Publications

2015
Georg Langs, Polina Golland, and Satrajit S Ghosh. 2015. “Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion.” Med Image Comput Comput Assist Interv, 9350, Pp. 313-20.Abstract

The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available.

Filip Szczepankiewicz, Samo Lasič, Danielle van Westen, Pia C Sundgren, Elisabet Englund, Carl-Fredrik Westin, Freddy Ståhlberg, Jimmy Lätt, Daniel Topgaard, and Markus Nilsson. 2015. “Quantification of Microscopic Diffusion Anisotropy Disentangles Effects of Orientation Dispersion from Microstructure: Applications in Healthy Volunteers and in Brain Tumors.” Neuroimage, 104, Pp. 241-52.Abstract
The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for "tissue integrity", especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.
Christian Wachinger and Polina Golland. 2015. “Sampling from Determinantal Point Processes for Scalable Manifold Learning.” Inf Process Med Imaging, 24, Pp. 687-98.Abstract
High computational costs of manifold learning prohibit its application for large datasets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the Nyström method. The two main challenges that arise are: (i) the landmarks selected in non-Euclidean geometries must result in a low reconstruction error, (ii) the graph constructed from sparsely sampled landmarks must approximate the manifold well. We propose to sample the landmarks from determinantal distributions on non-Euclidean spaces. Since current determinantal sampling algorithms have the same complexity as those for manifold learning, we present an efficient approximation with linear complexity. Further, we recover the local geometry after the sparsification by assigning each landmark a local covariance matrix, estimated from the original point set. The resulting neighborhood selection .based on the Bhattacharyya distance improves the embedding of sparsely sampled manifolds. Our experiments show a significant performance improvement compared to state-of-the-art landmark selection techniques on synthetic and medical data.
Sarah Parisot, Salim Arslan, Jonathan Passerat-Palmbach, William M Wells III, and Daniel Rueckert. 2015. “Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex.” Inf Process Med Imaging, 24, Pp. 600-12.Abstract

The analysis of the connectome of the human brain provides key insight into the brain's organisation and function, and its evolution in disease or ageing. Parcellation of the cortical surface into distinct regions in terms of structural connectivity is an essential step that can enable such analysis. The estimation of a stable connectome across a population of healthy subjects requires the estimation of a groupwise parcellation that can capture the variability of the connectome across the population. This problem has solely been addressed in the literature via averaging of connectivity profiles or finding correspondences between individual parcellations a posteriori. In this paper, we propose a groupwise parcellation method of the cortex based on diffusion MR images (dMRI). We borrow ideas from the area of cosegmentation in computer vision and directly estimate a consistent parcellation across different subjects and scales through a spectral clustering approach. The parcellation is driven by the tractography connectivity profiles, and information between subjects and across scales. Promising qualitative and quantitative results on a sizeable data-set demonstrate the strong potential of the method.

Klaus H Maier-Hein, Carl-Fredrik Westin, Martha E Shenton, Michael W Weiner, Ashish Raj, Philipp Thomann, Ron Kikinis, Bram Stieltjes, and Ofer Pasternak. 2015. “Widespread White Matter Degeneration Preceding the Onset of Dementia.” Alzheimers Dement, 11, 5, Pp. 485-93.Abstract

BACKGROUND: Brain atrophy in subjects with mild cognitive impairment (MCI) introduces partial volume effects, limiting the sensitivity of diffusion tensor imaging to white matter microstructural degeneration. Appropriate correction isolates microstructural effects in MCI that might be precursors of Alzheimer's disease (AD). METHODS: Forty-eight participants (18 MCI, 15 AD, and 15 healthy controls) had magnetic resonance imaging scans and clinical evaluations at baseline and follow-up after 36 months. Ten MCI subjects were diagnosed with AD at follow-up and eight remained MCI. Free-water (FW) corrected measures on the white matter skeleton were compared between groups. RESULTS: FW corrected radial diffusivity, but not uncorrected radial diffusivity, was increased across the brain of the converted group compared with the nonconverted group (P < .05). The extent of increases was similar to that found comparing AD with controls. CONCLUSION: Partial volume elimination reveals microstructural alterations preceding dementia. These alterations may prove to be an effective and feasible early biomarker of AD.

