Bariatric Surgery-Induced Changes in Intima-Media Thickness and Cardiovascular Risk Factors in Class 3 Obesity: A 3-Year Follow-Up Study.

OBJECTIVE: The impact of weight loss induced by bariatric surgery (BS) and nonsurgical approaches on cardiovascular risk factors (CVRFs) has not been fully elucidated. We assessed the effects of BS and a nonsurgical approach on carotid intima-media thickness (CIMT) and CVRFs in participants with class 3 obesity. METHODS: A total of 87 participants with obesity (59 women; 46 [37-52] years old; BMI, 43 [40-47]) and 75 controls were recruited; 21 (25%) participants with obesity underwent BS. BMI, blood pressure, cholesterol, triglycerides, fasting plasma glucose, C-reactive protein, CIMT, and Framingham Risk Score were measured at baseline and at 3-year follow-up. Independent factors for reduction in CIMT were analyzed. The literature on the effects of BS and CIMT was reviewed. RESULTS: After BS, BMI decreased from 45.45 to 27.28 (P < 0.001), and mean CIMT decreased from 0.64 mm (0.56-0.75 mm) to 0.54 mm (0.46-0.65) mm (P < 0.012), equivalent to 0.005 mm/kg of weight lost. At 3-year follow-up, participants who had undergone BS had similar CIMT and CVRFs to the control group. No changes in CVRFs were seen related to the nonsurgical approach. BMI reduction after BS had the strongest independent association with decreased CIMT. CONCLUSIONS: Weight loss after BS decreases CIMT and CVRFs in middle-aged participants with class 3 obesity, resulting in CIMT similar to that observed in lean participants.

SHNN-CAD⁺: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection.

To perform anomaly detection for trajectory data, we study the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD + . SHNN-CAD was introduced based on the theory of conformal prediction dealing with the problem of online detection. Unlike most related approaches requiring several not intuitive parameters, SHNN-CAD has the advantage of being parameter-light which enables the easy reproduction of experiments. We propose to adaptively determine the anomaly threshold during the online detection procedure instead of predefining it without any prior knowledge, which makes the algorithm more usable in practical applications. We present a modified Hausdorff distance measure that takes into account the direction difference and also reduces the computational complexity. In addition, the anomaly detection is more flexible and accurate via a re-do strategy. Extensive experiments on both real-world and synthetic data show that SHNN-CAD + outperforms SHNN-CAD with regard to accuracy and running time.

Novel Brain Complexity Measures Based on Information Theory.

Brain networks are widely used models to understand the topology and organization of the brain. These networks can be represented by a graph, where nodes correspond to brain regions and edges to structural or functional connections. Several measures have been proposed to describe the topological features of these networks, but unfortunately, it is still unclear which measures give the best representation of the brain. In this paper, we propose a new set of measures based on information theory. Our approach interprets the brain network as a stochastic process where impulses are modeled as a random walk on the graph nodes. This new interpretation provides a solid theoretical framework from which several global and local measures are derived. Global measures provide quantitative values for the whole brain network characterization and include entropy, mutual information, and erasure mutual information. The latter is a new measure based on mutual information and erasure entropy. On the other hand, local measures are based on different decompositions of the global measures and provide different properties of the nodes. Local measures include entropic surprise, mutual surprise, mutual predictability, and erasure surprise. The proposed approach is evaluated using synthetic model networks and structural and functional human networks at different scales. Results demonstrate that the global measures can characterize new properties of the topology of a brain network and, in addition, for a given number of nodes, an optimal number of edges is found for small-world networks. Local measures show different properties of the nodes such as the uncertainty associated to the node, or the uniqueness of the path that the node belongs. Finally, the consistency of the results across healthy subjects demonstrates the robustness of the proposed measures.

Some Order Preserving Inequalities for Cross Entropy and Kullback-Leibler Divergence.

Cross entropy and Kullback-Leibler (K-L) divergence are fundamental quantities of information theory, and they are widely used in many fields. Since cross entropy is the negated logarithm of likelihood, minimizing cross entropy is equivalent to maximizing likelihood, and thus, cross entropy is applied for optimization in machine learning. K-L divergence also stands independently as a commonly used metric for measuring the difference between two distributions. In this paper, we introduce new inequalities regarding cross entropy and K-L divergence by using the fact that cross entropy is the negated logarithm of the weighted geometric mean. We first apply the well-known rearrangement inequality, followed by a recent theorem on weighted Kolmogorov means, and, finally, we introduce a new theorem that directly applies to inequalities between K-L divergences. To illustrate our results, we show numerical examples of distributions.

Brain parcellation based on information theory.

