Publications

2016
Matthew Toews and William M Wells III. 4/2016. “Invariant Feature-Based Analysis of Medical Images: An Overview.” In IEEE Int Symp Biomed Imaging.
Lipeng Ning, Carl-Fredrik Westin, and Yogesh Rathi. 3/2016. “Estimation of Bounded and Unbounded Trajectories in Diffusion MRI.” Front Neurosci, 10, Pp. 129.Abstract

Disentangling the tissue microstructural information from the diffusion magnetic resonance imaging (dMRI) measurements is quite important for extracting brain tissue specific measures. The autocorrelation function of diffusing spins is key for understanding the relation between dMRI signals and the acquisition gradient sequences. In this paper, we demonstrate that the autocorrelation of diffusion in restricted or bounded spaces can be well approximated by exponential functions. To this end, we propose to use the multivariate Ornstein-Uhlenbeck (OU) process to model the matrix-valued exponential autocorrelation function of three-dimensional diffusion processes with bounded trajectories. We present detailed analysis on the relation between the model parameters and the time-dependent apparent axon radius and provide a general model for dMRI signals from the frequency domain perspective. For our experimental setup, we model the diffusion signal as a mixture of two compartments that correspond to diffusing spins with bounded and unbounded trajectories, and analyze the corpus-callosum in an ex-vivo data set of a monkey brain.

Tina Kapur and Clare M. Tempany. 3/2016. “Proceedings of the 8th Image Guided Therapy Workshop” 8, Pp. 1-68. 2016 IGT Workshop Proceedings
Yi Gao, Vadim Ratner, Liangjia Zhu, Tammy Diprima, Tahsin Kurc, Allen Tannenbaum, and Joel Saltz. 2/2016. “Hierarchical Nucleus Segmentation in Digital Pathology Images.” Proc SPIE Int Soc Opt Eng, 9791.Abstract

Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set.

Yi Gao, William Liu, Shipra Arjun, Liangjia Zhu, Vadim Ratner, Tahsin Kurc, Joel Saltz, and Allen Tannenbaum. 2/2016. “Multi-scale Learning Based Segmentation of Glands in Digital Colonrectal Pathology Images.” Proc SPIE Int Soc Opt Eng, 9791.Abstract

Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation. After the learning step, the dictionaries are used collectively to perform the classification and segmentation for the new image.

Jørn Bersvendsen, Matthew Toews, Adriyana Danudibroto, William M Wells III, Stig Urheim, Raúl San José Estépar, and Eigil Samset. 2/2016. “Robust Spatio-Temporal Registration of 4D Cardiac Ultrasound Sequences.” Proc SPIE Int Soc Opt Eng, 9790.Abstract

Registration of multiple 3D ultrasound sectors in order to provide an extended field of view is important for the appreciation of larger anatomical structures at high spatial and temporal resolution. In this paper, we present a method for fully automatic spatio-temporal registration between two partially overlapping 3D ultrasound sequences. The temporal alignment is solved by aligning the normalized cross correlation-over-time curves of the sequences. For the spatial alignment, corresponding 3D Scale Invariant Feature Transform (SIFT) features are extracted from all frames of both sequences independently of the temporal alignment. A rigid transform is then calculated by least squares minimization in combination with random sample consensus. The method is applied to 16 echocardiographic sequences of the left and right ventricles and evaluated against manually annotated temporal events and spatial anatomical landmarks. The mean distances between manually identified landmarks in the left and right ventricles after automatic registration were (mean ± SD) 4.3 ± 1.2 mm compared to a reference error of 2.8 ± 0.6 mm with manual registration. For the temporal alignment, the absolute errors in valvular event times were 14.4 ± 11.6 ms for Aortic Valve (AV) opening, 18.6 ± 16.0 ms for AV closing, and 34.6 ± 26.4 ms for mitral valve opening, compared to a mean inter-frame time of 29 ms.

Lipeng Ning, Kawin Setsompop, Oleg Michailovich, Nikos Makris, Martha E Shenton, Carl-Fredrik Westin, and Yogesh Rathi. 1/2016. “A Joint Compressed-sensing and Super-resolution Approach for Very High-resolution Diffusion Imaging.” Neuroimage, 125, Pp. 386-400.Abstract

Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time.

Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis, and Dagan D Feng. 2016. “Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging.” Front Aging Neurosci, 8, Pp. 23.Abstract
The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying therapies that can halt or slow the disease progression. Recently, neuroimaging are increasingly used as additional research-based markers to detect AD onset and predict conversion of MCI and normal control (NC) to AD. Researchers have proposed a variety of neuroimaging biomarkers to characterize the patterns of the pathology of AD and MCI, and suggested that multi-view neuroimaging biomarkers could lead to better performance than single-view biomarkers in AD staging. However, it is still unclear what leads to such synergy and how to preserve or maximize. In an attempt to answer these questions, we proposed a cross-view pattern analysis framework for investigating the synergy between different neuroimaging biomarkers. We quantitatively analyzed nine types of biomarkers derived from FDG-PET and T1-MRI, and evaluated their performance in a task of classifying AD, MCI, and NC subjects obtained from the ADNI baseline cohort. The experiment results showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression. Most importantly, we found that these features could be separated into clusters, each depicting a particular aspect; and the inter-cluster features could always achieve better performance than the intra-cluster features in AD staging.
Fan Zhang, Yang Song, Weidong Cai, Alexander G Hauptmann, Sidong Liu, Sonia Pujol, Ron Kikinis, Michael J Fulham, David Dagan Feng, and Mei Chen. 2016. “Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.” Neurocomputing, 177, Pp. 75-88.Abstract
Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.
Ivan Kolesov, Jehoon Lee, Gregory Sharp, Patricio Vela, and Allen Tannenbaum. 2016. “A Stochastic Approach to Diffeomorphic Point Set Registration with Landmark Constraints.” IEEE Trans Pattern Anal Mach Intell, 38, 2, Pp. 238-51.Abstract
This work presents a deformable point set registration algorithm that seeks an optimal set of radial basis functions to describe the registration. A novel, global optimization approach is introduced composed of simulated annealing with a particle filter based generator function to perform the registration. It is shown how constraints can be incorporated into this framework. A constraint on the deformation is enforced whose role is to ensure physically meaningful fields (i.e., invertible). Further, examples in which landmark constraints serve to guide the registration are shown. Results on 2D and 3D data demonstrate the algorithm's robustness to noise and missing information.
Katherine A Fu, Romil Nathan, Ivo D Dinov, Junning Li, and Arthur W Toga. 2016. “T2-Imaging Changes in the Nigrosome-1 Relate to Clinical Measures of Parkinson's Disease.” Front Neurol, 7, Pp. 174.Abstract
BACKGROUND: The nigrosome-1 region of the substantia nigra (SN) undergoes the greatest and earliest dopaminergic neuron loss in Parkinson's disease (PD). As T2-weighted magnetic resonance imaging (MRI) scans are often collected with routine clinical MRI protocols, this investigation aims to determine whether T2-imaging changes in the nigrosome-1 are related to clinical measures of PD and to assess their potential as a more clinically accessible biomarker for PD. METHODS: Voxel intensity ratios were calculated for T2-weighted MRI scans from 47 subjects from the Parkinson's Progression Markers Initiative database. Three approaches were used to delineate the SN and nigrosome-1: (1) manual segmentation, (2) automated segmentation, and (3) area voxel-based morphometry. Voxel intensity ratios were calculated from voxel intensity values taken from the nigrosome-1 and two areas of the remaining SN. Linear regression analyses were conducted relating voxel intensity ratios with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) sub-scores for each subject. RESULTS: For manual segmentation, linear regression tests consistently identified the voxel intensity ratio derived from the dorsolateral SN and nigrosome-1 (IR2) as predictive of nBehav (p = 0.0377) and nExp (p = 0.03856). For automated segmentation, linear regression tests identified IR2 as predictive of Subscore IA (nBehav) (p = 0.01134), Subscore IB (nExp) (p = 0.00336), Score II (mExp) (p = 0.02125), and Score III (mSign) (p = 0.008139). For the voxel-based morphometric approach, univariate simple linear regression analysis identified IR2 as yielding significant results for nBehav (p = 0.003102), mExp (p = 0.0172), and mSign (p = 0.00393). CONCLUSION: Neuroimaging biomarkers may be used as a proxy of changes in the nigrosome-1, measured by MDS-UPDRS scores as an indicator of the severity of PD. The voxel intensity ratio derived from the dorsolateral SN and nigrosome-1 was consistently predictive of non-motor complex behaviors in all three analyses and predictive of non-motor experiences of daily living, motor experiences of daily living, and motor signs of PD in two of the three analyses. These results suggest that T2 changes in the nigrosome-1 may relate to certain clinical measures of PD. T2 changes in the nigrosome-1 may be considered when developing a more accessible clinical diagnostic tool for patients with suspected PD.
Johanna Seitz, Jessica X Zuo, Amanda E Lyall, Nikos Makris, Zora Kikinis, Sylvain Bouix, Ofer Pasternak, Eli Fredman, Jonathan Duskin, Jill M Goldstein, Tracey L Petryshen, Raquelle I Mesholam-Gately, Joanne Wojcik, Robert W McCarley, Larry J Seidman, Martha E Shenton, Inga K Koerte, and Marek Kubicki. 2016. “Tractography Analysis of 5 White Matter Bundles and Their Clinical and Cognitive Correlates in Early-Course Schizophrenia.” Schizophr Bull, 42, 3, Pp. 762-71.Abstract
PURPOSE: Tractography is the most anatomically accurate method for delineating white matter tracts in the brain, yet few studies have examined multiple tracts using tractography in patients with schizophrenia (SCZ). We analyze 5 white matter connections important in the pathophysiology of SCZ: uncinate fasciculus, cingulum bundle (CB), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus, and arcuate fasciculus (AF). Additionally, we investigate the relationship between diffusion tensor imaging (DTI) markers and neuropsychological measures. METHODS: High-resolution DTI data were acquired on a 3 Tesla scanner in 30 patients with early-course SCZ and 30 healthy controls (HC) from the Boston Center for Intervention Development and Applied Research study. After manually guided tracts delineation, fractional anisotropy (FA), trace, radial diffusivity (RD), and axial diffusivity (AD) were calculated and averaged along each tract. The association of DTI measures with the Scales for the Assessment of Negative and Positive Symptoms and neuropsychological measures was evaluated. RESULTS: Compared to HC, patients exhibited reduced FA and increased trace and RD in the right AF, CB, and ILF. A discriminant analysis showed the possible use of FA of these tracts for better future group membership classifications. FA and RD of the right ILF and AF were associated with positive symptoms while FA and RD of the right CB were associated with memory performance and processing speed. CONCLUSION: We observed white matter alterations in the right CB, ILF, and AF, possibly caused by myelin disruptions. The structural abnormalities interact with cognitive performance, and are linked to clinical symptoms.
Sonia Pujol, Michael Baldwin, Joshua Nassiri, Ron Kikinis, and Kitt Shaffer. 2016. “Using 3D Modeling Techniques to Enhance Teaching of Difficult Anatomical Concepts.” Acad Radiol, 23, 4, Pp. 507-16.Abstract
RATIONALE AND OBJECTIVES: Anatomy is an essential component of medical education as it is critical for the accurate diagnosis in organs and human systems. The mental representation of the shape and organization of different anatomical structures is a crucial step in the learning process. The purpose of this pilot study is to demonstrate the feasibility and benefits of developing innovative teaching modules for anatomy education of first-year medical students based on three-dimensional (3D) reconstructions from actual patient data. MATERIALS AND METHODS: A total of 196 models of anatomical structures from 16 anonymized computed tomography datasets were generated using the 3D Slicer open-source software platform. The models focused on three anatomical areas: the mediastinum, the upper abdomen, and the pelvis. Online optional quizzes were offered to first-year medical students to assess their comprehension in the areas of interest. Specific tasks were designed for students to complete using the 3D models. RESULTS: Scores of the quizzes confirmed a lack of understanding of 3D spatial relationships of anatomical structures despite standard instruction including dissection. Written task material and qualitative review by students suggested that interaction with 3D models led to a better understanding of the shape and spatial relationships among structures, and helped illustrate anatomical variations from one body to another. CONCLUSIONS: The study demonstrates the feasibility of one possible approach to the generation of 3D models of the anatomy from actual patient data. The educational materials developed have the potential to supplement the teaching of complex anatomical regions and help demonstrate the anatomical variation among patients.
Lena KL Oestreich, Ofer Pasternak, Martha E Shenton, Marek Kubicki, Xue Gong, Xue Gong, Simon McCarthy-Jones, and Thomas J Whitford. 2016. “Abnormal White Matter Microstructure and Increased Extracellular Free-water in the Cingulum Bundle Associated with Delusions in Chronic Schizophrenia.” Neuroimage Clin, 12, Pp. 405-14.Abstract

