Interpretation of published meta-analytical studies affected by implementation errors in the GingerALE software

Garrison, Jane; Done, John; Simons, Jon S.

doi:10.1016/j.neubiorev.2017.10.027

Cited by 4 publications

(2 citation statements)

References 15 publications

(27 reference statements)

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“…The most conservative FDR correction yields a p value with no assumptions about how the data is correlated (FRN), and the least conservative FDR correction assumes independence or positive dependence (FID) with p < .05 and a minimum volume clustering value of 50 mm 3 . Note that, using a cluster‐level family‐wise error (FWE) correction implemented with a cluster‐extent threshold of p < .05 and a cluster‐forming threshold of p < .001 revealed virtually identical results (compared with FRN) (Eickhoff et al, ) as per previous reports (Garrison, Done, & Simons, ). For all tables presenting ALE cluster results, the size of each cluster is provided in mm 3 along with the associated MNI coordinates and maximum ALE score.…”

Section: Resultssupporting

confidence: 71%

Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta‐analysis

Fouragnan

Retzler

Philiastides

2018

Human Brain Mapping

137

126

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Learning occurs when an outcome differs from expectations, generating a reward prediction error signal (RPE). The RPE signal has been hypothesized to simultaneously embody the valence of an outcome (better or worse than expected) and its surprise (how far from expectations). Nonetheless, growing evidence suggests that separate representations of the two RPE components exist in the human brain. Meta-analyses provide an opportunity to test this hypothesis and directly probe the extent to which the valence and surprise of the error signal are encoded in separate or overlapping networks. We carried out several meta-analyses on a large set of fMRI studies investigating the neural basis of RPE, locked at decision outcome. We identified two valence learning systems by pooling studies searching for differential neural activity in response to categorical positive-versus-negative outcomes. The first valence network (negative > positive) involved areas regulating alertness and switching behaviours such as the midcingulate cortex, the thalamus and the dorsolateral prefrontal cortex whereas the second valence network (positive > negative) encompassed regions of the human reward circuitry such as the ventral striatum and the ventromedial prefrontal cortex. We also found evidence of a largely distinct surprise-encoding network including the anterior cingulate cortex, anterior insula and dorsal striatum. Together with recent animal and electrophysiological evidence this meta-analysis points to a sequential and distributed encoding of different components of the RPE signal, with potentially distinct functional roles.

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Section: Resultssupporting

confidence: 71%

Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta‐analysis

Fouragnan

Retzler

Philiastides

2018

Human Brain Mapping

137

126

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“…To date, Gr ezes and were among the first to use a meta-analytical approach to motor domain classification by investigating imagery, execution and observation. Such analysis only contained, however, a very small number of studies (only eight studies involving execution tasks were included) and, most importantly, it did not rely on specific statistical tests or validated procedures, but rather the authors identified commonalities across experiments by visually inspecting the foci of activation (Eickhoff et al, 2017;Garrison et al, 2019;Müller et al, 2018). Two current methodologies commonly used to ensure statistical reliability over large collections of functional data are image-based meta-analysis and coordinate-based meta-analysis (Laird et al, 2005b).…”

Section: Introductionmentioning

confidence: 99%

The topographical organization of motor processing: An ALE meta-analysis on six action domains and the relevance of Broca’s region

Papitto¹,

Friederici²,

Zaccarella³

2019

Preprint

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Action is a cover term used to refer to a large set of motor processes differing in domain specificities (e.g. execution or observation). Here we review neuroimaging evidence on action processing (N = 416; Subjects = 5912) using quantitative Activation Likelihood Estimation (ALE) and Meta-Analytic Connectivity Modelling (MACM) approaches to delineate the functional specificities of six domains: (1) Action Execution, (2) Action Imitation, (3) Motor Imagery, (4) Action Observation, (5) Motor Learning, (6) Motor Preparation. Our results show distinct functional patterns for the different domains with convergence in posterior BA44 (pBA44) for execution, imitation and imagery processing. The functional connectivity network seeding in the motor-based localized cluster of pBA44 differs from the connectivity network seeding in the (language-related) anterior BA44. The two networks implement distinct cognitive functions. We propose that the motor-related network encompassing pBA44 is recruited when processing movements requiring a mental representation of the action itself.

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Biomarkers of Pathological Dissociation: A Systematic Review

Roydeva

Reinders

2021

Neuroscience & Biobehavioral Reviews

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Interpretation of published meta-analytical studies affected by implementation errors in the GingerALE software

Cited by 4 publications

References 15 publications

Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta‐analysis

Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta‐analysis

The topographical organization of motor processing: An ALE meta-analysis on six action domains and the relevance of Broca’s region

Biomarkers of Pathological Dissociation: A Systematic Review

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