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Thomas E Nichols

Publications and source records attributed to Thomas E Nichols.

At least 19 recordsLinked to original sources
PubMed2006

Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.

Amyotrophic Lateral Sclerosis↗
PubMed2006

Diagnosis of single-subject and group fMRI data with SPMd.

Except for purely nonparametric methods, statistical methods depend on assumptions about the distribution of the data studied. While these assumptions are easily checked for a single univariate dataset with diagnostic plots, in the massively univariate model used with functional MRI (fMRI) it is impractical to check with a massive number of plots. In previous work we have demonstrated how to diagnose model assumptions and lack-of-fit for single-subject fMRI models using a working assumption of independent errors; our work depended on images and time series of summary statistics that, when simultaneously viewed dynamically, identify problem scans and voxels. In this article we extend our previous work to account for temporal autocorrelation in single-subject models and show how analogous methods can be used on group models where multiple subjects are studied. We apply these methods to the single-subject Functional Image Analysis Contest (FIAC) data and find several anomalies, but none that appear to invalidate the results for that subject. With the group FIAC data we find one subject (and possibly two more) that demonstrate a different pattern of activity. None of our conclusions would be arrived at by simply looking at images of t statistics, demonstrating the importance of model assessment through exploration of the data and diagnosis of model assumptions.

Artifacts↗
PubMed2005

Placebo effects mediated by endogenous opioid activity on mu-opioid receptors.

Reductions in pain ratings when administered a placebo with expected analgesic properties have been described and hypothesized to be mediated by the pain-suppressive endogenous opioid system. Using molecular imaging techniques, we directly examined the activity of the endogenous opioid system on mu-opioid receptors in humans in sustained pain with and without the administration of a placebo. Significant placebo-induced activation of mu-opioid receptor-mediated neurotransmission was observed in both higher-order and sub-cortical brain regions, which included the pregenual and subgenual rostral anterior cingulate, the dorsolateral prefrontal cortex, the insular cortex, and the nucleus accumbens. Regional activations were paralleled by lower ratings of pain intensity, reductions in its sensory and affective qualities, and in the negative emotional state of the volunteers. These data demonstrate that cognitive factors (e.g., expectation of pain relief) are capable of modulating physical and emotional states through the site-specific activation of mu-opioid receptor signaling in the human brain.

Adult↗
PubMed2005

Non-white noise in fMRI: does modelling have an impact?

The sources of non-white noise in Blood Oxygenation Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) are many. Familiar sources include low-frequency drift due to hardware imperfections, oscillatory noise due to respiration and cardiac pulsation and residual movement artefacts not accounted for by rigid body registration. These contributions give rise to temporal autocorrelation in the residuals of the fMRI signal and invalidate the statistical analysis as the errors are no longer independent. The low-frequency drift is often removed by high-pass filtering, and other effects are typically modelled as an autoregressive (AR) process. In this paper, we propose an alternative approach: Nuisance Variable Regression (NVR). By inclusion of confounding effects in a general linear model (GLM), we first confirm that the spatial distribution of the various fMRI noise sources is similar to what has already been described in the literature. Subsequently, we demonstrate, using diagnostic statistics, that removal of these contributions reduces first and higher order autocorrelation as well as non-normality in the residuals, thereby improving the validity of the drawn inferences. In addition, we also compare the performance of the NVR method to the whitening approach implemented in SPM2.

Algorithms↗
PubMed2005

Linkage disequilibrium: ancient history drives the new genetics.

This brief review provides a summary of the biological causes of genetic association between tightly linked markers--termed linkage disequilibrium--and unlinked markers--termed population structure. We also review the utility of linkage disequilibrium data in gene mapping in isolated populations, in the estimation of recombination rates and in studying the history of particular alleles, including the detection of natural selection. We discuss current understanding of the extent and patterns of linkage disequilibrium in the genome, and its promise for genetic association studies in complex disease. Finally, we highlight the importance of using appropriate statistical procedures, such as the false discovery rate, to maximize the chances of success in large scale association studies.

