Preclinical and clinical studies show that mild to moderate hypothermia is neuroprotective in sudden cardiac arrest, ischemic stroke, perinatal hypoxia/ischemia, traumatic brain injury, and seizures. Induction of hypothermia largely involves physical cooling therapies, which induce several clinical complications, while some molecules have shown to be efficient in pharmacologically induced hypothermia (PIH). Neurotensin (NT), a 13 amino acid neuropeptide that regulates body temperature, interacts with various receptors to mediate its peripheral and central effects. NT induces PIH when administered intracerebrally. However, these effects are not observed if NT is administered peripherally, due to its rapid degradation and poor passage of the blood-brain barrier (BBB). We conjugated NT to peptides that bind the low-density lipoprotein receptor (LDLR) to generate ‘vectorized’ forms of NT with enhanced BBB permeability. We evaluated their effects in epileptic conditions following peripheral administration. One of these conjugates, VH-N412, displayed improved stability, binding potential to both the LDLR and NTSR-1, rodent/human cross-reactivity and improved brain distribution. In a mouse model of kainate (KA)-induced status epilepticus (SE), VH-N412 elicited rapid hypothermia associated with anticonvulsant effects, potent neuroprotection, and reduced hippocampal inflammation. VH-N412 also reduced sprouting of the dentate gyrus mossy fibers and preserved learning and memory skills in the treated mice. In cultured hippocampal neurons, VH-N412 displayed temperature-independent neuroprotective properties. To the best of our knowledge, this is the first report describing the successful treatment of SE with PIH. In all, our results show that vectorized NT may elicit different neuroprotection mechanisms mediated by hypothermia and/or by intrinsic neuroprotective properties.

Reward-rate maximization is a prominent normative principle in behavioral ecology, neuroscience, economics, and AI. Here, we identify, compare, and analyze equations to maximize reward rate when assessing whether to initiate a pursuit. In deriving expressions for the value of a pursuit, we show that time’s cost consists of both apportionment and opportunity cost. Reformulating value as a discounting function, we show precisely how a reward-rate-optimal agent’s discounting function (1) combines hyperbolic and linear components reflecting apportionment and opportunity costs, and (2) is dependent not only on the considered pursuit’s properties but also on time spent and rewards obtained outside the pursuit. This analysis reveals how purported signs of suboptimal behavior (hyperbolic discounting, and the Delay, Magnitude, and Sign effects) are in fact consistent with reward-rate maximization. To better account for observed decision-making errors in humans and animals, we then analyze the impact of misestimating reward-rate-maximizing parameters and find that suboptimal decisions likely stem from errors in assessing time’s apportionment—specifically, underweighting time spent outside versus inside a pursuit—which we term the ‘Malapportionment Hypothesis’. This understanding of the true pattern of temporal decision-making errors is essential to deducing the learning algorithms and representational architectures actually used by humans and animals.