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Identification and quantification of speech variations in velar production across various phonological environments have always been an interesting topic in speech motor control studies. Dynamic magnetic resonance imaging has become a favorable tool for visualizing articulatory deformations and providing quantitative insights into speech activities over time. Based on this modality, it is proposed to employ a workflow of image analysis techniques to uncover potential deformation variations in the human tongue caused by changes in phonological environments by altering the placement of velar consonants in utterances. The speech deformations of four human subjects in three different consonant positions were estimated from magnetic resonance images using a spatiotemporal tracking method before being warped via image registration into a common space-a dynamic atlas space constructed using four-dimensional alignments-for normalized quantitative comparisons. Statistical tests and principal component analyses were conducted on the magnitude of deformations, consonant-specific deformations, and internal muscle strains. The results revealed an overall decrease in deformation intensity following the initial consonant production, indicating potential muscle adaptation behaviors at a later temporal position in one speech utterance., (© 2024 Acoustical Society of America.)

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).)

Background: Atherosclerosis is an arterial vessel wall disease characterized by slow, progressive lipid accumulation, smooth muscle disorganization, and inflammatory infiltration. Atherosclerosis often remains subclinical until extensive inflammatory injury promotes vulnerability of the atherosclerotic plaque to rupture with luminal thrombosis, which can cause the acute event of myocardial infarction or stroke. Current bioimaging techniques are unable to capture the pathognomonic distribution of cellular elements of the plaque and thus cannot accurately define its structural disorganization., Methods: We applied cardiovascular magnetic resonance spectroscopy (CMRS) and diffusion weighted CMR (DWI) with generalized Q-space imaging (GQI) analysis to architecturally define features of atheroma and correlated these to the microscopic distribution of vascular smooth muscle cells (SMC), immune cells, extracellular matrix (ECM) fibers, thrombus, and cholesteryl esters (CE). We compared rabbits with normal chow diet and cholesterol-fed rabbits with endothelial balloon injury, which accelerates atherosclerosis and produces advanced rupture-prone plaques, in a well-validated rabbit model of human atherosclerosis., Results: Our methods revealed new structural properties of advanced atherosclerosis incorporating SMC and lipid distributions. GQI with tractography portrayed the locations of these components across the atherosclerotic vessel wall and differentiated multi-level organization of normal, pro-inflammatory cellular phenotypes, or thrombus. Moreover, the locations of CE were differentiated from cellular constituents by their higher restrictive diffusion properties, which permitted chemical confirmation of CE by high field voxel-guided CMRS., Conclusions: GQI with tractography is a new method for atherosclerosis imaging that defines a pathological architectural signature for the atheromatous plaque composed of distributed SMC, ECM, inflammatory cells, and thrombus and lipid. This provides a detailed transmural map of normal and inflamed vessel walls in the setting of atherosclerosis that has not been previously achieved using traditional CMR techniques. Although this is an ex-vivo study, detection of micro and mesoscale level vascular destabilization as enabled by GQI with tractography could increase the accuracy of diagnosis and assessment of treatment outcomes in individuals with atherosclerosis., (© 2022. The Author(s).)

Accurate strain measurement in a deforming organ has been essential in motion analysis using medical images. In recent years, internal tissue's in vivo motion and strain computation has been mostly achieved through dynamic magnetic resonance (MR) imaging. However, such data lack information on tissue's intrinsic fiber directions, preventing computed strain tensors from being projected onto a direction of interest. Although diffusion-weighted MR imaging excels at providing fiber tractography, it yields static images unmatched with dynamic MR data. This work reports an algorithm workflow that estimates strain values in the diffusion MR space by matching corresponding tagged dynamic MR images. We focus on processing a dataset of various human tongue deformations in speech. The geometry of tongue muscle fibers is provided by diffusion tractography, while spatiotemporal motion fields are provided by tagged MR analysis. The tongue's deforming shapes are determined by segmenting a synthetic cine dynamic MR sequence generated from tagged data using a deep neural network. Estimated motion fields are transformed into the diffusion MR space using diffeomorphic registration, eventually leading to strain values computed in the direction of muscle fibers. The method was tested on 78 time volumes acquired during three sets of specific tongue deformations including both speech and protrusion motion. Strain in the line of action of seven internal tongue muscles was extracted and compared both intra- and inter-subject. Resulting compression and stretching patterns of individual muscles revealed the unique behavior of individual muscles and their potential activation pattern.

