Valid conjunction inference with the minimum statistic

Nichols, Thomas E.; Brett, Matthew; Andersson, Jesper; Wager, Tor D.; Poline, Jean‐Baptiste

doi:10.1016/j.neuroimage.2004.12.005

Cited by 1,782 publications

(1,414 citation statements)

References 7 publications

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“…First, we identified brain regions modulated by subjective SoE in both low and high time pressure phases by applying a "logical AND" conjunction analysis (Nichols et al, 2005). Voxels that passed the multiple comparison correction in both the LowP and HighP phases were included in the conjunction map.…”

Section: Region-of-interest Analysis Of Objective Soementioning

confidence: 99%

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

et al. 2017

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A B S T R A C TReal-life decision-making often involves combining multiple probabilistic sources of information under finite time and cognitive resources. To mitigate these pressures, people "satisfice", foregoing a full evaluation of all available evidence to focus on a subset of cues that allow for fast and "good-enough" decisions. Although this form of decision-making likely mediates many of our everyday choices, very little is known about the way in which the neural encoding of cue information changes when we satisfice under time pressure. Here, we combined human functional magnetic resonance imaging (fMRI) with a probabilistic classification task to characterize neural substrates of multi-cue decision-making under low (1500 ms) and high (500 ms) time pressure. Using variational Bayesian inference, we analyzed participants' choices to track and quantify cue usage under each experimental condition, which was then applied to model the fMRI data. Under low time pressure, participants performed nearoptimally, appropriately integrating all available cues to guide choices. Both cortical (prefrontal and parietal cortex) and subcortical (hippocampal and striatal) regions encoded individual cue weights, and activity linearly tracked trial-by-trial variations in the amount of evidence and decision uncertainty. Under increased time pressure, participants adaptively shifted to using a satisficing strategy by discounting the least informative cue in their decision process. This strategic change in decision-making was associated with an increased involvement of the dopaminergic midbrain, striatum, thalamus, and cerebellum in representing and integrating cue values. We conclude that satisficing the probabilistic inference process under time pressure leads to a cortical-to-subcortical shift in the neural drivers of decisions.

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Section: Region-of-interest Analysis Of Objective Soementioning

confidence: 99%

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

et al. 2017

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“…We performed fixed effects conjunction analyses with separate subject predictors because established random effects conjunction analysis statistics may be subject to error associated with the minimum-t problem (Nichols et al, 2005), according to which areas may be indicated as significant in the conjunction but that are not significant in all single contrasts.…”

Section: Fmri Acquisitionmentioning

confidence: 99%

Neural correlates of a ‘pessimistic’ attitude when anticipating events of unknown emotional valence

et al. 2007

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Since we do not know what future holds for us, we prepare for expected emotional events in order to deal with a pleasant or threatening environment. From an evolutionary perspective, it makes sense to be particularly prepared for the worst-case scenario. We were interested to evaluate whether this assumption is reflected in the central nervous information processing associated with expecting visual stimuli of unknown emotional valence. While being scanned with functional magnetic resonance imaging, healthy subjects were cued to expect and then perceive visual stimuli with a known emotional valence as pleasant, unpleasant, and neutral, as well as stimuli of unknown valence that could have been either pleasant or unpleasant. While anticipating pictures of unknown valence, the activity of emotion processing brain areas was similar to activity associated with expecting unpleasant pictures, but there were no areas in which the activity was similar to the activity when expecting pleasant pictures. The activity of the revealed regions, including bilateral insula, right inferior frontal gyrus, medial thalamus, and red nucleus, further correlated with the individual ratings of mood: the worse the mood, the higher the activity. These areas are supposedly involved in a network for internal adaptation and preparation processes in order to act according to potential or certain unpleasant events. Their activity appears to reflect a 'pessimistic' bias by anticipating the events of unknown valence to be unpleasant.

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“…This is a non-parametric equivalent of the conjunction method mentioned in the Introduction (Nichols et al, 2005). This combining function identifies the overlap of areas S 1 (v) > w and S 2 (v) > w, for an arbitrary threshold value w (Fig.…”

Section: A1 Changes In the Same Directionmentioning

confidence: 99%

“…Another approach in multi-modal coanalysis is the conjunction method, which identifies areas where significant changes overlap in multiple t statistic maps. This method, widely used in functional neuroimaging analyses Nichols et al, 2005), was extended to multiple imaging modalities to identify conjunction (Pell et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

A non-parametric approach for co-analysis of multi-modal brain imaging data: Application to Alzheimer's disease

Hayasaka

Duarte

et al. 2006

NeuroImage

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We developed a new flexible approach for a co-analysis of multimodal brain imaging data using a non-parametric framework. In this approach, results from separate analyses on different modalities are combined using a combining function and assessed with a permutation test. This approach identifies several cross-modality relationships, such as concordance and dissociation, without explicitly modeling the correlation between modalities. We applied our approach to structural and perfusion MRI data from an Alzheimer's disease (AD) study. Our approach identified areas of concordance, where both gray matter (GM) density and perfusion decreased together, and areas of dissociation, where GM density and perfusion did not decrease together. In conclusion, these results demonstrate the utility of this new non-parametric method to quantitatively assess the relationships between multiple modalities. D

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Valid conjunction inference with the minimum statistic

Cited by 1,782 publications

References 7 publications

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration

Neural correlates of a ‘pessimistic’ attitude when anticipating events of unknown emotional valence

A non-parametric approach for co-analysis of multi-modal brain imaging data: Application to Alzheimer's disease

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