Author name cluster

Andrew P. Holmes

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.

5 papers

1 author row

YNIMG Journal 2000 Journal Article

A Study of Analysis Parameters That Influence the Sensitivity of Event-Related fMRI Analyses

Joseph B. Hopfinger
Christian Büchel
Andrew P. Holmes
Karl J. Friston

To assess the effect of various analysis parameters on the sensitivity of event-related fMRI analyses, 36 analyses were performed on a single fMRI data-set, varying parameters along four axes: (1) resampled voxel size; (2) spatial smoothing; (3) temporal smoothing; and (4) the set of basis functions used to model event-related responses. Sensitivity (i. e. , the probability of detecting an activation given it exists) was assessed in terms of Z scores and by a metric for corrected P values, the negative log of the expected Euler characteristic. Sixteen brain regions distributed across cortical and subcortical areas were included in the meta-analysis. Main effects on sensitivity were found for resampled voxel size, spatial smoothing, temporal smoothing, and the set of basis functions chosen. The analysis parameters that generally produced the most sensitive analyses were a 2-mm3 resampled voxel size, 10-mm spatial smoothing, 4-s temporal smoothing, and a basis set comprising a hemodynamic response function and its temporal derivative.

Details DOI

YNIMG Journal 1999 Journal Article

How Many Subjects Constitute a Study?

Karl J. Friston
Andrew P. Holmes
Keith J. Worsley

In fMRI there are two classes of inference: one aims to make a comment about the “typical” characteristics of a population, and the other about “average” characteristics. The first pertains to studies of normal subjects that try to identify some qualitative aspect of normal functional anatomy. The second class necessarily applies to clinical neuroscience studies that want to make an inference about quantitative differences of a regionally specific nature. The first class of inferences is adequately serviced by conjunction analyses and fixed-effects models with relatively small numbers of subjects. The second requires random-effect analyses and larger cohorts.

Details DOI

YNIMG Journal 1999 Journal Article

Plurality and Resemblance in fMRI Data Analysis

Nicholas Lange
Stephen C. Strother
Jon R. Anderson
Finn Å. Nielsen
Andrew P. Holmes
Thomas Kolenda
Robert Savoy
Lars Kai Hansen

We apply nine analytic methods employed currently in imaging neuroscience to simulated and actual BOLD fMRI signals and compare their performances under each signal type. Starting with baseline time series generated by a resting subject during a null hypothesis study, we compare method performance with embedded focal activity in these series of three different types whose magnitudes and time courses are simple, convolved with spatially varying hemodynamic responses, and highly spatially interactive. We then apply these same nine methods to BOLD fMRI time series from contralateral primary motor cortex and ipsilateral cerebellum collected during a sequential finger opposition study. Paired comparisons of results across methods include a voxel-specific concordance correlation coefficient for reproducibility and a resemblance measure that accommodates spatial autocorrelation of differences in activity surfaces. Receiver-operating characteristic curves show considerable model differences in ranges less than 10% significance level (false positives) and greater than 80% power (true positives). Concordance and resemblance measures reveal significant differences between activity surfaces in both data sets. These measures can assist researchers by identifying groups of models producing similar and dissimilar results, and thereby help to validate, consolidate, and simplify reports of statistical findings. A pluralistic strategy for fMRI data analysis can uncover invariant and highly interactive relationships between local activity foci and serve as a basis for further discovery of organizational principles of the brain. Results also suggest that a pluralistic empirical strategy coupled formally with substantive prior knowledge can help to uncover new brain–behavior relationships that may remain hidden if only a single method is employed.

Details DOI

YNIMG Journal 1999 Journal Article

Robust Smoothness Estimation in Statistical Parametric Maps Using Standardized Residuals from the General Linear Model

Stefan J. Kiebel
Jean-Baptiste Poline
Karl J. Friston
Andrew P. Holmes
Keith J. Worsley

The assessment of significant activations in functional imaging using voxel-based methods often relies on results derived from the theory of Gaussian random fields. These results solve the multiple comparison problem and assume that the spatial correlation or smoothness of the data is known or can be estimated. End results (i. e. , P values associated with local maxima, clusters, or sets of clusters) critically depend on this assessment, which should be as exact and as reliable as possible. In some earlier implementations of statistical parametric mapping (SPM) (SPM94, SPM95) the smoothness was assessed on Gaussianized t-fields (Gt-f) that are not generally free of physiological signal. This technique has two limitations. First, the estimation is not stable (the variance of the estimator being far from negligible) and, second, physiological signal in the Gt-f will bias the estimation. In this paper, we describe an estimation method that overcomes these drawbacks. The new approach involves estimating the smoothness of standardized residual fields which approximates the smoothness of the component fields of the associated t-field. Knowing the smoothness of these component fields is important because it allows one to compute corrected P values for statistical fields other than the t-field or the Gt-f (e. g. , the F-map) and eschews bias due to deviation from the null hypothesis. We validate the method on simulated data and demonstrate it using data from a functional MRI study.

Details DOI

YNIMG Journal 1999 Journal Article

The Effect of Slice Order and Thickness on fMRI Activation Data Using Multislice Echo-Planar Imaging

Alistair M. Howseman
Sylke Grootoonk
David A. Porter
Joann Ramdeen
Andrew P. Holmes
Robert Turner

Multislice echo-planar imaging (EPI) is a commonly used technique for fMRI studies. Brain activation images acquired using fMRI are sensitive to T2* changes, reflecting the level of blood oxygenation (BOLD contrast), and may also contain an element of T1contrast which detects blood flow changes in large vessels. If slice inflow (T1) effects are significant in multislice EPI, then as the order in which the slices are acquired is changed, differences in the activation maps are predicted. However, in experiments presented here using visual stimulation, the data demonstrate that highly consistent results can be achieved for repetition times (TR) of 6. 0, 3. 0, and 1. 5 s. This suggests that, for whole-brain multislice EPI, fMRI activation is dominated by T2*, BOLD contrast. The thickness of the imaging slice is also an important parameter in these studies, having implications for spatial resolution, sensitivity, and acquisition time. In separate visual cortex experiments the effect on the values of the fMRIZscores and the number of activated voxels is investigated as a function of slice thickness (from 1 to 8 mm). The maximumZscores in the data are similar for all slice thicknesses and, after resampling to allow a direct comparison to be made, the volume of visual cortex detected as significantly activated increases with slice thickness.

Details DOI