Simon B. Eickhoff

A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data.

Correlating the activation foci identified in functional imaging studies of the human brain with structural (e.g., cytoarchitectonic) information on the activated areas is a major methodological challenge for neuroscience research. We here present a new approach to make use of three-dimensional probabilistic cytoarchitectonic maps, as obtained from the analysis of human post-mortem brains, for correlating microscopical, anatomical and functional imaging data of the cerebral cortex. We introduce a new, MATLAB based toolbox for the SPM2 software package which enables the integration of probabilistic cytoarchitectonic maps and results of functional imaging studies. The toolbox includes the functionality for the construction of summary maps combining probability of several cortical areas by finding the most probable assignment of each voxel to one of these areas. Its main feature is to provide several measures defining the degree of correspondence between architectonic areas and functional foci. The software, together with the presently available probability maps, is available as open source software to the neuroimaging community. This new toolbox provides an easy-to-use tool for the integrated analysis of functional and anatomical data in a common reference space.

Coordinate‐based activation likelihood estimation meta‐analysis of neuroimaging data: A random‐effects approach based on empirical estimates of spatial uncertainty

A widely used technique for coordinate‐based meta‐analyses of neuroimaging data is activation likelihood estimation (ALE). ALE assesses the overlap between foci based on modeling them as probability distributions centered at the respective coordinates. In this Human Brain Project/Neuroinformatics research, the authors present a revised ALE algorithm addressing drawbacks associated with former implementations. The first change pertains to the size of the probability distributions, which had to be specified by the used. To provide a more principled solution, the authors analyzed fMRI data of 21 subjects, each normalized into MNI space using nine different approaches. This analysis provided quantitative estimates of between‐subject and between‐template variability for 16 functionally defined regions, which were then used to explicitly model the spatial uncertainty associated with each reported coordinate. Secondly, instead of testing for an above‐chance clustering between foci, the revised algorithm assesses above‐chance clustering between experiments. The spatial relationship between foci in a given experiment is now assumed to be fixed and ALE results are assessed against a null‐distribution of random spatial association between experiments. Critically, this modification entails a change from fixed‐ to random‐effects inference in ALE analysis allowing generalization of the results to the entire population of studies analyzed. By comparative analysis of real and simulated data, the authors showed that the revised ALE‐algorithm overcomes conceptual problems of former meta‐analyses and increases the specificity of the ensuing results without loosing the sensitivity of the original approach. It may thus provide a methodologically improved tool for coordinate‐based meta‐analyses on functional imaging data. Hum Brain Mapp 2009.

ALE meta-analysis of action observation and imitation in the human brain.

Over the last decade, many neuroimaging studies have assessed the human brain networks underlying action observation and imitation using a variety of tasks and paradigms. Nevertheless, questions concerning which areas consistently contribute to these networks irrespective of the particular experimental design and how such processing may be lateralized remain unresolved. The current study aimed at identifying cortical areas consistently involved in action observation and imitation by combining activation likelihood estimation (ALE) meta-analysis with probabilistic cytoarchitectonic maps. Meta-analysis of 139 functional magnetic resonance and positron emission tomography experiments revealed a bilateral network for both action observation and imitation. Additional subanalyses for different effectors within each network revealed highly comparable activation patterns to the overall analyses on observation and imitation, respectively, indicating an independence of these findings from potential confounds. Conjunction analysis of action observation and imitation meta-analyses revealed a bilateral network within frontal premotor, parietal, and temporo-occipital cortex. The most consistently rostral inferior parietal area was PFt, providing evidence for a possible homology of this region to macaque area PF. The observation and imitation networks differed particularly with respect to the involvement of Brocas area: whereas both networks involved a caudo-dorsal part of BA 44, activation during observation was most consistent in a more rostro-dorsal location, i.e., dorsal BA 45, while activation during imitation was most consistent in a more ventro-caudal aspect, i.e., caudal BA 44. The present meta-analysis thus summarizes and amends previous descriptions of the human brain networks related to action observation and imitation.

