Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain’s functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. 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. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. 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. 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 suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

Human Super EEG 1 entails measuring ongoing activity from every cell in a living human brain at millisecond-scale temporal resolutions. Although direct cell-by-cell Super EEG recordings are impossible using existing methods, here we present a technique for inferring neural activity at arbitrarily high spatial resolutions using human intracranial electrophysiological recordings. Our approach, based on Gaussian process regression, relies on two assumptions. First, we assume that some of the correlational structure of people's brain activity is similar across individuals. Second, we resolve ambiguities in the data by assuming that neural activity from nearby sources will tend to be similar, all else being equal. One can then ask, for an arbitrary individual's brain: given what we know about the correlational structure of other people's brains, and given the recordings we made from electrodes implanted in this person's brain, how would those recordings most likely have looked at other locations throughout this person's brain?

We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people’s brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual’s brain: given recordings from a limited set of locations in that individual’s brain, along with the observed spatial correlations learned from other people’s recordings, how much can be inferred about ongoing activity at other locations throughout that individual’s brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.

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