Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies

Neural representation of abstract and concrete concepts: a meta-analysis of neuroimaging studies

A number of studies have investigated differences in neural correlates of abstract and concrete concepts with disagreement across results. A quantitative, coordinate-based meta-analysis combined data from 303 participants across 19 functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies to identify the differences in neural representation of abstract and concrete concepts. Studies that reported peak activations in standard space in contrast of abstract > concrete or concrete > abstract concepts at a whole brain level in healthy adults were included in this meta-analysis. Multilevel kernel density analysis (MKDA) was performed to identify the proportion of activated contrasts weighted by sample size and analysis type (fixed or random effects). Meta-analysis results indicated consistent and meaningful differences in neural representation for abstract and concrete concepts. Abstract concepts elicit greater activity in the inferior frontal gyrus and middle temporal gyrus compared to concrete concepts, while concrete concepts elicit greater activity in the posterior cingulate, precuneus, fusiform gyrus, and parahippocampal gyrus compared to abstract concepts. These results suggest greater engagement of the verbal system for processing of abstract concepts and greater engagement of the perceptual system for processing of concrete concepts, likely via mental imagery.

Cognitive neuroscience 2.0: building a cumulative science of human brain function

Cognitive neuroscientists increasingly recognize that continued progress in understanding human brain function will require not only the acquisition of new data, but also the synthesis and integration of data across studies and laboratories. Here we review ongoing efforts to develop a more cumulative science of human brain function. We discuss the rationale for an increased focus on formal synthesis of the cognitive neuroscience literature, provide an overview of recently developed tools and platforms designed to facilitate the sharing and integration of neuroimaging data, and conclude with a discussion of several emerging developments that hold even greater promise in advancing the study of human brain function.

fMRI studies of successful emotional memory encoding: A quantitative meta-analysis

Over the past decade, fMRI techniques have been increasingly used to interrogate the neural correlates of successful emotional memory encoding. These investigations have typically aimed to either characterize the contributions of the amygdala and medial temporal lobe (MTL) memory system, replicating results in animals, or delineate the neural correlates of specific behavioral phenomena. It has remained difficult, however, to synthesize these findings into a systems neuroscience account of how networks across the whole-brain support the enhancing effects of emotion on memory encoding. To this end, the present study employed a meta-analytic approach using activation likelihood estimates to assess the anatomical specificity and reliability of event-related fMRI activations related to successful memory encoding for emotional versus neutral information. The meta-analysis revealed consistent clusters within bilateral amygdala, anterior hippocampus, anterior and posterior parahippocampal gyrus, the ventral visual stream, left lateral prefrontal cortex and right ventral parietal cortex. The results within the amygdala and MTL support a wealth of findings from the animal literature linking these regions to arousal-mediated memory effects. The consistency of findings in cortical targets, including the visual, prefrontal, and parietal cortices, underscores the importance of generating hypotheses regarding their participation in emotional memory formation. In particular, we propose that the amygdala interacts with these structures to promote enhancements in perceptual processing, semantic elaboration, and attention, which serve to benefit subsequent memory for emotional material. These findings may motivate future research on emotional modulation of widespread neural systems and the implications of this modulation for cognition.

A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease

We reviewed case-control studies of diffusion tensor imaging (DTI) in patients with Alzheimer's dementia (AD) and mild cognitive impairment (MCI), in order to establish the relative severity and location of white matter microstructural changes. EMBASE and MEDLINE were searched using the keywords, (["diffusion tensor"] and ["Alzheimer*" or "mild cognitive impairment"]), as were reference lists of relevant papers. Forty-one diffusion tensor imaging studies contained data that were suitable for inclusion. Group means and standard deviations for fractional anisotropy and mean diffusivity, or p values from 2-sample tests, were extracted and pooled, using standard methods of meta-analysis and metaregression. Fractional anisotropy was decreased in AD in all regions except parietal white matter and internal capsule, while patients with MCI had lower values in all white matter regions except parietally and occipitally. Mean diffusivity was increased in AD in all regions, and in MCI in all but occipital and frontal regions.

Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder

BACKGROUND

We conducted a meta-analysis of gray matter abnormalities in bipolar disorder (BD) using voxel-based morphometry studies to help clarify the structural abnormalities underpinning this condition.

METHODS

A systematic review was conducted for voxel-based morphometry studies of patients with BD. Meta-analyses of gray matter differences between BD and control subjects were undertaken using "signed differential mapping," a novel method that, in contrast to previously used techniques, allows inclusion of negative findings and ensures that single studies do not exert undue influence on the results. Meta-regression and subgroup analyses were used to examine the effect of moderator variables on gray matter abnormalities.

