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Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
YNIMG Journal 2008 Journal Article
YNIMG Journal 2008 Journal Article
Activation patterns identified by fMRI processing pipelines or fMRI software packages are usually determined by the preprocessing options, parameters, and statistical models used. Previous studies that evaluated options of GLM (general linear model)-based fMRI processing pipelines are mainly based on simulated data with receiver operating characteristics (ROC) analysis, but evaluation of such fMRI processing pipelines on real fMRI data is rare. To understand the effect of processing options on performance of GLM-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly-used fMRI preprocessing steps; optimized the associated GLM-based single-subject processing pipelines; and quantitatively compared univariate GLM (in FSL. FEAT and NPAIRS. GLM) and multivariate CVA (canonical variates analysis) (in NPAIRS. CVA)-based analytic models in single-subject analysis with a recently developed fMRI processing pipeline evaluation system based on prediction accuracy (classification accuracy) and reproducibility performance metrics. For block-design data, we found that with GLM analysis (1) slice timing correction and global intensity normalization have little consistent impact on fMRI processing pipelines, spatial smoothing and high-pass filtering or temporal detrending significantly increases pipeline performance and thus are essential for robust fMRI statistical analysis; (2) combined optimization of spatial smoothing and temporal detrending improves pipeline performance; and (3) in general, the prediction performance of multivariate CVA is higher than that of the univariate GLM, while univariate GLM is more reproducible than multivariate CVA. Because of the different bias–variance trade-offs of univariate and multivariate models, it may be necessary to consider a consensus approach to obtain more accurate activation patterns in fMRI data.
YNIMG Journal 2007 Journal Article
An automated algorithm has been developed to segment stripped (non-brain tissue excluded) T1-weighted MRI brain volumes into left and right cerebral hemispheres and cerebellum+brainstem. The algorithm, which uses the Graph Cuts technique, performs a fully automated segmentation in approximately 30 s following pre-processing. It is robust and accurate and has been tested on datasets from two scanners using different field strengths and pulse sequences. We describe the Graph Cuts algorithm and compare the results of Graph Cuts segmentations against “gold standard” manual segmentations and segmentations produced by three popular software packages used by neuroimagers: BrainVisa, CLASP, and SurfRelax.
YNIMG Journal 2006 Journal Article
The segmentation of T1-weighted images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is a fundamental processing step in neuroimaging, the results of which affect many other structural imaging analyses. Variability in the segmentation process can decrease the power of a study to detect anatomical differences, and minimizing such variability can lead to more robust results. This paper outlines a straightforward strategy that can be used (1) to select more optimal data acquisition and processing protocols and (2) to quantify the impact of such optimization. Using this approach with multiple scans of a single subject, we found that the choice of a segmentation algorithm had the largest impact on variability, while the choice of a pulse sequence had the second largest impact. The data indicate that the classification of GM is the most variable, and that the optimal protocol may differ across tissue types. Therefore, the intended use of segmentation data should play a role in optimization. Examples are provided to demonstrate that the minimization of variability is not sufficient for optimization; the overall accuracy of the approach must also be considered. Simple volumetric computations are included to illustrate the potential gain of optimization; these results show that volume estimates from optimal pathways were on average three times less variable than estimates from suboptimal pathways. Therefore, the simple strategy illustrated here can be applied to many studies to optimize tissue segmentation, which should lead to a net increase in the power of structural neuroimaging studies.
YNIMG Journal 2006 Journal Article
Dysarthria is a significant feature of the dominantly inherited spinocerebellar ataxias (SCA), but little is known about the patterns of brain activity associated with this disorder of motor speech control. Positron emission tomography (PET) was used to study regional cerebral blood flow during speech and rest in a group of 24 subjects with hereditary ataxia with mild-to-moderate dysarthria. These data were compared to the results obtained from a group of 13 age-matched, normal speakers. In the ataxic subjects, speech rates during scanning were significantly slowed compared to normal speakers. Significant reductions in mean regional blood flow were found in the cerebellum but not in supratentorial regions in the ataxic subjects. Multiple linear regression was used to model speech rate from regional blood flow. Four regions were identified as having significant relationships with speech rate in the model: the left inferior frontal and transverse temporal regions, and the right inferior cerebellar region and caudate nucleus. The relationship between flow and rate was positive in the inferior frontal and cerebellar regions and negative in the caudate and the transverse temporal region. The ataxic model represents an elaboration of the relationship previously reported for normal speakers, likely reflecting both the effects of, and compensation for, cerebellar degeneration in motor speech control. Although the mean regional blood flow values presented a pattern of functional organization for motor speech control at odds with lesion data, the performance-based model was in agreement with clinical experience. Incorporating performance data in functional image analysis may be more revealing of system characteristics than simply examining mean blood flow values.