Tau neurofibrillary tangles, a pathophysiological hallmark of Alzheimer's disease (AD), exhibit a stereotypical spatiotemporal trajectory that is strongly correlated with disease progression and cognitive decline. Personalized prediction of tau progression is, therefore, vital for the early diagnosis and prognosis of AD. Evidence from both animal and human studies is suggestive of tau transmission along the brains preexisting neural connectivity conduits. We present here an analytic graph diffusion framework for individualized predictive modeling of tau progression along the structural connectome. To account for physiological processes that lead to active generation and clearance of tau alongside passive diffusion, our model uses an inhomogenous graph diffusion equation with a source term and provides closed-form solutions to this equation for linear and exponential source functionals. Longitudinal imaging data from two cohorts, the Harvard Aging Brain Study (HABS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI), were used to validate the model. The clinical data used for developing and validating the model include regional tau measures extracted from longitudinal positron emission tomography (PET) scans based on the 18 F-Flortaucipir radiotracer and individual structural connectivity maps computed from diffusion tensor imaging (DTI) by means of tractography and streamline counting. Two-timepoint tau PET scans were used to assess the goodness of model fit. Three-timepoint tau PET scans were used to assess predictive accuracy via comparison of predicted and observed tau measures at the third timepoint. Our results show high consistency between predicted and observed tau and differential tau from region-based analysis. While the prognostic value of this approach needs to be validated in a larger cohort, our preliminary results suggest that our longitudinal predictive model, which offers an in vivo macroscopic perspective on tau progression in the brain, is potentially promising as a personalizable predictive framework for AD., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Intelligible speech is produced by creating varying internal local muscle groupings-i.e., functional units-that are generated in a systematic and coordinated manner. There are two major challenges in characterizing and analyzing functional units. First, due to the complex and convoluted nature of tongue structure and function, it is of great importance to develop a method that can accurately decode complex muscle coordination patterns during speech. Second, it is challenging to keep identified functional units across subjects comparable due to their substantial variability. In this work, to address these challenges, we develop a new deep learning framework to identify common and subject-specific functional units of tongue motion during speech. Our framework hinges on joint deep graph-regularized sparse non-negative matrix factorization (NMF) using motion quantities derived from displacements by tagged Magnetic Resonance Imaging. More specifically, we transform NMF with sparse and graph regularizations into modular architectures akin to deep neural networks by means of unfolding the Iterative Shrinkage-Thresholding Algorithm to learn interpretable building blocks and associated weighting map. We then apply spectral clustering to common and subject-specific weighting maps from which we jointly determine the common and subject-specific functional units. Experiments carried out with simulated datasets show that the proposed method achieved on par or better clustering performance over the comparison methods.Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Purpose: Recent observations of several preferred orientations of diffusion in deep white matter may indicate either (a) that axons in different directions are independently bundled in thick sheets and function noninteractively, or more interestingly, (b) that the axons are closely interwoven and would exhibit branching and sharp turns. This study aims to investigate whether the dependence of dMRI Q-ball signal on the interpulse time Δ can decode the smaller-than-voxel-size brain structure, in particular, to distinguish scenarios (a) and (b)., Methods: High-resolution Q-ball images of a healthy brain taken with b = 8000  s/mm 2 for 3 different values of Δ were analyzed. The exchange of water molecules between crossing fibers was characterized by the fourth Fourier coefficient f 4 ( Δ ) of the signal profile in the plane of crossing. To interpret the empirical results, a model consisting of differently oriented parallel sheets of cylinders was developed. Diffusion of water molecules inside and outside cylinders was simulated by the Monte Carlo method., Results: Simulations predict that f 4 ( Δ ) , agreeing with the empirical results, must increase with Δ for large b-values, but may peak at a typical Δ that depends on the thickness of the cylinder sheets for intermediate b-values. Thus, the thickness of axon layers in voxels with 2 predominant orientations can be detected from empirical f 4 ( Δ ) taken at smaller b-values., Conclusion: Based on the simulation results, recommendations are made on how to design a dMRI experiment with optimal b-value and range of Δ in order to measure the thickness of axon sheets in the white matter, hence to distinguish (a) and (b)., (© 2021 International Society for Magnetic Resonance in Medicine.)

Tau tangles are a pathophysiological hallmark of Alzheimer's disease (AD) and exhibit a stereotypical pattern of spatiotemporal spread which has strong links to disease progression and cognitive decline. Preclinical evidence suggests that tau spread depends on neuronal connectivity rather than physical proximity between different brain regions. Here, we present a novel physics-informed geometric learning model for predicting tau buildup and spread that learns patterns directly from longitudinal tau imaging data while receiving guidance from governing physical principles. Implemented as a graph neural network with physics-based regularization in latent space, the model enables effective training with smaller data sizes. For training and validation of the model, we used longitudinal tau measures from positron emission tomography (PET) and structural connectivity graphs from diffusion tensor imaging (DTI) from the Harvard Aging Brain Study. The model led to higher peak signal-to-noise ratio and lower mean squared error levels than both an unregularized graph neural network and a differential equation solver. The method was validated using both two-timepoint and three-timepoint tau PET measures. The effectiveness of the approach was further confirmed by a cross-validation study.

The tongue is capable of producing intelligible speech because of successful orchestration of muscle groupings-i.e., functional units-of the highly complex muscles over time. Due to the different motions that tongues produce, functional units are transitional structures which transform muscle activity to surface tongue geometry and they vary significantly from one subject to another. In order to compare and contrast the location and size of functional units in the presence of such substantial inter-person variability, it is essential to study both common and subject-specific functional units in a group of people carrying out the same speech task. In this work, a new normalization technique is presented to simultaneously identify the common and subject-specific functional units defined in the tongue when tracked by tagged magnetic resonance imaging. To achieve our goal, a joint sparse non-negative matrix factorization framework is used, which learns a set of building blocks and subject-specific as well as common weighting matrices from motion quantities extracted from displacements. A spectral clustering technique is then applied to the subject-specific and common weighting matrices to determine the subject-specific functional units for each subject and the common functional units across subjects. Our experimental results using in vivo tongue motion data show that our approach is able to identify the common and subject-specific functional units with reduced size variability of tongue motion during speech.

The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders.

Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional units using tagged-magnetic resonance imaging (MRI) sheds light on the mechanisms of normal and pathological muscle coordination patterns, yielding improvement in surgical planning, treatment, or rehabilitation procedures. In this paper, to mine this information, we propose a matrix factorization and probabilistic graphical model framework to produce building blocks and their associated weighting map using motion quantities extracted from tagged-MRI. Our tagged-MRI imaging and accurate voxel-level tracking provide previously unavailable internal tongue motion patterns, thus revealing the inner workings of the tongue during speech or other lingual behaviors. We then employ spectral clustering on the weighting map to identify the cohesive regions defined by the tongue motion that may involve multiple or undocumented regions. To evaluate our method, we perform a series of experiments. We first use two-dimensional images and synthetic data to demonstrate the accuracy of our method. We then use three-dimensional synthetic and in vivo tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.

Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications. Here, we introduce a statistical multimodal atlas of 4D tongue motion using healthy subjects, which enables a combined quantitative characterization of tongue motion in a reference anatomical configuration. This atlas framework, termed Speech Map, combines cine- and tagged-MRI in order to provide both the anatomic reference and motion information during speech. Our approach involves a series of steps including (1) construction of a common reference anatomical configuration from cine-MRI, (2) motion estimation from tagged-MRI, (3) transformation of the motion estimations to the reference anatomical configuration, and (4) computation of motion quantities such as Lagrangian strain. Using this framework, the anatomic configuration of the tongue appears motionless, while the motion fields and associated strain measurements change over the time course of speech. In addition, to form a succinct representation of the high-dimensional and complex motion fields, principal component analysis is carried out to characterize the central tendencies and variations of motion fields of our speech tasks. Our proposed method provides a platform to quantitatively and objectively explain the differences and variability of tongue motion by illuminating internal motion and strain that have so far been intractable. The findings are used to understand how tongue function for speech is limited by abnormal internal motion and strain in glossectomy patients.