BACKGROUND: There is growing evidence to suggest that delusions associated with schizophrenia arise from altered structural brain connectivity. The present study investigated whether structural changes in three major fasciculi that interconnect the limbic system - the cingulum bundle, uncinate fasciculus and fornix - are associated with delusions in chronic schizophrenia patients. METHODS: Free-water corrected Diffusion Tensor Imaging was used to investigate the association between delusions and both microstructural changes within these three fasciculi and extracellular changes in the surrounding free-water. Clinical data and diffusion MRI scans were obtained from 28 healthy controls and 86 schizophrenia patients, of whom 34 had present state delusions, 35 had a lifetime history but currently remitted delusions, and 17 had never experienced delusions. RESULTS: While present state and remitted delusions were found to be associated with reduced free-water corrected fractional anisotropy (FAT) and increased free-water corrected radial diffusivity (RDT) in the cingulum bundle bilaterally, extracellular free-water (FW) in the left cingulum bundle was found to be specifically associated with present state delusions in chronic schizophrenia. No changes were observed in the remaining tracts. CONCLUSIONS: These findings suggest that state and trait delusions in chronic schizophrenia are associated with microstructural processes, such as myelin abnormalities (as indicated by decreased FAT and increased RDT) in the cingulum bundle and that state delusions are additionally associated with extracellular processes such as neuroinflammation or atrophy (as indicated by increased FW) in the left cingulum bundle.