YNIMG Journal 2004 Journal Article
Accurate identification of brain tissue and cerebrospinal fluid (CSF) in a whole-head MRI is a critical first step in many neuroimaging studies. Automating this procedure can eliminate intra- and interrater variance and greatly increase throughput for a labor-intensive step. Many available procedures perform differently across anatomy and under different acquisition protocols. We developed the Brain Extraction Meta-Algorithm (BEMA) to address these concerns. It executes many extraction algorithms and a registration procedure in parallel to combine the results in an intelligent fashion and obtain improved results over any of the individual algorithms. Using an atlas space, BEMA performs a voxelwise analysis of training data to determine the optimal Boolean combination of extraction algorithms to produce the most accurate result for a given voxel. This allows the provided extractors to be used differentially across anatomy, increasing both the accuracy and robustness of the procedure. We tested BEMA using modified forms of BrainSuite's Brain Surface Extractor (BSE), FSL's Brain Extraction Tool (BET), AFNI's 3dIntracranial, and FreeSurfer's MRI Watershed as well as FSL's FLIRT for the registration procedure. Training was performed on T1-weighted scans of 136 subjects from five separate data sets with different acquisition parameters on separate scanners. Testing was performed on 135 separate subjects from the same data sets. BEMA outperformed the individual algorithms, as well as interrater results from a subset of the scans, when compared for the mean Dice coefficient, a rating of the similarity of output masks to the manually defined gold standards.
YNIMG Journal 2001 Journal Article
Based on single-cell recordings in primates, the relationship between neuronal activity and force magnitude is thought to be monotonic, at least for a subset of pyramidal cells in the motor cortex. Functional neuroimaging studies have also suggested a monotonic relationship between cerebral activation and force magnitude. In order to more precisely define this relationship and to characterize the activation pattern(s) associated with the modulation of static force, we studied 40 normal subjects using [15O]water PET and a simple visuomotor task—application of static force on a micro force sensor with the thumb and index finger of the right hand. When our experimental design did not produce the expected result (evidence of a relationship between cerebral activation and force magnitude in ten subjects), we made serial changes in the experimental protocol, including the addition of control (baseline) trials, and increased the number of subjects in an effort to increase our sensitivity to variations in force magnitude. We compared univariate and multivariate data-analytic strategies, but we relied on our multivariate results to elucidate the interaction of attentional and motor networks. We found that increasing the number of subjects from 10 to 20 resulted in an increase in statistical power and a more stable (i. e. , more replicable) but qualitatively similar result, and that the inclusion of control trials in a 10-subject group did not enhance our ability to discern significant brain-behavior relationships. Our results suggest that sample sizes greater than 20 may be required to detect parametric variation in some instances and that failure to detect such variation may result from unanticipated neurobehavioral effects.
YNIMG Journal 2001 Journal Article
YNIMG Journal 2001 Journal Article
We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average κ indices of κ = 0. 746 ± 0. 114 for gray matter (GM) and κ = 0. 798 ± 0. 089 for white matter (WM) compared to expert labeled data. Our method achieved average κ indices κ = 0. 893 ± 0. 041 for GM and κ = 0. 928 ± 0. 039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.
YNIMG Journal 2001 Journal Article
The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms with different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscientific application, we evaluated the performance of six NUC algorithms. We used simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of the nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1. 5T and 3. 0T) MRI scanners in order to relate differences in algorithmic performance to intersubject variability and/or differences in scanner performance. In phantom studies, the correlation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial (top-to-bottom) direction. Two of the six algorithms demonstrated a high degree of stability, as measured by the iterative application of the algorithm to its corrected output. While none of the algorithms performed ideally under all circumstances, locally adaptive methods generally outperformed nonadaptive methods.
YNIMG Journal 2000 Journal Article
YNIMG Journal 2000 Journal Article
Imaging studies of visuomotor learning have reported practice-related activation in brain regions mediating sensorimotor functions. However, development and testing of functional motor learning models, based on the relationship between imaging and behavioral measures, is complicated by the multidimensional nature of motoric control. In the present study, multivariate techniques were used to analyze [15O]water PET and kinematic correlates of learning in a visuomotor tracing task. Fourteen subjects traced a geometric form over a series of eight tracing trials, preceded and followed by baseline trials in which they passively viewed the geometric form. Simultaneous evaluation of multiple behavioral measures indicated that performance improvement was most strongly associated with a global performance measure and least strongly associated with measures of fine motor control. Results of three independent analytic techniques (i. e. , intertrial correlation matrices, power function modeling, iterative canonical variate analysis) indicated that imaging and behavioral measures were most closely related on early learning trials. Performance improvement was associated with covarying increases in normalized activity among superior parietal, postcentral gyrus, and premotor regions and covarying decreases in normalized activity among cerebellar, inferior parietal, pallidal, and medial occipital regions. These findings suggest that performance improvement may be associated with increased activation in neural systems previously implicated in visually guided reaching and decreased activation in neural systems previously implicated in attentive visuospatial processing.
YNIMG Journal 1999 Journal Article
Although Positron Emission Tomography (PET) and functional magnetic resonance imaging (fMRI) studies commonly subtract data obtained during two or more experimental conditions to decompose a complex task, there have been few opportunities to evaluate this approach directly. In the present study, PET was used to study three motor speech tasks selected such that two were constituent components of the third, making possible a direct examination of decomposition by subtraction. In Experiment 1, a group of 13 right-handed normal volunteers participated in three activation studies: syllable repetition; phonation; and repetitive lip closure. A scanning session was devoted to a single task, repeated four times. In Experiment 2, six of the original subjects performed the same three activation studies during a single scanning session. Whether tasks were studied in separate scanning sessions or combined within a single session, the results of decomposition by compound subtraction differed significantly from the results obtained when individual tasks were compared to a simple baseline condition. These data failed to demonstrate task additivity, a necessary property if decomposition by subtraction is to provide an accurate characterization of the brain activity accompanying complex behavior.