Brain fiber pathways are presumed to follow smooth curves but recent high angular resolution diffusion MRI (dMRI) suggests that instead they follow 3 primary axes often nearly orthogonal. To investigate this, we analyzed axon pathways under monkey primary motor cortex with (1) dMRI tractography, (2) axon tract tracing, and (3) axon immunohistochemistry. dMRI tractography shows the predicted crossings of axons in mediolateral and dorsoventral orientations and does not show axon turns in this region. Axons labeled with tract tracer in the motor cortex dispersed in the centrum semiovale by microscopically sharp axonal turns and/or branches (radii ≤15 µm) into 2 sharply defined orientations, mediolateral and dorsoventral. Nearby sections processed with SMI-32 antibody to label projection axons and SMI-312 antibody to label all axons revealed axon distributions parallel to the tracer axons. All 3 histological methods confirmed preponderant axon distributions parallel with dMRI axes with few axons (<20%) following smooth curves or diagonal orientations. These findings indicate that axons navigate deep white matter via microscopic sharp turns and branches between primary axes. They support dMRI observations of primary fiber axes, as well as the prediction that fiber crossings include navigational events not yet directly resolved by dMRI. New methods will be needed to incorporate coherent microscopic navigation into dMRI of connectivity.

Amyotrophic Lateral Sclerosis (ALS) is a neurological disorder, which impairs tongue function for speech and swallowing. A widely used Diffusion Tensor Imaging (DTI) analysis pipeline is employed for quantifying differences in tongue fiber myoarchitecture between controls and ALS patients. This pipeline uses both high-resolution magnetic resonance imaging (hMRI) and DTI. hMRI is used to delineate tongue muscles, while DTI provides indices to reveal fiber connectivity within and between muscles. The preliminary results using five controls and two patients show quantitative differences between the groups. This work has the potential to provide insights into the detrimental effects of ALS on speech and swallowing.

Amyotrophic Lateral Sclerosis (ALS) is a neurological disease that causes death of neurons controlling muscle movements. Loss of speech and swallowing functions is a major impact due to degeneration of the tongue muscles. In speech studies using magnetic resonance (MR) techniques, diffusion tensor imaging (DTI) is used to capture internal tongue muscle fiber structures in three-dimensions (3D) in a non-invasive manner. Tagged magnetic resonance images (tMRI) are used to record tongue motion during speech. In this work, we aim to combine information obtained with both MR imaging techniques to compare the functionality characteristics of the tongue between normal and ALS subjects. We first extracted 3D motion of the tongue using tMRI from fourteen normal subjects in speech. The estimated motion sequences were then warped using diffeomorphic registration into the b0 spaces of the DTI data of two normal subjects and an ALS patient. We then constructed motion atlases by averaging all warped motion fields in each b0 space, and computed strain in the line of action along the muscle fiber directions provided by tractography. Strain in line with the fiber directions provides a quantitative map of the potential active region of the tongue during speech. Comparison between normal and ALS subjects explores the changing volume of compressing tongue tissues in speech facing the situation of muscle degradation. The proposed framework provides for the first time a dynamic map of contracting fibers in ALS speech patterns, and has the potential to provide more insight into the detrimental effects of ALS on speech.

The parameter selection for diffusion MRI experiments is dominated by the "k-q tradeoff" whereby the Signal to Noise Ratio (SNR) of the images is traded for either high spatial resolution (determined by the maximum k-value collected) or high diffusion sensitivity (effected by b-value or the q vector) but usually not both. Furthermore, different brain regions (such as gray matter and white matter) likely require different tradeoffs between these parameters due to the size of the structures to be visualized or the length-scale of the microstructure being probed. In this case, it might be advantageous to combine information from two scans - a scan with high q but low k (high angular resolution in diffusion but low spatial resolution in the image domain) to provide maximal information about white matter fiber crossing, and one low q but high k (low angular resolution but high spatial resolution) for probing the cortex. In this study, we propose a method, termed HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging, for acquiring and combining the information from these two complementary types of scan with the goal of studying diffusion in the cortex without compromising white matter fiber information. The white-gray boundary and pial surface obtained from anatomical scans are incorporated as prior information to guide the fusion. We study the complementary advantages of the fused datasets, and assess the quality of the HIBRID data compared to either alone., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Representation of human tongue motion using three-dimensional vector fields over time can be used to better understand tongue function during speech, swallowing, and other lingual behaviors. To characterize the inter-subject variability of the tongue's shape and motion of a population carrying out one of these functions it is desirable to build a statistical model of the four-dimensional (4D) tongue. In this paper, we propose a method to construct a spatio-temporal atlas of tongue motion using magnetic resonance (MR) images acquired from fourteen healthy human subjects. First, cine MR images revealing the anatomical features of the tongue are used to construct a 4D intensity image atlas. Second, tagged MR images acquired to capture internal motion are used to compute a dense motion field at each time frame using a phase-based motion tracking method. Third, motion fields from each subject are pulled back to the cine atlas space using the deformation fields computed during the cine atlas construction. Finally, a spatio-temporal motion field atlas is created to show a sequence of mean motion fields and their inter-subject variation. The quality of the atlas was evaluated by deforming cine images in the atlas space. Comparison between deformed and original cine images showed high correspondence. The proposed method provides a quantitative representation to observe the commonality and variability of the tongue motion field for the first time, and shows potential in evaluation of common properties such as strains and other tensors based on motion fields.

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Combined measurement of diverse molecular and anatomical traits that span multiple levels remains a major challenge in biology. Here, we introduce a simple method that enables proteomic imaging for scalable, integrated, high-dimensional phenotyping of both animal tissues and human clinical samples. This method, termed SWITCH, uniformly secures tissue architecture, native biomolecules, and antigenicity across an entire system by synchronizing the tissue preservation reaction. The heat- and chemical-resistant nature of the resulting framework permits multiple rounds (>20) of relabeling. We have performed 22 rounds of labeling of a single tissue with precise co-registration of multiple datasets. Furthermore, SWITCH synchronizes labeling reactions to improve probe penetration depth and uniformity of staining. With SWITCH, we performed combinatorial protein expression profiling of the human cortex and also interrogated the geometric structure of the fiber pathways in mouse brains. Such integrated high-dimensional information may accelerate our understanding of biological systems at multiple levels., (Copyright © 2015 Elsevier Inc. All rights reserved.)

