Mapping voxel-based statistical power on parametric images

Use of functional near-infrared spectroscopy to evaluate cognitive change when using healthcare simulation tools

Background

The use of brain imaging techniques in healthcare simulation is relatively rare. However, the use of mobile, wireless technique, such as functional near-infrared spectroscopy (fNIRS), is becoming a useful tool for assessing the unique demands of simulation learning. For this study, this imaging technique was used to evaluate cognitive load during simulation learning events.

Methods

This study took place in relation to six simulation activities, paired for similarity, and evaluated comparative cognitive change between the three task pairs. The three paired tasks were: receiving a (1) face-to-face and (2) video patient handover; observing a simulated scene in (1) two dimensions and (2) 360° field of vision; and on a simulated patient (1) taking a pulse and (2) taking a pulse and respiratory rate simultaneously. The total number of participants was n=12.

Results

In this study, fNIRS was sensitive to variations in task difficulty in common simulation tools and scenarios, showing an increase in oxygenated haemoglobin concentration and a decrease in deoxygenated haemoglobin concentration, as tasks increased in cognitive load.

Conclusion

Overall, findings confirmed the usefulness of neurohaemoglobin concentration markers as an evaluation tool of cognitive change in healthcare simulation. Study findings suggested that cognitive load increases in more complex cognitive tasks in simulation learning events. Task performance that increased in complexity therefore affected cognitive markers, with increase in mental effort required.

A stereotaxic MRI template set of mouse brain with fine sub-anatomical delineations: Application to MEMRI studies of 5XFAD mice

PURPOSE

Manganese-enhanced magnetic resonance imaging (MEMRI) can help us trace the active neurons and neuronal pathway in transgenic mouse AD model. 5XFAD has been widespread accepted as a valuable model system for studying brain dysfunction progresses in the courses of AD. To further understand the development of AD at early stages, an effective and objective data analysis platform for MEMRI studies should be constructed.

MATERIALS AND METHODS

A set of stereotaxic templates of mouse brain in Paxinos and Franklin space, "the Institute of High Energy Physics Mouse Template", or IMT for short, was constructed by iteratively registration and averaging. An atlas image was reconstructed from the Paxinos and Franklin atlas figures and each sub-anatomical segmentation was assigning a unique integer. An analysis SPM plug-in toolbox was further created, that automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and statistical analysis for MEMRI scans.

RESULTS

The IMT comprised a T2WI template image, a MEMRI template image, intracranial tissue segmentations, and accompany with a digital mouse brain atlas image, in which 707 sub-anatomical brain regions are delineated. Data analyses were performed on groups of developing 5XFAD mice to demonstrate the usage of IMT, and the results shows that abnormal neuronal activity occurs at early stage in 5XFAD mice.

CONCLUSION

We have constructed a stereotaxic template set of mouse brain named IMT with fine delineations of sub-anatomical structures, which is compatible with SPM. It will give a widely range of researchers a standardized coordinate system for localization of any mouse brain related data.

Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks

Studying brain connectivity is important due to potential differences in brain circuitry between health and disease. One drawback of graph-theoretic approaches to this is that their results are dependent on the spatial scale at which brain circuitry is examined and explicitly on how vertices and edges are defined in network models. To investigate this, magnetic resonance and diffusion tensor images were acquired from 136 healthy adults, and each subject's cortex was parceled into as many as 50,000 regions. Regions were represented as nodes in a reconstructed network representation, and interregional connectivity was inferred via deterministic tractography. Network model behavior was explored as a function of nodal number and connectivity weighing. Three distinct regimes of quantitative behavior assumed by network models as a function of spatial scale are identified, and their existence may be modulated by the spatial folding scale of the cortex. The maximum number of network nodes used to model human brain circuitry in this study (∼50,000) is larger than in previous macroscale neuroimaging studies. Results suggest that network model properties vary appreciably as a function of vertex assignment convention and edge weighing scheme and that graph-theoretic analysis results should not be compared across spatial scales without appropriate understanding of how spatial scale and model topology modulate network model properties. These findings have implications for comparing macro- to mesoscale studies of brain network models and understanding how choosing network-theoretic parameters affects the interpretation of brain connectivity studies.

Altered viscerotopic cortical innervation in patients with irritable bowel syndrome

BACKGROUND

Studies have demonstrated the existence of regional gray matter and white matter (WM) alterations in the brains of patients with irritable bowel syndrome (IBS), but the extent to which altered anatomical connectivity between brain regions is altered in IBS remains incompletely understood.