BACKGROUND AND OBJECTIVE: In computational neuroimaging, brain parcellation methods subdivide the brain into individual regions that can be used to build a network to study its structure and function. Using anatomical or functional connectivity, hierarchical clustering methods aim to offer a meaningful parcellation of the brain at each level of granularity. However, some of these methods have been only applied to small regions and strongly depend on the similarity measure used to merge regions. The aim of this work is to present a robust whole-brain hierarchical parcellation that preserves the global structure of the network. METHODS: Brain regions are modeled as a random walk on the connectome. From this model, a Markov process is derived, where the different nodes represent brain regions and in which the structure can be quantified. Functional or anatomical brain regions are clustered by using an agglomerative information bottleneck method that minimizes the overall loss of information of the structure by using mutual information as a similarity measure. RESULTS: The method is tested with synthetic models, structural and functional human connectomes and is compared with the classic k-means. Results show that the parcellated networks preserve the main properties and are consistent across subjects. CONCLUSION: This work provides a new framework to study the human connectome using functional or anatomical connectivity at different levels.

Information theory-based automatic multimodal transfer function design.

In this paper, we present a new framework for multimodal volume visualization that combines several information-theoretic strategies to define both colors and opacities of the multimodal transfer function. To the best of our knowledge, this is the first fully automatic scheme to visualize multimodal data. To define the fused color, we set an information channel between two registered input datasets, and afterward, we compute the informativeness associated with the respective intensity bins. This informativeness is used to weight the color contribution from both initial 1-D transfer functions. To obtain the opacity, we apply an optimization process that minimizes the informational divergence between the visibility distribution captured by a set of viewpoints and a target distribution proposed by the user. This distribution is defined either from the dataset features, from manually set importances, or from both. Other problems related to the multimodal visualization, such as the computation of the fused gradient and the histogram binning, have also been solved using new information-theoretic strategies. The quality and performance of our approach are evaluated on different datasets.

Multimodal Data Fusion Based on Mutual Information.

Multimodal visualization aims at fusing different data sets so that the resulting combination provides more information and understanding to the user. To achieve this aim, we propose a new information-theoretic approach that automatically selects the most informative voxels from two volume data sets. Our fusion criteria are based on the information channel created between the two input data sets that permit us to quantify the information associated with each intensity value. This specific information is obtained from three different ways of decomposing the mutual information of the channel. In addition, an assessment criterion based on the information content of the fused data set can be used to analyze and modify the initial selection of the voxels by weighting the contribution of each data set to the final result. The proposed approach has been integrated in a general framework that allows for the exploration of volumetric data models and the interactive change of some parameters of the fused data set. The proposed approach has been evaluated on different medical data sets with very promising results.

Reliability of the ABC/2 method in determining acute infarct volume.

BACKGROUND AND PURPOSE: Infarct volume is used as a surrogate outcome measure in clinical trials of therapies for acute ischemic stroke. ABC/2 is a fast volumetric method, but its accuracy remains to be determined. We aimed to study the accuracy and reproducibility of ABC/2 in determining acute infarct volume with diffusion-weighted imaging. METHODS: We studied 86 consecutive patients with acute ischemic stroke. Three blinded observers determined volume with the ABC/2 method, and the results were compared with those of the manual planimetric method. RESULTS: The ABC/2 technique overestimated infarct volume by a median false increase (variable ABC/2 volume minus planimetric volume) of 7.33 cm(3) (1.29, 22.170, representing a 162.56% increase over the value of the gold standard (variable ABC/2 volume over planimetric volume) (121.70, 248.52). In each method, the interrater reliability was excellent: the intraclass correlations were .992 and .985 for the ABC/2 technique and planimetric method, respectively. CONCLUSIONS: ABC/2 is volumetric method with clinical value but it consistently overestimates the real infarct volume.

Automatic transfer functions based on informational divergence.

In this paper we present a framework to define transfer functions from a target distribution provided by the user. A target distribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is based on a communication channel between a set of viewpoints and a set of bins of a volume data set, and it supports 1D as well as 2D transfer functions including the gradient information. The transfer functions are obtained by minimizing the informational divergence or Kullback-Leibler distance between the visibility distribution captured by the viewpoints and a target distribution selected by the user. The use of the derivative of the informational divergence allows for a fast optimization process. Different target distributions for 1D and 2D transfer functions are analyzed together with importance-driven and view-based techniques.

Magnetic resonance imaging biomarkers of ischemic stroke: criteria for the validation of primary imaging biomarkers.

Ischemic stroke is associated with a high rate of disability and death. Establishing valid biomarkers could help accelerate the approval of promising new therapies for stroke. Whereas many serum biomarkers have been evaluated, possible imaging biomarkers of stroke lack validation. Magnetic resonance imaging (MRI) is a very sensitive technique to study acute stroke and MRI parameters have been established to assess the outcome of acute stroke. This review reassesses the criteria for the validation of MRI biomarkers of acute ischemic stroke (MRI-BAS). Seven criteria were used to review the validity of the main MRI-BAS: vascular status, lesion volume, reversibility on diffusion-weighted imaging, perfusion alteration, penumbra studied with diffusion-perfusion mismatch, clinical-diffusion mismatch, diffusion-angiography mismatch and hemorrhagic transformation. We analyzed the definitions of these biomarkers and the extent to which each fulfills the criteria for validation and found that few MRI-BAS have been fully validated. Further studies should help to improve the validation of current MRI-BAS and develop new biomarkers.

Image segmentation using information bottleneck method.

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.