The central autonomic network (CAN) has been described in animal models but has been difficult to elucidate in humans. Potential confounds include physiological noise artifacts affecting brainstem neuroimaging data, and difficulty in deriving non-invasive continuous assessments of autonomic modulation. We have developed and implemented a new method which relates cardiac-gated fMRI timeseries with continuous-time heart rate variability (HRV) to estimate central autonomic processing. As many autonomic structures of interest are in brain regions strongly affected by cardiogenic pulsatility, we chose to cardiac-gate our fMRI acquisition to increase sensitivity. Cardiac-gating introduces T1-variability, which was corrected by transforming fMRI data to a fixed TR using a previously published method (Guimaraes et al. 1998). The electrocardiogram was analyzed with a novel point process adaptive-filter algorithm for computation of the high-frequency (HF) index, reflecting the time-varying dynamics of efferent cardiovagal modulation. Central command of cardiovagal outflow was inferred by using the HF timeseries resampled at as a regressor to the fMRI data. A grip task was used to perturb the autonomic nervous system. Our combined HRV-fMRI approach demonstrated HF correlation with fMRI activity in the hypothalamus, cerebellum, parabrachial nucleus/locus ceruleus, periaqueductal gray, amygdala, hippocampus, thalamus, and dorsomedial/dorsolateral prefrontal, posterior insular, and middle temporal cortices. While some regions consistent with central cardiovagal control in animal models gave corroborative evidence for our methodology, other mostly higher cortical or limbic-related brain regions may be unique to humans. Our approach should be optimized and applied to study the human brain correlates of autonomic modulation for various stimuli in both physiological and pathological states.

Highlights d Senescent cancer cells respond differently to senolytic ABT-263 d SASP expression in cancer is heterogeneous and influenced by cell origin d The SENCAN classifier detects cancer cell senescence in vitro d The Cancer SENESCopedia contains transcriptome data from 37 senescence models

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Designing specific therapies for drug-resistant cancers is arguably the ultimate challenge in cancer therapy. While much emphasis has been put on the study of genetic alterations that give rise to drug resistance, much less is known about the non-genetic adaptation mechanisms that operate during the early stages of drug resistance development. Drug-tolerant persister cells have been suggested to be key players in this process. These cells are thought to have undergone non-genetic adaptations that enable survival in the presence of a drug, from which full-blown resistant cells may emerge. Such initial adaptations often involve engagement of stress response programs to maintain cancer cell viability. In this review, we discuss the nature of drug-tolerant cancer phenotypes, as well as the non-genetic adaptations involved. We also discuss how malignant cells employ homeostatic stress response pathways to mitigate the intrinsic costs of such adaptations. Lastly, we discuss which vulnerabilities are introduced by these adaptations and how these might be exploited therapeutically.