2014
Thomas Kahn, Ferenc A Jolesz, and Jonathan S Lewin. 10/2014. “Proceedings of the 10th Interventional MRI Symposium.” 10th Interventional MRI Symposium 10, Pp. 1-85. 2014 iMRI Symposium Proceedings
Ivan Kolesov, L Zhu, Peter Karasev, and Allen Tannenbaum. 9/2014. “A Control Framework for Interactive Deformable Image Registration.” Int Conf Med Image Comput Comput Assist Interv. Workshop on Interactive Methods 17 (WS). Kolesov MICCAI 2014
Liangija Zhu, Ivan Kolesov, Yi Gao, Ron Kikinis, and Allen Tannenbaum. 9/2014. “An Effective Interactive Medical Image Segmentation Method using Fast GrowCut.” Int Conf Med Image Comput Comput Assist Interv. Workshop on Interactive Methods. 17 (WS).Abstract
Segmentation of anatomical structures in medical imagery is a key step in a variety of clinical applications. Designing a generic, automated method that works for various structures and imaging modalities is a daunting task. In this paper, we present an effective interactive segmentation method that reformulates the GrowCut algorithm as a clustering problem and computes a fast, approximate solution. The method is further improved by using an efficient updating scheme requiring only local computations when new user input becomes available, making it applicable to high resolution images. The algorithm may easily be included as a user-oriented software module in any number of available medical imaging/image processing platforms such as 3D Slicer. The efficiency and effectiveness of the algorithm are demonstrated through tests on several challenging data sets where it is also compared to standard GrowCut.
Zhu MICCAI WS 2014
Ramesh Sridharan, Adrian V Dalca, and Polina Golland. 9/2014. “An Interactive Visualization Tool for Nipype Medical Image Computing Pipelines.” Int Conf Med Image Comput Comput Assist Interv. Workshop on Interactive Medical Image Computing 17.
Tina Kapur, Clare M. Tempany, and Ferenc A. Jolesz. 9/2014. “Proceedings of the 7th Image Guided Therapy Workshop.” Image Guided Therapy Workshop 7, Pp. 1-60. 2014 IGT Workshop Proceedings
Adrian V Dalca, Ramesh Sridharan, Natalia S Rost, and Polina Golland. 9/2014. “tipiX: Rapid Visualization of Large Image Collections.” Int Conf Med Image Comput Comput Assist Interv. Workshop on Interactive Medical Image Computing 14.
Christian Wachinger, Polina Golland, and Martin Reuter. 9/2014. “BrainPrint: Identifying Subjects by Their Brain.” Med Image Comput Comput Assist Interv, 17, Pt 3, Pp. 41-8.Abstract

Introducing BrainPrint, a compact and discriminative representation of anatomical structures in the brain. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the 2D and 3D Laplace-Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. We derive a robust classifier for this representation that identifies the subject in a new scan, based on a database of brain scans. In an example dataset containing over 3000 MRI scans, we show that BrainPrint captures unique information about the subject's anatomy and permits to correctly classify a scan with an accuracy of over 99.8%. All processing steps for obtaining the compact representation are fully automated making this processing framework particularly attractive for handling large datasets.

Christian Wachinger, Polina Golland, Martin Reuter, and William M Wells III. 9/2014. “Gaussian Process Interpolation for Uncertainty Estimation in Image Registration.” Med Image Comput Comput Assist Interv, 17, Pt 1, Pp. 267-74.Abstract

Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods.

Yogesh Rathi, Ofer Pasternak, Peter Savadjiev, Oleg V Michailovich, Sylvain Bouix, Marek Kubicki, Carl-Fredrik Westin, Nikos Makris, and Martha E Shenton. 8/2014. “Gray Matter Alterations in Early Aging: A Diffusion Magnetic Resonance Imaging Study.” Hum Brain Mapp, 35, 8, Pp. 3841-56.Abstract

Many studies have observed altered neurofunctional and structural organization in the aging brain. These observations from functional neuroimaging studies show a shift in brain activity from the posterior to the anterior regions with aging (PASA model), as well as a decrease in cortical thickness, which is more pronounced in the frontal lobe followed by the parietal, occipital, and temporal lobes (retrogenesis model). However, very little work has been done using diffusion MRI (dMRI) with respect to examining the structural tissue alterations underlying these neurofunctional changes in the gray matter. Thus, for the first time, we propose to examine gray matter changes using diffusion MRI in the context of aging. In this work, we propose a novel dMRI based measure of gray matter "heterogeneity" that elucidates these functional and structural models (PASA and retrogenesis) of aging from the viewpoint of diffusion MRI. In a cohort of 85 subjects (all males, ages 15-55 years), we show very high correlation between age and "heterogeneity" (a measure of structural layout of tissue in a region-of-interest) in specific brain regions. We examine gray matter alterations by grouping brain regions into anatomical lobes as well as functional zones. Our findings from dMRI data connects the functional and structural domains and confirms the "retrogenesis" hypothesis of gray matter alterations while lending support to the neurofunctional PASA model of aging in addition to showing the preservation of paralimbic areas during healthy aging.