Mao Li, Karol Miller, Grand Roman Joldes, Ron Kikinis, and Adam Wittek. 2016. “Biomechanical Model for Computing Deformations for Whole-body Image Registration: A Meshless Approach.” Int J Numer Method Biomed Eng, 32, 12.Abstract

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time-consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2D models and computing single organ deformations. In this study, 3D comprehensive patient-specific nonlinear biomechanical models implemented using meshless Total Lagrangian explicit dynamics algorithms are applied to predict a 3D deformation field for whole-body image registration. Unlike a conventional approach that requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the fuzzy c-means algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. Copyright © 2016 John Wiley & Sons, Ltd.

Alireza Mehrtash, Sandeep N Gupta, Dattesh Shanbhag, James V Miller, Tina Kapur, Fiona M Fennessy, Ron Kikinis, and Andriy Fedorov. 2016. “Bolus Arrival Time and its Effect on Tissue Characterization with Dynamic Contrast-enhanced Magnetic Resonance Imaging.” J Med Imaging (Bellingham), 3, 1, Pp. 014503.Abstract

Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant ([Formula: see text]) and extravascular extracellular volume fraction ([Formula: see text]) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of [Formula: see text] and [Formula: see text] values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.

Zhenrui Chen, Yanmei Tie, Olutayo Olubiyi, Fan Zhang, Alireza Mehrtash, Laura Rigolo, Pegah Kahali, Isaiah Norton, Ofer Pasternak, Yogesh Rathi, Alexandra J Golby, and Lauren J O'Donnell. 2016. “Corticospinal Tract Modeling for Neurosurgical Planning by Tracking through Regions of Peritumoral Edema and Crossing Fibers using Two-Tensor Unscented Kalman Filter Tractography.” Int J Comput Assist Radiol Surg, 11, 8, Pp. 1475-86.Abstract

PURPOSE: The aim of this study was to present a tractography algorithm using a two-tensor unscented Kalman filter (UKF) to improve the modeling of the corticospinal tract (CST) by tracking through regions of peritumoral edema and crossing fibers. METHODS: Ten patients with brain tumors in the vicinity of motor cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-T magnetic resonance imaging (MRI) including functional MRI (fMRI) and a diffusion-weighted data set with 31 directions. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography methods. A two-region-of-interest approach was used to delineate the CST. Results from the two tractography methods were compared visually and quantitatively. fMRI was applied to identify the functional fiber tracts. RESULTS: Single-tensor streamline tractography underestimated the extent of tracts running through the edematous areas and could only track the medial projections of the CST. In contrast, two-tensor UKF tractography tracked fanning projections of the CST despite peritumoral edema and crossing fibers. Based on visual inspection, the two-tensor UKF tractography delineated tracts that were closer to motor fMRI activations, and it was apparently more sensitive than single-tensor streamline tractography to define the tracts directed to the motor sites. The volume of the CST was significantly larger on two-tensor UKF than on single-tensor streamline tractography ([Formula: see text]). CONCLUSION: Two-tensor UKF tractography tracks a larger volume CST than single-tensor streamline tractography in the setting of peritumoral edema and crossing fibers in brain tumor patients.

