Mapping histamine pathway networks in the human brain across cognition and psychiatric disorders – Nature Mental Health

Neuronal histamine plays a crucial role in the regulation of brain function, serving as a neuromodulator with widespread influence across multiple neurotransmitter systems^1,2,3,4. Originating primarily from histaminergic neurons in the tuberomammillary nucleus of the hypothalamus⁵, histamine contributes to a wide range of physiological and cognitive processes, including arousal, attention, learning and memory, while also modulating behavioral states such as sleep–wake cycles, feeding and emotion^3,6,7,8. Dysregulation of the histaminergic system has been implicated in several neuropsychiatric conditions such as anxiety, depression, schizophrenia and autism spectrum disorder, as well as neurodegenerative diseases, including Alzheimer’s, Parkinson’s and Huntington’s diseases^3,9. Therefore, targeting the brain histamine system has garnered significant attention as a potential new therapeutic strategy for treating these disorders, with pharmacological interventions aimed at modulating histamine receptor activity showing promise in preclinical models⁹.

Histamine exerts its effects through four known histamine receptors (histamine receptor H₁ (encoded by HRH1), H₂ (HRH2), H₃ (HRH3) and H₄ (HRH4)), each of which mediates distinct functions, including neurotransmitter release, synaptic plasticity and network excitability^10,11. Among them, the H₃ receptor acts predominantly as an autoreceptor on presynaptic histaminergic neurons, where it tightly regulates histamine release and synthesis through a feedback inhibition mechanism¹². This autoregulatory role of H₃ is critical for maintaining homeostasis in histaminergic signaling as it modulates neurotransmitter release not only at histaminergic synapses but also within broader neuromodulatory networks by influencing other neurotransmitter systems, such as dopamine, serotonin and acetylcholine¹³. The system’s activity is regulated by two key enzymes—L-histidine decarboxylase (HDC), which synthesizes histamine from L-histidine (using a dual mechanism for translocation and degradation that ensures restriction of histamine synthesis to axonal terminals and at the same time rapid loading into synaptic vesicles¹⁴), and histamine N-methyltransferase (HNMT), which catalyzes its degradation^15,16.

Comparative studies in animal models, particularly rodents¹⁷, have significantly advanced our understanding of the histamine system’s fundamental properties^{16,18,19,20,21,22}. These studies have revealed the diverse roles that histamine plays in regulating behavior and interacting with other neuromodulatory systems²³. However, notable interspecies differences in the distribution and density of histamine receptors and neurons highlight the challenges of extrapolating findings from model organisms to humans²⁴. For example, recent studies have shown significant disparities in the spatial organization of histaminergic neurons and their projections between rodents and primates, underscoring the need for species-specific investigations^{16,18,19,20,21}. Moreover, non-human primate studies suggest greater histaminergic involvement in higher-order cognitive functions in primates than in rodents, further emphasizing the importance of comprehensive mapping in the human brain to fully delineate the histamine system’s unique roles in complex cognition and behavior^11,25.

Despite its importance, the molecular and functional architecture of the histaminergic system in the human brain remains comparatively undercharacterized relative to better-studied neuromodulatory systems such as dopamine and serotonin, for which detailed information is available on the anatomical distribution of key molecular components, interactions with other neurotransmitter systems, and system-level circuitry underlying cognition and behavior²⁶. This relative lack of integrated, human-specific data has hindered translational progress, limiting our ability to develop targeted interventions and to understand how histaminergic dysregulation contributes to brain disorders. Notably, a substantial body of evidence has been derived from positron emission tomography (PET) and single-photon emission computed tomography studies of histamine H₁ and H₃ receptors in humans^9,27, which has been invaluable for delineating aspects of histaminergic molecular circuitry. However, these insights have largely relied on smaller or heterogeneous datasets and have only occasionally been integrated with functional measures, pharmacological challenges or broader neurobiological context to assess target engagement or system-level relationships. As a result, a comprehensive, multiscale mapping of the histamine system in the human brain remains a critical unmet need. Recent advances in transcriptomic profiling and neuroimaging now enable the investigation of the spatial organization of histamine-related genes in the human brain at unprecedented resolution. Crucially, these approaches allow the integration of molecular data—such as gene expression and receptor distribution—with functional and structural brain imaging, offering new opportunities to elucidate how histaminergic signaling shapes neural circuits, cognition and behavior.

