A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents

Demidenko, Michael I.; Mumford, Jeanette A.; Ram, Nilam; Poldrack, Russell A.

doi:10.1016/j.dcn.2023.101337

Cited by 3 publications

(2 citation statements)

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“…This suggests that across each of the three samples, the MID task is relatively effective at eliciting a group-level activation map; however, the individual estimates are lower and more variable. In the context of the MID task, the between-run and between-session effects may be the result of within-session effects decreasing across runs (Demidenko, Mumford, et al, 2024). Notably, the higher between-session than between-run reliabilities is inconsistent with values reported in previous work (Fröhner et al, 2019), this is likely the result of those between-run estimates being based on randomly split-half (within runs) which are inflated as a result of dependencies in the model estimates within runs (Mumford et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…The analytic decisions at the GLM modeling phase demonstrated improvements in reliability from between-run to between-session. Higher between-session reliability may be related to decreasing activity from early to later runs (Demidenko, Mumford, et al, 2024) or based on the sessions being an average of two runs/increased trials (Han et al, 2022;Ooi et al, 2024). In the current analyses, we focused on univariate maps and the parametric, voxelwise ICC estimation procedures (ICC [3,1]).…”

Section: Discussionmentioning

confidence: 99%

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Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task

Demidenko,

Mumford,

Poldrack

2024

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Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 120) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .44 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization, however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.

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Section: Discussionmentioning

confidence: 99%