Arab, F. | Search bioRxiv

bioRxiv2024

Whole-brain causal connectivity during decoded neurofeedback: a meta study

Decoded Neurofeedback (DecNef) enables modulation of targeted brain activity patterns without subjective awareness through multivariate pattern analysis, reinforcement learning, and real-time fMRI feedback. Despite its proven effectiveness, the causal mechanisms underlying DecNef and the neural dynamics that distinguish successful learners from those who struggle remain poorly understood. We conducted a meta-study using fMRI data from five DecNef experiments and extracted causal network dynamics as well as their associations with performance differences. Across studies, we found that connectivity within a posterior control hub-consisting of posterior cingulate, precuneus, and lateral posterior parietal cortices-is stronger during DecNef and positively correlates with neurofeedback success. Comparisons across cognition- and perception-targeted DecNef revealed separation in connections to somatomotor network, where connections between somatomotor and control-default-attention networks are larger during cognitive neurofeedback while connections between somatomotor and subcortical-visual-limbic networks are larger during perceptive DecNef. Whole-brain causal connectivity during DecNef further exhibited distinct network reorganizations, with greater subject-to-subject variability, increased engagement of control, limbic and visual, and decreased engagement of ventral and dorsal attention networks. Our results distill the complex and distributed network mechanisms underlying DecNef into dissociable roles for well-known functional subnetworks, thus advancing the research and clinical applications of decoded neurofeedback.

neuroscience↗

bioRxiv2023

Whole-Brain Causal Discovery Using fMRI

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger Causality and Dynamic Causal Modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested alternatives. From resting-state human fMRI, CaLLTiF learns causal connectomes that are highly consistent across individuals, show clear top-down flow of causal effect from attention and default mode to sensorimotor networks, exhibit Euclidean distance-dependence in causal interactions, and are highly dominated by contemporaneous effects. Overall, this work takes a major step in enhancing causal discovery from whole-brain fMRI and defines a new standard for future investigations. AUTHOR SUMMARYDiscovering causal relationships from fMRI data is challenging due to contemporaneous effects and latent causes. Popular methods like Granger Causality and Dynamic Causal Modeling struggle with these issues, especially in large networks. To address this, we introduce CaLLTiF, a scalable method that uses both lagged and contemporaneous variables to identify causal relationships. CaLLTiF outperforms various existing techniques in accuracy and scalability on simulated fMRI data. When applied to human resting-state fMRI, it reveals consistent and biologically-plausible patterns across individuals, with a clear top-down causal flow from attention and default mode networks to sensorimotor areas. Overall, this work advances the field of causal discovery in large-scale fMRI studies.

neuroscience↗