Andriy Fedorov, William M Wells III, Ron Kikinis, Clare M Tempany, and Mark G Vangel. 7/2014. “Application of Tolerance Limits to the Characterization of Image Registration Performance.” IEEE Trans Med Imaging, 33, 7, Pp. 1541-50.Abstract

Deformable image registration is used increasingly in image-guided interventions and other applications. However, validation and characterization of registration performance remain areas that require further study. We propose an analysis methodology for deriving tolerance limits on the initial conditions for deformable registration that reliably lead to a successful registration. This approach results in a concise summary of the probability of registration failure, while accounting for the variability in the test data. The (β, γ) tolerance limit can be interpreted as a value of the input parameter that leads to successful registration outcome in at least 100β% of cases with the 100γ% confidence. The utility of the methodology is illustrated by summarizing the performance of a deformable registration algorithm evaluated in three different experimental setups of increasing complexity. Our examples are based on clinical data collected during MRI-guided prostate biopsy registered using publicly available deformable registration tool. The results indicate that the proposed methodology can be used to generate concise graphical summaries of the experiments, as well as a probabilistic estimate of the registration outcome for a future sample. Its use may facilitate improved objective assessment, comparison and retrospective stress-testing of deformable.

Jong Woo Lee, Andrew D Norden, Keith L Ligon, Alexandra J Golby, Rameen Beroukhim, John Quackenbush, William M Wells III, Kristen Oelschlager, Derek Maetzold, and Patrick Y Wen. 7/2014. “Tumor Associated Seizures in Glioblastomas are Influenced by Survival Gene Expression in a Region-specific Manner: A Gene Expression Imaging Study.” Epilepsy Res, 108, 5, Pp. 843-52.Abstract

Tumor associated seizures (TAS) are common and cause significant morbidity. Both imaging and gene expression features play significant roles in determining TAS, with strong interactions between them. We describe gene expression imaging tools which allow mapping of brain regions where gene expression has significant influence on TAS, and apply these methods to study 77 patients who underwent surgical evaluation for supratentorial glioblastomas. Tumor size and location were measured from MRI scans. A 9-set gene expression profile predicting long-term survivors was obtained from RNA derived from formalin-fixed paraffin embedded tissue. A total of 32 patients (42%) experienced preoperative TAS. Tumor volume was smaller (31.1 vs. 58.8 cubic cm, p<0.001) and there was a trend toward median survival being higher (48.4 vs. 32.7 months, p=0.055) in patients with TAS. Although the expression of only OLIG2 was significantly lower in patients with TAS in a groupwise analysis, gene expression imaging analysis revealed regions with significantly lower expression of OLIG2 and RTN1 in patients with TAS. Gene expression imaging is a powerful technique that demonstrates that the influence of gene expression on TAS is highly region specific. Regional variability should be evaluated with any genomic or molecular markers of solid brain lesions.

Peter Savadjiev, Thomas J Whitford, ME Hough, Christian Clemm von Hohenberg, Sylvain Bouix, Carl-Fredrik Westin, Martha E Shenton, Tim J Crow, James A, and Marek Kubicki. 5/2014. “Sexually Dimorphic White Matter Geometry Abnormalities in Adolescent Onset Schizophrenia.” Cereb Cortex, 24, 5, Pp. 1389-96.Abstract

The normal human brain is characterized by a pattern of gross anatomical asymmetry. This pattern, known as the "torque", is associated with a sexual dimorphism: The male brain tends to be more asymmetric than that of the female. This fact, along with well-known sex differences in brain development (faster in females) and onset of psychosis (earlier with worse outcome in males), has led to the theory that schizophrenia is a disorder in which sex-dependent abnormalities in the development of brain torque, the correlate of the capacity for language, cause alterations in interhemispheric connectivity, which are causally related to psychosis (Crow TJ, Paez P, Chance SE. 2007. Callosal misconnectivity and the sex difference in psychosis. Int Rev Psychiatry. 19(4):449-457.). To provide evidence toward this theory, we analyze the geometry of interhemispheric white matter connections in adolescent-onset schizophrenia, with a particular focus on sex, using a recently introduced framework for white matter geometry computation in diffusion tensor imaging data (Savadjiev P, Kindlmann GL, Bouix S, Shenton ME, Westin CF. 2010. Local white geometry from diffusion tensor gradients. Neuroimage. 49(4):3175-3186.). Our results reveal a pattern of sex-dependent white matter geometry abnormalities that conform to the predictions of Crow's torque theory and correlate with the severity of patients' symptoms. To the best of our knowledge, this is the first study to associate geometrical differences in white matter connectivity with torque in schizophrenia.

