHU group reflect microstructural injury to frontal circuitries, and these may underlie the reduced cognitive control amid heightened reward sensitivity associated with resumption of heavy drinking.

Determination of three-dimensional ventricular strain distributions in gene-targeted mice using tagged MRI

A model-based method for calculating three-dimensional (3D) cardiac wall strain distributions in the mouse has been developed and tested in a genetically engineered mouse model of dilated cardiomyopathy. Data from MR tagging and harmonic phase (HARP) tracking were used to measure material point displacements, and 3D Lagrangian strains were calculated throughout the entire left ventricle (LV) with a deformable parametric model. A mouse model where cardiomyocytes are specifically made deficient in vinculin (VclKO) were compared to wild-type (WT) littermates. 3D strain analysis revealed differences in LV wall mechanics between WT and VclKO mice at 8 weeks of age when systolic function had just begun to decline. Most notably, end-systolic radial strain and torsional shear were reduced in VclKO hearts which contributed to regional mechanical dysfunction. This study demonstrates the feasibility of using MRI tagging methods to detect alterations in 3D myocardial strain distributions in genetically engineered mouse models of cardiovascular disease.

The disappearing third dimension

Three-dimensional computing is driving what many would call a revolution in scientific visualization. However, its power and advancement are held back by the absence of sustainable archives for raw data and derivative visualizations. Funding agencies, professional societies, and publishers each have unfulfilled roles in archive design and data management policy.

A simulation environment for diffusion weighted MR experiments in complex media

Simulations of diffusion in neural tissues have traditionally been limited to analytical solutions, to grid-based solvers, or to small-scale Monte Carlo simulations. None of these approaches has had the capability to simulate realistic complex neural tissues on the scale of even a single voxel of reasonable (i.e., clinical) size. An approach is described that combines a Monte Carlo Brownian dynamics simulator capable of simulating diffusion in arbitrarily complex polygonal geometries with a signal integrator flexible enough to handle a variety of pulse sequences. Taken together, this package provides a complete and general simulation environment for diffusion MRI experiments. The simulator is validated against analytical solutions for unbounded diffusion and diffusion between parallel plates. Further results are shown for aligned fibers, varying packing density and permeability, and for crossing straight fibers.

Altered white matter microstructure in adolescent substance users

Chronic marijuana use during adolescence is frequently comorbid with heavy alcohol consumption and associated with CNS alterations, yet the influence of early cannabis and alcohol use on microstructural white matter integrity is unclear. Building on evidence that cannabinoid receptors are present in myelin precursors and affect glial cell processing, and that excessive ethanol exposure is associated with persistently impaired myelination, we used diffusion tensor imaging (DTI) to characterize white matter integrity in heavy substance using and non-using adolescents. We evaluated 36 marijuana and alcohol-using (MJ+ALC) adolescents (ages 16-19) and 36 demographically similar non-using controls with DTI. The diffusion parameters fractional anisotropy (FA) and mean diffusivity (MD) were subjected to whole-brain voxelwise group comparisons using tract-based spatial statistics (Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E., 2006. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487-1505). MJ+ALC teens had significantly lower FA than controls in 10 regions, including left superior longitudinal fasciculus (SLF), left postcentral gyrus, bilateral crus cerebri, and inferior frontal and temporal white matter tracts. These diminutions occurred in the context of increased FA in right occipital, internal capsule, and SLF regions. Changes in MD were less distributed, but increased MD was evident in the right occipital lobe, whereas the left inferior longitudinal fasciculus showed lower MD in MJ+ALC users. Findings suggest that fronto-parietal circuitry may be particularly impacted in adolescent users of the most prevalent intoxicants: marijuana and alcohol. Disruptions to white matter in this young group could indicate aberrant axonal and myelin maturation with resultant compromise of fiber integrity. Findings of increased anisotropic diffusion in alternate brain regions suggest possible neuroadaptive processes and can be examined in future studies of connectivity to determine how aberrancies in specific tracts might influence efficient cognitive processing.

High efficiency, low distortion 3D diffusion tensor imaging with variable density spiral fast spin echoes (3D DW VDS RARE)

We present an acquisition and reconstruction method designed to acquire high resolution 3D fast spin echo diffusion tensor images while mitigating the major sources of artifacts in DTI-field distortions, eddy currents and motion. The resulting images, being 3D, are of high SNR, and being fast spin echoes, exhibit greatly reduced field distortions. This sequence utilizes variable density spiral acquisition gradients, which allow for the implementation of a self-navigation scheme by which both eddy current and motion artifacts are removed. The result is that high resolution 3D DTI images are produced without the need for eddy current compensating gradients or B(0) field correction. In addition, a novel method for fast and accurate reconstruction of the non-Cartesian data is employed. Results are demonstrated in the brains of normal human volunteers.

High-field diffusion MR histology: image-based correction of eddy-current ghosts in diffusion-weighted rapid acquisition with relaxation enhancement (DW-RARE)

High-resolution, diffusion-weighted (DW) MR microscopy is gaining increasing acceptance as a nondestructive histological tool for the study of fixed tissue samples. Spin-echo sequences are popular for high-field diffusion imaging due to their high tolerance to B(0) field inhomogeneities. Volumetric DW rapid acquisition with relaxation enhancement (DW-RARE) currently offers the best tradeoff between imaging efficiency and image quality, but is relatively sensitive to residual eddy-current effects on the echo train phase, resulting in encoding direction-dependent ghosting in the DW images. We introduce two efficient, image-based phase corrections for ghost artifact reduction in DW-RARE of fixed tissue samples, neither of which require navigator echo acquisition. Both methods rely on the phase difference in k-space between the unweighted reference image and a given DW image and assume a constant, per-echo phase error arising from residual eddy-current effects in the absence of sample motion. Significant qualitative and quantitative ghost artifact reductions are demonstrated for individual DW and calculated diffusion tensor images.