C. Wang, F Ji, Z Hong, JS Poh, R. Krishnan, J. Lee, G Rekhi, RSE Keefe, RA Adcock, SJ Wood, Alex Fornito, Ofer Pasternak, MW Chee, and J Zhou. 2016. “Disrupted Salience Network Functional Connectivity and White-Matter Microstructure in Persons at Risk For Psychosis: Findings from the LYRIKS Study.” Psychol Med, 46, 13, Pp. 2771-83.Abstract

BACKGROUND: Salience network (SN) dysconnectivity has been hypothesized to contribute to schizophrenia. Nevertheless, little is known about the functional and structural dysconnectivity of SN in subjects at risk for psychosis. We hypothesized that SN functional and structural connectivity would be disrupted in subjects with At-Risk Mental State (ARMS) and would be associated with symptom severity and disease progression. METHOD: We examined 87 ARMS and 37 healthy participants using both resting-state functional magnetic resonance imaging and diffusion tensor imaging. Group differences in SN functional and structural connectivity were examined using a seed-based approach and tract-based spatial statistics. Subject-level functional connectivity measures and diffusion indices of disrupted regions were correlated with CAARMS scores and compared between ARMS with and without transition to psychosis. RESULTS: ARMS subjects exhibited reduced functional connectivity between the left ventral anterior insula and other SN regions. Reduced fractional anisotropy (FA) and axial diffusivity were also found along white-matter tracts in close proximity to regions of disrupted functional connectivity, including frontal-striatal-thalamic circuits and the cingulum. FA measures extracted from these disrupted white-matter regions correlated with individual symptom severity in the ARMS group. Furthermore, functional connectivity between the bilateral insula and FA at the forceps minor were further reduced in subjects who transitioned to psychosis after 2 years. CONCLUSIONS: Our findings support the insular dysconnectivity of the proximal SN hypothesis in the early stages of psychosis. Further developed, the combined structural and functional SN assays may inform the prognosis of persons at-risk for psychosis.

Zhang Fan, Savadjev Peter, Weidong Cai, Yang Song, Ragini Verma, Westin Carl-Fredrik, and Lauren J O'Donnell. 2016. “Fiber Clustering Based White Matter Connectivity Analysis for Prediction of Autism Spectrum Disorder using Diffusion Tensor Imaging.” IEEE Int Symp Biomed Imaging.Abstract
Autism Spectrum Disorder (ASD) has been suggested to associate with alterations in brain connectivity. In this study, we focus on a fiber clustering tractography segmentation strategy to observe white matter connectivity alterations in ASD. Compared to another popular parcellation-based approach for tractography segmentation based on cortical regions, we hypothesized that the clustering-based method could provide a more anatomically correspondent division of white matter. We applied this strategy to conduct a population-based group statistical analysis for the automated prediction of ASD. We obtained a maximum classification accuracy of 81.33% be- tween ASDs and controls, compared to the results of 78.00% from the parcellation-based method.
 
Zhang ISBI 2016
Georg Langs, Danhong Wang, Polina Golland, Sophia Mueller, Ruiqi Pan, Mert R Sabuncu, Wei Sun, Kuncheng Li, and Hesheng Liu. 2016. “Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability.” Cereb Cortex, 26, 10, Pp. 4004-14.Abstract
The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units. The functional connectivity patterns observed in resting-state data were mapped in the embedding space and the maps were aligned across individuals. A clustering algorithm was performed on the aligned embedding maps and the resulting clusters were transformed back to the unique anatomical space of each individual. This novel approach identified functional systems that were reproducible within subjects, but were distributed across different anatomical locations in different subjects. Using this approach for intersubject alignment improved the predictability of individual differences in language laterality when compared with anatomical alignment alone. Our results further revealed that the strength of association between function and macroanatomy varied across the cortex, which was strong in unimodal sensorimotor networks, but weak in association networks.

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