Cancer-related fatigue is a prominent and debilitating side effect of cancer and its treatment. It can develop prior to diagnosis, generally peaks during cancer treatment, and can persist long after treatment completion. Its mechanisms are multifactorial, and its expression is highly variable. Unfortunately, treatment options are limited. Our research uses syngeneic murine models of cancer and cisplatin-based chemotherapy to better understand these mechanisms. Our data indicate that both peripherally and centrally processes may contribute to the developmental of fatigue. These processes include metabolic alterations, mitochondrial dysfunction, pre-cachexia, and inflammation. However, our data has revealed that behavioral fatigue can persist even after the toxicity associated with cancer and its treatment recover. For example, running during cancer treatment attenuates kidney toxicity while also delaying recovery from fatigue-like behavior. Additionally, administration of anesthetics known to disrupt memory consolidation at the time treatment can promote recovery, and treatment-related cues can re-instate fatigue after recovery. Cancer-related fatigue can also promote habitual behavioral patterns, as observed using a devaluation task. We interpret this data to suggest that limit metabolic resources during cancer promote the utilization of habit-based behavioral strategies that serve to maintain fatigue behavior into survivorship. This line of work is exciting as it points us toward novel interventional targets for the treatment of persistent cancer-related fatigue.

Modeling brain mechanisms is often confined to a given scale, such as single-cell models, network models or whole-brain models, and it is often difficult to relate these models. Here, we show an approach to build models across scales, starting from the level of circuits to the whole brain. The key is the design of accurate population models derived from biophysical models of networks of excitatory and inhibitory neurons, using mean-field techniques. Such population models can be later integrated as units in large-scale networks defining entire brain areas or the whole brain. We illustrate this approach by the simulation of asynchronous and slow-wave states, from circuits to the whole brain. At the mesoscale (millimeters), these models account for travelling activity waves in cortex, and at the macroscale (centimeters), the models reproduce the synchrony of slow waves and their responsiveness to external stimuli. This approach can also be used to evaluate the impact of sub-cellular parameters, such as receptor types or membrane conductances, on the emergent behavior at the whole-brain level. This is illustrated with simulations of the effect of anesthetics. The program codes are open source and run in open-access platforms (such as EBRAINS).

Local anesthetics decrease the excitability of all neurons by blocking voltage-gated sodium channels non-selectively. We have developed a technology to silence only those sensory neurons – the nociceptors – that trigger pain, itch, and cough. I will tell you why and how we devised the strategy, the way we showed that it works, and will also discuss its implications for treating multiple human disorders.

Local anesthetics decrease the excitability of all neurons by blocking voltage-gated sodium channels non-selectively. We have developed a technology to silence only those sensory neurons – the nociceptors – that trigger pain, itch, and cough. I will tell you why and how we devised the strategy, the way we showed that it works, and will also discuss its implications for treating multiple human disorders.

General anesthesia is a drug-induced, reversible condition comprised of five behavioral states: unconsciousness, amnesia (loss of memory), antinociception (loss of pain sensation), akinesia (immobility), and hemodynamic stability with control of the stress response. Our work shows that a primary mechanism through which anesthetics create these altered states of arousal is by initiating and maintaining highly structured oscillations. These oscillations impair communication among brain regions. We illustrate this effect by presenting findings from our human studies of general anesthesia using high-density EEG recordings and intracranial recordings. These studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness due to propofol. We show how these dynamics change systematically with different anesthetic classes and with age. As a consequence, we have developed a principled, neuroscience-based paradigm for using the EEG to monitor the brain states of patients receiving general anesthesia. We demonstrate that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. Finally, we demonstrate that the state of general anesthesia can be controlled using closed loop feedback control systems. The success of our research has depended critically on tight coupling of experiments, signal processing research and mathematical modeling.