Chromosome Mapping↗
PubMed2005

A comparison of random field theory and permutation methods for the statistical analysis of MEG data.

We describe the use of random field and permutation methods to detect activation in cortically constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps event-related MEG to a coregistered cortical surface. These approaches also extend directly to images computed from event-related EEG data. We determine statistical thresholds that control the familywise error rate (FWER) across space or across both space and time. Both random field and permutation methods use the distribution of the maximum statistic under the null hypothesis to find FWER thresholds. The former methods make assumptions on the distribution and smoothness of the data and use approximate analytical solutions, the latter resample the data and rely on empirical distributions. Both methods account for spatial and temporal correlation in the cortical maps. Unlike previous nonparametric work in neuroimaging, we address the problem of nonuniform specificity that can arise without a Gaussianity assumption. We compare and evaluate the methods on simulated data and experimental data from a somatosensory-evoked response study. We find that the random field methods are conservative with or without smoothing, though with a 5 vertex FWHM smoothness, they are close to exact. Our permutation methods demonstrated exact specificity in simulation studies. In real data, the permutation method was not as sensitive as the RF method, although this could be due to violations of the random field theory assumptions.

Data Interpretation, Statistical↗
PubMed2005

Toward a taxonomy of attention shifting: individual differences in fMRI during multiple shift types.

Although task switching is often considered one of the fundamental abilities underlying executive functioning and general intelligence, there is little evidence that switching is a unitary construct and little evidence regarding the relationship between brain activity and switching performance. We examined individual differences in multiple types of attention shifting in order to determine whether behavioral performance and fMRI activity are correlated across different types of shifting. The participants (n = 39) switched between objects and attributes both when stimuli were perceptually available (external) and when stimuli were stored in memory (internal). We found that there were more switch-related activations in many regions associated with executive control--including the dorsolateral and medial prefrontal and parietal cortices--when behavioral switch costs were higher (poor performance). Conversely, activation in the ventromedial prefrontal cortex (VMPFC) and the rostral anterior cingulate was consistently correlated with good performance, suggesting a general role for these areas in efficient attention shifting. We discuss these findings in terms of a model of cognitive-emotional interaction in attention shifting, in which reward-related signals in the VMPFC guide efficient selection of tasks in the lateral prefrontal and parietal cortices.

Adolescent↗
PubMed2004

Nonstationary cluster-size inference with random field and permutation methods.

Because of their increased sensitivity to spatially extended signals, cluster-size tests are widely used to detect changes and activations in brain images. However, when images are nonstationary, the cluster-size distribution varies depending on local smoothness. Clusters tend to be large in smooth regions, resulting in increased false positives, while in rough regions, clusters tend to be small, resulting in decreased sensitivity. Worsley et al. proposed a random field theory (RFT) method that adjusts cluster sizes according to local roughness of images [Worsley, K.J., 2002. Nonstationary FWHM and its effect on statistical inference of fMRI data. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Available on CD-ROM in NeuroImage 16 (2) 779-780; Hum. Brain Mapp. 8 (1999) 98]. In this paper, we implement this method in a permutation test framework, which requires very few assumptions, is known to be exact [J. Cereb. Blood Flow Metab. 16 (1996) 7] and is robust [NeuroImage 20 (2003) 2343]. We compared our method to stationary permutation, stationary RFT, and nonstationary RFT methods. Using simulated data, we found that our permutation test performs well under any setting examined, whereas the nonstationary RFT test performs well only for smooth images under high df. We also found that the stationary RFT test becomes anticonservative under nonstationarity, while both nonstationary RFT and permutation tests remain valid under nonstationarity. On a real PET data set we found that, though the nonstationary tests have reduced sensitivity due to smoothness estimation variability, these tests have better sensitivity for clusters in rough regions compared to stationary cluster-size tests. We include a detailed and consolidated description of Worsley nonstationary RFT cluster-size test.

Brain↗
PubMed2004

Combining voxel intensity and cluster extent with permutation test framework.