A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis

Whether we feel sympathy for another, listen to our heartbeat, experience pain or negotiate, the insular cortex is thought to integrate perceptions, emotions, thoughts, and plans into one subjective image of “our world”. The insula has hence been ascribed an integrative role, linking information from diverse functional systems. Nevertheless, various anatomical and functional studies in humans and non-human primates also indicate a functional differentiation of this region. In order to investigate this functional differentiation as well as the mechanisms of the functional integration in the insula, we performed activation-likelihood-estimation (ALE) meta-analyses of 1,768 functional neuroimaging experiments. The analysis revealed four functionally distinct regions on the human insula, which map to the social-emotional, the sensorimotor, the olfacto-gustatory, and the cognitive network of the brain. Sensorimotor tasks activated the mid-posterior and social-emotional tasks the anterior-ventral insula. In the central insula activation by olfacto-gustatory stimuli was found, and cognitive tasks elicited activation in the anterior-dorsal region. A conjunction analysis across these domains revealed that aside from basic somatosensory and motor processes all tested functions overlapped on the anterior-dorsal insula. This overlap might constitute a correlate for a functional integration between different functional systems and thus reflect a link between them necessary to integrate different qualities into a coherent experience of the world and setting the context for thoughts and actions.

An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.

Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.

Behavioral interpretations of intrinsic connectivity networks

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

Activation likelihood estimation meta-analysis revisited

A widely used technique for coordinate-based meta-analysis of neuroimaging data is activation likelihood estimation (ALE), which determines the convergence of foci reported from different experiments. ALE analysis involves modelling these foci as probability distributions whose width is based on empirical estimates of the spatial uncertainty due to the between-subject and between-template variability of neuroimaging data. ALE results are assessed against a null-distribution of random spatial association between experiments, resulting in random-effects inference. In the present revision of this algorithm, we address two remaining drawbacks of the previous algorithm. First, the assessment of spatial association between experiments was based on a highly time-consuming permutation test, which nevertheless entailed the danger of underestimating the right tail of the null-distribution. In this report, we outline how this previous approach may be replaced by a faster and more precise analytical method. Second, the previously applied correction procedure, i.e. controlling the false discovery rate (FDR), is supplemented by new approaches for correcting the family-wise error rate and the cluster-level significance. The different alternatives for drawing inference on meta-analytic results are evaluated on an exemplary dataset on face perception as well as discussed with respect to their methodological limitations and advantages. In summary, we thus replaced the previous permutation algorithm with a faster and more rigorous analytical solution for the null-distribution and comprehensively address the issue of multiple-comparison corrections. The proposed revision of the ALE-algorithm should provide an improved tool for conducting coordinate-based meta-analyses on functional imaging data.

Minimizing Within-Experiment and Within-Group Effects in Activation Likelihood Estimation Meta-Analyses

Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate‐based meta‐analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALEs theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within‐experiment effects. However, we show that these effects only account for 2–3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7–9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within‐experiment or within‐group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within‐experiment and within‐group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. Hum Brain Mapp, 2012.

Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps.

The statistical inference on functional imaging data is severely complicated by the embedded multiple testing problem. Defining a region of interest (ROI) where the activation is hypothesized a priori helps to circumvent this problem, since in this case the inference is restricted to fewer simultaneous tests, rendering it more sensitive. Cytoarchitectonic maps obtained from postmortem brains provide objective, a priori ROIs that can be used to test anatomically specified hypotheses about the localization of functional activations. We here analyzed three methods for the definition of ROIs based on probabilistic cytoarchitectonic maps. (1) ROIs defined by the volume assigned to a cytoarchitectonic area in the summary map of all areas (maximum probability map, MPM), (2) ROIs based on thresholding the individual probabilistic maps and (3) spherical ROIs build around the cytoarchitectonic center of gravity. The quality with which the thus defined ROIs represented the respective cytoarchitectonic areas as well as their sensitivity for detecting functional activations was subsequently statistically evaluated. Our data showed that the MPM method yields ROIs, which reflect most adequately the underlying anatomical hypotheses. These maps also show a high degree of sensitivity in the statistical analysis. We thus propose the use of MPMs for the definition of ROIs. In combination with thresholding based on the Gaussian random field theory, these ROIs can then be applied to test anatomically specified hypotheses in functional neuroimaging studies.

Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging

This study aimed at identifying the impact of subcortical stroke on the interaction of cortical motor areas within and across hemispheres during the generation of voluntary hand movements.