By employing an integrative multimodal approach, this study provides a comprehensive analysis of the genetic, molecular and functional architecture of the histamine system in the human brain (see Fig. 1 for a summary of our pipeline). Using regional transcriptomic data and single-cell RNA sequencing data from the Allen Human Brain Atlas (AHBA), alongside in vivo molecular imaging normative templates²⁸, we examine the spatial distribution of histamine-related genes and their spatial covariance with the distribution of molecular targets of neurotransmitter systems. These data can provide a descriptive anchor to better understand existing collected data (for example, cellular and regional distribution mapping, time courses) but also generate hypotheses alongside, for example, regarding brain processes for which the histamine system might contribute based on analyses of regional patterns of co-distribution (for example, high expression of elements of the histamine machinery in regions highly important for a specific cognitive process or brain disorder). Therefore, in addition, we examined how the spatial distribution of histaminergic gene expression and meta-analytical maps of cognitive functions/behavior correlate with structural brain changes in the main psychiatric disorders to generate hypotheses about potential functional implications and relevance to mental health. Finally, we characterized the developmental trajectories of histaminergic brain gene expression⁷, shedding light on how this system matures across critical periods of brain development.

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Summary of the analytical pipeline and data sources. a, Adult transcriptomic data. b, Developmental transcriptomic data. DK, Desikan–Killiany; MRI, magnetic resonance imaging.

Cellular distribution of histaminergic gene expression

Single-nucleus RNA sequencing data revealed distinct expression patterns of histaminergic genes across major cortical cell types. HNMT, which encodes the key enzyme for histamine degradation, exhibited a widespread distribution, with significant expression in both neurons and non-neuronal cells. Aldehyde dehydrogenase enzyme 7 family member A1 (ALDH7A1) was highly expressed by astrocytes and monoamine oxidase B (MAOB) in excitatory neurons. Among histamine receptors, HRH3 demonstrated the most neuronally restricted profile, predominantly expressed in inhibitory neurons, while HRH1 and HRH2 displayed broader neuronal expression, predominating in excitatory neurons. Data were not available for HRH4, HDC and AOC1. These findings establish a cellular framework for histamine signaling in the human cortex (Fig. 2).

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The heatmap displays normalized single-cell mRNA expression of histamine-related genes, normalized to highlight variation across main cell clusters. Gene expression was measured in multiple cortical areas of the human brain using RNA sequencing. Data are available as part of the Allen Human Brain Cell Atlas¹⁸ and can be downloaded from ref. ¹⁰⁵. OPC, oligodendrocyte precursor cells; VLMC, vascular leptomeningeal cells.

Regional expression of histaminergic genes in the adult human brain

Postmortem microarray data from the AHBA revealed heterogeneous regional expression of histaminergic genes across the brain. HRH1 showed relatively high expression in the frontal cortex and limbic structures such as the entorhinal, amygdala, parahippocampal cortices and cingulate gyrus. HRH2 is more variably expressed, with lower levels in the limbic system and cingulate cortex but relatively higher expression in some primary sensory regions such as the cuneus and lateral occipital cortex. HRH3 has lower-than-average expression in occipital and parietal cortices but is highly expressed in frontal regions, anterior cingulate and subcortical regions such as the caudate, putamen and thalamus. HRH4 has more localized expression, with moderate levels in the middle cingulate, entorhinal cortex, hippocampus and amygdala. The enzymes involved in histamine metabolism also show region-specific expression. HDC, which synthesizes histamine, is particularly expressed in limbic regions such as the entorhinal and parahippocampal cortices, amygdala and hippocampus. HNMT, responsible for histamine degradation, is broadly expressed but with lower-than-average levels in visual and occipital regions and higher-than-average expression in the orbitofrontal and middle cingulate, hippocampus and amygdala. MAOB, which encodes another enzyme contributing to histamine metabolism, is highly expressed in subcortical structures such as the putamen and pallidum, orbitofrontal cortex and cingulate. ALDH7A1, linked to histamine catabolism, exhibits strong expression in the middle cingulate, thalamus and basal ganglia (Fig. 3).

