Biomedical subjects

Stephen LaConte

Publications and source records attributed to Stephen LaConte.

6 recordsLinked to original sources

PubMed2006

Activity and effective connectivity of parietal and occipital cortical regions during haptic shape perception.

It is now widely accepted that visual cortical areas are active during normal tactile perception, but the underlying mechanisms are still not clear. The goal of the present study was to use functional magnetic resonance imaging (fMRI) to investigate the activity and effective connectivity of parietal and occipital cortical areas during haptic shape perception, with a view to potentially clarifying the role of top-down and bottom-up inputs into visual areas. Subjects underwent fMRI scanning while engaging in discrimination of haptic shape or texture, and in separate runs, visual shape or texture. Accuracy did not differ significantly between tasks. Haptic shape-selective regions, identified on a contrast between the haptic shape and texture conditions in individual subjects, were found bilaterally in the postcentral sulcus (PCS), multiple parts of the intraparietal sulcus (IPS) and the lateral occipital complex (LOC). The IPS and LOC foci tended to be shape-selective in the visual modality as well. Structural equation modelling was used to study the effective connectivity among the haptic shape-selective regions in the left hemisphere, contralateral to the stimulated hand. All possible models were tested for their fit to the correlations among the observed time-courses of activity. Two equivalent models emerged as the winners. These models, which were quite similar, were characterized by both bottom-up paths from the PCS to parts of the IPS, and top-down paths from the LOC and parts of the IPS to the PCS. We conclude that interactions between unisensory and multisensory cortical areas involve bidirectional information flow.

Adult↗

PubMed2005

Support vector machines for temporal classification of block design fMRI data.

This paper treats support vector machine (SVM) classification applied to block design fMRI, extending our previous work with linear discriminant analysis [LaConte, S., Anderson, J., Muley, S., Ashe, J., Frutiger, S., Rehm, K., Hansen, L.K., Yacoub, E., Hu, X., Rottenberg, D., Strother S., 2003a. The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics. NeuroImage 18, 10-27; Strother, S.C., Anderson, J., Hansen, L.K., Kjems, U., Kustra, R., Siditis, J., Frutiger, S., Muley, S., LaConte, S., Rottenberg, D. 2002. The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework. NeuroImage 15, 747-771]. We compare SVM to canonical variates analysis (CVA) by examining the relative sensitivity of each method to ten combinations of preprocessing choices consisting of spatial smoothing, temporal detrending, and motion correction. Important to the discussion are the issues of classification performance, model interpretation, and validation in the context of fMRI. As the SVM has many unique properties, we examine the interpretation of support vector models with respect to neuroimaging data. We propose four methods for extracting activation maps from SVM models, and we examine one of these in detail. For both CVA and SVM, we have classified individual time samples of whole brain data, with TRs of roughly 4 s, thirty slices, and nearly 30,000 brain voxels, with no averaging of scans or prior feature selection.

Algorithms↗

PubMed2005

Connectivity exploration with structural equation modeling: an fMRI study of bimanual motor coordination.

The present fMRI study explores the connectivity among motor areas in a bimanual coordination task using the analysis framework of structural equation modeling (SEM). During bimanual finger tapping at different frequency ratios, temporal correlations of activations between left/right primary motor cortices (MI), left/right PMdc (caudal dorsal premotor area) and supplementary motor cortex (SMA) were detected and used as inputs to the SEM analysis. SEM was extended from its traditional role as a confirmatory analysis to be used as an exploratory technique to determine the most statistically significant connectivity model given a set of cortical areas based on anatomic constraints. The resultant network exhibits coupling from left MI to right MI, links from both PMs to the two MIs, a negative interaction from left PM to right PM, and functional influence from SMA to right MI and right PM, revealing contributions of these areas to bimanual coordination.

Adolescent↗

PubMed2004

Inherent insensitivity to RF inhomogeneity in FLASH imaging.

Radiofrequency (RF) field inhomogeneity is an unavoidable problem in MRI, and it becomes severe at high magnetic fields due to the dependence of B1 on the sample. It leads to nonuniformities in image intensity and contrast, causing difficulties in quantitative interpretation and image segmentation. In this work, it is observed that with the fast low-angle shot (FLASH) sequence, which is often used for anatomic imaging and morphometric studies, sensitivity to RF inhomogeneity can be substantially reduced when the same coil is used for both transmission and reception, and an appropriate nominal flip angle is employed. This observation can help us understand the signal behavior of FLASH in the presence of RF inhomogeneity, and provide a guide for selecting parameters in FLASH imaging.

Brain Mapping↗

PubMed2003

The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics.

This work proposes an alternative to simulation-based receiver operating characteristic (ROC) analysis for assessment of fMRI data analysis methodologies. Specifically, we apply the rapidly developing nonparametric prediction, activation, influence, and reproducibility resampling (NPAIRS) framework to obtain cross-validation-based model performance estimates of prediction accuracy and global reproducibility for various degrees of model complexity. We rely on the concept of an analysis chain meta-model in which all parameters of the preprocessing steps along with the final statistical model are treated as estimated model parameters. Our ROC analog, then, consists of plotting prediction vs. reproducibility results as curves of model complexity for competing meta-models. Two theoretical underpinnings are crucial to utilizing this new validation technique. First, we explore the relationship between global signal-to-noise and our reproducibility estimates as derived previously. Second, we submit our model complexity curves in the prediction versus reproducibility space as reflecting classic bias-variance tradeoffs. Among the particular analysis chains considered, we found little impact in performance metrics with alignment, some benefit with temporal detrending, and greatest improvement with spatial smoothing.

Adult↗

PubMed2002

The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework.

We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPMs). Our NPAIRS (nonparametric prediction, activation, influence, and reproducibility resampling) framework provides an alternative to simulations and ROC curves by using real PET and fMRI data sets to examine the relationship between prediction accuracy and the signal-to-noise ratios (SNRs) associated with reproducible SPMs. Using cross-validation resampling we plot training-test set predictions of the experimental design variables (e.g., brain-state labels) versus reproducibility SNR metrics for the associated SPMs. We demonstrate the utility of this framework across the wide range of performance metrics obtained from [(15)O]water PET studies of 12 age- and sex-matched data sets performing different motor tasks (8 subjects/set). For the 12 data sets we apply NPAIRS with both univariate and multivariate data-analysis approaches to: (1) demonstrate that this framework may be used to obtain reproducible SPMs from any data-analysis approach on a common Z-score scale (rSPM[Z]); (2) demonstrate that the histogram of a rSPM[Z] image may be modeled as the sum of a data-analysis-dependent noise distribution and a task-dependent, Gaussian signal distribution that scales monotonically with our reproducibility performance metric; (3) explore the relation between prediction and reproducibility performance metrics with an emphasis on bias-variance tradeoffs for flexible, multivariate models; and (4) measure the broad range of reproducibility SNRs and the significant influence of individual subjects. A companion paper describes learning curves for four of these 12 data sets, which describe an alternative mutual-information prediction metric and NPAIRS reproducibility as a function of training-set sizes from 2 to 18 subjects. We propose the NPAIRS framework as a validation tool for testing and optimizing methodological choices and tools in functional neuroimaging.

Adult↗