Quantifying the systemic risk and fragility of financial systems is of vital importance in analyzing market efficiency, deciding on portfolio allocation, and containing financial contagions. At a high level, financial systems may be represented as weighted graphs that characterize the complex web of interacting agents and information flow (for example, debt, stock returns, and shareholder ownership). Such a representation often turns out to provide keen insights. We show that fragility is a system-level characteristic of "business-as-usual" market behavior and that financial crashes are invariably preceded by system-level changes in robustness. This was done by leveraging previous work, which suggests that Ricci curvature, a key geometric feature of a given network, is negatively correlated to increases in network fragility. To illustrate this insight, we examine daily returns from a set of stocks comprising the Standard and Poor's 500 (S&P 500) over a 15-year span to highlight the fact that corresponding changes in Ricci curvature constitute a financial "crash hallmark." This work lays the foundation of understanding how to design (banking) systems and policy regulations in a manner that can combat financial instabilities exposed during the 2007-2008 crisis.
We propose a novel method to harmonize diffusion MRI data acquired from multiple sites and scanners, which is imperative for joint analysis of the data to significantly increase sample size and statistical power of neuroimaging studies. Our method incorporates the following main novelties: i) we take into account the scanner-dependent spatial variability of the diffusion signal in different parts of the brain; ii) our method is independent of compartmental modeling of diffusion (e.g., tensor, and intra/extra cellular compartments) and the acquired signal itself is corrected for scanner related differences; and iii) inter-subject variability as measured by the coefficient of variation is maintained at each site. We represent the signal in a basis of spherical harmonics and compute several rotation invariant spherical harmonic features to estimate a region and tissue specific linear mapping between the signal from different sites (and scanners). We validate our method on diffusion data acquired from seven different sites (including two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. Since the extracted rotation invariant spherical harmonic features depend on the accuracy of the brain parcellation provided by Freesurfer, we propose a feature based refinement of the original parcellation such that it better characterizes the anatomy and provides robust linear mappings to harmonize the dMRI data. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across multiple sites before and after data harmonization. We also show results using tract-based spatial statistics before and after harmonization for independent validation of the proposed methodology. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.
More than half of all cancer patients receive radiotherapy in their treatment process. However, our understanding of abnormal transcriptional responses to radiation remains poor. In this study, we employ an extended definition of Ollivier-Ricci curvature based on LI-Wasserstein distance to investigate genes and biological processes associated with ionizing radiation (IR) and ultraviolet radiation (UV) exposure using a microarray dataset. Gene expression levels were modeled on a gene interaction topology downloaded from the Human Protein Reference Database (HPRD). This was performed for IR, UV, and mock datasets, separately. The difference curvature value between IR and mock graphs (also between UV and mock) for each gene was used as a metric to estimate the extent to which the gene responds to radiation. We found that in comparison of the top 200 genes identified from IR and UV graphs, about 20~30% genes were overlapping. Through gene ontology enrichment analysis, we found that the metabolic-related biological process was highly associated with both IR and UV radiation exposure.
Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disorder (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with disease subtypes. It has been shown that the disease subtypes and textures are linked to physiological indicators and prognosis, although neither is well characterized clinically. Most previous computational approaches to modeling emphysema imaging data have focused on supervised classification of lung textures in patches of CT scans. In this work, we describe a generative model that jointly captures heterogeneity of disease subtypes and of the patient population. We also describe a corresponding inference algorithm that simultaneously discovers disease subtypes and population structure in an unsupervised manner. This approach enables us to create image-based descriptors of emphysema beyond those that can be identified through manual labeling of currently defined phenotypes. By applying the resulting algorithm to a large data set, we identify groups of patients and disease subtypes that correlate with distinct physiological indicators.
The question whether our brain pathways adhere to a geometric grid structure has been a popular topic of debate in the diffusion imaging and neuroscience societies. Wedeen et al. (2012a, b) proposed that the brain's white matter is organized like parallel sheets of interwoven pathways. Catani et al. (2012) concluded that this grid pattern is most likely an artifact, resulting from methodological biases that cause the tractography pathways to cross in orthogonal angles. To date, ambiguities in the mathematical conditions for a sheet structure to exist (e.g. its relation to orthogonal angles) combined with the lack of extensive quantitative evidence have prevented wide acceptance of the hypothesis. In this work, we formalize the relevant terminology and recapitulate the condition for a sheet structure to exist. Note that this condition is not related to the presence or absence of orthogonal crossing fibers, and that sheet structure is defined formally as a surface formed by two sets of interwoven pathways intersecting at arbitrary angles within the surface. To quantify the existence of sheet structure, we present a novel framework to compute the sheet probability index (SPI), which reflects the presence of sheet structure in discrete orientation data (e.g. fiber peaks derived from diffusion MRI). With simulation experiments we investigate the effect of spatial resolution, curvature of the fiber pathways, and measurement noise on the ability to detect sheet structure. In real diffusion MRI data experiments we can identify various regions where the data supports sheet structure (high SPI values), but also areas where the data does not support sheet structure (low SPI values) or where no reliable conclusion can be drawn. Several areas with high SPI values were found to be consistent across subjects, across multiple data sets obtained with different scanners, resolutions, and degrees of diffusion weighting, and across various modeling techniques. Under the strong assumption that the diffusion MRI peaks reflect true axons, our results would therefore indicate that pathways do not form sheet structures at every crossing fiber region but instead at well-defined locations in the brain. With this framework, sheet structure location, extent, and orientation could potentially serve as new structural features of brain tissue. The proposed method can be extended to quantify sheet structure in directional data obtained with techniques other than diffusion MRI, which is essential for further validation.
We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.
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.