Histamine boost helps the brain remember, decide, and learn from loss

A human trial shows that increasing histaminergic signaling may sharpen memory retrieval, support adaptive decision-making, and stabilize learning from aversive experiences, revealing a neglected neurotransmitter’s role in cognition.

Study: Histamine shapes the neurocomputational dynamics of human learning. Image Credit: Juan Gaertner / Shutterstock

Brain Histamine and Memory Formation

How does your brain make decisions regarding which experiences should be remembered? The neurotransmitter histamine plays a significant role in memory and learning. This is surprising because, while histamine was the first monoamine discovered in the mammalian brain, less is known about its function than that of dopamine or serotonin.

As shown in studies of animal memory, activation of the histaminergic system enhances memory, promotes attentional function, and modulates the retention of fear-based memories, but the available evidence regarding the role of histamine in human memory is limited.

Understanding its effects is increasingly important because drugs targeting histamine signaling are being explored for cognitive disorders and psychiatric conditions. Further research is needed to clarify its mechanisms in human cognition.

Pitolisant Memory Study Design

In the present randomized, double-blind, placebo-controlled study, researchers studied 58 healthy participants who received either a single 36-milligram dose of pitolisant hydrochloride, an inverse agonist of the histamine H3 receptor, or a placebo.

All volunteers were screened to exclude neurological, psychiatric, and medical conditions that could affect the results. Testing began approximately three hours after drug administration, when pitolisant reached high brain receptor occupancy.

Participants completed several behavioral and neuroimaging tasks. A multi-stage memory paradigm assessed image encoding, post-learning resting-state functional Magnetic Resonance Imaging (fMRI), additional encoding of familiar and novel images, and a recognition memory test.

fMRI data were analyzed to examine activity within temporal-hippocampal networks associated with memory processing. The researchers used computational modeling techniques, such as Drift Diffusion Models (DDMs), to analyze the accumulation of evidence and decision-making during memory retrieval.

Working memory performance was assessed by participants' performance on a verbal n-back task with increasing levels of difficulty. Reinforcement learning was assessed using a probabilistic instrumental learning task in which participants learned to maximize gains and avoid losses.

Computational Q-learning models estimated learning rates and decision parameters. In addition, other analyses examined blood perfusion, subjective feelings about mood, alertness, side effects, and blinding integrity to ensure that observed effects were not explained by nonspecific factors.

Histamine Enhances Memory Networks

Pharmacological elevation of histaminergic signaling by pitolisant significantly modulated learning- and memory-related brain networks.

During the resting period that followed initial learning, machine-learning analyses distinguished participants receiving pitolisant from those receiving a placebo with 88.5% accuracy. These differences were associated with enhanced connectivity between the hippocampus and the mammillary zone, including the mammillary bodies and the tuberomammillary nucleus, regions closely linked to memory and histamine signaling. These findings suggested that histamine modifies offline brain activity that supports memory consolidation.

During subsequent learning of new images, participants receiving pitolisant showed greater activation in bilateral hippocampal subregions, the basal forebrain, entorhinal cortex, and perirhinal cortex. Additionally, there was prolonged persistence of activity in the left medial entorhinal cortex following the learning of new images.

Prolonged persistence of neural activity after an acquisition event is thought to support consolidation, as it keeps the newly learned information active after it has been acquired. Enhanced hippocampal-mammillary zone connectivity predicted increased hippocampal activation during learning and prolonged persistence of entorhinal activity afterward.

Memory recognition performance improved substantially in the pitolisant group. Participants identified previously learned images more accurately and made decisions more quickly. Computational modeling revealed that histamine increased drift rate, a measure of evidence accumulation efficiency, for previously encoded images.

At the same time, it reduced the decision threshold required when evaluating unfamiliar distractor images.

These findings indicate that pitolisant-induced enhancement of histamine signaling asymmetrically altered retrieval computations, improving evidence accumulation for learned images while lowering the evidence threshold for unfamiliar distractors.

Multi-stage memory paradigm flow and modelling of H3R-weighted network dynamics during post-learning rest. A The paradigm comprised four stages: (1) encoding of visual pictures (landscapes/animals) through repeated presentation; (2) postlearning rest (10.4 mins rsfMRI) to capture network dynamics following encoding; (3) further encoding of novel (n = 48) and previously learned (n = 8) images during fMRI, enabling the novel > familiar contrast; and (4) recognition testing of previously learned (n = 56) and novel distractor (n = 27) images. B Histamine neurons are densely distributed along a pathway critical for memory consolidation and encoding, spanning the mammillary zone (including the TMN) to the hippocampus via the fornix 9,30. C During post-learning rest (stage two), signal was extracted from memory-relevant ROIs with evidence of H3R sensitivity. Signals were weighted by regional H3R density derived from PET maps 70. D Weighted signals were assembled into individual covariance matrices and E classified via linear discriminant analysis. Cross-validated models distinguished pitolisant from placebo with 88.5% accuracy. F Univariate analysis showed that network changes were driven by stronger mammillary zone ↔ hippocampus connectivity in the pitolisant group (permutation testing [pitolisant > placebo]: t[51] = 2.92, p = 0.0266, FWE-corrected, Cohen’s d = 0.81 [95% CI 0.24, 1.37]). Boxplots depict the interquartile range (IQR, central line = median), whiskers = ±1.5 × IQR, and half-violins show data distribution. *p ≤ 0.05, pe

Working Memory and Reinforcement Learning

In the working memory task, pitolisant increased overall accuracy and drift rate, reflecting more efficient evidence accumulation during decision-making. Non-decision time increased with task complexity, suggesting an adaptive shift in pre-decisional processing under higher cognitive load.

Neuroimaging results demonstrated increased activation in the left dorsolateral prefrontal cortex, and a positive correlation between dorsolateral prefrontal cortex activity and drift rate was observed.

In reinforcement learning tasks, pitolisant improved the overall selection of optimal choices. The most prominent effect occurred during loss-related learning. Participants receiving pitolisant showed reduced learning rates when processing aversive outcomes.

Lower learning rates are advantageous in stable environments because they prevent excessive reactions to individual negative events and promote more consistent decision-making. The lower learning rates associated with higher task performance indicate that histamine provides stability in value updating rather than making individuals overly reactive to losses.

Cerebral blood perfusion, mood, alertness, fatigue, side effects, or awareness of the treatment allocation did not differ significantly between groups. These findings strengthened the conclusion that the observed behavioral and neural changes were consistent with histaminergic modulation rather than general arousal, expectancy, or perfusion effects, although downstream effects on other neuromodulatory systems cannot be fully excluded.

Histamine-Based Cognitive Therapy Potential

The study shows that histamine plays a broad and previously underappreciated role in human learning and cognition. By increasing histamine signaling through histamine H3 receptor blockade, researchers observed enhanced memory encoding, neural markers consistent with memory consolidation, improved recognition performance, more efficient working memory processing, and more stable learning from negative outcomes.

These findings identify histamine as an important regulator of neurocomputational processes and suggest that histamine-based therapies warrant further investigation for conditions characterized by cognitive impairment, including neurodegenerative and psychiatric disorders.

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Journal reference:

• Colwell, M. J., van Uum, F. J. E., Cowen, P. J., Martens, M. A. G., Browning, M., Barron, H. C., Harmer, C. J., & Murphy, S. E. (2026). Histamine shapes the neurocomputational dynamics of human learning. Nature Communications. DOI: 10.1038/s41467-026-73865-9 https://www.nature.com/articles/s41467-026-73865-9