Lipeng Ning, Frederik Laun, Yaniv Gur, Edward VR DiBella, Samuel Deslauriers-Gauthier, Thinhinane Megherbi, Aurobrata Ghosh, Mauro Zucchelli, Gloria Menegaz, Rutger Fick, Samuel St-Jean, Michael Paquette, Ramon Aranda, Maxime Descoteaux, Rachid Deriche, Lauren O'Donnell, and Yogesh Rathi. 2015. “Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?” Med Image Anal, 26, 1, Pp. 316-31.Abstract
Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements, a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SPArse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimaging study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications.
Revanth Reddy Garlapati, Ahmed Mostayed, Grand Roman Joldes, Adam Wittek, Barry Doyle, and Karol Miller. 2015. “Towards measuring neuroimage misalignment.” Comput Biol Med, 64, Pp. 12-23.Abstract
To enhance neuro-navigation, high quality pre-operative images must be registered onto intra-operative configuration of the brain. Therefore evaluation of the degree to which structures may remain misaligned after registration is critically important. We consider two Hausdorff Distance (HD)-based evaluation approaches: the edge-based HD (EBHD) metric and the Robust HD (RHD) metric as well as various commonly used intensity-based similarity metrics such as Mutual Information (MI), Normalised Mutual Information (NMI), Entropy Correlation Coefficient (ECC), Kullback-Leibler Distance (KLD) and Correlation Ratio (CR). We conducted the evaluation by applying known deformations to simple sample images and real cases of brain shift. We conclude that the intensity-based similarity metrics such as MI, NMI, ECC, KLD and CR do not correlate well with actual alignment errors, and hence are not useful for assessing misalignment. On the contrary, the EBHD and the RHD metrics correlated well with actual alignment errors; however, they have been found to underestimate the actual misalignment. We also note that it is beneficial to present HD results as a percentile-HD curve rather than a single number such as the 95-percentile HD. Percentile-HD curves present the full range of alignment errors and also facilitate the comparison of results obtained using different approaches. Furthermore, the qualities that should be possessed by an ideal evaluation metric were highlighted. Future studies could focus on developing such an evaluation metric.
Toshiyuki Ohtani, Sylvain Bouix, Amanda E Lyall, Taiga Hosokawa, Yukiko Saito, Eric Melonakos, Carl-Fredrik Westin, Larry J Seidman, Jill Goldstein, Raquelle Mesholam-Gately, Tracey Petryshen, Joanne Wojcik, and Marek Kubicki. 2015. “Abnormal White Matter Connections between Medial Frontal Regions Predict Symptoms in Patients with First Episode Schizophrenia.” Cortex, 71, Pp. 264-76.Abstract
INTRODUCTION: The medial orbitofrontal cortex (mOFC) and rostral part of anterior cingulate cortex (rACC) have been suggested to be involved in the neural network of salience and emotional processing, and associated with specific clinical symptoms in schizophrenia. Considering the schizophrenia dysconnectivity hypothesis, the connectivity abnormalities between mOFC and rACC might be associated with clinical characteristics in first episode schizophrenia patients (FESZ). METHODS: After parcellating mOFC into the anterior and posterior part, diffusion properties of the mOFC-rACC white matter connections for 21 patients with FESZ and 21 healthy controls (HCs) were examined using stochastic tractography, one of the most effective Diffusion Tensor Imaging (DTI) methods for examining tracts between adjacent gray matter (GM) regions. RESULTS: Fractional anisotropy (FA) reductions were observed in bilateral posterior, but not anterior mOFC-rACC connections (left: p < .0001; right: p < .0001) in FESZ compared to HCs. In addition, reduced FA in the left posterior mOFC-rACC connection was associated with more severe anhedonia-asociality (rho = -.633, p = .006) and total score (rho = -.520, p = .032) in the Scale for the Assessment of Negative Symptoms (SANS); reduced FA in the right posterior mOFC-rACC connection was associated with more severe affective flattening (rho = -.644, p = .005), total score (rho = -.535, p = .027) in SANS, hallucinations (rho = -.551, p = .018), delusions (rho = -.632, p = .005) and total score (rho = -.721, p = .001) in the Scale for the Assessment of Positive Symptoms (SAPS) in FESZ. CONCLUSIONS: The observed white matter abnormalities within the connections between mOFC and rACC might be associated with the psychopathology of the early stage of schizophrenia.