METHODS

In this study, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) were used to identify significant brain connectivity differences between IBS patients and healthy control (HC) subjects. Based on MRI and DTI volumes acquired from 66 IBS patients and 23 HC subjects, multivariate regression was used to investigate whether subject age, sex, cortical thickness, or the mean fractional anisotropy (FA) of WM connections innervating each location on the cortex could predict IBS diagnosis.

KEY RESULTS

HC and IBS subjects were found to differ significantly within both left and right viscerotopic portions of the primary somatosensory cortex (S1), with the mean FA of WM bundles innervating S1 being the predictor variable responsible for these significant differences.

CONCLUSIONS & INFERENCES

These preliminary findings illustrate how a chronic visceral pain syndrome and brain structure are related in the cohort examined, and because of their indication that IBS diagnosis is associated with anatomic neuropathology of potential neurological relevance in this patient sample.

Alternative-based thresholding with application to presurgical fMRI

Functional magnetic reasonance imaging (fMRI) plays an important role in pre-surgical planning for patients with resectable brain lesions such as tumors. With appropriately designed tasks, the results of fMRI studies can guide resection, thereby preserving vital brain tissue. The mass univariate approach to fMRI data analysis consists of performing a statistical test in each voxel, which is used to classify voxels as either active or inactive-that is, related, or not, to the task of interest. In cognitive neuroscience, the focus is on controlling the rate of false positives while accounting for the severe multiple testing problem of searching the brain for activations. However, stringent control of false positives is accompanied by a risk of false negatives, which can be detrimental, particularly in clinical settings where false negatives may lead to surgical resection of vital brain tissue. Consequently, for clinical applications, we argue for a testing procedure with a stronger focus on preventing false negatives. We present a thresholding procedure that incorporates information on false positives and false negatives. We combine two measures of significance for each voxel: a classical p-value, which reflects evidence against the null hypothesis of no activation, and an alternative p-value, which reflects evidence against activation of a prespecified size. This results in a layered statistical map for the brain. One layer marks voxels exhibiting strong evidence against the traditional null hypothesis, while a second layer marks voxels where activation cannot be confidently excluded. The third layer marks voxels where the presence of activation can be rejected.

Development of PowerMap: a software package for statistical power calculation in neuroimaging studies

Although there are a number of statistical software tools for voxel-based massively univariate analysis of neuroimaging data, such as fMRI (functional MRI), PET (positron emission tomography), and VBM (voxel-based morphometry), very few software tools exist for power and sample size calculation for neuroimaging studies. Unlike typical biomedical studies, outcomes from neuroimaging studies are 3D images of correlated voxels, requiring a correction for massive multiple comparisons. Thus, a specialized power calculation tool is needed for planning neuroimaging studies. To facilitate this process, we developed a software tool specifically designed for neuroimaging data. The software tool, called PowerMap, implements theoretical power calculation algorithms based on non-central random field theory. It can also calculate power for statistical analyses with FDR (false discovery rate) corrections. This GUI (graphical user interface)-based tool enables neuroimaging researchers without advanced knowledge in imaging statistics to calculate power and sample size in the form of 3D images. In this paper, we provide an overview of the statistical framework behind the PowerMap tool. Three worked examples are also provided, a regression analysis, an ANOVA (analysis of variance), and a two-sample T-test, in order to demonstrate the study planning process with PowerMap. We envision that PowerMap will be a great aide for future neuroimaging research.

Do semantic categories activate distinct cortical regions? Evidence for a distributed neural semantic system

A key issue in cognitive neuroscience concerns the neural representation of conceptual knowledge. Currently, debate focuses around the issue of whether there are neural regions specialised for the processing of specific semantic attributes or categories, or whether concepts are represented in an undifferentiated neural system. Neuropsychological studies of patients with selective semantic deficits and previous neuroimaging studies do not unequivocally support either account. We carried out a PET study to determine whether there is any regional specialisation for the processing of concepts from different semantic categories using picture stimuli and a semantic categorisation task. We found robust activation of a large semantic network extending from left inferior frontal cortex into the inferior temporal lobe and including occipital cortex and the fusiform gyrus. The only category effect that we found was additional activation for animals in the right occipital cortex, which we interpret as being due to the extra visual processing demands required in order to differentiate one animal from another. We also carried out analyses in specific cortical regions that have been claimed to be preferentially activated for various categories, but found no evidence of any differential activation as a function of category. We interpret these data within the framework of cognitive accounts in which conceptual knowledge is represented within a nondifferentiated distributed system.

