[Show abstract][Hide abstract] ABSTRACT: In the present study, we explored how Age of Acquisition (AoA) of L2 affected brain structures in bilingual individuals. Thirty-six native English speakers who were bilingual were scanned with high resolution MRI. After MRI signal intensity inhomogeneity correction, we applied both voxel-based morphometry (VBM) and surface-based morphometry (SBM) approaches to the data. VBM analysis was performed using FSL's standard VBM processing pipeline. For the SBM analysis, we utilized a semi-automated sulci delineation procedure, registered the brains to an atlas, and extracted measures of twenty four pre-selected regions of interest. We addressed three questions: (1) Which areas are more susceptible to differences in AoA? (2) How do AoA, proficiency and current level of exposure work together in predicting structural differences in the brain? And (3) What is the direction of the effect of AoA on regional volumetric and surface measures? Both VBM and SBM results suggested that earlier second language exposure was associated with larger volumes in the right parietal cortex. Consistently, SBM showed that the cortical area of the right superior parietal lobule increased as AoA decreased. In contrast, in the right pars orbitalis of the inferior frontal gyrus, AoA, proficiency, and current level of exposure are equally important in accounting for the structural differences. We interpret our results in terms of current theory and research on the effects of L2 learning on brain structures and functions.
Journal of Neurolinguistics 11/2015; 36. DOI:10.1016/j.jneuroling.2015.05.001 · 1.49 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We present a method that quantifies point-wise changes in surface morphology of the bones of the human wrist. The proposed method, referred to as Registration-based Bone Morphometry (RBM), consists of two steps: an atlas selection step and an atlas warping step. The atlas for individual wrist bones was selected based on the shortest l2 distance to the ensemble of wrist bones from a database of a healthy population of subjects. The selected atlas was then warped to the corresponding bones of individuals in the population using a non-linear registration method based on regularized l2 distance minimization. The displacement field thus calculated showed local differences in bone shape that then were used for the analysis of group differences. Our results indicate that RBM has potential to provide a standardized approach to shape analysis of bones of the human wrist. We demonstrate the performance of RBM for examining group differences in wrist bone shapes based on sex and between those of the right and left wrists in healthy individuals. We also present data to show the application of RBM for tracking bone erosion status in rheumatoid arthritis.
[Show abstract][Hide abstract] ABSTRACT: Directed graph representations of brain networks are increasingly being used to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network.
Information processing in medical imaging: proceedings of the ... conference 07/2015; 24:399-410.
[Show abstract][Hide abstract] ABSTRACT: Magnetic field inhomogeneities in echo planar images (EPI) can cause large distortion in the phase encoding dimension. In functional MRI (fMRI), this distortion can shift activation loci, increase inter subject variability, and reduce statistical power during group analysis. Distortion correction methods that make use of acquired magnetic field maps have been developed, however, field maps are not always acquired or may not be available to researchers. An alternative approach, which we pursue in this paper, is to estimate the distortion retrospectively by spatially registering the EPI to a structural MRI. We describe a constrained non-linear registration method for correcting fMRI distortion that uses T1-weighted images and does not require field maps. We compared resting state results from uncorrected fMRI, fMRI data corrected with field maps, and fMRI data corrected with our proposed method in data from 20 subjects. The results show that the estimated field maps were similar to the acquired field maps and that the proposed method reduces the overall error in independent component location.
[Show abstract][Hide abstract] ABSTRACT: Late preterm birth confers increased risk of developmental delay, academic difficulties and social deficits. The late third trimester may represent a critical period of development of neural networks including the default mode network (DMN), which is essential to normal cognition. Our objective is to identify functional and structural connectivity differences in the posteromedial cortex related to late preterm birth.
Thirty-eight preadolescents (ages 9-13; 19 born in the late preterm period (≥32 weeks gestational age) and 19 at term) without access to advanced neonatal care were recruited from a low socioeconomic status community in Brazil. Participants underwent neurocognitive testing, 3-dimensional T1-weighted imaging, diffusion-weighted imaging and resting state functional MRI (RS-fMRI). Seed-based probabilistic diffusion tractography and RS-fMRI analyses were performed using unilateral seeds within the posterior DMN (posterior cingulate cortex, precuneus) and lateral parietal DMN (superior marginal and angular gyri).
