Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or “virtual brains”, whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs) and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic), in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states display lower complexity than virtual brains modeling normal neural function. We finally discuss the implications of our results for the neurobiology of health and disease.

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The proceedings of the 3rd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, imaging, and computational work on DBS for the treatment of neurological and neuropsychiatric disease. Significant innovations of the past year are emphasized. The Think Tank's contributors represent a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers, and members of industry. Presentations and discussions covered a broad range of topics, including policy and advocacy considerations for the future of DBS, connectomic approaches to DBS targeting, developments in electrophysiology and related strides toward responsive DBS systems, and recent developments in sensor and device technologies.

The proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies.

Objective The Shannon model is often used to define an expected boundary between non-damaging and damaging modes of electrical neurostimulation. Numerous preclinical studies have been performed by manufacturers of neuromodulation devices using different animal models and a broad range of stimulation parameters while developing devices for clinical use. These studies are mostly absent from peer-reviewed literature, which may lead to this information being overlooked by the scientific community. We aimed to locate summaries of these studies accessible via public regulatory databases and to add them to a body of knowledge available to a broad scientific community. Methods We employed web search terms describing device type, intended use, neural target, therapeutic application, company name, and submission number to identify summaries for premarket approval (PMA) devices and 510(k) devices. We filtered these records to a subset of entries that have sufficient technical information relevant to safety of neurostimulation. Results We identified 13 product codes for 8 types of neuromodulation devices. These led us to devices that have 22 PMAs and 154 510(k)s and six transcripts of public panel meetings. We found one PMA for a brain, peripheral nerve, and spinal cord stimulator and five 510(k) spinal cord stimulators with enough information to plot in Shannon coordinates of charge and charge density per phase. Conclusions Analysis of relevant entries from public regulatory databases reveals use of pig, sheep, monkey, dog, and goat animal models with deep brain, peripheral nerve, muscle and spinal cord electrode placement with a variety of stimulation durations (hours to years); frequencies (10–10,000 Hz) and magnitudes (Shannon k from below zero to 4.47). Data from located entries indicate that a feline cortical model that employs acute stimulation might have limitations for assessing tissue damage in diverse anatomical locations, particularly for peripheral nerve and spinal cord simulation.