In a massively univariate analysis of brain image data, statistical inference is typically based on intensity or spatial extent of signals. Voxel intensity-based tests provide great sensitivity for high intensity signals, whereas cluster extent-based tests are sensitive to spatially extended signals. To benefit from the strength of both, the intensity and extent information needs to be combined. Various ways of combining voxel intensity and cluster extent are possible, and a few such combining methods have been proposed. Poline et al.'s [NeuroImage 16 (1997) 83] minimum P value approach is sensitive to signals whose either intensity or extent is significant. Bullmore et al.'s [IEEE Trans. Med. Imag. 18 (1999) 32] cluster mass method can detect signals whose intensity and extent are sufficiently large, even when they are not significant by intensity or extent alone. In this work, we study such combined inference methods using combining functions (Pesarin, F., 2001. Multivariate Permutation Tests. Wiley, New York) and permutation framework [Holmes et al., J. Cereb. Blood Flow Metab. 16 (1996) 7], which allow us to examine different ways of combining voxel intensity and cluster extent information without knowing their distribution. We also attempt to calibrate combined inference by using weighted combining functions, which adjust the test according to signals of interest. Furthermore, we propose meta-combining, a combining function of combining functions, which integrates strengths of multiple combining functions into a single statistic. We found that combined tests are able to detect signals that are not detected by voxel or cluster size test alone. We also found that the weighted combining functions can calibrate the combined test according to the signals of interest, emphasizing either intensity or extent as appropriate. Though not necessarily more sensitive than individual combining functions, the meta-combining function is sensitive to all types of signals and thus can be used as a single test summarizing all the combining functions.

Artifacts↗
PubMed2003

Genes regulated by learning in the hippocampus.

The enduring changes in long-term memory probably depend on regulation of gene expression in the hippocampus. To seek genes regulated by learning, we used microarray technology to compare hippocampal gene expression in mice undergoing training in the Morris water maze and control mice forced to swim for the same period in the absence of a hidden platform. ANOVA was employed to prioritize genes for further study, and three genes were confirmed by real-time PCR as being regulated during learning. One of the genes was the alpha subunit of the platelet-derived growth factor receptor (Pdgfra); another showed homology to DnaJ and cAMP response element-binding protein 2 (CREB2); and a third was novel. These genes may provide useful insights into the molecular mechanisms of hippocampal learning.

Activating Transcription Factor 4↗
PubMed2003

Activation of the medial prefrontal cortex and extended amygdala by individual ratings of emotional arousal: a fMRI study.

BACKGROUND: Significant differences between individual responses to emotional stimuli can be important for the study of emotion. We investigated whether incorporating individual ratings of emotional arousal in the analysis of functional magnetic resonance imaging (fMRI) data improves the detection of activation in the medial prefrontal cortex (MPFC) and sublenticular extended amygdala (SLEA), areas implicated in the processing of emotional salience. METHODS: Healthy subjects viewed counterbalanced blocks of aversive, nonaversive, and blank images. Outside the scanner, they rated the intensity of emotional arousal (salience) of each presented picture. RESULTS: Incorporating the subject's response to each stimulus by using individualized regressors produced more robust activations within MPFC and SLEA compared with a simple boxcar regressor, identical for all subjects. CONCLUSIONS: Our findings demonstrate that individual behavioral data are useful in improving detection of activation in block-design functional imaging studies.

Adult↗
PubMed2003

Spatiotemporal localization of significant activation in MEG using permutation tests.

We describe the use of non-parametric permutation tests to detect activation in cortically-constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps event-related MEG to a coregistered cortical surface. To determine an appropriate threshold to apply to statistics computed from these maps, it is important to control for the multiple testing problem associated with testing 10's of thousands of hypotheses (one per surface element). By randomly permuting pre- and post-stimuius data from the collection of individual epochs in an event related study, we develop thresholds that control the familywise (type 1) error rate. These thresholds are based on the distribution of the maximum intensity, which implicitly accounts for spatial and temporal correlation in the cortical maps. We demonstrate the method in application to simulated data and experimental data from a somatosensory evoked response study.

