Visualizing data mining results with the brede tools

Mapping the semantic structure of cognitive neuroscience

Cognitive neuroscience, as a discipline, links the biological systems studied by neuroscience to the processing constructs studied by psychology. By mapping these relations throughout the literature of cognitive neuroscience, we visualize the semantic structure of the discipline and point to directions for future research that will advance its integrative goal. For this purpose, network text analyses were applied to an exhaustive corpus of abstracts collected from five major journals over a 30-month period, including every study that used fMRI to investigate psychological processes. From this, we generate network maps that illustrate the relationships among psychological and anatomical terms, along with centrality statistics that guide inferences about network structure. Three terms--prefrontal cortex, amygdala, and anterior cingulate cortex--dominate the network structure with their high frequency in the literature and the density of their connections with other neuroanatomical terms. From network statistics, we identify terms that are understudied compared with their importance in the network (e.g., insula and thalamus), are underspecified in the language of the discipline (e.g., terms associated with executive function), or are imperfectly integrated with other concepts (e.g., subdisciplines like decision neuroscience that are disconnected from the main network). Taking these results as the basis for prescriptive recommendations, we conclude that semantic analyses provide useful guidance for cognitive neuroscience as a discipline, both by illustrating systematic biases in the conduct and presentation of research and by identifying directions that may be most productive for future research.

Brede tools and federating online neuroinformatics databases

As open science neuroinformatics databases the Brede Database and Brede Wiki seek to make distribution and federation of their content as easy and transparent as possible. The databases rely on simple formats and allow other online tools to reuse their content. This paper describes the possible interconnections on different levels between the Brede tools and other databases.

Meta-analytic clustering of the insular cortex: characterizing the meta-analytic connectivity of the insula when involved in active tasks

The human insula has been parcellated on the basis of resting state functional connectivity and diffusion tensor imaging. Little is known about the organization of the insula when involved in active tasks. We explored this issue using a novel meta-analytic clustering approach. We queried the BrainMap database asking for papers involving normal subjects that recorded activations in the insular cortex, retrieving 1305 papers, involving 22,872 subjects and a total of 2957 foci. Data were analyzed with several different methodologies, some of which expressly designed for this work. We used meta-analytic connectivity modeling and meta-analytic clustering of data obtained from the BrainMap database. We performed cluster analysis to subdivide the insula in areas with homogeneous connectivity, and density analysis of the activated foci using Voronoi tessellation. Our results confirm and extend previous findings obtained investigating the resting state connectivity of the anterior-posterior and left-right insulae. They indicate, for the first time, that some blocks of the anterior insula play the role of hubs between the anterior and the posterior insulae, as confirmed by their activation in several different paradigms. This finding supports the view that the network to which the anterior insula belongs is related to saliency detection. The insulae of both sides can be parcellated in two clusters, the anterior and the posterior: the anterior is characterized by an attentional pattern of connectivity with frontal, cingulate, parietal, cerebellar and anterior insular highly connected areas, whereas the posterior is characterized by a more local connectivity pattern with connections to sensorimotor, temporal and posterior cingulate areas. This antero-posterior subdivision, better characterized on the right side, results sharper with the connectivity based clusterization than with the behavioral based clusterization. The circuits belonging to the anterior insula are very homogeneous and their blocks in multidimensional scaling of MACM-based profiles are in central position, whereas those belonging to the posterior insula, especially on the left, are located at the periphery and sparse, thus suggesting that the posterior circuits bear a more heterogeneous connectivity. The anterior cluster is mostly activated by cognition, whereas the posterior is mostly activated by interoception, perception and emotion.

Mental attributes and temporal brain dynamics during bargaining: EEG source localization and neuroinformatic mapping

It was previously shown by fMRI studies that unfair offers during an ultimatum bargaining game activate regions in the brain associated with emotions and conflict, leading to decisions inconsistent with standard economic theory. The temporal dynamics of emotional processing and mental attributes were not clear due to the coarse temporal resolution in those studies (∼2 s). Here, the ultimatum game was studied by EEG recorded from the responders in 19 channels. EEG time series were first in-put to independent component analysis. An equivalent current dipole model was used to localize the sources of the independent components in EEGLAB. The Talairach coordinates of the dipoles were matched with references in the Brede neuroinformatics database. Dipole magnitudes, anatomical regions, and mental attributes were used to explain the rejection of the offers by applying multiple regression as a function of time in epochs with a median resolution of 250 ms. The results are consistent with previous studies regarding responder behavior and activated regions (e.g., anterior cingulate cortex, frontal gyrus, insular cortex). There are three main findings observed with the higher temporal resolution: (1) regression results from fine-scale temporal data showed activations not captured when the analysis was done by using time-averaged data; (2) temporal analysis detected the individual significant epochs and fluctuations (positive and negative correlations) in regions and for the associated mental attributes (e.g., reward/harm perception, anger, unfairness); (3) there was a sequential activation of anterior cingulate cortex and insular cortex, respectively, leading to the rejection response. Overall, regression models could explain a large percentage (∼80%) of out-of-sample behavioral responses. The results are promising for the prospect of using EEG and source localization techniques in neuroeconomics to study finer temporal dynamics of neural activation.

Using Gaussian-process regression for meta-analytic neuroimaging inference based on sparse observations

The purpose of neuroimaging meta-analysis is to localize the brain regions that are activated consistently in response to a certain intervention. As a commonly used technique, current coordinate-based meta-analyses (CBMA) of neuroimaging studies utilize relatively sparse information from published studies, typically only using (x,y,z) coordinates of the activation peaks. Such CBMA methods have several limitations. First, there is no way to jointly incorporate deactivation information when available, which has been shown to result in an inaccurate statistic image when assessing a difference contrast. Second, the scale of a kernel reflecting spatial uncertainty must be set without taking the effect size (e.g., Z-stat) into account. To address these problems, we employ Gaussian-process regression (GPR), explicitly estimating the unobserved statistic image given the sparse peak activation "coordinate" and "standardized effect-size estimate" data. In particular, our model allows estimation of effect size at each voxel, something existing CBMA methods cannot produce. Our results show that GPR outperforms existing CBMA techniques and is capable of more accurately reproducing the (usually unavailable) full-image analysis results.