RESULTS

A total of 21 studies comparing gray matter volumes of 660 BD patients and 770 healthy control subjects were included. Gray matter reduction in left rostral anterior cingulate cortex (ACC) and right fronto-insular cortex was associated with BD. Fronto-insular cortex abnormality was not evident in early phases of the illness. In chronic patients, longer duration of illness was associated with increased gray matter in a cluster that included basal ganglia, subgenual ACC, and amygdala. Lithium treatment was associated with enlargement of ACC gray matter volumes, which overlapped with the region where gray matter was reduced in BD.

CONCLUSIONS

The most robust gray matter reductions in BD occur in anterior limbic regions, which may be related to the executive control and emotional processing abnormalities seen in this patient population. Clinical factors such as illness duration and lithium treatment also impact on case-control comparisons of gray matter volume.

Multivariate models of inter-subject anatomical variability

This paper presents a very selective review of some of the approaches for multivariate modelling of inter-subject variability among brain images. It focusses on applying probabilistic kernel-based pattern recognition approaches to pre-processed anatomical MRI, with the aim of most accurately modelling the difference between populations of subjects. Some of the principles underlying the pattern recognition approaches of Gaussian process classification and regression are briefly described, although the reader is advised to look elsewhere for full implementational details. Kernel pattern recognition methods require matrices that encode the degree of similarity between the images of each pair of subjects. This review focusses on similarity measures derived from the relative shapes of the subjects' brains. Pre-processing is viewed as generative modelling of anatomical variability, and there is a special emphasis on the diffeomorphic image registration framework, which provides a very parsimonious representation of relative shapes. Although the review is largely methodological, excessive mathematical notation is avoided as far as possible, as the paper attempts to convey a more intuitive understanding of various concepts. The paper should be of interest to readers wishing to apply pattern recognition methods to MRI data, with the aim of clinical diagnosis or biomarker development. It also tries to explain that the best models are those that most accurately predict, so similar approaches should also be relevant to basic science. Knowledge of some basic linear algebra and probability theory should make the review easier to follow, although it may still have something to offer to those readers whose mathematics may be more limited.

Meta-analysis of neuroimaging data

As the number of neuroimaging studies that investigate psychological phenomena grows, it becomes increasingly difficult to integrate the knowledge that has accrued across studies. Meta-analyses are designed to serve this purpose, as they allow the synthesis of findings not only across studies but also across laboratories and task variants. Meta-analyses are uniquely suited to answer questions about whether brain regions or networks are consistently associated with particular psychological domains, including broad categories such as working memory or more specific categories such as conditioned fear. Meta-analysis can also address questions of specificity, which pertains to whether activation of regions or networks is unique to a particular psychological domain, or is a feature of multiple types of tasks. This review discusses several techniques that have been used to test consistency and specificity in published neuroimaging data, including the kernel density analysis (KDA), activation likelihood estimate (ALE), and the recently developed multilevel kernel density analysis (MKDA). We discuss these techniques in light of current and future directions in the field. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Neuroscience > Cognition.

Performance-dependent inhibition of pain by an executive working memory task

It is widely assumed that distraction reduces pain. Similarly, it is assumed that pain distracts from concurrent, unrelated cognitive processing, reducing performance on difficult tasks. Taken together, these assumptions suggest pain processing and cognitive function engage an overlapping set of domain-general, capacity-limited mental resources. However, experimental tests of this proposal have yielded mixed results, leading to alternative proposals that challenge the common model of a bidirectional relationship between concurrent pain and task performance. We tested these contrasting positions using a novel concurrent pain and executive working memory paradigm. Both task difficulty and nociceptive stimulus intensity were individually calibrated for each participant. Participants reported less pain during the working memory task than a visually matched control condition. Conversely, increasing levels of heat incrementally reduced task performance. Path analyses showed that variations in pain completely mediated this effect, and that even within a given heat level, trial-by-trial fluctuations in pain predicted decrements in performance. In sum, these findings argue that overlapping cognitive resources play a role in both pain processing and executive working memory. Future studies could use this paradigm to understand more precisely which components of executive function or other cognitive resources contribute to the experience of pain.

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 Broca's 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.

Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling

The default mode network (DMN) comprises a set of regions that exhibit ongoing, intrinsic activity in the resting state and task-related decreases in activity across a range of paradigms. However, DMN regions have also been reported as task-related increases, either independently or coactivated with other regions in the network. Cognitive subtractions and the use of low-level baseline conditions have generally masked the functional nature of these regions. Using a combination of activation likelihood estimation, which assesses statistically significant convergence of neuroimaging results, and tools distributed with the BrainMap database, we identified core regions in the DMN and examined their functional heterogeneity. Meta-analytic coactivation maps of task-related increases were independently generated for each region, which included both within-DMN and non-DMN connections. Their functional properties were assessed using behavioral domain metadata in BrainMap. These results were integrated to determine a DMN connectivity model that represents the patterns of interactions observed in task-related increases in activity across diverse tasks. Subnetwork components of this model were identified, and behavioral domain analysis of these cliques yielded discrete functional properties, demonstrating that components of the DMN are differentially specialized. Affective and perceptual cliques of the DMN were identified, as well as the cliques associated with a reduced preference for motor processing. In summary, we used advanced coordinate-based meta-analysis techniques to explicate behavior and connectivity in the default mode network; future work will involve applying this analysis strategy to other modes of brain function, such as executive function or sensorimotor systems.

Is subcortical-cortical midline activity in depression mediated by glutamate and GABA? A cross-species translational approach

Major depressive disorder has recently been characterized by abnormal resting state hyperactivity in anterior midline regions. The neurochemical mechanisms underlying resting state hyperactivity remain unclear. Since animal studies provide an opportunity to investigate subcortical regions and neurochemical mechanisms in more detail, we used a cross-species translational approach comparing a meta-analysis of human data to animal data on the functional anatomy and neurochemical modulation of resting state activity in depression. Animal and human data converged in showing resting state hyperactivity in various ventral midline regions. These were also characterized by abnormal concentrations of glutamate and gamma-aminobutyric acid (GABA) as well as by NMDA receptor up-regulation and AMPA and GABA receptor down-regulation. This cross-species translational investigation suggests that resting state hyperactivity in depression occurs in subcortical and cortical midline regions and is mediated by glutamate and GABA metabolism. This provides insight into the biochemical underpinnings of resting state activity in both depressed and healthy subjects.

Pooling FMRI data: meta-analysis, mega-analysis and multi-center studies

The quantitative analysis of pooled data from related functional magnetic resonance imaging (fMRI) experiments has the potential to significantly accelerate progress in brain mapping. Such data-pooling can be achieved through meta-analysis (the pooled analysis of published results), mega-analysis (the pooled analysis of raw data) or multi-site studies, which can be seen as designed mega-analyses. Current limitations in function-location brain mapping and how data-pooling can be used to remediate them are reviewed, with particular attention to power aggregation and mitigation of false positive results. Some recently developed analysis tools for meta- and mega-analysis are also presented, and recommendations for the conduct of valid fMRI data pooling are formulated.

Visualizing data mining results with the brede tools

A few neuroinformatics databases now exist that record results from neuroimaging studies in the form of brain coordinates in stereotaxic space. The Brede Toolbox was originally developed to extract, analyze and visualize data from one of them - the BrainMap database. Since then the Brede Toolbox has expanded and now includes its own database with coordinates along with ontologies for brain regions and functions: The Brede Database. With Brede Toolbox and Database combined, we setup automated workflows for extraction of data, mass meta-analytic data mining and visualizations. Most of the Web presence of the Brede Database is established by a single script executing a workflow involving these steps together with a final generation of Web pages with embedded visualizations and links to interactive three-dimensional models in the Virtual Reality Modeling Language. Apart from the Brede tools I briefly review alternate visualization tools and methods for Internet-based visualization and information visualization as well as portals for visualization tools.

Correspondence of the brain's functional architecture during activation and rest

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

Lost in localization--but found with foci?!

Commentaries by Derrfuss and Mar [Derrfuss, J., Mar, R.A., 2009. Lost in localization: the need for a universal coordinate database. Neuroimage (doi:10.1016/j.neuroimage.2009.01.053).], Nielsen [Nielsen, F.A., 2009. Lost in localization: a solution with neuroinformatics 2.0? Neuroimage.], Hamilton [Hamilton, A., 2009. Lost in localization: a minimal middle way. Neuroimage.], and Laird and Fox [Laird, A.R., Fox, P.T., 2009 Lost in localization? The focus is meta-analysis. Neuroimage.] agree on the need for a comprehensive database of published stereotaxic coordinates but offer diverse views on how best to achieve this objective. Here, I summarize recent enhancements to the SumsDB database that increase its utility and decrease the impediments to data submission, thereby making it attractive as a resource that can approach comprehensive content in a realistic time frame.