Biology subjects

Giessing, C.

Publications and source records attributed to Giessing, C..

3 recordsLinked to original sources

bioRxiv2024

A dynamic functional connectivity toolbox for multiverse analysis

In network neuroscience, a broad range of methods for estimating dynamic functional connectivity from fMRI data and subsequent analyses using graph-theoretic approaches have been introduced in recent years. However, in the absence of ground truths regarding the validity of analytical steps in capturing true brain dynamics, researchers are often faced with a multitude of arbitrary, yet defensible, analytical choices, raising concerns regarding the robustness of results. Here, we aim to address this issue by implementing a comprehensive suite of dynamic functional connectivity methods in a unified Python software package, allowing for a diverse exploration of brain dynamics. Anchored in the framework of multiverse analysis, the present work introduces a workflow for systematically exploring different methodological choices. The developed toolbox includes a graphical user interface for ease of use and accessibility for those who wish to operate outside of a script-based pipeline. Comprehensive documentation and demo scripts are included to support adoption and usability. By promoting transparency and robustness, Comet aims to promote best practice in the study of brain dynamics.

neuroscience↗

bioRxiv2024

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

The large number of different analytical choices researchers use may be partly responsible for the replication challenge in neuroimaging studies. For robustness analysis, knowledge of the full space of options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable options. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the options within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) distinct sequencing of steps, and 3) parameter tuning within steps. To facilitate access to the data, we developed a decision support app with high educational value called METEOR, which allows researchers to explore the space of choices as reference for well-informed robustness (multiverse) analysis. HighlightsO_LIData analysis variability in neuroimaging hinders replicability. C_LIO_LIAnalysis across multiple defensible options examines the robustness of results. C_LIO_LIWe conducted a systematic literature review to identify analytical options. C_LIO_LIWe identified 61 steps and 102 options in performing graph-fMRI analysis. C_LIO_LIInteractive visualization of these steps and options is available as a Shiny app. C_LI

neuroscience↗

bioRxiv2022

Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation

Integration and segregation are two fundamental principles of brain organization. The brain manages the transitions and balance between different functional segregated or integrated states through neuromodulatory systems. Recently, computational and experimental studies suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology in healthy subjects during resting-state conditions and during an attentional task. Then, we employed a whole-brain neural mass model interconnected using a human connectome to simulate the effects of nicotine and investigate causal mechanisms for these changes. The drug effect was modeled decreasing both the global coupling and local feedback inhibition parameters, consistent with the known cellular effects of acetylcholine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during the task, but not in the resting state. In-task performance correlates with functional segregation, establishing a link between functional network topology and behavior. Furthermore, we found in the empirical data that the regional density of the nicotinic acetylcholine 4{beta}2 correlates with the decrease in functional nodal strength by nicotine during the task. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks.

neuroscience↗