Nabgha Farhat. 1/2014. “Tutorial: Preparing Data for 3-D Printing using 3D Slicer”.Abstract
This tutorial demonstrates how to prepare data for 3D printing using the open source software 3D Slicer. The following topics are highlighted in the tutorial: introduction to the 3D Slicer interface, loading data into 3D Slicer, volume rendering, cropping image volumes, creating label maps, creating surface models, and saving data in file formats appropriate for 3D printing.
3D Printing Tutorial.mov
Samantha Huang, Stephanie Rossi, Matti Hämäläinen, and Jyrki Ahveninen. 2014. “Auditory conflict resolution correlates with medial-lateral frontal theta/alpha phase synchrony.” PLoS One, 9, 10, Pp. e110989.Abstract
When multiple persons speak simultaneously, it may be difficult for the listener to direct attention to correct sound objects among conflicting ones. This could occur, for example, in an emergency situation in which one hears conflicting instructions and the loudest, instead of the wisest, voice prevails. Here, we used cortically-constrained oscillatory MEG/EEG estimates to examine how different brain regions, including caudal anterior cingulate (cACC) and dorsolateral prefrontal cortices (DLPFC), work together to resolve these kinds of auditory conflicts. During an auditory flanker interference task, subjects were presented with sound patterns consisting of three different voices, from three different directions (45° left, straight ahead, 45° right), sounding out either the letters "A" or "O". They were asked to discriminate which sound was presented centrally and ignore the flanking distracters that were phonetically either congruent (50%) or incongruent (50%) with the target. Our cortical MEG/EEG oscillatory estimates demonstrated a direct relationship between performance and brain activity, showing that efficient conflict resolution, as measured with reduced conflict-induced RT lags, is predicted by theta/alpha phase coupling between cACC and right lateral frontal cortex regions intersecting the right frontal eye fields (FEF) and DLPFC, as well as by increased pre-stimulus gamma (60-110 Hz) power in the left inferior fontal cortex. Notably, cACC connectivity patterns that correlated with behavioral conflict-resolution measures were found during both the pre-stimulus and the pre-response periods. Our data provide evidence that, instead of being only transiently activated upon conflict detection, cACC is involved in sustained engagement of attentional resources required for effective sound object selection performance.
Takeshi Asami, Sang Hyuk Lee, Sylvain Bouix, Yogesh Rathi, Thomas J Whitford, Margaret Niznikiewicz, Paul Nestor, Robert W McCarley, Martha E Shenton, and Marek Kubicki. 2014. “Cerebral white matter abnormalities and their associations with negative but not positive symptoms of schizophrenia.” Psychiatry Res, 222, 1-2, Pp. 52-9.Abstract
Although diffusion tensor imaging (DTI) studies have reported fractional anisotropy (FA) abnormalities in multiple white matter (WM) regions in schizophrenia, relationship between abnormal FA and negative symptoms has not been fully explored. DTI data were acquired from twenty-four patients with chronic schizophrenia and twenty-five healthy controls. Regional brain abnormalities were evaluated by conducting FA comparisons in the cerebral and each lobar WMs between groups. Focal abnormalities were also evaluated with a voxel-wise tract specific method. Associations between structural WM changes and negative symptoms were assessed using the Scale for the Assessment of Negative Symptoms (SANS). The patient group showed decreased FA in the cerebrum, especially in the frontal lobe, compared with controls. A voxel-wise analysis showed FA decreases in almost all WM tracts in schizophrenia. Correlation analyses demonstrated negative relationships between FA in the cerebrum, particularly in the left hemisphere, and SANS global and global rating scores (Anhedonia-Asociality, Attention, and Affective-Flattening), and also associations between FA of left frontal lobe and SANS global score, Anhedonia-Asociality, and Attention. This study demonstrates that patients with chronic schizophrenia evince widespread cerebral FA abnormalities and that these abnormalities, especially in the left hemisphere, are associated with negative symptoms.

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