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Surface-rendered cortical and subcortical maps show regionally normalized (Z-scored) expression of key histamine-related genes (HDC, HNMT, ALDH7A1, MAOB, HRH1–HRH4) across the human brain. For each gene, lateral and medial cortical views and subcortical projections are displayed. PCA of regional gene expression profiles captures the dominant axis of histaminergic molecular variation (top right). Bottom panels show normative PET templates of histamine H₃ receptor binding derived from two independent radiotracers, [¹¹C]GSK-189254 and [¹¹C]MK-8278, displayed as Z-scored regional distributions. The heatmap summarizes Spearman correlations between regional PET binding patterns and gene expression maps (including PCA scores). Asterisks indicate statistically significant correlations after correction for spatial autocorrelation (spin rotation permutation two-sided tests). Color bars indicate Z-score ranges (blue represents lower relative expression or binding; red represents higher relative expression or binding).

Principal component analysis (PCA) on histaminergic gene expression identified a dominant spatial component that captured shared variance across histamine-related genes, reflecting a functional gradient from primary sensorimotor areas to multimodal association regions. This first component alone explained 41.1% of the total variance (Supplementary Fig. 1). The loadings of each gene into PCA1 can be seen in Fig. 4. Except for HRH2, which loaded negatively, all other genes had positive loadings. PCA1 scores were higher and lower than chance, respectively, in the limbic and visual 7 Yeo networks. For the von Economo cytoarchitectonic classes, we found PCA1 scores to be higher and lower than chance, respectively, in the limbic and primary sensory cortices (Supplementary Fig. 2).

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The left panel shows Spearman correlations between the regional histaminergic molecular score (derived from the PCA of histamine-related gene expression) and normative PET templates of major neurotransmitter receptors and transporters across the human brain. Colors indicate correlation strength (red = positive, blue = negative), and asterisks denote associations surviving false discovery rate (FDR) correction for multiple comparisons. Right panels summarize cognitive and behavioral domains significantly associated with the histaminergic molecular axis using Neurosynth meta-analytic decoding. Word clouds depict terms showing significant positive (top) and negative (bottom) correlations after FDR correction, with word size reflecting the relative strength of association. Positive associations preferentially map onto affective, stress-related and emotion regulation processes, whereas negative associations are enriched for visual, attentional and perceptual functions. Significance was assessed using spin rotation two-sided permutation tests.

Spatial correlations with H₃ receptor binding and neurotransmitter systems

To validate our inferred transcriptomic signature, we compared histaminergic gene expression maps with in vivo PET imaging of H₃ receptor distribution. Regional H₃ receptor bindings showed the highest positive significant correlation with HRH3 expression but also correlated positively with HRH1, MAOB and PCA1 scores, and negatively with HRH2 (Table 1 and Fig. 3). For [¹¹C]MK-8278 only, we also found significant positive correlations with the expression of HNMT. These findings support our assumption that mRNA expression data might be a good proxy mapping of the architecture of the human brain histamine system.

In addition, PCA-derived histaminergic expression patterns correlated with receptor densities of multiple neurotransmitter systems. Notably, the spatial distribution of histaminergic PCA1 scores positively correlated with the one for serotonin receptors 5-HT_1A and 5-HT_2A, dopamine receptor D1 and mu opioid receptor, although only the first survived correction for multiple comparisons. Moreover, it negatively correlated with the distribution of serotonin receptors 5-HT_1B, 5-HT₄ and 5-HT₆, dopamine transporter and dopamine D2 receptor, nicotinic receptor A4B2, muscarinic receptor M1 and acetylcholine transporter, noradrenaline transporter, cannabinoid receptor 1, and glutamate N-methyl-d-aspartate (NMDA) and mGlu5R receptors (Table 2 and Fig. 4). We did not find any significant correlation between the spatial distribution of PCA1 scores and any of the six magnetoencephalography (MEG) cortical maps of oscillatory power across different bands (Supplementary Table 1 and Supplementary Fig. 3).

Macroscale functional decoding of histaminergic gene expression

To explore the functional implications of histaminergic gene expression, we correlated PCA-derived histaminergic expression patterns with meta-analytic functional neuroimaging maps from Neurosynth. Brain regions with higher histaminergic gene expression were significantly associated with brain response during tasks indexing the following cognitive domains related to emotion, emotion regulation, stress, fear and anxiety processing, salience detection, impulsivity and cognitive control, loss, risk and addiction, sleep and memory, eating and pain. Brain regions with lower-than-average histaminergic gene expression were significantly associated with visual function, attention, gaze, multisensory integration, mental imagery and reading (Fig. 4 and Supplementary Table 1).