A diffusion-weighted (DW) template in a standard coordinate system is often necessary for the analysis of white matter (WM) structures using DW images. Although several DW templates have been constructed in the ICBM-152 space, a template for diffusion spectrum imaging (DSI) is still lacking. In this study, we developed a DSI template in the ICBM-152 space from 122 healthy adults. This high quality template, NTU-DSI-122, was built through incorporating the macroscopic anatomical information using high-resolution T1 -weighted images and the microscopic structural information obtained from DSI datasets. Two evaluations were conducted to examine the quality of NTU-DSI-122. The first evaluation examined the anatomical consistency of NTU-DSI-122 in matching to the ICBM-152 coordinate system. The results showed that this template matched to the ICBM-152 templates very well across the whole brain, not only in the deep white matter regions as other DW templates but also in the superficial white matter regions. In the second evaluation, a large number of independent diffusion tensor imaging (DTI) datasets were registered to the DTI template derived from NTU-DSI-122. The examination was performed by quantifying the anatomical consistency among the registered DTI datasets. The results showed that using NTU-DSI-122 as the registration template the registered DTI datasets can achieve high anatomical alignment. Both evaluations demonstrate that NTU-DSI-122 is a useful high quality DW template. Therefore, NTU-DSI-122 can serve as a representative DSI dataset for a healthy adult population, and will be of potential value for brain research and clinical applications. The NTU-DSI-122 template is available at http://www.nitrc.org/projects/ntu-dsi-122/., (© 2015 Wiley Periodicals, Inc.)

The cytoarchitecture of the human brain is of great interest in diverse fields: neuroanatomy, neurology, neuroscience, and neuropathology. Traditional histology is a method that has been historically used to assess cell and fiber content in the ex vivo human brain. However, this technique suffers from significant distortions. We used a previously demonstrated optical coherence microscopy technique to image individual neurons in several square millimeters of en-face tissue blocks from layer II of the human entorhinal cortex, over 50 µm in depth. The same slices were then sectioned and stained for Nissl substance. We registered the optical coherence tomography (OCT) images with the corresponding Nissl stained slices using a nonlinear transformation. The neurons were then segmented in both images and we quantified the overlap. We show that OCT images contain information about neurons that is comparable to what can be obtained from Nissl staining, and thus can be used to assess the cytoarchitecture of the ex vivo human brain with minimal distortion. With the future integration of a vibratome into the OCT imaging rig, this technique can be scaled up to obtain undistorted volumetric data of centimeter cube tissue blocks in the near term, and entire human hemispheres in the future.

One of the major goals of the NIH Blueprint Human Connectome Project was to map and quantify the white matter connections in the brain using diffusion tractography. Given the prevalence of complex white matter structures, the capability of resolving local white matter geometries with multiple crossings in the diffusion magnetic resonance imaging (dMRI) data is critical. Increasing b-value has been suggested for delineation of the finer details of the orientation distribution function (ODF). Although increased gradient strength and duration increase sensitivity to highly restricted intra-axonal water, gradient strength limitations require longer echo times (TE) to accommodate the increased diffusion encoding times needed to achieve a higher b-value, exponentially lowering the signal-to-noise ratio of the acquisition. To mitigate this effect, the MGH-USC Connectom scanner was built with 300 mT/m gradients, which can significantly reduce the TE of high b-value diffusion imaging. Here we report comparisons performed across b-values based on q-ball ODF metrics to investigate whether high b-value diffusion imaging on the Connectom scanner can improve resolving complex white matter structures. The q-ball ODF features became sharper as the b-value increased, with increased power fraction in higher order spherical harmonic series of the ODF and increased peak heights relative to the overall size of the ODF. Crossing structures were detected in an increasingly larger fraction of white matter voxels and the spatial distribution of two-way and three-way crossing structures was largely consistent with known anatomy. Results indicate that dMRI with high diffusion encoding on the Connectom system is a promising tool to better characterize, and ultimately understand, the underlying structural organization and motifs in the human brain.

Background: The arrangement of myofibers in the heart is highly complex and must be replicated by injected cells to produce functional myocardium. A novel approach to characterize the microstructural response of the myocardium to ischemia and cell therapy, with the use of serial diffusion tensor magnetic resonance imaging tractography of the heart in vivo, is presented., Methods and Results: Validation of the approach was performed in normal (n=6) and infarcted mice (n=6) as well as healthy human volunteers. Mice (n=12) were then injected with bone marrow mononuclear cells 3 weeks after coronary ligation. In half of the mice the donor and recipient strains were identical, and in half the strains were different. A positive response to cell injection was defined by a decrease in mean diffusivity, an increase in fractional anisotropy, and the appearance of new myofiber tracts with the correct orientation. A positive response to bone marrow mononuclear cell injection was seen in 1 mouse. The response of the majority of mice to bone marrow mononuclear cell injection was neutral (9/12) or negative (2/12). The in vivo tractography findings were confirmed with histology., Conclusions: Diffusion tensor magnetic resonance imaging tractography was able to directly resolve the ability of injected cells to generate new myofiber tracts and provided a fundamental readout of their regenerative capacity. A highly novel and translatable approach to assess the efficacy of cell therapy in the heart is thus presented.

Spectral domain optical coherence tomography (SD-OCT) is a high resolution imaging technique that generates excellent contrast based on intrinsic optical properties of the tissue, such as neurons and fibers. The SD-OCT data acquisition is performed directly on the tissue block, diminishing the need for cutting, mounting and staining. We utilized SD-OCT to visualize the laminar structure of the isocortex and compared cortical cytoarchitecture with the gold standard Nissl staining, both qualitatively and quantitatively. In histological processing, distortions routinely affect registration to the blockface image and prevent accurate 3D reconstruction of regions of tissue. We compared blockface registration to SD-OCT and Nissl, respectively, and found that SD-OCT-blockface registration was significantly more accurate than Nissl-blockface registration. Two independent observers manually labeled cortical laminae (e.g. III, IV and V) in SD-OCT images and Nissl stained sections. Our results show that OCT images exhibit sufficient contrast in the cortex to reliably differentiate the cortical layers. Furthermore, the modalities were compared with regard to cortical laminar organization and showed good agreement. Taken together, these SD-OCT results suggest that SD-OCT contains information comparable to standard histological stains such as Nissl in terms of distinguishing cortical layers and architectonic areas. Given these data, we propose that SD-OCT can be used to reliably generate 3D reconstructions of multiple cubic centimeters of cortex that can be used to accurately and semi-automatically perform standard histological analyses., (© 2013.)