Late preterm children demonstrated increased functional connectivity within the posterior default mode networks and increased anti-correlation with the central-executive network when seeded from the posteromedial cortex (PMC). Key differences were demonstrated between PMC components with increased anti-correlation with the salience network seen only with posterior cingulate cortex seeding but not with precuneus seeding. Probabilistic tractography showed increased streamlines within the right inferior longitudinal fasciculus and inferior fronto-occipital fasciculus within late preterm children while decreased intrahemispheric streamlines were also observed. No significant differences in neurocognitive testing were demonstrated between groups.
Late preterm preadolescence is associated with altered functional connectivity from the PMC and lateral parietal cortex to known distributed functional cortical networks despite no significant executive neurocognitive differences. Selective increased structural connectivity was observed in the setting of decreased posterior interhemispheric connections. Future work is needed to determine if these findings represent a compensatory adaptation employing alternate neural circuitry or could reflect subtle pathology resulting in emotional processing deficits not seen with neurocognitive testing.
PLoS ONE 06/2015; 10(6):e0130686. DOI:10.1371/journal.pone.0130686 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Late preterm birth is increasingly recognized as a risk factor for cognitive and social deficits. The prefrontal cortex is particularly vulnerable to injury in late prematurity because of its protracted development and extensive cortical connections. Our study examined children born late preterm without access to advanced postnatal care to assess structural and functional connectivity related to the prefrontal cortex. Thirty-eight preadolescents [19 born late preterm (34-36 /7 weeks gestational age) and 19 at term] were recruited from a developing community in Brazil. Participants underwent neuropsychological testing. Individuals underwent three-dimensional T1-weighted, diffusion-weighted, and resting state functional MRI. Probabilistic tractography and functional connectivity analyses were carried out using unilateral seeds combining the medial prefrontal cortex and the anterior cingulate cortex. Late preterm children showed increased functional connectivity within regions of the default mode, salience, and central-executive networks from both right and left frontal cortex seeds. Decreased functional connectivity was observed within the right parahippocampal region from left frontal seeding. Probabilistic tractography showed a pattern of decreased streamlines in frontal white matter pathways and the corpus callosum, but also increased streamlines in the left orbitofrontal white matter and the right frontal white matter when seeded from the right. Late preterm children and term control children scored similarly on neuropsychological testing. Prefrontal cortical connectivity is altered in late prematurity, with hyperconnectivity observed in key resting state networks in the absence of neuropsychological deficits. Abnormal structural connectivity indicated by probabilistic tractography suggests subtle changes in white matter development, implying disruption of normal maturation during the late gestational period.
[Show abstract][Hide abstract] ABSTRACT: Several studies comparing adult musicians and non-musicians have provided compelling evidence for functional and anatomical differences in the brain systems engaged by musical training. It is not known, however, whether those differences result from long-term musical training or from pre-existing traits favoring musicality. In an attempt to begin addressing this question, we have launched a longitudinal investigation of the effects of childhood music training on cognitive, social and neural development. We compared a group of 6- to 7-year old children at the start of intense after-school musical training, with two groups of children: one involved in high intensity sports training but not musical training, another not involved in any systematic training. All children were tested with a comprehensive battery of cognitive, motor, musical, emotional, and social assessments and underwent magnetic resonance imaging and electroencephalography. Our first objective was to determine whether children who participate in musical training were different, prior to training, from children in the control groups in terms of cognitive, motor, musical, emotional, and social behavior measures as well as in structural and functional brain measures. Our second objective was to determine whether musical skills, as measured by a music perception assessment prior to training, correlates with emotional and social outcome measures that have been shown to be associated with musical training. We found no neural, cognitive, motor, emotional, or social differences among the three groups. In addition, there was no correlation between music perception skills and any of the social or emotional measures. These results provide a baseline for an ongoing longitudinal investigation of the effects of music training.
