Magno R. Guillen

Sub-patterns of language network reorganization in pediatric localization related epilepsy: A multisite study

To study the neural networks reorganization in pediatric epilepsy, a consortium of imaging centers was established to collect functional imaging data. Common paradigms and similar acquisition parameters were used. We studied 122 children (64 control and 58 LRE patients) across five sites using EPI BOLD fMRI and an auditory description decision task. After normalization to the MNI atlas, activation maps generated by FSL were separated into three sub‐groups using a distance method in the principal component analysis (PCA)‐based decisional space. Three activation patterns were identified: (1) the typical distributed network expected for task in left inferior frontal gyrus (Brocas) and along left superior temporal gyrus (Wernickes) (60 controls, 35 patients); (2) a variant left dominant pattern with greater activation in IFG, mesial left frontal lobe, and right cerebellum (three controls, 15 patients); and (3) activation in the right counterparts of the first pattern in Brocas area (one control, eight patients). Patients were over represented in Groups 2 and 3 (P < 0.0004). There were no scanner (P = 0.4) or site effects (P = 0.6). Our data‐driven method for fMRI activation pattern separation is independent of a priori notions and bias inherent in region of interest and visual analyses. In addition to the anticipated atypical right dominant activation pattern, a sub‐pattern was identified that involved intensity and extent differences of activation within the distributed left hemisphere language processing network. These findings suggest a different, perhaps less efficient, cognitive strategy for LRE group to perform the task. Hum Brain Mapp, 2011.

A 3-D Liver Segmentation Method with Parallel Computing for Selective Internal Radiation Therapy

This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.

Classification of FMRI Patterns—A Study of the Language Network Segregation in Pediatric Localization Related Epilepsy

This article describes a pattern classification algorithm for pediatric epilepsy using fMRI language‐related activation maps. 122 fMRI datasets from a control group (64) and localization related epilepsy patients (58) provided by five childrens hospitals were used. Each subject performed an auditory description decision task. Using the artificial data as training data, incremental Principal Component Analysis was used in order to generate the feature space while overcoming memory requirements of large datasets. The nearest‐neighbor classifier (NNC) and the distance‐based fuzzy classifier (DFC) were used to perform group separation into left dominant, right dominant, bilateral, and others. The results show no effect of age, age at seizure onset, seizure duration, or seizure etiology on group separation. Two sets of parameters were significant for group separation, the patient vs. control populations and handedness. Of the 122 real datasets, 90 subjects gave the same classification results across all the methods (three raters, LI, bootstrap LI, NNC, and DFC). For the remaining datasets, 18 cases for the IPCA‐NNC and 21 cases for the IPCA‐DFC agreed with the majority of the five classification results (three visual ratings and two LI results). Kappa values vary from 0.59 to 0.73 for NNC and 0.61 to 0.75 for DFC, which indicate good agreement between NNC or DFC with traditional methods. The proposed method as designed can serve as an alternative method to corroborate existing LI and visual rating classification methods and to resolve some of the cases near the boundaries in between categories. Hum Brain Mapp 35:1446–1460, 2014.

A Decisional Space for fMRI Pattern Separation Using the Principal Component Analysis –A Comparative Study of Language Networks in Pediatric Epilepsy

Atypical functional magnetic resonance imaging (fMRI) language patterns may be identified by visual inspection or by region of interest (ROI)‐based laterality indices (LI) but are constrained by a priori assumptions. We compared a data‐driven novel application of principal component analysis (PCA) to conventional methods. We studied 122 fMRI data sets from control and localization‐related epilepsy patients provided by five childrens hospitals. Each subject performed an auditory description decision task. The data sets, acquired with different scanners but similar acquisition parameters, were processed through fMRIB software library to obtain 3D activation maps in standard space. A PCA analysis was applied to generate the decisional space and the data cluster into three distinct activation patterns. The classified activation maps were interpreted by (1) blinded reader rating based on predefined language patterns and (2) by language area ROI‐based LI (i.e., fixed threshold vs. bootstrap approaches). The different classification results were compared through κ inter‐rater agreement statistics. The unique decisional space classified activation maps into three clusters (a) lower intensity typical language representation, (b) higher intensity typical, as well as (c) higher intensity atypical representation. Inter‐rater agreements among the three raters were excellent (Fleiss κ = 0.85, P = 0.05). There was substantial to excellent agreement between the conventional visual rating and LI methods (κ = 0.69–0.82, P = 0.05). The PCA‐based method yielded excellent agreement with conventional methods (κ = 0.82, P = 0.05). The automated and data‐driven PCA decisional space segregates language‐related activation patterns in excellent agreement with current clinical rating and ROI‐based methods. Hum Brain Mapp 34:2330–2342, 2013.

