Biomedical subjects

Ziad S Saad

Publications and source records attributed to Ziad S Saad.

4 recordsLinked to original sources

PubMed2006

Simplified intersubject averaging on the cortical surface using SUMA.

Task and group comparisons in functional magnetic resonance imaging (fMRI) studies are often accomplished through the creation of intersubject average activation maps. Compared with traditional volume-based intersubject averages, averages made using computational models of the cortical surface have the potential to increase statistical power because they reduce intersubject variability in cortical folding patterns. We describe a two-step method for creating intersubject surface averages. In the first step cortical surface models are created for each subject and the locations of the anterior and posterior commissures (AC and PC) are aligned. In the second step each surface is standardized to contain the same number of nodes with identical indexing. An anatomical average from 28 subjects created using the AC-PC technique showed greater sulcal and gyral definition than the corresponding volume-based average. When applied to an fMRI dataset, the AC-PC method produced greater maximum, median, and mean t-statistics in the average activation map than did the volume average and gave a better approximation to the theoretical-ideal average calculated from individual subjects. The AC-PC method produced average activation maps equivalent to those produced with surface-averaging methods that use high-dimensional morphing. In comparison with morphing methods, the AC-PC technique does not require selection of a template brain and does not introduce deformations of sulcal and gyral patterns, allowing for group analysis within the original folded topology of each individual subject. The tools for performing AC-PC surface averaging are implemented and freely available in the SUMA software package.

Brain↗

PubMed2006

Functional imaging analysis contest (FIAC) analysis according to AFNI and SUMA.

The Functional Imaging Analysis Contest (FIAC) datasets were analyzed with the AFNI software package. Two types of linear regression analyses were carried out: "fixed shape" hemodynamic response, where a preselected incomplete gamma function is used to model each brief activation episode, and "variable shape" analysis, where the temporal shape of the response model in each stimulus block class is allowed to vary separately in each voxel. These time series regressions were carried out both in the volume and on the original data projected to individual standardized cortical surface models. Intersubject analyses were carried out voxel-wise on the regression amplitudes obtained from these time series results, using a multi-way within-subject analysis of variance (ANOVA). Group analysis of the block design demonstrated a significant repetition suppression of the BOLD signal within blocks in the superior and middle temporal gyrus. This effect may represent differences in the response to the first stimulus following a period of silence compared to the remaining sentences in the block. Analyzing the event-related data, Brodmann area 31 showed significant sentence effect and consecutive-sentence repetition effect. However, no significant speaker effect was found; these results may be consistent with the instructions to the subjects that they would be tested on the sentence content. Sentence by speaker interaction effects were found in bilateral middle temporal gyrus, left inferior frontal, and left inferior temporal gyrus.

Brain Mapping↗

PubMed2003

Estimation of FMRI response delays.

We present an efficient algorithm using the Hilbert Transform for estimating the delay of the BOLD response to neuronal stimulation. With minimal additional computations, the algorithm estimates parameters generated in the widely used cross-correlation method and simplifies the interpolation required to estimate the response delay from the cross-correlation function. We examined errors in the Hilbert-based delay estimate associated with the use of DFT on short-duration discrete signals and proposed a method for minimizing these errors. Furthermore, we compared the delay estimates obtained with the Hilbert method to those obtained using the onset of the BOLD response. The Hilbert method resulted in less variance in the delay estimate despite the potential for higher variability in the latter part of the BOLD response. This improved delay estimate was attributed to the reduced sensitivity of the Hilbert method to noise contamination compared to the onset method.

Algorithms↗

PubMed2003

The spatial extent of the BOLD response.

Functional magnetic resonance imaging is routinely used to localize brain function, with multiple brain scans averaged together to reveal activation volumes. In this study, we examine the seldom-studied effect of multiple scan averaging on the extent of activation volume. Using restricted visual field stimulation, we obtained a large number of scan repetitions and analyzed changes in activation volume with progressively increased averaging and across single scans. Activation volume increased monotonically with averaging and failed to asymptote when as many as 22 scans were averaged together. Expansions in the spatial extent of activation were not random; rather, they were centered about activation loci that appear with little or no averaging. Using empirical and simulated data, changes with averaging in activation volumes and cross correlation coefficient distributions revealed the presence of considerably more activated voxels than commonly surmised. Many voxels have low SNR and remain undetected without extensive averaging. The primary source of such voxels was not downstream venous drainage since there was no significant and consistent delay difference between voxels activated at different averaging levels. Voxels with low SNR may reflect a diffuse subthreshold activity centered about spiking neurons, dephasing gradients from distal veins, or simply a blood flow response extending beyond the locus of neuronal firing. Across single scans, as much as twofold changes in activation volume were observed. These changes were not correlated with the order of scan acquisition, subject task performance, or signal and noise properties of activated voxels. Instead, they may reflect subtle changes between overlapping noise and signal frequency components.

Brain↗