Jon R. Anderson

Reproducibility of BOLD-Based Functional MRI Obtained at 4 T

The reproducibility of activation patterns in the whole brain obtained by functional magnetic resonance imaging (fMRI) experiments at 4 Tesla was studied with a simple finger‐opposition task. Six subjects performed three runs in one session, and each run was analyzed separately with the t‐test as a univariate method and Fishers linear discriminant analysis as a multivariate method. Detrending with a first‐ and third‐order polynomial as well as logarithmic transformation as preprocessing steps for the t‐test were tested for their impact on reproducibility. Reproducibility across the whole brain was studied by using scatter plots of statistical values and calculating the correlation coefficient between pairs of activation maps. In order to compare reproducibility of “activated” voxels across runs, subjects and models, 2% of all voxels in the brain with the highest statistical values were classified as activated. The analysis of reproducible activated voxels was performed for the whole brain and within regions of interest. We found considerable variability in reproducibility across subjects, regions of interest, and analysis methods. The t‐test on the linear detrended data yielded better reproducibility than Fishers linear discriminant analysis, and therefore seems to be a robust although conservative method. Preliminary data indicate that these modeling results may be reversed by preprocessing to reduce respiratory and cardiac physiological noise effects. The reproducibility of both the position and number of activated voxels in the sensorimotor cortex was highest, while that of the supplementary motor area was much lower, with reproducibility of the cerebellum falling in between the other two areas. Hum. Brain Mapping 7:267–283, 1999.

Quantitative comparisons of image registration techniques based on high-resolution MRI of the brain.

Objective A variety of methods for matching intrasubject MRI-MRI, PET-PET, or MRI-PET image pairs have been proposed. Based on the rigid body transformations needed to align pairs of high-resolution MRI scans and/or simulated PET scans (derived from these MRI scans), we obtained general comparisons of four intrasubject image registration techniques: Talairach coordinates, head and hat, equivalent internal points, and ratio image uniformity. In addition, we obtained a comparison of stereotaxic Z frames with a customized head mold for MRI-MRI image pairs. Materials and Methods and Results Each technique was quantitatively evaluated using the mean and maximum voxel registration errors for matched voxel pairs within the brain volumes being registered. Conclusion We conclude that fiducial markers such as stereotaxic Z frames that are not rigidly fixed to a patients skull are inaccurate compared with other registration techniques, Talairach coordinate transformations provide surprisingly good registration, and minimizing the variance of MRI-MRI, PET-PET, or MRI-PET ratio images provides significantly better registration than all other techniques tested. Registration optimization based on measurement of the similarity of spatial distributions of voxel values is superior to techniques that do not use such information.

Principal Component Analysis and the Scaled Subprofile Model Compared to Intersubject Averaging and Statistical Parametric Mapping: I. “Functional Connectivity” of the Human Motor System Studied with [15O]Water PET

Using [15O]water PET and a previously well studied motor activation task, repetitive finger-to-thumb opposition, we compared the spatial activation patterns produced by (1) global normalization and intersubject averaging of paired-image subtractions, (2) the mean differences of ANCOVA-adjusted voxels in Statistical Parametric Mapping, (3) ANCOVA-adjusted voxels followed by principal component analysis (PCA), (4) ANCOVA-adjustment of mean image volumes (mean over subjects at each time point) followed by F-masking and PCA, and (5) PCA with Scaled Subprofile Model pre- and postprocessing. All data analysis techniques identified large positive focal activations in the contralateral sensorimotor cortex and ipsilateral cerebellar cortex, with varying levels of activation in other parts of the motor system, e.g., supplementary motor area, thalamus, putamen; techniques 1–4 also produced extensive negative areas. The activation signal of interest constitutes a very small fraction of the total nonrandom signal in the original dataset, and the exact choice of data preprocessing steps together with a particular analysis procedure have a significant impact on the identification and relative levels of activated regions. The challenge for the future is to identify those preprocessing algorithms and data analysis models that reproducibly optimize the identification and quantification of higher-order sensorimotor and cognitive responses.

Are Brain Functions Really Additive

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.

Enhancing the multivariate signal of [/sup 15/O] water PET studies with a new nonlinear neuroanatomical registration algorithm [MRI application]

This paper addresses the problem of neuro-anatomical registration across individuals for functional [15O] water PET activation studies. A new algorithm for three-dimensional (3-D) nonlinear structural registration (warping) of MR scans is presented. The method performs a hierarchically scaled search for a displacement field, maximizing one of several voxel similarity measures derived from the two-dimensional (2-D) histogram of matched image intensities, subject to a regularizer that ensures smoothness of the displacement field. The effect of the nonlinear structural registration is studied when it is computed on anatomical MR scans and applied to coregistered [15O] water PET scans from the same subjects: in this experiment, a study of visually guided saccadic eye movements. The performance of the nonlinear warp is evaluated using multivariate functional signal and noise measures. These measures prove to be useful for comparing different intersubject registration approaches, e.g., affine versus nonlinear. A comparison of 12-parameter affine registration versus non-linear registration demonstrates that the proposed nonlinear method increases the number of voxels retained in the cross-subject mask. We demonstrate that improved structural registration may result in an improved multivariate functional signal-to-noise ratio (SNR). Furthermore, registration of PET scans using the 12-parameter affine transformations that align the coregistered MR images does not improve registration, compared to 12-parameter affine alignment of the PET images directly.