Histaminergic gene expression and neuropsychiatric disorders

Histaminergic gene expression patterns were next examined in relation to structural brain alterations observed in major neuropsychiatric disorders. Using ENIGMA consortium data, we identified significant correlations between PCA-derived histaminergic expression and disorder-specific cortical alterations in thickness and surface area. PCA1 scores showed the strongest negative associations with thickness changes in attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD), and a significant positive association with thickness changes in anorexia nervosa. For the surface area, we found significant positive associations with the spatial distribution of changes in anorexia nervosa and schizophrenia (Supplementary Fig. 4).

Developmental trajectories of histaminergic gene expression

Analysis of the BrainSpan transcriptomic developmental atlas revealed dynamic changes in histaminergic gene expression across lifespan stages, with distinct regional time courses likely shaping histamine-related functions. HDC expression peaked during late fetal development and early postnatal stages, consistent with the early establishment of histaminergic neurotransmission. HRH3 exhibited a protracted developmental trajectory, increasing steadily from childhood to adulthood, mirroring the maturation of prefrontal cognitive control networks. HNMT expression remained relatively stable across development, probably reflecting its housekeeping role in histamine metabolism (Fig. 5).

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The heatmap displays whole-brain normalized gene expression across developmental stages. Data were extracted from the BrainSpan Atlas of the Developing Human Brain (https://www.brainspan.org/). Significance was assessed using permutation testing where we shuffled developmental stages across samples. The * denotes P < 0.05 (two-sided) after FDR correction for the number of genes. RPKM, reads per kilobase of transcript per million mapped reads.

We also mapped regional time courses of histaminergic gene expression and identified region-specific maturation patterns of PCA1 scores summarizing developmental histaminergic brain gene expression. PCA1 explained 66.10% of the variance and had negative loadings from all candidate genes. We found PCA1 scores to follow a delayed trajectory in association cortices, reaching peak expression in late adolescence, whereas its expression in primary sensorimotor regions stabilized earlier. Moreover, it showed early enrichment in subcortical structures, reinforcing histamine’s role in early neuromodulation (Supplementary Fig. 5).

This study provides an integrated characterization of the histaminergic system in the human brain, leveraging transcriptomic, neuroimaging and functional datasets to delineate its molecular organization and relevance to brain function underlying cognition and psychiatric disorders. The findings reveal that histaminergic genes exhibit distinct cellular and regional expression profiles, closely aligning with known histaminergic neuroanatomy and function^{16,18,19,20,21}. HRH1 and HRH2 show the highest expression in excitatory neurons, reinforcing histamine’s role in modulating cortical excitability^4,29. By contrast, HNMT, which degrades histamine, is more broadly expressed, highlighting its role in shaping histaminergic tone across multiple brain regions. The receptor profiles further underscore functional specialization within the histaminergic system, with HRH3 exhibiting a neuronally restricted distribution in inhibitory circuits, suggesting a central role in feedback regulation^3,23. Although single-nucleus RNA sequencing revealed enrichment of HRH3 expression within inhibitory neuronal populations, this finding should be interpreted in light of the methodological scope of transcriptomic data, which cannot resolve presynaptic versus postsynaptic localization or distinguish local interneurons from long-range projecting cells. The H₃ receptor serves dual roles as both an autoreceptor and heteroreceptor, modulating histamine release as well as the release of other neuromodulators, and these functions are not incompatible with transcriptional enrichment in inhibitory neurons¹². Rather, this pattern may reflect a role for H₃ receptors in regulating cortical excitability via inhibitory microcircuits. Consistent with this interpretation, regional H₃ receptor binding measured using PET showed strong correspondence with HRH3 gene expression, supporting the relevance of the transcriptomic findings despite cell-type-level ambiguities³⁰.

