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.

No abstract

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.

5Our thoughts arise from coordinated patterns of interactions between brain structures that change with 6 our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different 7 subgraphs of the brain's connectome that display homologous lower-level dynamic correlations. We tested 8 the hypothesis that high-level cognition is supported by high-order dynamic correlations in brain activity 9 patterns. We developed an approach to estimating high-order dynamic correlations in timeseries data, and 10 we applied the approach to neuroimaging data collected as human participants either listened to a ten-11 minute story, listened to a temporally scrambled version of the story, or underwent a resting state scan. We 12 trained across-participant pattern classifiers to decode (in held-out data) when in the session each neural 13 activity snapshot was collected. We found that classifiers trained to decode from high-order dynamic 14 correlations yielded the best performance on data collected as participants listened to the (unscrambled) 15 story. By contrast, classifiers trained to decode data from scrambled versions of the story or during 16 the resting state scan yielded the best performance when they were trained using first-order dynamic 17 correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, 18 they are supported by higher-order patterns of dynamic network interactions throughout the brain.

Quantitative biomarkers of depression are critical for development of rational therapeutics, but limitations of current low-resolution, indirect brain assays may impede their discovery. We applied graph theory and machine learning to a large unique dataset of intracranial electrophysiological recordings to generate a four-dimensional whole-brain model of neural activity. Using this model, we found patterns of network activity that correctly classified depression in over 80% of individuals. These complex patterns were especially evident in alpha and beta spectral power across frontal and occipital brain regions, respectively. Our findings reveal a widespread network of abnormal activity that may inform advanced personalized treatment.

We applied dimensionality reduction algorithms and pattern classifiers to functional neuroimaging data collected as participants listened to a story, temporally scrambled versions of the story, or underwent a resting state scanning session. These experimental conditions were intended to require different depths of processing and inspire different levels of cognitive engagement. We considered two primary aspects of the data. First, we treated the maximum achievable decoding accuracy across participants as an indicator of the "informativeness" of the recorded patterns. Second, we treated the number of features (components) required to achieve a threshold decoding accuracy as a proxy for the "compressibility" of the neural patterns (where fewer components indicate greater compression). Overall, we found that the peak decoding accuracy (achievable without restricting the numbers of features) was highest in the intact (unscrambled) story listening condition. However, the number of features required to achieve comparable classification accuracy was also lowest in the intact story listening condition. Taken together, our work suggests that our brain networks flexibly reconfigure according to ongoing task demands, and that the activity patterns associated with higher-order cognition and high engagement are both more informative and more compressible than the activity patterns associated with lower-order tasks and lower levels of engagement.