Article

Model-based clustering of meta-analytic functional imaging data.

Max-Planck-Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, Germany.
Human Brain Mapping (Impact Factor: 6.88). 02/2008; 29(2):177-92. DOI: 10.1002/hbm.20380
Source: PubMed

ABSTRACT We present a method for the analysis of meta-analytic functional imaging data. It is based on Activation Likelihood Estimation (ALE) and subsequent model-based clustering using Gaussian mixture models, expectation-maximization (EM) for model fitting, and the Bayesian Information Criterion (BIC) for model selection. Our method facilitates the clustering of activation maxima from previously performed imaging experiments in a hierarchical fashion. Regions with a high concentration of activation coordinates are first identified using ALE. Activation coordinates within these regions are then subjected to model-based clustering for a more detailed cluster analysis. We demonstrate the usefulness of the method in a meta-analysis of 26 fMRI studies investigating the well-known Stroop paradigm.

0 Bookmarks
 · 
81 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Several studies have attempted to characterize morphological brain changes due to chronic pain. Although it has repeatedly been suggested that longstanding pain induces gray matter modifications, there is still some controversy surrounding the direction of the change (increase or decrease in gray matter) and the role of psychological and psychiatric comorbidities. In this study, we propose a novel, network-oriented, meta-analytic approach to characterize morphological changes in chronic pain. We used network decomposition to investigate whether different kinds of chronic pain are associated with a common or specific set of altered networks. Representational similarity techniques, network decomposition and model-based clustering were employed: i) to verify the presence of a core set of brain areas commonly modified by chronic pain; ii) to investigate the involvement of these areas in a large-scale network perspective; iii) to study the relationship between altered networks and; iv) to find out whether chronic pain targets clusters of areas. Our results showed that chronic pain causes both core and pathology-specific gray matter alterations in large-scale networks. Common alterations were observed in the prefrontal regions, in the anterior insula, cingulate cortex, basal ganglia, thalamus, periaqueductal gray, post-and pre-central gyri and inferior parietal lobule. We observed that the salience and attentional networks were targeted in a very similar way by different chronic pain pathologies. Conversely, alterations in the sensorimotor and attention circuits were differentially targeted by chronic pain pathologies. Moreover, model-based clustering revealed that chronic pain, in line with some neurodegenerative diseases, selectively targets some large-scale brain networks. Altogether these findings indicate that chronic pain can be better conceived and studied in a network perspective. c 2014 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 3.0 /).
    Frontiers in Human Neuroscience 01/2014; 4:676-686. · 2.91 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Age-related increases in right frontal cortex activation are a common finding in the neuroimaging literature. However, neurocognitive factors contributing to right frontal over-recruitment remain poorly understood. Here we investigated the influence of age-related reaction time (RT) slowing and white matter (WM) microstructure reductions as potential explanatory factors for age-related increases in right frontal activation during task switching. Groups of younger (N = 32) and older (N = 33) participants completed a task switching paradigm while functional magnetic resonance imaging (fMRI) was performed, and rested while diffusion tensor imaging (DTI) was performed. Two right frontal regions of interest (ROIs), the dorsolateral prefrontal cortex (DLPFC) and insula, were selected for further analyses from a common network of regions recruited by both age groups during task switching. Results demonstrated age-related activation increases in both ROIs. In addition, the older adult group showed longer RT and decreased fractional anisotropy in regions of the corpus callosum with direct connections to the fMRI ROIs. Subsequent mediation analyses indicated that age-related increases in right insula activation were mediated by RT slowing and age-related increases in right DLPFC activation were mediated by WM microstructure. Our results suggest that age-related RT slowing and WM microstructure declines contribute to age-related increases in right frontal activation during cognitive task performance.
    Neuroscience 08/2014; · 3.12 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Spatial normalization-applying standardized coordinates as anatomical addresses within a reference space-was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.
    Annual Review of Neuroscience 07/2014; 37:409-434. · 20.61 Impact Factor

Full-text (2 Sources)

View
26 Downloads
Available from
Jun 3, 2014