Background: TMS activations of white matter depend not only on the distance from the coil, but also on the orientation of the axons relative to the TMS-induced electric field, and especially on axonal bends that create strong local field gradient maxima. Therefore, tractography contains potentially useful information for TMS targeting., Objective/methods: Here, we utilized 1-mm resolution diffusion and structural T1-weighted MRI to construct large-scale tractography models, and localized TMS white matter activations in motor cortex using electromagnetic forward modeling in a boundary element model (BEM)., Results: As expected, in sulcal walls, pyramidal cell axonal bends created preferred sites of activation that were not found in gyral crowns. The model agreed with the well-known coil orientation sensitivity of motor cortex, and also suggested unexpected activation distributions emerging from the E-field and tract configurations. We further propose a novel method for computing the optimal coil location and orientation to maximally stimulate a pre-determined axonal bundle., Conclusions: Diffusion MRI tractography with electromagnetic modeling may improve spatial specificity and efficacy of TMS., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The optimum sequence parameters of diffusion spectrum MRI (DSI) on clinical scanners were investigated previously. However, the scan time of approximately 30 min is still too long for patient studies. Additionally, relatively large sampling interval in the diffusion-encoding space may cause aliasing artifact in the probability density function when Fourier transform is undertaken, leading to estimation error in fiber orientations. Therefore, this study proposed a non-Cartesian sampling scheme, body-centered-cubic (BCC), to avoid the aliasing artifact as compared to the conventional Cartesian grid sampling scheme (GRID). Furthermore, the accuracy of DSI with the use of half-sphere sampling schemes, i.e. GRID102 and BCC91, was investigated by comparing to their full-sphere sampling schemes, GRID203 and BCC181, respectively. In results, smaller deviation angle and lower angular dispersion were obtained by using the BCC sampling scheme. The half-sphere sampling schemes yielded angular precision and accuracy comparable to the full-sphere sampling schemes. The optimum b(max) was approximately 4750 s/mm(2) for GRID and 4500 s/mm(2) for BCC. In conclusion, the BCC sampling scheme could be implemented as a useful alternative to the GRID sampling scheme. Combination of BCC and half-sphere sampling schemes, that is BCC91, may potentially reduce the scan time of DSI from 30 min to approximately 14 min while maintaining its precision and accuracy., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio represent significant hurdles for many in vivo applications. This article presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstruction method that takes advantage of the high level of structural correlation observed in typical datasets. Compared to existing denoising methods, the proposed method performs reconstruction directly from the measured complex k-space data, allowing for gaussian noise modeling and theoretical characterizations of the resolution and signal-to-noise ratio of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling and q-space modeling). The joint reconstruction method can provide significant improvements in signal-to-noise ratio relative to conventional reconstruction techniques, with a relatively minor corresponding loss in image resolution. Results are shown in the context of diffusion spectrum imaging tractography and diffusion tensor imaging, illustrating the potential of this signal-to-noise ratio-enhancing joint reconstruction approach for a range of different diffusion imaging experiments., (Copyright © 2011 Wiley Periodicals, Inc.)

In diffusion MRI, simultaneous multi-slice single-shot EPI acquisitions have the potential to increase the number of diffusion directions obtained per unit time, allowing more diffusion encoding in high angular resolution diffusion imaging (HARDI) acquisitions. Nonetheless, unaliasing simultaneously acquired, closely spaced slices with parallel imaging methods can be difficult, leading to high g-factor penalties (i.e., lower SNR). The CAIPIRINHA technique was developed to reduce the g-factor in simultaneous multi-slice acquisitions by introducing inter-slice image shifts and thus increase the distance between aliased voxels. Because the CAIPIRINHA technique achieved this by controlling the phase of the RF excitations for each line of k-space, it is not directly applicable to single-shot EPI employed in conventional diffusion imaging. We adopt a recent gradient encoding method, which we termed "blipped-CAIPI", to create the image shifts needed to apply CAIPIRINHA to EPI. Here, we use pseudo-multiple replica SNR and bootstrapping metrics to assess the performance of the blipped-CAIPI method in 3× simultaneous multi-slice diffusion studies. Further, we introduce a novel image reconstruction method to reduce detrimental ghosting artifacts in these acquisitions. We show that data acquisition times for Q-ball and diffusion spectrum imaging (DSI) can be reduced 3-fold with a minor loss in SNR and with similar diffusion results compared to conventional acquisitions., (Copyright © 2012 Elsevier Inc. All rights reserved.)

In response to Catani et al., we show that corticospinal pathways adhere via sharp turns to two local grid orientations; that our studies have three times the diffusion resolution of those compared; and that the noted technical concerns, including crossing angles, do not challenge the evidence of mathematically specific geometric structure. Thus, the geometric thesis gives the best account of the available evidence.

Simultaneous multislice Echo Planar Imaging (EPI) acquisition using parallel imaging can decrease the acquisition time for diffusion imaging and allow full-brain, high-resolution functional MRI (fMRI) acquisitions at a reduced repetition time (TR). However, the unaliasing of simultaneously acquired, closely spaced slices can be difficult, leading to a high g-factor penalty. We introduce a method to create interslice image shifts in the phase encoding direction to increase the distance between aliasing pixels. The shift between the slices is induced using sign- and amplitude-modulated slice-select gradient blips simultaneous with the EPI phase encoding blips. This achieves the desired shifts but avoids an undesired "tilted voxel" blurring artifact associated with previous methods. We validate the method in 3× slice-accelerated spin-echo and gradient-echo EPI at 3 T and 7 T using 32-channel radio frequency (RF) coil brain arrays. The Monte-Carlo simulated average g-factor penalty of the 3-fold slice-accelerated acquisition with interslice shifts is <1% at 3 T (compared with 32% without slice shift). Combining 3× slice acceleration with 2× inplane acceleration, the g-factor penalty becomes 19% at 3 T and 10% at 7 T (compared with 41% and 23% without slice shift). We demonstrate the potential of the method for accelerating diffusion imaging by comparing the fiber orientation uncertainty, where the 3-fold faster acquisition showed no noticeable degradation., (Copyright © 2011 Wiley Periodicals, Inc.)