Christian Wachinger, Polina Golland, William Kremen, Bruce Fischl, Martin Reuter, and Martin Reuter. 2015. “BrainPrint: A Discriminative Characterization of Brain Morphology.” Neuroimage, 109, Pp. 232-48.Abstract
We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace-Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.
René CW Mandl, Ofer Pasternak, Wiepke Cahn, Marek Kubicki, René S Kahn, Martha E Shenton, and Hilleke E Hulshoff Pol. 2015. “Comparing Free Water Imaging and Magnetization Transfer Measurements in Schizophrenia.” Schizophr Res, 161, 1, Pp. 126-32.Abstract
Diffusion weighted imaging (DWI) has been extensively used to study the microarchitecture of white matter in schizophrenia. However, popular DWI-derived measures such as fractional anisotropy (FA) may be sensitive to many types of pathologies, and thus the interpretation of reported differences in these measures remains difficult. Combining DWI with magnetization transfer ratio (MTR) - a putative measure of white matter myelination - can help us reveal the underlying mechanisms. Previous findings hypothesized that MTR differences in schizophrenia are associated with free water concentrations, which also affect the DWIs. In this study we use a recently proposed DWI-derived method called free-water imaging to assess this hypothesis. We have reanalyzed data from a previous study by using a fiber-based analysis of free-water imaging, providing a free-water fraction, as well as mean diffusivity and FA corrected for free-water, in addition to MTR along twelve major white matter fiber bundles in 40 schizophrenia patients and 40 healthy controls. We tested for group differences in each fiber bundle and for each measure separately and computed correlations between the MTR and the DWI-derived measures separately for both groups. Significant higher average MTR values in patients were found for the right uncinate fasciculus, the right arcuate fasciculus and the right inferior-frontal occipital fasciculus. No significant results were found for the other measures. No significant differences in correlations were found between MTR and the DWI-derived measures. The results suggest that MTR and free-water imaging measures can be considered complementary, promoting the acquisition of MTR in addition to DWI to identify group differences, as well as to better understand the underlying mechanisms in schizophrenia.
Christian Wachinger, Karl Fritscher, Greg Sharp, and Polina Golland. 2015. “Contour-Driven Atlas-Based Segmentation.” IEEE Trans Med Imaging, 34, 12, Pp. 2492-505.Abstract
We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images.
Sonia Pujol, William M Wells III, Carlo Pierpaoli, Caroline Brun, James Gee, Guang Cheng, Baba Vemuri, Olivier Commowick, Sylvain Prima, Aymeric Stamm, Maged Goubran, Ali Khan, Terry Peters, Peter Neher, Klaus H Maier-Hein, Yundi Shi, Antonio Tristan-Vega, Gopalkrishna Veni, Ross Whitaker, Martin Styner, Carl-Fredrik Westin, Sylvain Gouttard, Isaiah Norton, Laurent Chauvin, Hatsuho Mamata, Guido Gerig, Arya Nabavi, Alexandra Golby, and Ron Kikinis. 2015. “The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery.” J Neuroimaging, 25, 6, Pp. 875-82.Abstract
BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop. METHODS: Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods. RESULTS: The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues. CONCLUSIONS: The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.
Lipeng Ning, Carl-Fredrik Westin, and Yogesh Rathi. 2015. “Estimating diffusion propagator and its moments using directional radial basis functions.” IEEE Trans Med Imaging, 34, 10, Pp. 2058-78.Abstract
The ensemble average diffusion propagator (EAP) obtained from diffusion MRI (dMRI) data captures important structural properties of the underlying tissue. As such, it is imperative to derive an accurate estimate of the EAP from the acquired diffusion data. In this work, we propose a novel method for estimating the EAP by representing the diffusion signal as a linear combination of directional radial basis functions scattered in q-space. In particular, we focus on a special case of anisotropic Gaussian basis functions and derive analytical expressions for the diffusion orientation distribution function (ODF), the return-to-origin probability (RTOP), and mean-squared-displacement (MSD). A significant advantage of the proposed method is that the second and the fourth order moment tensors of the EAP can be computed explicitly. This allows for computing several novel scalar indices (from the moment tensors) such as mean-fourth-order-displacement (MFD) and generalized kurtosis (GK)-which is a generalization of the mean kurtosis measure used in diffusion kurtosis imaging. Additionally, we also propose novel scalar indices computed from the signal in q-space, called the q-space mean-squared-displacement (QMSD) and the q-space mean-fourth-order-displacement (QMFD), which are sensitive to short diffusion time scales. We validate our method extensively on data obtained from a physical phantom with known crossing angle as well as on in-vivo human brain data. Our experiments demonstrate the robustness of our method for different combinations of b-values and number of gradient directions.
