Xiaozhen You

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.

Atypical modulation of distant functional connectivity by cognitive state in children with Autism Spectrum Disorders

We examined whether modulation of functional connectivity by cognitive state differed between pre-adolescent children with Autism Spectrum Disorders (ASD) and age and IQ-matched control children. Children underwent functional magnetic resonance imaging (fMRI) during two states, a resting state followed by a sustained attention task. A voxel-wise method was used to characterize functional connectivity at two levels, local (within a voxels 14 mm neighborhood) and distant (outside of the voxels 14 mm neighborhood to the rest of the brain) and regions exhibiting Group × State interaction were identified for both types of connectivity maps. Distant functional connectivity of regions in the left frontal lobe (dorsolateral [BA 11, 10]; supplementary motor area extending into dorsal anterior cingulate [BA 32/8]; and premotor [BA 6, 8, 9]), right parietal lobe (paracentral lobule [BA 6]; angular gyrus [BA 39/40]), and left posterior middle temporal cortex (BA 19/39) showed a Group × State interaction such that relative to the resting state, connectivity reduced (i.e., became focal) in control children but increased (i.e., became diffuse) in ASD children during the task state. Higher state-related increase in distant connectivity of left frontal and right angular gyrus predicted worse inattention in ASD children. Two graph theory measures (global efficiency and modularity) were also sensitive to Group × State differences, with the magnitude of state-related change predicting inattention in the ASD children. Our results indicate that as ASD children transition from an unconstrained to a sustained attentional state, functional connectivity of frontal and parietal regions with the rest of the brain becomes more widespread in a manner that may be maladaptive as it was associated with attention problems in everyday life.

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.

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.

Reduced Language Connectivity in Pediatric Epilepsy

Functional connectivity (FC) among language regions is decreased in adults with epilepsy compared to controls, but less is known about FC in children with epilepsy. We sought to determine if language FC is reduced in pediatric epilepsy, and examined clinical factors that associate with language FC in this population.

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.

Effect of Adolescent Bariatric Surgery on the Brain and Cognition: A Pilot Study

Neurocognitive deficits in pediatric obesity relate to poor developmental outcomes. We sought preliminary evidence for changes in brain and cognitive functioning relevant to obesogenic behavior following vertical sleeve gastrectomy (VSG) in adolescents relative to wait‐listed (WL) and healthy controls (HC).

A New Algorithm as an Extension to the Gradient Descent Method for Functional Brain Activation Classification

The functional activation of the brain gets affected in conditions such as brain-tumor, localization-related epilepsy (LRE) and lesions. Typical brain activation is such that the left brain is dominant as compared to the right brain. In order to distinguish between the two groups -typical and atypical - the patients undergo functional Magnetic Resonance Imaging (fMRI) test. Based on the processed fMRI maps, nonlinear decision functions (NDF) are used to determine the laterality. Here an alternate algorithm called the ‘Iterative Random Training-Testing Algorithm’, a modification of the well known gradient descent algorithm, which is used as a means for enhancing the results of the classification, is presented. The algorithm aims at improving the sensitivity of results obtained in earlier studies reported in literature. Improving the sensitivity is of prime importance since sensitivity suggests the proportion of false negatives in the classification results. False negatives are critical in clinical decision making. The algorithm divides the training data set randomly into a pure-training set and cross-validation training set. The decision function is trained with the elements assigned to the pure training set and then tested with the element of the cross validation training set. The whole process is repeated a number of times with the aim that the random division of the data set would take into consideration various formations of the data yielding better results. The results of the algorithm showed an improvement in the sensitivity of 2 to 5% with no significant changes in the accuracy, specificity or precision.

The Merit of Principal Component Analysis in fMRI Language Pattern Recognition for Pediatric Epilepsy

Atypical language activation pattern analysis is of significant clinical relevance in neuroscience research, especially when surgical interventions are deemed necessary. Epilepsy patient populations provide a means for validating these methods because of known heterogeneity of language dominance. Florida International University (FIU), in collaboration with 13 worldwide health care institutions, has established a multisite repository for language re-organization analysis on normal and pediatric epilepsy fMRI data. Quantitative region of interest (ROI) analysis, Laterality Index (LI) calculation, and visual rating are common methods for determining language dominance. Limitations of subjective ROI analysis with priori assumption or subjective visual rating motivate us to seek a data-driven method. Here we propose a new configuration and application of the Principal Component Analysis for fMRI language activation pattern recognition among a heterogeneous population. The top eigenvectors are proposed to objectively automate the recognition of ROI among fMRI datasets. 122 subjects’ fMRI activation maps were processed, visually rated by clinical investigators. ROI identified through the PCA-based method generally encompass Broca’s and Wernicke’s areas. fMRI datasets masked by the ROI were applied as input to the proposed PCA method. Different numbers of top eigenvectors were examined in comparison to their spatial distributions of LI and their respective visual ratings. These PCA-based brain activation distributions suggest a potential of using eigenvectors to separate and classify fMRI language activation patterns.