The structure of the brain as a product of morphogenesis is difficult to reconcile with the observed complexity of cerebral connectivity. We therefore analyzed relationships of adjacency and crossing between cerebral fiber pathways in four nonhuman primate species and in humans by using diffusion magnetic resonance imaging. The cerebral fiber pathways formed a rectilinear three-dimensional grid continuous with the three principal axes of development. Cortico-cortical pathways formed parallel sheets of interwoven paths in the longitudinal and medio-lateral axes, in which major pathways were local condensations. Cross-species homology was strong and showed emergence of complex gyral connectivity by continuous elaboration of this grid structure. This architecture naturally supports functional spatio-temporal coherence, developmental path-finding, and incremental rewiring with correlated adaptation of structure and function in cerebral plasticity and evolution.

A diffusion deconvolution method is proposed to apply deconvolution to the diffusion orientation distribution function (dODF) and calculate the fiber orientation distribution function (fODF), which is defined as the orientation distribution of the fiber spin density. The dODF can be obtained from q-space imaging methods such as q-ball imaging (QBI), diffusion spectrum imaging (DSI), and generalized q-sampling imaging (GQI), and thus the method can be applied to various diffusion sampling schemes. A phantom study was conducted to compare the angular resolution of the fODF with the dODF, and the in vivo datasets were acquired using single-shell, two-shell, and grid sampling schemes, which were then reconstructed by QBI, GQI, and DSI, respectively. The phantom study showed that the fODF significantly improved the angular resolution over the dODF at 45- and 60-degree crossing angles. The in vivo study showed consistent fODF regardless of the applied sampling schemes and reconstruction methods, and the ability to resolve crossing fibers was improved in reduced sampling condition. The fiber spin density obtained from deconvolution showed a higher contrast-to-noise ratio than the fractional anisotropy (FA) mapping, and further application on tractography showed that the fiber spin density can be used to determine the termination of fiber tracts. In conclusion, the proposed deconvolution method is generally applicable to different q-space imaging methods. The calculated fODF improves the angular resolution and also provides a quantitative index of fiber spin density to refine fiber tracking., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The immature cortex (cortical plate [CP]) and underlying subplate (SP), a transient cell layer just below the CP, play critical roles in the formation of intracerebral connections. The purpose of this study was to examine the diffusion characteristics of the developing cortex and subcortical structures and compare to histology. We obtained high-resolution diffusion spectrum images of postnatal day (P) 0 (newborn), P35 (pediatric), and P100 (adult) cat brains, performed tractography analysis, and correlated with histological findings. Tractography revealed radial organization and radial afferent/efferent tracts not only in the CP but also in external SP at P0. Radial organization persisted only in the cortex but decreased at P35 and P100. Radial organization correlated with radial cellular organization, with highest cellular density at P0 (Cresyl Violet staining). At P0, the internal SP contained abundant corticocortical and projection tractography pathways, crossing at wide angles in areas with no myelination by Luxol Fast Blue staining. At P35 and P100, increased directional coherence of white matter was observed, with fewer local tracts, but more long association pathways. These results suggest that diffusion tractography can differentially characterize internal and external SP zones and their transition into mature cortical pathways.

From toddler to late teenager, the macroscopic pattern of axonal projections in the human brain remains largely unchanged while undergoing dramatic functional modifications that lead to network refinement. These functional modifications are mediated by increasing myelination and changes in axonal diameter and synaptic density, as well as changes in neurochemical mediators. Here we explore the contribution of white matter maturation to the development of connectivity between ages 2 and 18 y using high b-value diffusion MRI tractography and connectivity analysis. We measured changes in connection efficacy as the inverse of the average diffusivity along a fiber tract. We observed significant refinement in specific metrics of network topology, including a significant increase in node strength and efficiency along with a decrease in clustering. Major structural modules and hubs were in place by 2 y of age, and they continued to strengthen their profile during subsequent development. Recording resting-state functional MRI from a subset of subjects, we confirmed a positive correlation between structural and functional connectivity, and in addition observed that this relationship strengthened with age. Continuously increasing integration and decreasing segregation of structural connectivity with age suggests that network refinement mediated by white matter maturation promotes increased global efficiency. In addition, the strengthening of the correlation between structural and functional connectivity with age suggests that white matter connectivity in combination with other factors, such as differential modulation of axonal diameter and myelin thickness, that are partially captured by inverse average diffusivity, play an increasingly important role in creating brain-wide coherence and synchrony.

To demonstrate the relationship between lingual myoarchitecture and mechanics during swallowing, we performed a finite-element (FE) simulation of lingual deformation employing mesh aligned with the vector coordinates of myofiber tracts obtained by diffusion tensor imaging with tractography in humans. Material properties of individual elements were depicted in terms of Hill's three-component phenomenological model, assuming that the FE mesh was composed of anisotropic muscle and isotropic connective tissue. Moreover, the mechanical model accounted for elastic constraints by passive and active elements from the superior and inferior directions and the effect of out-of-plane muscles and connective tissue. Passive bolus effects were negligible. Myofiber tract activation was simulated over 500 ms in 1-ms steps following lingual tip association with the hard palate and incorporated specifically the accommodative and propulsive phases of the swallow. Examining the displacement field, active and passive muscle stress, elemental stretch, and strain rate relative to changes of global shape, we demonstrate that lingual reconfiguration during these swallow phases is characterized by (in sequence) the following: 1) lingual tip elevation and shortening in the anterior-posterior direction; 2) inferior displacement related to hyoglossus contraction at its inferior-most position; and 3) dominant clockwise rotation related to regional contraction of the genioglossus and contraction of the hyoglossus following anterior displacement. These simulations demonstrate that lingual deformation during the indicated phases of swallowing requires temporally patterned activation of intrinsic and extrinsic muscles and delineate a method to ascertain the mechanics of normal and pathological swallowing.