A Client-Server Architecture for Context-Aware Search Application

This paper develops a client-side context-aware search application which is built on the context-aware infrastructure. A context-aware architecture is designed to collect the mobile user’s context information, derive mobile user’s current context, distribute user context among context-aware applications, and support the context-aware applications. The context acquisition is centralized at the context server to ensure the reusability of context information among mobile devices, while context reasoning remains at the application level. Algorithms are proposed to consider the user context profiles. By promoting feedback on the dynamics of the system, prior user selection is now saved for further analysis expediting a subsequent search. A software-based proxy is set up at the client side which includes the context reasoning component. Implementation of such a proxy supports that the context applications are able to derive the user context profiles. To meet the practical demands required of a testing environment, a software simulation using Yahoo search API is provided as a means to evaluate the effectiveness of the design approach in a realistic way. The integration of user context into Yahoo search engines proves how context-aware searches can meet user demands for tailored services and products in and around the user’s environment.

fMRI activation pattern recognition: A novel application of PCA in language network of pediatric localization related epilepsy

In this study, a novel application of Principal Component Analysis (PCA) is proposed to detect language activation map patterns. These activation patterns were obtained by processing functional Magnetic Resonance Imaging (fMRI) studies on both control and localization related epilepsy (LRE) patients as they performed an auditory word definition task. Most group statistical analyses of fMRI datasets look for “commonality” under the assumption of the homogeneity of the sample. However, inter-subject variance may be expected to increase in large “normal” or otherwise heterogeneous patient groups. In such cases, certain different patterns may share a common feature, comprising of small categorical sub-groups otherwise hidden within the main pooling statistical procedure. These variant patterns may be of importance both in normal and patient groups. fMRI atypical-language patterns can be separated by qualitative visual inspection or by means of Laterality Indices (LI) based on region of interest. LI is a coefficient related to the asymmetry of distribution of activated voxels with respect to the midline and it lacks specific spatial and graphical information. We describe a mathematical and computational method for the automatic discrimination of variant spatial patterns of fMRI activation in a mixed population of control subjects and LRE patients. Unique in this study is the provision of a data-driven mechanism to automatically extract brain activation patterns from a heterogeneous population. This method will lead to automatic self-clustering of the datasets provided by different institutions often with different acquisition parameters.

Presurgical thalamocortical connectivity is associated with response to vagus nerve stimulation in children with intractable epilepsy

Although chronic vagus nerve stimulation (VNS) is an established treatment for medically-intractable childhood epilepsy, there is considerable heterogeneity in seizure response and little data are available to pre-operatively identify patients who may benefit from treatment. Since the therapeutic effect of VNS may be mediated by afferent projections to the thalamus, we tested the hypothesis that intrinsic thalamocortical connectivity is associated with seizure response following chronic VNS in children with epilepsy. Twenty-one children (ages 5–21 years) with medically-intractable epilepsy underwent resting-state fMRI prior to implantation of VNS. Ten received sedation, while 11 did not. Whole brain connectivity to thalamic regions of interest was performed. Multivariate generalized linear models were used to correlate resting-state data with seizure outcomes, while adjusting for age and sedation status. A supervised support vector machine (SVM) algorithm was used to classify response to chronic VNS on the basis of intrinsic connectivity. Of the 21 subjects, 11 (52%) had 50% or greater improvement in seizure control after VNS. Enhanced connectivity of the thalami to the anterior cingulate cortex (ACC) and left insula was associated with greater VNS efficacy. Within our test cohort, SVM correctly classified response to chronic VNS with 86% accuracy. In an external cohort of 8 children, the predictive model correctly classified the seizure response with 88% accuracy. We find that enhanced intrinsic connectivity within thalamocortical circuitry is associated with seizure response following VNS. These results encourage the study of intrinsic connectivity to inform neural network-based, personalized treatment decisions for children with intractable epilepsy.