Algorithms↗
PubMed2003

Validating cluster size inference: random field and permutation methods.

Cluster size tests used in analyses of brain images can have more sensitivity compared to intensity based tests. The random field (RF) theory has been widely used in implementation of such tests, however the behavior of such tests is not well understood, especially when the RF assumptions are in doubt. In this paper, we carried out a simulation study of cluster size tests under varying smoothness, thresholds, and degrees of freedom, comparing RF performance to that of the permutation test, which is known to be exact. For Gaussian images, we find that the RF methods are generally conservative, especially for low smoothness and low threshold. For t images, the RF tests are found to be conservative at lower thresholds and do not perform well unless the threshold is high and images are sufficiently smooth. The permutation test performs well for any settings though the discreteness in cluster size must be accounted for. We make specific recommendations on when permutation tests are to be preferred to RF tests.

Algorithms↗
PubMed2003

Switching attention and resolving interference: fMRI measures of executive functions.

Is there a single executive process or are there multiple executive processes that work together towards the same goal in some task? In these experiments, we use counter switching and response inhibition tasks to examine the neural underpinnings of two cognitive processes that have often been identified as potential executive processes: the switching of attention between tasks, and the resolution of interference between competing task responses. Using functional magnetic resonance imaging (fMRI), for both event-related and blocked design tasks, we find evidence for common neural areas across both tasks in bilateral parietal cortex (BA 40), left dorsolateral prefrontal cortex (DLPFC; BA 9), premotor cortex (BA 6) and medial frontal cortex (BA 6/32). However, we also find areas preferentially involved in the switching of attention between mental counts (BA 7, BA 18) and the inhibition of a prepotent motor response (BA 6, BA 10), respectively. These findings provide evidence for the separability of cognitive processes underlying executive control.

Adolescent↗
PubMed2003

Optimization of experimental design in fMRI: a general framework using a genetic algorithm.

This article describes a method for selecting design parameters and a particular sequence of events in fMRI so as to maximize statistical power and psychological validity. Our approach uses a genetic algorithm (GA), a class of flexible search algorithms that optimize designs with respect to single or multiple measures of fitness. Two strengths of the GA framework are that (1) it operates with any sort of model, allowing for very specific parameterization of experimental conditions, including nonstandard trial types and experimentally observed scanner autocorrelation, and (2) it is flexible with respect to fitness criteria, allowing optimization over known or novel fitness measures. We describe how genetic algorithms may be applied to experimental design for fMRI, and we use the framework to explore the space of possible fMRI design parameters, with the goal of providing information about optimal design choices for several types of designs. In our simulations, we considered three fitness measures: contrast estimation efficiency, hemodynamic response estimation efficiency, and design counterbalancing. Although there are inherent trade-offs between these three fitness measures, GA optimization can produce designs that outperform random designs on all three criteria simultaneously.

Algorithms↗
PubMed2003

Diagnosis and exploration of massively univariate neuroimaging models.

The goal of this work is to establish the validity of neuroimaging models and inferences through diagnosis and exploratory data analysis. While model diagnosis and exploration are integral parts of any statistical modeling enterprise, these aspects have been mostly neglected in functional neuroimaging. We present methods that make diagnosis and exploration of neuroimaging data feasible. We use three- and one-dimensional summaries that characterize the model fit and the four-dimensional residuals. The statistical tools are diagnostic summary statistics with tractable null distributions and the dynamic graphical tools which allow the exploration of multiple summaries in both spatial and temporal/interscan aspects, with the ability to quickly jump to spatiotemporal detail. We apply our methods to a fMRI data set, demonstrating their ability to localize subtle artifacts and to discover systematic experimental variation not captured by the model.

Algorithms↗
PubMed2002

Nonparametric permutation tests for functional neuroimaging: a primer with examples.

Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices.

Brain↗