Demidenko, M. I. | Search bioRxiv

bioRxiv2024

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

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, 119) 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 .43 - .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.

neuroscience↗

bioRxiv2022

Auditory cortex encodes lipreading information through spatially distributed activity

Watching a speakers face improves speech perception accuracy. These benefits are owed, in part, to implicit lipreading abilities present in the general population. While it is established that lipreading can alter the perception of a heard word, it is unknown how information that is extracted from lipread words is transformed into a neural code that the auditory system can use. One influential, but untested, hypothesis is that visual speech modulates the population coded representations of phonetic and phonemic features in the auditory system. This model is largely supported by data showing that silent lipreading evokes activity in auditory cortex, but these activations could alternatively reflect general effects of arousal or attention, or the encoding of non-linguistic features such as visual timing information. This gap limits our understanding of how vision supports speech perception processes. To test the hypothesis that the auditory system encodes visual speech information, we acquired fMRI data from healthy adults and intracranial recordings from electrodes implanted in patients with epilepsy during auditory and visual speech perception tasks. Across both methods, linear classifiers successfully decoded the identity of silently lipread words using the spatial pattern of auditory cortex responses. Examining the time-course of classification using intracranial recordings, lipread words were classified at significantly earlier time-points relative to heard words, suggesting a predictive mechanism for facilitating speech. These results support a model in which the auditory system combines the joint neural distributions evoked by heard and lipread words to generate a more precise estimate of what was said. Significance StatementWhen we listen to someone speak in a noisy environment, watching their face can help us understand them better, largely due to automatic lipreading abilities. However, it unknown how lipreading information is transformed into a neural code that the auditory system can use. We used fMRI and intracranial recordings in patients to study how the brain processes silently lipread words and found that the auditory system encodes the identity of lipread words through spatially distributed activity. These results suggest that the auditory system combines information from both lipreading and hearing to generate more precise estimates of what is said, potentially by both activating the corresponding representation of the heard word and suppressing incorrect phonemic representations.

neuroscience↗