Frontiers in Human Neuroscience 09/2014; 8:690. DOI:10.3389/fnhum.2014.00690 · 3.63 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background / Purpose:
The main goal of this study was to develop a method to reliably estimate functional connectivity in the presence of cross-talk with interference in EEG and MEG data.
We show LC is invariant to linear mixing when only two signals are present whereas C, IC and PLI change as the degree of mixing changes. However, this bivariate framework ignores the interference that occurs when additional sources mix into the two signals of interest. By regressing out reference signals from the interfering regions using real regression coefficients we aimed to improve estimation of true interaction between these two signals. The resulting method (PLC) uses L1-regularization to control the degree of signal suppression in the regression.
20th Annual Meeting of the Organization for Human Brain Mapping (OHBM) 2014; 08/2014
[Show abstract][Hide abstract] ABSTRACT: We investigate using dual time-point PET data to perform Patlak modeling. This approach can be used for whole body dynamic PET studies in which we compute voxel-wise estimates of Patlak parameters using two frames of data for each bed position. Our approach directly uses list-mode arrival times for each event to estimate the Patlak parametric image. We use a penalized likelihood method in which the penalty function uses spatially variant weighting to ensure a count independent local impulse response. We evaluate performance of the method in comparison to fractional changes in SUV values (%DSUV) between the two frames using Cramer Rao analysis and Monte Carlo simulation. Receiver operating characteristic (ROC) curves are used to compare performance in differentiating tumors relative to background based on the dynamic data sets. Using area under the ROC curve as a performance metric, we show superior performance of Patlak relative to %DSUV over a range of dynamic data sets and parameters. These results suggest that Patlak analysis may be appropriate for analysis of dual time-point whole body PET data and could lead to superior detection of tumors relative to %DSUV metrics.
IEEE Transactions on Medical Imaging 04/2014; 33(4):913-924. DOI:10.1109/TMI.2014.2298868 · 3.39 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Obesity is a global health problem, particularly in the U.S. where one third of adults are obese. A reliable and accurate method of quantifying obesity is necessary. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are two measures of obesity that reflect different associated health risks, but accurate measurements in humans or rodent models are difficult. In this paper we present an automatic, registration-based segmentation method for mouse adiposity studies using microCT images. We co-register the subject CT image and a mouse CT atlas. Our method is based on surface matching of the microCT image and an atlas. Surface-based elastic volume warping is used to match the internal anatomy. We acquired a whole body scan of a C57BL6/J mouse injected with contrast agent using microCT and created a whole body mouse atlas by manually delineate the boundaries of the mouse and major organs. For method verification we scanned a C57BL6/J mouse from the base of the skull to the distal tibia. We registered the obtained mouse CT image to our atlas. Preliminary results show that we can warp the atlas image to match the posture and shape of the subject CT image, which has significant differences from the atlas. We plan to use this software tool in longitudinal obesity studies using mouse models.
Proceedings of SPIE - The International Society for Optical Engineering 03/2014; 9038. DOI:10.1117/12.2043744 · 0.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We present a method based on spectral theory for the shape analysis of carpal bones of the human wrist. We represent the cortical surface of the carpal bone in a coordinate system based on the eigensystem of the two-dimensional Helmholtz equation. We employ a metric-global point signature (GPS)-that exploits the scale and isometric invariance of eigenfunctions to quantify overall bone shape. We use a fast finite-element-method to compute the GPS metric. We capitalize upon the properties of GPS representation-such as stability, a standard Euclidean (ℓ(2)) metric definition, and invariance to scaling, translation and rotation-to perform shape analysis of the carpal bones of ten women and ten men from a publicly-available database. We demonstrate the utility of the proposed GPS representation to provide a means for comparing shapes of the carpal bones across populations.