No effects of short-term GSM mobile phone radiation on cerebral blood flow measured using positron emission tomography

The present study investigated the effects of 902.4 MHz global system for mobile communications (GSM) mobile phone radiation on cerebral blood flow using positron emission tomography (PET) with the (15) O-water tracer. Fifteen young, healthy, right-handed male subjects were exposed to phone radiation from three different locations (left ear, right ear, forehead) and to sham exposure to test for possible exposure effects on brain regions close to the exposure source. Whole-brain [¹⁵O]H₂O-PET images were acquired 12 times, 3 for each condition, in a counterbalanced order. Subjects were exposed for 5 min in each scan while performing a simple visual vigilance task. Temperature was also measured in the head region (forehead, eyes, cheeks, ear canals) during exposure. The exposure induced a slight temperature rise in the ear canals but did not affect brain hemodynamics and task performance. The results provided no evidence for acute effects of short-term mobile phone radiation on cerebral blood flow.

Statistical parametric mapping for effects of verapamil on olfactory connections of rat brain in vivo using manganese-enhanced MR imaging

PURPOSE

We investigated the effect of verapamil on the transport of manganese in the olfactory connections of rat brains in vivo using statistical parametric mapping and manganese-enhanced magnetic resonance (MR) imaging.

METHODS

We divided 12 7-week-old male Sprague-Dawley rats into 2 groups of six and injected 10 μL of saline into the right nasal cavities of the first group and 10 μL of verapamil (2.5 mg/mL) into the other group. Twenty minutes after the initial injection, we injected 10 μL of MnCl(2) (1 mol/L) into the right nasal cavities of both groups. We obtained serial T(1)-weighted MR images before administering the verapamil or saline and at 0.5, one, 24, 48, and 72 hours and 7 days after administering the MnCl(2), spatially normalized the MR images on the rat brain atlas, and analyzed the data using voxel-based statistical comparison.

RESULTS

Statistical parametric maps demonstrated the transport of manganese. Manganese ions created significant enhancement (t-score = 36.6) 24 hours after MnCl(2) administration in the group administered saline but not at the same time point in the group receiving verapamil. The extent of significantly enhanced regions peaked at 72 hours in both groups and both sides of the brain. The peak of extent in the right side brain in the group injected with saline was 70.2 mm(3) and in the group with verapamil, 92.4 mm(3). The extents in the left side were 64.0 mm(3) for the group with saline and 53.2 mm(3) for the group with verapamil.

CONCLUSION

We applied statistical parametric mapping using manganese-enhanced MR imaging to demonstrate in vivo the transport of manganese in the olfactory connections of rat brains with and without verapamil and found that verapamil did affect this transport.

Power calculations for multicenter imaging studies controlled by the false discovery rate

Magnetic resonance imaging (MRI) is widely used in brain imaging research (neuroimaging) to explore structural and functional changes across dispersed neural networks visible only via multisubject experiments. Multicenter investigations are an effective way to increase recruitment rates. This article describes image-based power calculations for a two-group, cross-sectional design specified by the mean effect size and its standard error, sample size, false discovery rate (FDR), and size of the network (i.e., proportion of image locations) that truly demonstrates an effect. Minimum sample size (for fixed effect size) and the minimum effect size (for fixed sample size) are calculated by specifying the acceptable power threshold. Within-center variance was estimated in five participating centers by repeat MRI scanning of 12 healthy participants from whom distributions of gray matter were estimated. The effect on outcome measures when varying FDR and the proportion of true positives is presented. Their spatial patterns reflect within-center variance, which is consistent across centers. Sample sizes 3-6 times larger are needed when detecting effects in subcortical regions compared to the neocortex. Hypothesized multicenter studies of patients with first episode psychosis and control participants were simulated with varying proportions of the cohort recruited at each center. There is little penalty to sample size for recruitment at five centers compared to the center with the lowest variance alone. At 80% power 80 participants per group are required to observe differences in gray matter in high variance regions.

How reliable are the results from functional magnetic resonance imaging?

Functional magnetic resonance imaging (fMRI) is one of the most important methods for in vivo investigation of cognitive processes in the human brain. Within the last two decades, an explosion of research has emerged using fMRI, revealing the underpinnings of everything from motor and sensory processes to the foundations of social cognition. While these results have revealed the potential of neuroimaging, important questions regarding the reliability of these results remain unanswered. In this paper, we take a close look at what is currently known about the reliability of fMRI findings. First, we examine the many factors that influence the quality of acquired fMRI data. We also conduct a review of the existing literature to determine if some measure of agreement has emerged regarding the reliability of fMRI. Finally, we provide commentary on ways to improve fMRI reliability and what questions remain unanswered. Reliability is the foundation on which scientific investigation is based. How reliable are the results from fMRI?