The myoarchitecture of the ventricular wall provides a structural template dictating tissue-scale patterns of mechanical function. We studied whether myofiber tract imaging performed with MR diffusion spectrum imaging (DSI) tractography has the capacity to resolve abnormalities of ventricular myoarchitecture in a model of congenital hypertrophic cardiomyopathy (HCM) associated with the ablation of myosin binding protein-C (MyBP-C). Homozygous MyBP-C knockout mice were generated by deletion of exons 3-10 from the endogenous MyBP-C gene. Fiber alignment in the left ventricular wall of wild type mice was depicted through DSI tractography (and confirmed by multi-slice two-photon microscopy) as a set of helical structures whose angles display a continuous transition from negative in the subepicardium to positive in the subendocardium. In contrast, the hearts obtained from the MyBP-C knockouts displayed substantial myoarchitectural disarray, characterized by a loss of voxel-to-voxel orientational coherence for fibers principally located in the mid-myocardium-subendocardium and impairment of the transmural progression of helix angles. These results substantiate the use of DSI tractography in determining myoarchitectural disarray in models of cardiomyopathy and suggest a biological association between myofilament expression, cardiac fiber alignment, and torsional rotation in the setting of congenital HCM.

Based on the Fourier transform relation between diffusion magnetic resonance (MR) signals and the underlying diffusion displacement, a new relation is derived to estimate the spin distribution function (SDF) directly from diffusion MR signals. This relation leads to an imaging method called generalized q-sampling imaging (GQI), which can obtain the SDF from the shell sampling scheme used in q-ball imaging (QBI) or the grid sampling scheme used in diffusion spectrum imaging (DSI). The accuracy of GQI was evaluated by a simulation study and an in vivo experiment in comparison with QBI and DSI. The simulation results showed that the accuracy of GQI was comparable to that of QBI and DSI. The simulation study of GQI also showed that an anisotropy index, named quantitative anisotropy, was correlated with the volume fraction of the resolved fiber component. The in vivo images of GQI demonstrated that SDF patterns were similar to the ODFs reconstructed by QBI or DSI. The tractography generated from GQI was also similar to those generated from QBI and DSI. In conclusion, the proposed GQI method can be applied to grid or shell sampling schemes and can provide directional and quantitative information about the crossing fibers.

Diffusion spectrum imaging (DSI) is a generalization of diffusion tensor imaging to map fibrous structure of white matter and potentially very sensitive to alterations of the cingulum bundles in dementia. In this in-vivo 4T study, DSI parameters especially spatial resolution and diffusion encoding bandwidth were optimized on humans to segment the cingulum bundles for tract level measurements of diffusion. The careful tailoring of the DSI acquisitions in conjunction with fiber tracking provided an optimal DSI setting for a reliable quantification of the cingulum bundle tracts. The optimization of tracking the cingulum bundle was verified using fiber tract quantifications, including coefficients of variability of DSI measurements along the fibers between and within healthy subjects in back-to-back studies and variogram analysis of spatial correlations between diffusion orientation distribution functions (ODF) along the cingulum bundle tracts. The results demonstrate that the identification of the cingulum bundle in human brain is reproducible using an optimized DSI parameter for maximum b-value and high spatial resolution of the DSI acquisition with a feasible acquisition time of whole brain in clinical practice. This optimized DSI setting should be useful for detecting alterations along the cingulum bundle in Alzheimer disease and related neurodegenerative disorders., (Published by Elsevier Ireland Ltd.)

Examination of the three-dimensional axonal pathways in the developing brain is key to understanding the formation of cerebral connectivity. By tracing fiber pathways throughout the entire brain, diffusion tractography provides information that cannot be achieved by conventional anatomical MR imaging or histology. However, standard diffusion tractography (based on diffusion tensor imaging, or DTI) tends to terminate in brain areas with low water diffusivity, indexed by low diffusion fractional anisotropy (FA), which can be caused by crossing fibers as well as fibers with less myelin. For this reason, DTI tractography is not effective for delineating the structural changes that occur in the developing brain, where the process of myelination is incomplete, and where crossing fibers exist in greater numbers than in the adult brain. Unlike DTI, diffusion spectrum imaging (DSI) can define multiple directions of water diffusivity; as such, diffusion tractography based on DSI provides marked flexibility for delineation of fiber tracts in areas where the fiber architecture is complex and multidirectional, even in areas of low FA. In this study, we showed that FA values were lower in the white matter of newborn (postnatal day 0; P0) cat brains than in the white matter of infant (P35) and juvenile (P100) cat brains. These results correlated well with histological myelin stains of the white matter: the newborn kitten brain has much less myelin than that found in cat brains at later stages of development. Using DSI tractography, we successfully identified structural changes in thalamo-cortical and cortico-cortical association tracts in cat brains from one stage of development to another. In newborns, the main body of the thalamo-cortical tract was smooth, and fibers branching from it were almost straight, while the main body became more complex and branching fibers became curved reflecting gyrification in the older cats. Cortico-cortical tracts in the temporal lobe were smooth in newborns, and they formed a sharper angle in the later stages of development. The cingulum bundle and superior longitudinal fasciculus became more visible with time. Within the first month after birth, structural changes occurred in these tracts that coincided with the formation of the gyri. These results show that DSI tractography has the potential for mapping morphological changes in low FA areas associated with growth and development. The technique may also be applicable to the study of other forms of brain plasticity, including future studies in vivo.

Histological studies have shown that the myocardium consists of an array of crossing helical fiber tracts. Changes in myocardial fiber architecture occur in ischemic heart disease and heart failure, and can be imaged non-destructively with diffusion-encoded MR. Several diffusion-encoding schemes have been developed, ranging from scalar measurements of mean diffusivity to a 6-dimensional imaging technique known as diffusion spectrum imaging or DSI. The properties of DSI make it particularly suited to the generation of 3-dimensional tractograms of myofiber architecture. In this article we review the physical basis of diffusion-tractography in the myocardium and the attributes of the available techniques, placing particular emphasis on DSI. The application of DSI in ischemic heart disease is reviewed, and the requisites for widespread clinical translation of diffusion MR tractography in the heart are discussed.

