Multilevel Kernel Density Analysis Research Articles

The Brain Network for Deductive Reasoning: A Quantitative Meta-analysis of 28 Neuroimaging Studies

Over the course of the past decade, contradictory claims have been made regarding the neural bases of deductive reasoning. Researchers have been puzzled by apparent inconsistencies in the literature. Some have even questioned the effectiveness of the methodology used to study the neural bases of deductive reasoning. However, the idea that neuroimaging findings are inconsistent is not based on any quantitative evidence. Here, we report the results of a quantitative meta-analysis of 28 neuroimaging studies of deductive reasoning published between 1997 and 2010, combining 382 participants. Consistent areas of activations across studies were identified using the multilevel kernel density analysis method. We found that results from neuroimaging studies are more consistent than what has been previously assumed. Overall, studies consistently report activations in specific regions of a left fronto-parietal system, as well as in the left BG. This brain system can be decomposed into three subsystems that are specific to particular types of deductive arguments: relational, categorical, and propositional. These dissociations explain inconstancies in the literature. However, they are incompatible with the notion that deductive reasoning is supported by a single cognitive system relying either on visuospatial or rule-based mechanisms. Our findings provide critical insight into the cognitive organization of deductive reasoning and need to be accounted for by cognitive theories.

Meta-Analysis of Functional Neuroimaging Studies of Emotion Perception and Experience in Schizophrenia

Neuroimaging studies of emotion in schizophrenia have reported abnormalities in amygdala and other regions, although divergent results and heterogeneous paradigms complicate conclusions from single experiments. To identify more consistent patterns of dysfunction, a meta-analysis of functional imaging studies of emotion was undertaken. Searching Medline and PsycINFO databases through January 2011, 88 potential articles were identified, of which 26 met inclusion criteria, comprising 450 patients with schizophrenia and 422 healthy comparison subjects. Contrasts were selected to include emotion perception and emotion experience. Foci from individual studies were subjected to a voxelwise meta-analysis using multilevel kernel density analysis. For emotional experience, comparison subjects showed greater activation in the left occipital pole. For emotional perception, schizophrenia subjects showed reduced activation in bilateral amygdala, visual processing areas, anterior cingulate cortex, dorsolateral frontal cortex, medial frontal cortex, and subcortical structures. Schizophrenia subjects showed greater activation in the cuneus, parietal lobule, precentral gyrus, and superior temporal gyrus. Combining across studies and eliminating studies that did not balance on effort and stimulus complexity eliminated most differences in visual processing regions as well as most areas where schizophrenia subjects showed a greater signal. Reduced reactivity of the amygdala appeared primarily in implicit studies of emotion, whereas deficits in anterior cingulate cortex activity appeared throughout all contrasts. Processing emotional stimuli, schizophrenia patients show reduced activation in areas engaged by emotional stimuli, although in some conditions, schizophrenia patients exhibit increased activation in areas outside those traditionally associated with emotion, possibly representing compensatory processing.

A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps

Meta-analyses are essential to summarize the results of the growing number of neuroimaging studies in psychiatry, neurology and allied disciplines. Image-based meta-analyses use full image information (i.e. the statistical parametric maps) and well-established statistics, but images are rarely available making them highly unfeasible. Peak-probability meta-analyses such as activation likelihood estimation (ALE) or multilevel kernel density analysis (MKDA) are more feasible as they only need reported peak coordinates. Signed-differences methods, such as signed differential mapping (SDM) build upon the positive features of existing peak-probability methods and enable meta-analyses of studies comparing patients with controls. In this paper we present a new version of SDM, named Effect Size SDM (ES-SDM), which enables the combination of statistical parametric maps and peak coordinates and uses well-established statistics. We validated the new method by comparing the results of an ES-SDM meta-analysis of studies on the brain response to fearful faces with the results of a pooled analysis of the original individual data. The results showed that ES-SDM is a valid and reliable coordinate-based method, whose performance might be additionally increased by including statistical parametric maps. We anticipate that ES-SDM will be a helpful tool for researchers in the fields of psychiatry, neurology and allied disciplines.

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.

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.

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

With the rapid growth of neuroimaging research and accumulation of neuroinformatic databases the synthesis of consensus findings using meta-analysis is becoming increasingly important. Meta-analyses pool data across many studies to identify reliable experimental effects and characterize the degree of agreement across studies. Coordinate-based meta-analysis (CBMA) methods are the standard approach, where each study entered into the meta-analysis has been summarized using only the (x, y, z) locations of peak activations (with or without activation magnitude) reported in published reports. Image-based meta-analysis (IBMA) methods use the full statistic images, and allow the use of hierarchical mixed effects models that account for differing intra-study variance and modeling of random inter-study variation. The purpose of this work is to compare image-based and coordinate-based meta-analysis methods applied to the same dataset, a group of 15 fMRI studies of pain, and to quantify the information lost by working only with the coordinates of peak activations instead of the full statistic images. We apply a 3-level IBMA mixed model for a "mega-analysis", and highlight important considerations in the specification of each model and contrast. We compare the IBMA result to three CBMA methods: ALE (activation likelihood estimation), KDA (kernel density analysis) and MKDA (multi-level kernel density analysis), for various CBMA smoothing parameters. For the datasets considered, we find that ALE at σ=15 mm, KDA at ρ=25–30 mm and MKDA at ρ=15 mm give the greatest similarity to the IBMA result, and that ALE was the most similar for this particular dataset, though only with a Dice similarity coefficient of 0.45 (Dice measure ranges from 0 to 1). Based on this poor similarity, and the greater modeling flexibility afforded by hierarchical mixed models, we suggest that IBMA is preferred over CBMA. To make IBMA analyses practical, however, the neuroimaging field needs to develop an effective mechanism for sharing image data, including whole-brain images of both effect estimates and their standard errors.