![]() ![]() Studies of the neural code to date have primarily focused on univariate or multivariate neural patterns 2, or (more recently) on patterns of dynamic first-order correlations (i.e., interactions between pairs of brain structures 11, 12, 18, 20, 21, 22). Graph measures characterize each unit’s participation in its associated network. Dimensionality reduction algorithms project the patterns onto lower-dimensional spaces whose dimensions reflect weighted combinations or nonlinear transformations of the dimensions in the original space. To efficiently compute with complex neural patterns, it can be useful to characterize the patterns using summary measures. Each of these patterns may be static (e.g., averaging over time) or dynamic. Order 0 patterns involve individual nodes order 1 patterns involve node-node interactions order 2 (and higher) patterns relate to interactions between homologous networks. Univariate analyses characterize the activities of individual units (e.g., nodes, small networks, hierarchies of networks, etc.), whereas multivariate analyses characterize the patterns of activity across units. Within-brain analyses are carried out within a single brain, whereas across-brain analyses compare neural patterns across two or more individuals' brains. ![]() An emerging theme in this literature is that cognition is mediated by dynamic interactions between brain structures 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25.Ī A space of neural features. For example, prior work has used region of interest analyses to estimate the anatomical locations of specific neural representations 10, or to compare the relative contributions to the neural code of multivariate activity patterns versus dynamic correlations between neural activity patterns 11, 12. 1a) can also help to elucidate which specific aspects of neural activity patterns are informative about cognition and, by extension, which types of neural activity patterns might compose the neural code. Training decoding models on different types of neural features (Fig. One means of testing models of the neural code is to ask how accurately that model is able to “translate” neural activity patterns into known (or hypothesized) mental states or cognitive representations 1, 2, 3, 4, 5, 6, 7, 8, 9. ![]() ![]() We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.Ī central goal in cognitive neuroscience is to elucidate the neural code: i.e., the mapping between (a) mental states or cognitive representations and (b) neural activity patterns. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain’s functional connectome that display homologous lower-level dynamic correlations. Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. ![]()
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