Objective. In ischemic stroke, treatments to protect neurons from irreversible damage are urgently needed. Studies in animal models have shown that neuroprotective treatments targeting neuronal silencing improve brain recovery, but in clinical trials none of these were effective in patients. This failure of translation poses doubts on the real efficacy of treatments tested and on the validity of animal models for human stroke. Here, we established a human neuronal model of the ischemic penumbra by using human induced pluripotent stem cells and we provided an in-depth characterization of neuronal responses to hypoxia and treatment strategies at the network level. Approach. We generated neurons from induced pluripotent stem cells derived from healthy donor and we cultured them on micro-electrode arrays. We measured the electrophysiological activity of human neuronal networks under controlled hypoxic conditions. We tested the effect of different treatment strategies on neuronal network functionality. Main results. Human neuronal networks are vulnerable to hypoxia reflected by a decrease in activity and synchronicity under low oxygen conditions. We observe that full, partial or absent recovery depend on the timing of re-oxygenation and we provide a critical time threshold that, if crossed, is associated with irreversible impairments. We found that hypoxic preconditioning improves resistance to a second hypoxic insult. Finally, in contrast to previously tested, ineffective treatments, we show that stimulatory treatments counteracting neuronal silencing during hypoxia, such as optogenetic stimulation, are neuroprotective. Significance. We presented a human neuronal model of the ischemic penumbra and we provided insights that may offer the basis for novel therapeutic approaches for patients after stroke. The use of human neurons might improve drug discovery and translation of findings to patients and might open new perspectives for personalized investigations.

PurposeArterial spin labeling (ASL) acquisitions at multiple post‐labeling delays may provide more accurate quantification of cerebral blood flow (CBF), by fitting appropriate kinetic models and simultaneously estimating relevant parameters such as the arterial transit time (ATT) and arterial cerebral blood volume (aCBV). We evaluate the effects of denoising strategies on model fitting and parameter estimation when accounting for the dispersion of the label bolus through the vasculature in cerebrovascular disease.MethodsWe analyzed multi‐delay ASL data from 17 cerebral small vessel disease patients (50 ± 9 y) and 13 healthy controls (52 ± 8 y), by fitting an extended kinetic model with or without bolus dispersion. We considered two denoising strategies: removal of structured noise sources by independent component analysis (ICA) of the control‐label image timeseries; and averaging the repetitions of the control‐label images prior to model fitting.ResultsModeling bolus dispersion improved estimation precision and impacted parameter values, but these effects strongly depended on whether repetitions were averaged before model fitting. In general, repetition averaging improved model fitting but adversely affected parameter values, particularly CBF and aCBV near arterial locations in patients. This suggests that using all repetitions allows better noise estimation at the earlier delays. In contrast, ICA denoising improved model fitting and estimation precision while leaving parameter values unaffected.ConclusionOur results support the use of ICA denoising to improve model fitting to multi‐delay ASL and suggest that using all control‐label repetitions improves the estimation of macrovascular signal contributions and hence perfusion quantification near arterial locations. This is important when modeling flow dispersion in cerebrovascular pathology.