No abstract

Overall, this study suggests that, unlike many other illicit drugs, severity of use and problems associated with use were not elevated in METH-dependent men compared to women. In fact, several factors indicated more severe patterns of use or risk factors in women.

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.

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.

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.