Physics in Medicine and Biology 02/2014; 59(4):961-73. DOI:10.1088/0031-9155/59/4/961 · 2.76 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Time-of-flight (TOF) information improves the signal-to-noise ratio in positron emission tomography (PET). The computation cost in processing TOF-PET sinograms is substantially higher than for nonTOF data because the data in each line of response is divided among multiple TOF bins. This additional cost has motivated research into methods for rebinning TOF data into lower dimensional representations that exploit redundancies inherent in TOF data. We have previously developed approximate Fourier methods that rebin TOF data into either three-dimensional (3D) nonTOF or 2D nonTOF formats. We refer to these methods respectively as FORET-3D and FORET-2D. Here we describe maximum a posteriori (MAP) estimators for use with FORET rebinned data. We first derive approximate expressions for the variance of the rebinned data. We then use these results to rescale the data so that the variance and mean are approximately equal allowing us to use the Poisson likelihood model for MAP reconstruction. MAP reconstruction from these rebinned data uses a system matrix in which the detector response model accounts for the effects of rebinning. Using these methods we compare the performance of FORET-2D and 3D with TOF and nonTOF reconstructions using phantom and clinical data. Our phantom results show a small loss in contrast recovery at matched noise levels using FORET compared to reconstruction from the original TOF data. Clinical examples show FORET images that are qualitatively similar to those obtained from the original TOF-PET data but with a small increase in variance at matched resolution. Reconstruction time is reduced by a factor of 5 and 30 using FORET3D+MAP and FORET2D+MAP respectively compared to 3D TOF MAP, which makes these methods attractive for clinical applications.
Physics in Medicine and Biology 02/2014; 59(4):925-949. DOI:10.1088/0031-9155/59/4/925 · 2.76 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks.
[Show abstract][Hide abstract] ABSTRACT: Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM).
NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch.
To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised [Formula: see text] PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches - Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches.
The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.
PLoS ONE 12/2013; 8(12):e81390. DOI:10.1371/journal.pone.0081390 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The estimation and analysis of kinetic parameters in dynamic PET is frequently confounded by tissue heterogeneity and partial volume effects. We propose a new constrained model of dynamic PET to address these limitations. The proposed formulation incorporates an explicit mixture model in which each image voxel is represented as a mixture of different pure tissue types with distinct temporal dynamics.We use Cram´er-Rao lower bounds to demonstrate that the use of prior information is important to stabilize parameter estimation with this model. As a result, we propose a constrained formulation of the estimation problem that we solve using a two-stage algorithm. In the first stage, a sparse signal processing method is applied to estimate the rate parameters for the different tissue compartments from the noisy PET time series. In the second stage, tissue fractions and the linear parameters of different time activity curves are estimated using a combination of spatial-regularity and fractional mixture constraints. A block coordinate descent algorithm is combined with a manifold search to robustly estimate these parameters. The method is evaluated with both simulated and experimental dynamic PET data.
[Show abstract][Hide abstract] ABSTRACT: Coherence is a widely used measure to investigate interaction between electrophysiological signals obtained from EEG or MEG. However spurious coherence between locations can be introduced through cross-talk or linear mixing between signals that occurs as a result of diffuse lead-field sensitivity (in sensor data) or limited spatial resolution (in cortical current density maps). Measures including the imaginary part of the coherence (IC), phase lag index (PLI) and Lagged coherence (LC) have all been proposed to overcome this problem. Each of these metrics use the fact that cross-talk will produce instantaneous or zero phase-lag interactions between signals. By constructing measures such as these, all of which have zero value in the case of instantaneous mixing only, we can reduce sensitivity to cross-talk. However, none of these measures considers the effect of interference from external sources. In this paper we first investigate the relative performance of IC, PLI and LC. We then propose and evaluate a novel measure, partial lagged coherence (PLC), which is more robust to cross-talk in the presence of interfering signals.
2013 Asilomar Conference on Signals, Systems and Computers; 11/2013
[Show abstract][Hide abstract] ABSTRACT: Measurements of electrophysiological activity can be used to infer interactions between different regions of the human brain. In this work, we consider the use of an autoregressive conditional heteroscedasticity (ARCH) model to estimate causality in variance between different brain regions in simulation and continuously measured EEG data. We propose an efficient new algorithm for ARCH model estimation and demonstrate that the proposed approach provides promising results that are distinct from the causality estimates obtained from simpler and more conventional signal causality models.
2013 Asilomar Conference on Signals, Systems and Computers; 11/2013