Graphical analysis of MR feature space for measurement of CSF, gray-matter, and white-matter volumes.

The problem of volume averaging in quantitating CSF, gray-matter, and white-matter fractions in the brain is solved using a three-compartment model and a simple graphical analysis of a multispectral MR feature space. Compartmentalization is achieved without the ambiguities of thresholding techniques or the need to assume that the underlying pixel probability distributions have a particular form. A 2D feature space is formed by double SE (proton density-and T2-weighted) MR data with image nonuniformity removed by a novel technique in which the brain itself serves as a uniformity reference. Compartments other than the basic three were rejected by the tailoring of limits in feature space. Phantom scans substantiate this approach, and the importance of the careful selection and standardization of pure tissue reference signals is demonstrated. Compartmental profiles from standardized subvolumes of three normal brains, based on a 3D (Talairach) coordinate system, demonstrate slice-by-slice detail; longitudinal studies confirm reproducibility. Compartmentalization may be described graphically and algebraically, complementing data displays in feature space and images of compartmentalized brain scans. These studies anticipate the application of our compartmentalization technique to patients with neurological disorders.

Multivariate predictive relationship between kinematic and functional activation patterns in a PET study of visuomotor learning.

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.

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

In functional magnetic resonance imaging (fMRI) analysis, although the univariate general linear model (GLM) is currently the dominant approach to brain activation detection, there is growing interest in multivariate approaches such as principal component analysis, canonical variate analysis (CVA), independent component analysis and cluster analysis, which have the potential to reveal neural networks and functional connectivity in the brain. To understand the effect of processing options on performance of multivariate model-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly used fMRI preprocessing steps and optimized the associated multivariate CVA-based, single-subject processing pipelines with the NPAIRS (nonparametric prediction, activation, influence and reproducibility resampling) performance metrics [prediction accuracy and statistical parametric image (SPI) reproducibility] on the Fiswidgets platform. We also compared the single-subject SPIs of univariate GLM with multivariate CVA-based processing pipelines from SPM, FSL.FEAT, NPAIRS.GLM and NPAIRS.CVA software packages (or modules) using a novel second-level CVA. We found that for the block-design data, (a) slice timing correction and global intensity normalization have little consistent impact on the fMRI processing pipeline, but spatial smoothing, temporal detrending or high-pass filtering, and motion correction significantly improved pipeline performance across all subjects; (b) the combined optimization of spatial smoothing, temporal detrending and CVA model parameters on average improved between-subject reproducibility; and (c) the most important pipeline choices include univariate or multivariate statistical models and spatial smoothing. This study suggests that considering options other than simply using GLM with a fixed spatial filter may be of critical importance in determining activation patterns in BOLD fMRI studies.

Comparison of matched BOLD and FAIR 4.0T-fMRI with [15O]water PET brain volumes

Valid comparisons of functional activation volumes from fMRI and PET require accurate registration, matched spatial resolution, and if possible matched noise. We coregistered 4.0T-fMRI and PET volumes, using a series of linear and nonlinear transformations applied to the PET volumes. Because of the limited number of fMRI slices that were available, PET volumes were transformed to the fMRI space. Since 4.0T-fMRI and 4.0T-MRI volumes have significant spatial distortion due to magnet inhomogeneities, high resolution 1.5T-MRI volumes were nonlinearly transformed to 4.0T-MRI volumes as part of the transformation chain. The smoothing effects of these registration transformations were measured, in order to match the spatial resolution of the coregistered fMRI and PET volumes. Spatial resolution of the transformed PET volumes in the fMRI space was degraded by up to 60% due to the transformation process. Due to both the image acquisition characteristics and the coregistration process, the transformed PET volumes had a spatial resolution that was lower than that of tMRI. Therefore, significant smoothing of fMRI volumes was necessary to match their spatial resolution with that of the transformed PET volumes. Matching the spatial resolution of the fMRI volumes to those of the transformed PET volumes was achieved by matching the shape of their point spread functions. In order to do this, Gaussian kernels were employed to smooth the fMRI volumes. We were unable to simultaneously match the resolution and noise of fMRI and PET signals in the motor cortex. Activation maps derived from transformed PET and smoothed fMRI volumes were compared. Contralateral motor cortex was active in all modalities but there were large variations in the size of the activated region and its signal to noise ratio across BOLD, FAIR, and PET images within each subject. Nevertheless, the relative CBF changes measured by FAIR were consistent with those determined by PET.

A spatially robust ICA algorithm for multiple fMRI data sets

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model extracts independent components common to all data sets and independent data-set-specific components. We use time-delayed autocorrelations to obtain independent signal components and base our algorithm on prediction analysis. We applied this method to functional brain mapping using functional magnetic resonance imaging (fMRI). The results of our 3-subject analysis demonstrate the robustness of the algorithm to the spatial misalignment intrinsic in multiple-subject fMRI data sets.