More Like this

1. Advances in fMRI Real-Time Neurofeedback

   https://doi.org/10.1016/j.tics.2018.05.007  

   Watanabe, Takeo; Sasaki, Yuka; Shibata, Kazuhisa; Kawato, Mitsuo (August 2018, Trends in Cognitive Sciences)
2. Connectivity analyses for task-based fMRI

   https://doi.org/10.1016/j.plrev.2024.04.012  

   Huang, Shenyang; De_Brigard, Felipe; Cabeza, Roberto; Davis, Simon W (July 2024, Physics of Life Reviews)
3. Advances in fMRI Real-Time Neurofeedback

   https://doi.org/10.1016/j.tics.2017.09.010  

   Watanabe, Takeo; Sasaki, Yuka; Shibata, Kazuhisa; Kawato, Mitsuo (December 2017, Trends in Cognitive Sciences)
4. Multivariate Spatial Feature Selection in fMRI

   https://doi.org/10.1093/scan/nsab010  

   Jolly, E; Chang, L J (January 2021, Social Cognitive and Affective Neuroscience)

   null (Ed.)

   Abstract Multivariate neuroimaging analyses constitute a powerful class of techniques to identify psychological representations. However, not all psychological processes are represented at the same spatial scale throughout the brain. This heterogeneity is apparent when comparing hierarchically organized local representations of perceptual processes to flexible transmodal representations of more abstract cognitive processes such as social and affective operations. An open question is how the spatial scale of analytic approaches interacts with the spatial scale of the representations under investigation. In this article, we describe how multivariate analyses can be viewed as existing on a spatial spectrum, anchored by searchlights used to identify locally distributed patterns of information on one end, whole brain approach used to identify diffuse neural representations at the other, and region-based approaches in between. We describe how these distinctions are an important and often overlooked analytic consideration and provide heuristics to compare these different techniques to choose based on the analyst’s inferential goals. 

   more » « less
5. Whole-brain causal discovery using fMRI

   https://doi.org/10.1162/netn_a_00438  

   Arab, Fahimeh; Ghassami, AmirEmad; Jamalabadi, Hamidreza; Peters, Megan_A_K; Nozari, Erfan (March 2025, Network Neuroscience)

   Abstract 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. 

   more » « less