Ofer Pasternak, Carl-Fredrik Westin, Brian Dahlben, Sylvain Bouix, and Marek Kubicki. 2015. “The Extent of Diffusion MRI Markers of Neuroinflammation and White Matter Deterioration in Chronic Schizophrenia.” Schizophr Res, 161, 1, Pp. 113-8.Abstract
In a previous study we have demonstrated, using a novel diffusion MRI analysis called free-water imaging, that the early stages of schizophrenia are more likely associated with a neuroinflammatory response and less so with a white matter deterioration or a demyelination process. What is not known is how neuroinflammation and white matter deterioration change along the progression of the disorder. In this study we apply the free-water measures on a population of 29 chronic schizophrenia subjects and compare them with 25 matching controls. Our aim was to compare the extent of free-water imaging abnormalities in chronic subjects with the ones previously obtained for subjects at their first psychotic episode. We find that chronic subjects showed a limited extent of abnormal increase in the volume of the extracellular space, suggesting a less extensive neuroinflammatory response relative to patients at the onset of schizophrenia. At the same time, the chronic schizophrenia subjects had greater extent of reduced fractional anisotropy compared to the previous study, suggesting increased white matter deterioration along the progression of the disease. Our findings substantiate the role of neuroinflammation in the earlier stages of the disorder, and the effect of neurodegeneration that is worsening in the chronic phase.
Nematollah K Batmanghelich, Ardavan Saeedi, Michael Cho, Raul San Jose Estepar, and Polina Golland. 2015. “Generative Method to Discover Genetically Driven Image Biomarkers.” Inf Process Med Imaging, 24, Pp. 30-42.Abstract
We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint generative model of image and genetic markers to identify disease subtypes guided by known genetic influences. Our model is based on a variant of the so-called topic models that uncover the latent structure in a collection of data. We derive an efficient variational inference algorithm to extract patterns of co-occurrence and to quantify the presence of heterogeneous disease processes in each patient. We evaluate the method on simulated data and illustrate its use in the context of Chronic Obstructive Pulmonary Disease (COPD) to characterize the relationship between image and genetic signatures of COPD subtypes in a large patient cohort.
Danielle F Pace, Adrian V Dalca, Tal Geva, Andrew J Powell, Mehdi H Moghari, and Polina Golland. 2015. “Interactive Whole-Heart Segmentation in Congenital Heart Disease.” Med Image Comput Comput Assist Interv, 9351, Pp. 80-8.Abstract
We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.
Yi Gao, Liangjia Zhu, Joshua Cates, Rob S MacLeod, Sylvain Bouix, and Allen Tannenbaum. 2015. “A Kalman Filtering Perspective for Multiatlas Segmentation.” SIAM J Imaging Sci, 8, 2, Pp. 1007-29.Abstract
In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.
George H Chen, Devavrat Shah, and Polina Golland. 2015. “A Latent Source Model for Patch-Based Image Segmentation.” Med Image Comput Comput Assist Interv, 9351, Pp. 140-8.Abstract

Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.

Lipeng Ning, Tryphon T Georgiou, Allen Tannenbaum, and Stephen P Boyd. 2015. “Linear Models Based on Noisy Data and the Frisch Scheme.” SIAM Rev Soc Ind Appl Math, 57, 2, Pp. 167-97.Abstract

We address the problem of identifying linear relations among variables based on noisy measurements. This is a central question in the search for structure in large data sets. Often a key assumption is that measurement errors in each variable are independent. This basic formulation has its roots in the work of Charles Spearman in 1904 and of Ragnar Frisch in the 1930s. Various topics such as errors-in-variables, factor analysis, and instrumental variables all refer to alternative viewpoints on this problem and on ways to account for the anticipated way that noise enters the data. In the present paper we begin by describing certain fundamental contributions by the founders of the field and provide alternative modern proofs to certain key results. We then go on to consider a modern viewpoint and novel numerical techniques to the problem. The central theme is expressed by the Frisch-Kalman dictum, which calls for identifying a noise contribution that allows a maximal number of simultaneous linear relations among the noise-free variables-a rank minimization problem. In the years since Frisch's original formulation, there have been several insights, including trace minimization as a convenient heuristic to replace rank minimization. We discuss convex relaxations and theoretical bounds on the rank that, when met, provide guarantees for global optimality. A complementary point of view to this minimum-rank dictum is presented in which models are sought leading to a uniformly optimal quadratic estimation error for the error-free variables. Points of contact between these formalisms are discussed, and alternative regularization schemes are presented.

