Identifying Predictive Regions from fMRI with TV-L1 Prior

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Frank-Wolfe Splitting via Augmented Lagrangian Method (2018, background): “, 2012) or find a set of brain maps which are both sparse and piecewise constant by constraining both the `1 and total variation pseudonorm (Gramfort et al., 2013).”
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models (2018, unknown): “Over the recent years, the whole-brain lasso, as well as closely re-lated decoding approaches (e.g., [Ryali et al., 2010, Gramfort et al., 2013, McIntosh and Lobaugh, 2004]), have found widespread application throughout the neuroscience literature (e.g., [Chang et al., 2015, Wager et al., 2013]).”
Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer's Disease (2015, methodology): “Besides the metabolic prior, we also investigate the usefulness of a spatial prior called the total-variation TV-`1 prior proposed in [1].”
Assessing and tuning brain decoders: Cross‐validation, caveats, and guidelines (2016, background): “8Similar decoders adding a regularization that captures spatiotemporal correlations among the voxels are well suited for neuroimaging [15, 16, 25, 36].”