Reliability of dissimilarity measures for multi-voxel pattern analysis

By Alexander Walther, Hamed Nili, N. Ejaz, Arjen Alink, Nikolaus Kriegeskorte, Jörn Diedrichsen. NeuroImage, 2016

524 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4c85935bc541ad162ff7d92d199f1f354e85bbe7

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In Cognitive Neuroscience

Citations in the corpus

On the distribution of cross-validated Mahalanobis distances (2016, background): “This range of regularization constants is recommended, as it also maximizes the reliability of the resultant distance measures [25].”
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data (2016, background): “, correlation between the activation patterns, or difference in classifier performance (Walther et al., 2015).”
Strong Evidence for Pattern Separation in Human Dentate Gyrus (2016, methodology): “…from our multivoxel patterns before our MVPA because recent studies have highlighted the negative effects of mean pattern removal (e.g., inducing negative correlations; Garrido et al., 2013; Walther et al., 2015), but also argue against the removal of the mean value (Davis and Poldrack, 2013).”
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis (2017, methodology): “If multivariate noisenormalization [19] was completely successful, then this would be the identity matrix.”
Deconstructing multivariate decoding for the study of brain function (2017, methodology): “CV-MANOVA is intimately related to the popular cross-validated Mahalanobis (crossnobis) distance estimate that is based on the linear discriminant (Walther et al., 2016).”
Neural Coding of Cognitive Control: The Representational Similarity Analysis Approach. (2020, background): “Unbiased similarity measures Type of similarity measure for which the expected value is not impacted by measurement error + Useful for unbalanced designs – Increased variance – Each condition must appear in >1 run + Also robust to 'design bias' issues Techniques: [13,96,97,124] Unbalanced design: e.”
Hierarchical Reconfiguration of Neurocognitive Task Set Representations Mediates Cognitive Flexibility (2026, unknown): “Within each searchlight sphere, signals were first “whitened” by applying the noise covariance matrix (equation 1) estimated across all trials and voxels in that sphere to remove shared noise correlations (equation 2) before measuring pattern shifts (Walther et al., 2016).”
Novel Verbal Instructions Recruit Abstract Neural Patterns of Time-Variable Information Dimensionality (2025, result): “This cross-validated approach diminishes the risk of bias in the results (Grootswagers et al., 2018; Walther et al., 2016).”