Enhancing Precision in Human Neuroscience

Nebe, Stephan; Reutter, Mario; Baker, Daniel H.; Bölte, Jens; Domes, Gregor; Gamer, Matthias; Gärtner, Anne; Gießing, Carsten; Mann, Caroline Gurr née; Hilger, Kirsten; Jawinski, Philippe; Kulke, Louisa; Lischke, Alexander; Markett, Sebastian; Meier, Maria; Merz, Christian J.; Popov, Tzvetan; Puhlmann, Lara; Quintana, Daniel; Schäfer, Tim; Schubert, Anna‐Lena; Sperl, Matthias F.J.; Vehlen, Antonia; Lonsdorf, Tina B.; Feld, Gordon B.

doi:10.31234/osf.io/m8c4k

Cited by 5 publications

(4 citation statements)

References 247 publications

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“…One would assume that all these factors lead to reduced correlations between the first and second half of a data set, so that our results may underestimate the true reliability of the investigated measures. Better estimates of split-half reliability may involve trial-based splits of the data (such as odd-even), which are also discussed in more detail elsewhere (see Nebe et al, 2023;Pronk et al, 2022). However, here the second factor comes into play, that is, that the analyzed measures of variability and complexity require a minimum number of time frames to be reliably…”

Section: Discussionmentioning

confidence: 99%

Reliability of variability and complexity measures for task and task‐free BOLD fMRI

Wehrheim,

Faskowitz,

Schubert

et al. 2024

Human Brain Mapping

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Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between‐person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures—which is an important precondition for robust individual differences as well as longitudinal research—is not yet sufficiently studied. We examined reliability (split‐half correlations) and test–retest correlations for task‐free (resting‐state) BOLD fMRI as well as split‐half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split‐half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test–retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time‐resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region‐specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well‐suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function.Practitioner Points Variability and complexity measures of BOLD activation show good split‐half reliability and moderate test–retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

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Section: Discussionmentioning

confidence: 99%

Reliability of variability and complexity measures for task and task‐free BOLD fMRI

Wehrheim,

Faskowitz,

Schubert

et al. 2024

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…One would assume that all these factors lead to reduced correlations between the first and second half of a dataset, so that our results may underestimate the true reliability of the investigated measures. Better estimates of split-half reliability may involve trial-based splits of the data (such as odd-even), which are also discussed in more detail elsewhere (see Nebe et al, 2023;Pronk et al, 2022). However, here the second factor comes into play, i.e., that the analyzed measures of variability and complexity require a minimum number of time frames to be reliably estimated, which are not typically reached by single trials of event-related functional activation experiments (often well under 10 seconds in length).…”

Section: Discussionmentioning

confidence: 99%

Reliability of Variability and Complexity Measures for Task and Task-Free BOLD fMRI

Wehrheim,

Faskowitz,

Schubert

et al. 2023

Preprint

View full text Add to dashboard Cite

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, particularly also with respect to between-person differences in brain function. For example, temporal variability and complexity have been used to study changes in brain function over the course of development or between clinical populations and healthy control groups. However, the psychometric reliability of brain signal variability and complexity measures - which is an important precondition for robust individual differences research - is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting state) BOLD fMRI as well as split-half correlations for seven functional task datasets from the Human Connectome Project to evaluate their reliability. We observed good to excellent reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and good test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (fuzzy entropy, as well as three measures of dimensionality) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series, and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Lastly, we also show that reliability scores are not dependent on differences in scan length. These results suggest that variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function.

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“…Electrodermal activity was recorded with the Movisens EdaMove 4 sensor at a sampling rate of 32 Hz. We followed the guidelines by Nebe et al (2023) and participants' skin was prepared using lukewarm water (no soap, alcohol, or abrasion) and the electrodes were attached to the thenar and hypothenar eminences of the participants' non-dominant hand.…”

Section: Apparatusmentioning

confidence: 99%

Studying the Influence of Single Social Interactions on Approach and Avoidance Behavior – A Multimodal Investigation in Immersive Virtual Reality

Gado,

Gamer

2024

Preprint

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Adaptive social approach and avoidance behavior is of utmost importance for social functioning and an imbalance in these behavioral tendencies may constitute a risk factor for the etiology and maintenance of mental illness. To investigate these processes in a naturalistic environment with high ecological validity and experimental control, we implemented an immersive virtual environment that combines a social conditioning procedure with a social approach-avoidance test. In this environment, we conducted two experiments (both with N = 48 female participants; data collected in summer 2023 and winter 2023/24) to examine whether participants differing in trait social anxiety develop adaptive approach and avoidance tendencies towards differently conditioned virtual agents on a behavioral, subjective, and autonomic level. Overall, we observed significant effects of the social conditioning procedure on participants’ exploration behavior. After initial hypervigilance, participants exhibited avoidance of social threats as indicated by a higher interpersonal distance and decreased visual attention towards the negatively conditioned virtual agent. We found no association between hypervigilance and trait social anxiety, but observed higher fear ratings and enhanced avoidance of social threats in participants with elevated anxiety levels. This study demonstrates the potential of immersive virtual environments for examining social learning processes under conditions resembling real-life social encounters.

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Enhancing Precision in Human Neuroscience

Cited by 5 publications

References 247 publications

Reliability of variability and complexity measures for task and task‐free BOLD fMRI

Reliability of variability and complexity measures for task and task‐free BOLD fMRI

Reliability of Variability and Complexity Measures for Task and Task-Free BOLD fMRI

Studying the Influence of Single Social Interactions on Approach and Avoidance Behavior – A Multimodal Investigation in Immersive Virtual Reality

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