At the regional level, our summary histaminergic gene expression signature follows a heterogeneous spatial distribution, supporting the notion of a functional specialization across the brain. HRH1 is prominently expressed in the frontal cortex and limbic regions, such as the entorhinal cortex, amygdala and cingulate gyrus, highlighting its putative role in emotional regulation and higher-order cognitive functions^31,32. By contrast, HRH2 shows relatively higher expression in primary sensory areas such as the cuneus and lateral occipital cortex, suggesting a putative role in sensory processing. HRH3 exhibits strong expression in subcortical regions, including the caudate, putamen and thalamus, as well as frontal areas, consistent with its role in modulating motor control, reward processing and neurotransmitter release³³. HRH4 is more localized, with moderate presence in limbic regions such as the hippocampus, amygdala and entorhinal cortex, aligning with its putative role in the brain’s immune responses as these regions are also known to contain a higher density of cells with immune function³⁴. Enzymes regulating histamine metabolism also show distinct regional expression: HDC, responsible for histamine synthesis, is particularly active in limbic areas; HNMT, involved in histamine degradation, is broadly expressed in the brain with higher levels in the orbitofrontal cortex, hippocampus and cingulate; and MAOB and ALDH7A1, which are also involved in histamine metabolism, exhibit strong expression in subcortical and cingulate regions. Together, these patterns hint at the possibility of diverse and region-specific roles of histaminergic signaling in sensory processing, cognition, emotion and motor control, with implications for understanding its involvement in both normal brain function and neuropsychiatric disorders^35,36.

The validation of the inferred transcriptomic signature through comparison with in vivo PET imaging of H₃ receptor distribution as measured using two different tracers in two different cohorts provides critical support for our spatial transcriptomic mapping of the human brain’s histaminergic system. Although also expressed postsynaptically, including in the striatum, being an autoreceptor expressed in histaminergic presynaptic terminals, H₃ receptor binding is likely to offer a reliable proxy for histaminergic innervation¹³. The strong positive correlations between regional H₃ receptor binding and HRH3 expression highlight the congruence between mRNA expression and receptor localization, confirming the robustness of transcriptomic data as a reliable spatial representation of histaminergic architecture.

Whether the transcriptomic signature identified here also predicts regional variation in histamine H₁ receptor availability remains an open empirical question. While human H₁ receptor PET studies exist, they are currently limited to a small number of cohorts and are not yet available as harmonized, shareable whole-brain templates suitable for large-scale multimodal integration. Future efforts aimed at generating standardized H₁ receptor PET templates—ideally with prospective attention to data sharing and cross-site harmonization—would therefore represent a valuable extension of the present work. Notwithstanding these limitations, existing clinical and imaging evidence underscores the translational relevance of histaminergic signaling across neuropsychiatric conditions, including depression, anorexia nervosa, schizophrenia and aging-related neurodegeneration. In particular, altered H₁ receptor binding has been reported in individuals with anorexia nervosa, implicating histaminergic mechanisms in the regulation of appetite, arousal and affective processing^37,38. In this context, the positive association we observed between the principal component summarizing histaminergic gene expression and brain alterations in anorexia nervosa provides convergent, systems-level support for a role of histamine in eating disorders. Although the clinical literature remains relatively sparse, recent work highlights histamine’s involvement in visceral processing, stress responsivity and mood regulation in this population, encouraging further preclinical investigation and the exploration of histaminergic targets in future clinical studies³⁹.

The positive correlation of histaminergic PCA1 scores with serotonin receptor 5-HT_1A speaks in favor of histamine’s role in regulating serotonergic transmission and aligns well with preclinical evidence showing that histaminergic neurons project to the raphe nuclei, where H₃ receptor activation reduces serotonin release, potentially influencing mood and cognition^23,40,41. We note however that even if, for example, the H₃ receptor has been found to co-exist in heteromers with the dopamine receptor D1⁴², NMDA receptors⁴² and the adenosine receptor A1⁴³, to date there has been no reports of heteromers with the 5-HT_1AR, and our data did not specifically show significant positive correlations with D1 or NMDA binding (which was strongly anticorrelated). Nevertheless, we should highlight that even if not reaching significance in our strict spin test, D1 was among the targets with the strongest positive correlations, an observation that urges some caution in excluding the presence of heteromers on the basis of our data exclusively. The observed negative correlations in receptor expression between the histaminergic system and other neuromodulatory systems (for example, serotonin other than 5-HT_1A, dopamine other than D1 receptors, glutamate, acetylcholine) is interesting as it suggests there are region-specific specializations and/or feedback regulation in the gene expression of these neuromodulatory systems^11,23. Supporting the later interpretation, histamine can suppress dopamine and glutamate release via H₃ receptor-mediated inhibition while simultaneously enhancing dopamine output through H₁ receptor activation^40,44,45. It also modulates norepinephrine release and facilitates cholinergic transmission through projections to key regions such as the prefrontal cortex, striatum and basal forebrain^40,46,47. Thus, our findings extend this preclinical evidence to suggest similar interactions may occur in humans. We interpret the inverse patterns of receptor expression as representing regulatory feedback, whereby elevated histaminergic tone reduces the necessity for receptor upregulation in other systems to preserve neurotransmitter balance. While this interpretation needs testing in humans, collectively, these findings underscore the potential integrative role of histamine as a dynamic regulator of multiple neuromodulatory pathways, wherein functional interactions are shaped by system-level feedback.

