A novel meta-analytic approach: Mining frequent co-activation patterns in neuroimaging databases

Abstract

In recent years, coordinate-based meta-analyses have become a powerful and widely used tool to study coactivity across neuroimaging experiments, a development that was supported by the emergence of large-scale neuroimaging databases like BrainMap. However, the evaluation of co-activation patterns is constrained by the fact that previous coordinate-based meta-analysis techniques like Activation Likelihood Estimation (ALE) and Multilevel Kernel Density Analysis (MKDA) reveal all brain regions that show convergent activity within a dataset without taking into account actual within-experiment co-occurrence patterns. To overcome this issue we here propose a novel meta-analytic approach named PaMiNI that utilizes a combination of two well-established data-mining techniques, Gaussian mixture modeling and the Apriori algorithm. By this, PaMiNI enables a data-driven detection of frequent co-activation patterns within neuroimaging datasets. The feasibility of the method is demonstrated by means of several analyses on simulated data as well as a real application. The analyses of the simulated data show that PaMiNI identifies the brain regions underlying the simulated activation foci and perfectly separates the co-activation patterns of the experiments in the simulations. Furthermore, PaMiNI still yields good results when activation foci of distinct brain regions become closer together or if they are non-Gaussian distributed. For the further evaluation, a real dataset on working memory experiments is used, which was previously examined in an ALE meta-analysis and hence allows a cross-validation of both methods. In this latter analysis, PaMiNI revealed a fronto-parietal “core” network of working memory and furthermore indicates a left-lateralization in this network. Finally, to encourage a widespread usage of this new method, the PaMiNI approach was implemented into a publicly available software system.

Frontal Dysfunctions of Impulse Control – A Systematic Review in Borderline Personality Disorder and Attention-Deficit/Hyperactivity Disorder

“…Therefore, differences in activation patterns may not only rely on differences in transient and sustained attention but also in differences in error processing (which is a general drawback of blocked designs in impulse control research). However, regions implicated in error processing are rather anterior insula and dorsal ACC (Aron and Poldrack, 2006 ; Agam et al, 2014 ; Erika-Florence et al, 2014 ; Steele et al, 2014 ) than the DLPFC, which in turn has been implicated in working memory performance (Brunoni and Vanderhasselt, 2014 ; Caspers et al, 2014 ) as well as in proactive inhibition (Chikazoe et al, 2009 ; Aron, 2011 ; Jahfari et al, 2012 ). Increased DLPFC activity in patients with ADHD resulting from the block-wise analysis might hence indicate increased working memory demands or proactive stimulus interference.…”

“…Dorsolateral deficits implicate rather deficits in cognitive or executive functions involved in discounting such as deliberative processes involved in the comparison of choice options (Sonuga-Barke and Fairchild, 2012 ). These processes will most likely include working memory processes, e.g., by holding choice alternatives in mind, which are linked to DLPFC function (Brunoni and Vanderhasselt, 2014 ; Caspers et al, 2014 ). Similarly, DLPFC dysfunctions in ADHD have been implicated stimulus interference, especially in block-wise analysis.…”

The Coordinate-Based Meta-Analysis of Neuroimaging Data

“…Early works mainly utilised exploratory data analysis and visualisation techniques to blend the results from different studies (Fox, Parsons and Lancaster, 1998) and it was not until the early 2000’s that the first methods for CBMA were proposed (Fox et al, 1997; Turkeltaub et al, 2002; Nielsen and Hansen, 2002; Wager et al, 2003). Since then, many new methods and modifications appeared in the neuroimaging (Laird et al, 2005; Wager, Lindquist and Kaplan, 2007; Radua and Mataix-Cols, 2009; Turkeltaub et al, 2012; Caspers et al, 2014, to name a few) as well as the statistics (Kang et al, 2011; Yue, Lindquist and Loh, 2012; Kang et al, 2014; Montagna et al, 2017) literature.…”

Objectifying “Pain” in the Modern Neurosciences: A Historical Account of the Visualization Technologies Used in the Development of an “Algesiogenic Pathology”, 1850 to 2000

“…), much of the normal documentation of practical human endeavors, involving comforting and retraction behaviors in pain conditions, as well as written linguistic descriptions of pain-related experiences get transformed and “objectively” presented in aesthetic ways, which often render them “understandable” only to the experts’ eyes (such as the “lightening up” of the nucleus accumbens in an fMRI done with a patient in acute pain). This is often the result of dedicated interdisciplinary work by larger teams of neuroscientists, electronic designers, bioinformaticians and physical scientists, which has also led to the development of new interdisciplinary forms of reasoning, epistemic approaches, and forms of neuroscientific experimentation [ 80 ]. As such, we have come to witness an important co-evolution in the relationship between the life sciences and visualization techniques, as these had previously contributed to the investigative process of studying pain since the early 1980s.…”