Power and sample size calculation for neuroimaging studies by non-central random field theory

Determining power and sample size in neuroimaging studies is a challenging task because of the massive multiple comparisons among tens of thousands of correlated voxels. To facilitate this task, we propose a power analysis method based on random field theory (RFT) by modeling signal areas within images as non-central random field. With this framework, power can be calculated for specific areas of anticipated signals within the brain while accounting for the 3D nature of signals. This framework can also be extended to visualize local variability in sensitivity as a power map and a sample size map. We validated our non-central RFT framework based on Monte-Carlo simulations. Moreover, we applied our method to a blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) data set with a small sample size in order to demonstrate its use in study planning. From the simulations, we found that our method was able to estimate power quite accurately. In the fMRI data analysis, despite the small sample size, we were able to determine power and the number of subjects required to detect signals.

Skull-stripping magnetic resonance brain images using a model-based level set

The segmentation of brain tissue from nonbrain tissue in magnetic resonance (MR) images, commonly referred to as skull stripping, is an important image processing step in many neuroimage studies. A new mathematical algorithm, a model-based level set (MLS), was developed for controlling the evolution of the zero level curve that is implicitly embedded in the level set function. The evolution of the curve was controlled using two terms in the level set equation, whose values represented the forces that determined the speed of the evolving curve. The first force was derived from the mean curvature of the curve, and the second was designed to model the intensity characteristics of the cortex in MR images. The combination of these forces in a level set framework pushed or pulled the curve toward the brain surface. Quantitative evaluation of the MLS algorithm was performed by comparing the results of the MLS algorithm to those obtained using expert segmentation in 29 sets of pediatric brain MR images and 20 sets of young adult MR images. Another 48 sets of elderly adult MR images were used for qualitatively evaluating the algorithm. The MLS algorithm was also compared to two existing methods, the brain extraction tool (BET) and the brain surface extractor (BSE), using the data from the Internet brain segmentation repository (IBSR). The MLS algorithm provides robust skull-stripping results, making it a promising tool for use in large, multi-institutional, population-based neuroimaging studies.

Olanzapine-induced cerebral metabolic changes related to symptom improvement in schizophrenia

The pattern of brain metabolic changes produced by olanzapine has yet to be described, despite the theoretical and clinical interest of this new antipsychotic. We studied a group of 17 schizophrenic patients who underwent two fluoro-deoxyglucose-positron emission tomography (FDG-PET) studies under two different conditions: a baseline scan during treatment with either conventional antipsychotics (n=15) or risperidone (n=2) and a second scan performed 17-24 weeks after switching to olanzapine. PET scans were obtained while performing a standard cognitive paradigm (Continuous Performance Test) and analysed by means of Statistical Parametric Mapping. No significant metabolic changes were found in the comparison between pre- and post-olanzapine conditions. A brain map of the statistical power of our design showed that changes up to 3% in the frontal and up to 8% in the occipital region were not likely to exist (1-beta=0.8). The degree of improvement in positive symptoms was related to the amount of activity decrease in the right orbital region and to the amount of activity increase in the primary visual area. Improvement in negative symptoms was associated with an activity increase in the dorsal prefrontal cortex, and a higher baseline activity in both temporal poles. These correlation patterns suggest that the functional mechanism of action of olanzapine may share traits from both typical and atypical neuroleptics.

The role of the rostral frontal cortex (area 10) in prospective memory: a lateral versus medial dissociation

Using the H(2)(15)O PET method, we investigated whether previous findings of regional cerebral blood flow (rCBF) changes in the polar and superior rostral aspects of the frontal lobes (principally Brodmann's area (BA) 10) during prospective memory (PM) paradigms (i.e. those involving carrying out an intended action after a delay) can be attributed merely to the greater difficulty of such tasks over the baseline conditions typically employed. Three different tasks were administered under four conditions: baseline simple RT; attention-demanding ongoing task only; ongoing task plus a delayed intention (unpracticed); ongoing task plus delayed intention (practiced). Under prospective memory conditions, we found significant rCBF decreases in the superior medial aspects of the rostral prefrontal cortex (BA 10) relative to the baseline or ongoing task only conditions. However more lateral aspects of area 10 (plus the medio-dorsal thalamus) showed the opposite pattern, with rCBF increases in the prospective memory conditions relative to the other conditions. These patterns were broadly replicated over all three tasks. Since both the medial and lateral rostral regions showed: (a) instances where rCBF was lower during a more effortful condition (as estimated by increased RTs and error rates) than in a less effortful one; and (b) there was no correlation between rCBF and RT durations or number of errors in these regions, a simple task difficulty explanation of the rCBF changes in the rostral aspects of the frontal lobes during prospective memory tasks is rejected. Instead, the favoured explanation concentrates upon the particular processing demands made by these situations irrespective of the precise stimuli used or the exact nature of the intention. Moreover, the results suggest different roles for medial and lateral rostral prefrontal cortex, with the former involved in suppressing internally-generated thought, and the latter in maintaining it.

Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses

Estimation of statistical power in functional MRI (fMRI) requires knowledge of the expected percent signal change between two conditions as well as estimates of the variability in percent signal change. Variability can be divided into intra-subject variability, reflecting noise within the time series, and inter-subject variability, reflecting subject-to-subject differences in activation. The purpose of this study was to obtain estimates of percent signal change and the two sources of variability from fMRI data, and then use these parameter estimates in simulation experiments in order to generate power curves. Of interest from these simulations were conclusions concerning how many subjects are needed and how many time points within a scan are optimal in an fMRI study of cognitive function. Intra-subject variability was estimated from resting conditions, and inter-subject variability and percent signal change were estimated from verbal working memory data. Simulations derived from these parameters illustrate how percent signal change, intra- and inter-subject variability, and number of time points affect power. An empirical test experiment, using fMRI data acquired during somatosensory stimulation, showed good correspondence between the simulation-based power predictions and the power observed within somatosensory regions of interest. Our analyses suggested that for a liberal threshold of 0.05, about 12 subjects were required to achieve 80% power at the single voxel level for typical activations. At more realistic thresholds, that approach those used after correcting for multiple comparisons, the number of subjects doubled to maintain this level of power.

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.

The problem of functional localization in the human brain

Functional imaging gives us increasingly detailed information about the location of brain activity. To use this information, we need a clear conception of the meaning of location data. Here, we review methods for reporting location in functional imaging and discuss the problems that arise from the great variability in brain anatomy between individuals. These problems cause uncertainty in localization, which limits the effective resolution of functional imaging, especially for brain areas involved in higher cognitive function.

Accuracy and sensitivity of detection of activation foci in the brain via statistical parametric mapping: a study using a PET simulator

Statistical parametric mapping (SPM) is currently the most widely used method for analysis of functional activation images. This paper reports a quantitative evaluation of the sensitivity and accuracy of SPM, using a realistic simulator of PET image formation, which accounted for the main physical processes involved in PET, including attenuation, scatter, randoms, Poisson noise, and limited detector resolution. Activation foci of the brain were simulated by placing spheres of specified activities in particular locations. Using these data, the sensitivity and accuracy of SPM in detecting activation foci was measured for different versions of the SPM spatial normalization method and for an elastic warping method referred to as STAR (spatial transformation algorithm for registration). The STAR method resulted in relatively better registration and hence better detection of the activation foci. A secondary goal of the paper was to evaluate the improvement in detection sensitivity obtained by applying an atlas-based adaptive smoothing method instead of the usual Gaussian filtering method. The results indicate some limitations of statistical parametric mapping, assist in the correct interpretation of the SPM maps, and point to future research directions in functional image analysis.

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference.

Statistical limitations in functional neuroimaging. II. Signal detection and statistical inference

The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low-pass filtering in relation to functional-anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non-inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.

Human brain activation under controlled thermal stimulation and habituation to noxious heat: an fMRI study

Brain activity was studied with functional magnetic resonance imaging (fMRI) following thermal stimulation. Two groups (n = 6/group) of human male volunteers were given up to four noxious (46 degrees C) and four non-noxious (41 degrees C) stimuli. In the 46 degrees C experiment, positive signal changes were found in the frontal gyri, anterior and posterior cingulate gyrus, thalamus, motor cortex, somatosensory cortex (SI and SII), supplementary motor area, insula, and cerebellum. Low-level negative signal changes appeared in the amygdala and hypothalamus. All regions activated by 46 degrees C were also activated by 41 degrees C. However, except for SI and thalamus, significantly more activation was observed for the 46 degrees C stimulus. A significant attenuation of the signal change was observed by the third stimulus for the 46 degrees C, but not for 41 degrees C experiment. Similar findings were replicated in the second group. These fMRI findings specify differences between somatosensory and pain sensation and suggest a number of rich avenues for future research.