Background: Changes in myocardial microstructure are important components of the tissue response to infarction but are difficult to resolve with current imaging techniques. A novel technique, diffusion spectrum MRI tractography (DSI tractography), was thus used to image myofiber architecture in normal and infarcted myocardium. Unlike diffusion tensor imaging, DSI tractography resolves multiple myofiber populations per voxel, thus generating accurate 3D tractograms, which we present in the myocardium for the first time., Methods and Results: DSI tractography was performed at 4.7 T in excised rat hearts 3 weeks after left coronary artery ligation (n=4) and in 4 age-matched controls. Fiber architecture in the control hearts varied smoothly from endocardium to epicardium, producing a symmetrical array of crossing helical structures in which orthogonal myofibers were separated by fibers with intermediate helix angles. Fiber architecture in the infarcted hearts was severely perturbed. The infarct boundary in all cases was highly irregular and punctuated repeatedly by residual myofibers extending from within the infarct to the border zones. In all infarcts, longitudinal myofibers extending toward the basal-anterior wall and transversely oriented myofibers extending toward the septum lay in direct contact with each other, forming nodes of orthogonal myofiber intersection or contact., Conclusions: DSI tractography resolves 3D myofiber architecture and reveals a complex network of orthogonal myofibers within infarcted myocardium. Meshlike networks of orthogonal myofibers in infarcted myocardium may resist mechanical remodeling but also probably increase the risk for lethal reentrant arrhythmias. DSI tractography thus provides a new and important readout of tissue injury after myocardial infarction.

Purpose: To increase the efficiency of densely encoded diffusion imaging of the brain, such as diffusion spectrum imaging (DSI), we time-multiplex multiple slices within the same readout using simultaneous image refocusing echo-planar imaging (SIR-EPI)., Materials and Methods: Inefficiency in total scan time results from the long time of diffusion encoding gradient pulses which must be repeated for each and every image. We present a highly efficient multiplexing method, simultaneous image refocusing (SIR), for reducing the total scan time of diffusion imaging by nearly one-half. SIR DSI is performed in 10 minutes rather than 21 minutes, acceptable for routine clinical application., Results: Two identical studies were completed, comparing conventional single-slice EPI DSI and SIR-EPI DSI, showing equal signal-to-noise ratio (SNR) and contrast and small differences in registration, likely due to typical subject motion. Comparison of DSI and DTI tractographs showed matching quality and detection of white matter tracts., Conclusion: The net reduction to nearly half the number of diffusion encoding gradient pulses in SIR-EPI significantly reduces acquisition times of DSI and DTI., (Copyright (c) 2009 Wiley-Liss, Inc.)

Background: We used diffusion-tensor cardiac MR to investigate myocardial microstructure changes, including tissue integrity (mean diffusivity [MD], fractional anisotropy) and fiber architecture (helix angles) in patients with recent myocardial infarction (MI). This study aimed to investigate the sequential changes of myocardial microstructure and its relationships with changes of macrostructure and function of the left ventricle post-MI., Methods and Results: Seventeen patients (age, 55.1+/-11.5 years; all men) participated in the follow-up study. Diffusion-tensor cardiac MR, cine gradient echo for left ventricle function, and late gadolinium enhancement for viability were measured from recent to chronic MI (median interval, 191 days). When compared with the remote zone, the infarct-adjacent zone showed overall increase of MD (2-way MANOVA, F(1,16)=36.3; P<0.001), decrease of fractional anisotropy (F(1,16)=5.8; P=0.029), and decrease of mean helix angles (F(1,16)=62.0; P<0.001). From recent to chronic MI, there was overall sequential decrease of MD (F(1,16)=22.6; P<0.001) and increase of fractional anisotropy (F(1,16)=7.8; P=0.013). Multiple linear regression showed that the improvement of wall thickening in the infarct-adjacent zone correlated with sequential decrease of MD in the infarct-adjacent zone (r=-0.70; P=0.002) and increase of mean helix angles (ie, more right-handed helical myofiber reorientation, predominantly subendocardial location) in the remote zone (r=0.60; P=0.011). Likewise, wall thickening in the remote zone correlated with MD in the remote zone (r=-0.72; P=0.001) and mean helix angles in the infarct-adjacent zone (r=0.72; P=0.001)., Conclusions: Diffusion-tensor cardiac MR suggests that sequential zonal improvement of tissue integrity and fiber architecture remodeling both associate with sequential recovery of zonal wall thickening of the left ventricle from recent to chronic MI.

The tongue consists of a complex, multiscale array of myofibers that comprise the anatomical underpinning of lingual mechanical function. 3-D myoarchitecture was imaged in mouse tongues with diffusion spectrum magnetic resonance imaging (DSI) at 9.4 T (b(max) 7000 smm, 150-microm isotropic voxels), a method that derives the preferential diffusion of water/voxel, and high-throughput (10 fps) two-photon microscope (TPM). Net fiber alignment was represented for each method in terms of the local maxima of an orientational distribution function (ODF) derived from the local diffusion (DSI) and 3-D structural autocorrelation (TPM), respectively. Mesoscale myofiber tracts were generated by alignment of the principal orientation vectors of the ODFs. These data revealed a consistent relationship between the properties of the respective ODFs and the virtual superimposition of the distributed mesoscale myofiber tracts. The identification of a mesoscale anatomical construct, which specifically links the microscopic and macroscopic spatial scales, provides a method for relating the orientation and distribution of cells and subcellular components with overall tissue morphology, thus contributing to the development of multiscale methods for mechanical analysis.

MRI tractography is the mapping of neural fiber pathways based on diffusion MRI of tissue diffusion anisotropy. Tractography based on diffusion tensor imaging (DTI) cannot directly image multiple fiber orientations within a single voxel. To address this limitation, diffusion spectrum MRI (DSI) and related methods were developed to image complex distributions of intravoxel fiber orientation. Here we demonstrate that tractography based on DSI has the capacity to image crossing fibers in neural tissue. DSI was performed in formalin-fixed brains of adult macaque and in the brains of healthy human subjects. Fiber tract solutions were constructed by a streamline procedure, following directions of maximum diffusion at every point, and analyzed in an interactive visualization environment (TrackVis). We report that DSI tractography accurately shows the known anatomic fiber crossings in optic chiasm, centrum semiovale, and brainstem; fiber intersections in gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex. In contrast, none of these examples of fiber crossing and complex structure was identified by DTI analysis of the same data sets. These findings indicate that DSI tractography is able to image crossing fibers in neural tissue, an essential step toward non-invasive imaging of connectional neuroanatomy.