Web supplementary material for "A Bayesian Heirachical Spatial Point Process Model for Multi-type Neuroimaging Meta-analysis"

Web supplementary material for "A Bayesian Heirachical Spatial Point Process Model for Multi-type Neuroimaging Meta-analysis

Estimating the prevalence of ‘file drawer’ studies in coordinate-based meta-analysis

Poster submitted to the 2015 Organization for Human Brain Mapping (OHBM) in Hawaii, 14-18 June

Strain of social workers dealing with the benefits of help in material deprivation illustrated by the social reform

The degree work deals with the issues of psychic and physical strain of social workers who are in charge of the benefits of help in material deprivation. More deeply it describes the social work difficulties and problems connected to performing this profession, especially after the social reform of 01/01/2012. All aspcts of the strain that social workers at the benefits of help in material deprivation can meet, e. g. overwork, burn-out syndrome, growing sickness rate, communication problems, conflicts at work places, agressive clients. Neither prevention nor coping with strain can be omitted. In the background of the degree work the social work before and after the social reform is compared and the consequent proposals for the improvement of the situation of social workers at job centres are introduced

Web-based material for "Analysis of multiple sclerosis lesions via spatially varying coefficients"

Web-based material for "Analysis of multiple sclerosis lesions via spatially varying coefficients

Spatial point process modelling of coordinate-based meta-analysis data

Poster submitted to the 2015 Organization for Human Brain Mapping (OHBM) in Hawaii, 14-18 June

Le Courrier

20 juin 18281828/06/20 (N172)

A Bayesian Model of Category-Specific Emotional Brain Responses

<div><p>Understanding emotion is critical for a science of healthy and disordered brain function, but the neurophysiological basis of emotional experience is still poorly understood. We analyzed human brain activity patterns from 148 studies of emotion categories (2159 total participants) using a novel hierarchical Bayesian model. The model allowed us to classify which of five categories—fear, anger, disgust, sadness, or happiness—is engaged by a study with 66% accuracy (43-86% across categories). Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures. The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks. The model provides a precise summary of the prototypical patterns for each emotion category, and demonstrates that a sufficient characterization of emotion categories relies on (a) differential patterns of involvement in neocortical systems that differ between humans and other species, and (b) distinctive patterns of cortical-subcortical interactions. Thus, these findings are incompatible with several contemporary theories of emotion, including those that emphasize emotion-dedicated brain systems and those that propose emotion is localized primarily in subcortical activity. They are consistent with componential and constructionist views, which propose that emotions are differentiated by a combination of perceptual, mnemonic, prospective, and motivational elements. Such brain-based models of emotion provide a foundation for new translational and clinical approaches.</p></div

Co-activation graphs for each emotion category.

<p>A) Force-directed graphs for each emotion category, based on the Fruchterman-Reingold spring algorithm (134). The nodes (circles) are regions or networks, color-coded by anatomical system. The edges (lines) reflect co-activation between pairs of regions or networks, assessed based on the joint distribution of activation intensity in the Bayesian model (Pearson’s r across all MCMC iterations) and thresholded at P <. 05 corrected based on a permutation test. The size of each circle reflects its betweenness-centrality (48, 49), a measure of how strongly it connects disparate networks. (B) The same connections in the anatomical space of the brain. One location is depicted for each cortical network for visualization purposes, though the networks were distributed across regions (see Fig 3A). C) Global network efficiency (see refs. (135, 136)) within (diagonal elements) and between (off-diagonals) brain systems. Global efficiency (135, 136) is defined as the inverse of the average minimum path length between all members of each group of regions/nodes. Minimum path length is the minimum number of intervening nodes that must be traversed to reach one node from another, counting only paths with statistically significant associations and with distance values proportional to (2—Pearson’s r), rather than binary values, to better reflect the actual co-activation values. Higher efficiency reflects more direct relationships among the systems. Values of 0 indicates disjoint systems, with no significant co-activation paths connecting any pair of regions/networks, and values of 1 indicate the upper bound of efficiency, with a perfect association between each pair of regions. Co-activation is related to connectivity and network integration, though all fMRI-based connectivity measures only indirectly reflect actual neural connections. Efficiency is related to the average correlation among regions (r = 0.76) but not the average intensity (r = 0.02; see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004066#pcbi.1004066.s009" target="_blank">S5 Fig</a>).</p

Population centers and 5-way emotion-classification performance.

<p><b>Note</b>: Number of population centers refers to the estimated number of discrete brain regions activated for each emotion category. SD denotes standard deviation, and UCL and LCL denote upper and lower 95% posterior credible intervals, respectively. Model performance includes the hits rate (sensitivity), correct rejection rate (specificity), positive and negative predictive values (PPV and NPV) for a test classifying each study as belonging to an emotion category or not based on its reported brain foci. Performance statistics are based on leave-one-study-out cross-validated results.</p><p>Population centers and 5-way emotion-classification performance.</p

Emotion-predictive patterns of activity across cortical networks and subcortical regions.

<p>A) Left: Seven resting-state connectivity networks from the Buckner Lab with cortical, basal ganglia, and cerebellar components. Colors reflect the network membership. Right: Published anatomical parcellations were used to supplement the resting-state networks to identify sub-regions in amygdala (131), hippocampus (131, 132), and thalamus (133). dAN: dorsal attention network; Def: default mode network; FPN: fronto-parietal network; Limbic: limbic network; SMN: somatomotor network; vAN: ventral attention network; Vis: visual network. B) The profile of activation intensity across the 7 cortical and basal ganglia resting-state networks, and anatomical amygdalar and thalamic regions. Colors indicate different emotion categories, as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004066#pcbi.1004066.g001" target="_blank">Fig. 1</a>. Red: anger; green: disgust; purple: fear; yellow: happiness; blue: sadness. Values farther toward the solid circle indicate greater average intensity in the network (i.e., more expected study centers). C) Two canonical patterns estimated using non-negative matrix factorization, and the distribution of intensity values for each emotion across the two canonical patterns. The colored area shows the 95% joint confidence interval (confidence ellipsoids) derived from the 10,000 Markov chain Monte Carlo samples in the Bayesian model. Non-overlapping confidence ellipsoids indicate significant differences across categories in the expression of each profile.</p