A Comparative Study on the Performance of the Parallel and Distributing Computing Operation in MatLab

This study describes the performance results on testing MatLab applications using the parallel computing and the distributed computing toolboxes under different platforms with different hardware and operating systems. Each trial was executed keeping the hardware fixed and changing the operating system to obtain unbiased results. To standardize the benchmarking test, Fast Fourier Transform (FFT), discrete cosine transform (DCT), edge detection and matrix multiplication algorithms were executed. The results show that the leveraging of multicore platforms can speed up considerably the processing of images through the use of parallel computing tools in MatLab. Two different system hardware platforms (systems 1 and 2) were used in a series of experiments. Four rounds of experiments were performed benchmarking the FFT algorithm using the parallel tool box, by changing system platform, number of workers, image size and number of images. The results of the ANOVA test suggest that although there is no statistical significance on the factor represented by the operating system (OS) on system 1, the OS plays a significant roll on system 2. Moreover, on both systems there is statistical significance on the factors represented by the number of workers utilized and the number of images processed, yielding more than a 500% performance increase by using 8 MatLab workers on a dual quad-core machine.

A knowledge-based database system for visual rating of fMRI activation patterns for brain language networks

This paper describes a novel multimedia tool to facilitate visual assessment of Functional Magnetic Resonance Imaging (fMRI) activation patterns by human experts. A great effort is placed by radiologists and neurologists to present a consistent methodology to provide assessment for brain activation map images. Since each radiologist has his own way to perform the visual analysis on the images and present the findings, rating a large and heterogeneous group of images is a hard task. Although this tool is focused on assessing fMRI activation patterns related to brain language network paradigms, the tool can be extended to other brain activation maps, such as motor, reading, and working memory. Moreover, the same tool can be used for assessing images acquired using different recording modalities as long as these images are saved in standard image formats such as JPEG, BMP, or PNG. The use of this tool is independent of the methodology used to generate the brain activation map, which can be done using specialized software tools such as Statistical Parametric Mapping (SPM) or fMRI Software Library (FSL). The main benefits of using this tool for brain activation image scoring are the systematic approach for rating the activation maps, the automatic descriptive statistics applied to the results and the reduction of assessment time from several minutes to seconds. For each study, the proposed system presents the activation pattern image, based on which the rater is asked to indicate the level and type of activation observed in general, and in specific on the following areas: frontal, temporal, and supplemental motor area.

The spinning dancer illusion and spontaneous brain fluctuations: an fMRI study.

The brain activation associated with the Spinning Dancer Illusion, a cognitive visual illusion, is not entirely known. Inferences from other study modalities point to the involvement of the dorso-parieto-occipital areas in the spontaneous switchings of perception in other bistable non-kinetic illusions. fMRI is a mature technique used to investigate the brain responses associated with mental changes. Resting-state fMRI is a novel technique that may help ascertain the effects of spontaneous brain changes in the top-down regulation of visual perception. The purpose of this report is to describe the brain activation associated with the subjective illusory changes of perception of a kinetic bistable stimulus. We hypothesize that there is a relationship between the perception phases with the very slow cortical spontaneous fluctuations, recently described. A single normal subject who was trained to produce voluntarily perception phase switches underwent a series of fMRI studies whose blocks were either defined post-hoc or accordingly with a predefined timeline to assess spontaneous and voluntarily evoked visual perception switches, respectively. Correlation of findings with resting-state fMRI and independent component analysis of the task series was sought. Phases of the rotation direction were found associated with right parietal activity. Independent component analysis of the task series and their comparison with basal resting-state components suggest that this activity is related to one of the very slow spontaneous brain fluctuations. The spontaneous fluctuations of the cortical activity may explain the subjective changes in perception of direction of the Spinning Dancer Illusion. This observation is a proof-of-principle, suggesting that the spontaneous brain oscillations may influence top-down sensory regulation.