Lipeng Ning, Tryphon T Georgiou, and Allen Tannenbaum. 2015. “On Matrix-Valued Monge-Kantorovich Optimal Mass Transport.” IEEE Trans Automat Contr, 60, 2, Pp. 373-82.Abstract
We present a particular formulation of optimal transport for matrix-valued density functions. Our aim is to devise a geometry which is suitable for comparing power spectral densities of multivariable time series. More specifically, the value of a power spectral density at a given frequency, which in the matricial case encodes power as well as directionality, is thought of as a proxy for a "matrix-valued mass density." Optimal transport aims at establishing a natural metric in the space of such matrix-valued densities which takes into account differences between power across frequencies as well as misalignment of the corresponding principle axes. Thus, our transportation cost includes a cost of transference of power between frequencies together with a cost of rotating the principle directions of matrix densities. The two endpoint matrix-valued densities can be thought of as marginals of a joint matrix-valued density on a tensor product space. This joint density, very much as in the classical Monge-Kantorovich setting, can be thought to specify the transportation plan. Contrary to the classical setting, the optimal transport plan for matrices is no longer supported on a thin zero-measure set.
Sidong Liu, Weidong Cai, Siqi Liu, Fan Zhang, Michael Fulham, Dagan Feng, Sonia Pujol, and Ron Kikinis. 2015. “Multimodal Neuroimaging Computing: A Review of the Applications in Neuropsychiatric Disorders.” Brain Inform, 2, Pp. 167-80.Abstract
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.
Sidong Liu, Weidong Cai, Siqi Liu, Fan Zhang, Michael Fulham, Dagan Feng, Sonia Pujol, and Ron Kikinis. 2015. “Multimodal Neuroimaging Computing: The Workflows, Methods, and Platforms.” Brain Inform, 2, Pp. 181-95.Abstract
The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.
Christian Wachinger, Polina Golland, Caroline Magnain, Bruce Fischl, and Martin Reuter. 2015. “Multi-modal Robust Inverse-consistent Linear Registration.” Hum Brain Mapp, 36, 4, Pp. 1365-80.Abstract
Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for cross-modal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss-Newton update and show that it is equivalent to the efficient second-order minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images.
Stefanie Eriksson, Samo Lasič, Markus Nilsson, Carl-Fredrik Westin, and Daniel Topgaard. 2015. “NMR Diffusion-Encoding with Axial Symmetry and Variable Anisotropy: Distinguishing Between Prolate and Oblate Microscopic Diffusion Tensors with Unknown Orientation Distribution.” J Chem Phys, 142, 10, Pp. 104201.Abstract
We introduce a nuclear magnetic resonance method for quantifying the shape of axially symmetric microscopic diffusion tensors in terms of a new diffusion anisotropy metric, DΔ, which has unique values for oblate, spherical, and prolate tensor shapes. The pulse sequence includes a series of equal-amplitude magnetic field gradient pulse pairs, the directions of which are tailored to give an axially symmetric diffusion-encoding tensor b with variable anisotropy bΔ. Averaging of data acquired for a range of orientations of the symmetry axis of the tensor b renders the method insensitive to the orientation distribution function of the microscopic diffusion tensors. Proof-of-principle experiments are performed on water in polydomain lyotropic liquid crystals with geometries that give rise to microscopic diffusion tensors with oblate, spherical, and prolate shapes. The method could be useful for characterizing the geometry of fluid-filled compartments in porous solids, soft matter, and biological tissues.
Danhong Wang, Randy L Buckner, Michael D Fox, Daphne J Holt, Avram J Holmes, Sophia Stoecklein, Georg Langs, Ruiqi Pan, Tianyi Qian, Kuncheng Li, Justin T Baker, Steven M Stufflebeam, Kai Wang, Xiaomin Wang, Bo Hong, and Hesheng Liu. 2015. “Parcellating Cortical Functional Networks in Individuals.” Nat Neurosci, 18, 12, Pp. 1853-60.Abstract

The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.