The correlation between PCA-derived histaminergic gene expression patterns and meta-analytic functional neuroimaging maps from Neurosynth indicates that brain regions with higher histaminergic gene expression were strongly associated with cognitive domains and processes related to emotion, emotional regulation, stress, fear and anxiety processing, salience detection, impulsivity, cognitive control, risk-taking, addiction, sleep, memory, eating and pain. These findings provide evidence for a role of the histaminergic system in linking physiological arousal (which leads to histamine release in the brain) with higher-order cognitive and affective processes⁴⁸. The associations with stress, anxiety and emotional regulation extend previous rodent evidence for histamine’s role in adapting to environmental challenges and maintaining emotional homeostasis⁴⁹ to indicate this is the case in humans. The link to salience detection and decision-making suggests histaminergic involvement in facilitating the prioritization of relevant stimuli and modulating cognitive control⁵⁰. Furthermore, the strong associations with impulsivity, addiction and risk-taking behaviors highlight histamine’s contribution to reward processing and behavioral regulation within circuits governing motivation and decision-making under uncertainty^49,51. The connections to sleep⁵², eating⁵³ and pain⁵⁴ further suggest a broader role for histamine in managing homeostatic processes, supporting its involvement in maintaining the body’s internal balance and response to environmental demands. Together, these results provide an integrated view of how histaminergic signaling supports the brain’s ability to navigate affective, cognitive and physiological challenges, making it a good candidate for a critical regulator of brain physiology in both health and neuropsychiatric disorders^11,55.

The potential relevance of histaminergic signaling to neuropsychiatric disorders is further underscored by its associations with structural brain changes observed in conditions such as schizophrenia, depression, ADHD and anorexia. PCA1 scores, representing an aggregate histaminergic gene expression pattern, correlated positively with cortical thickness alterations in anorexia nervosa but negatively with thickness changes in ADHD and MDD. The negative correlation between PCA1 and cortical thinning in ADHD aligns well with the idea that disrupted histamine signaling may contribute to attentional and cognitive dysfunction⁵⁶. This is consistent with preclinical studies showing that histamine depletion leads to impairments in attention and executive function⁵⁷. The evidence linking histamine to depression is manifold. Chronic treatment with pitolisant, an inverse H₃ agonist, was shown to prevent the development of anxiety-like behaviors in a rodent model of corticosterone-induced depression⁵⁸. Selective serotonin reuptake inhibitors, which constitute first-line treatments for the treatment of depression, were shown to require an intact histaminergic system to produce therapeutic effects in a depression-like rodent model⁵⁹. In addition, increased brain histamine has been shown to likely mediate suppression of SSRI-induced increases in serotonin during acute inflammation⁶⁰, offering a new angle to our understanding of treatment-resistant depression often observed in patients with low-grade chronic inflammation⁶¹. In terms of surface area changes, PCA1 scores showed a positive correlation with alterations in anorexia and schizophrenia. These findings align with previous evidence implicating the histaminergic system in schizophrenia’s pathophysiology, particularly its role in modulating neurotransmitter systems such as dopamine and glutamate, both of which are dysregulated in the disorder^62,63,64. Preclinical and clinical trial data have also demonstrated the therapeutic potential of targeting histamine H₂ and H₃ receptors in improving both cognitive dysfunction and negative symptoms in schizophrenia^29,65. For example, H₃ receptor antagonists, such as pitolisant, have shown promise in enhancing cognitive performance in animal models of psychosis, with early clinical trials in humans showing tolerability and potential improvements in attention and memory^35,66. In addition, the positive association between PCA1 scores and cortical thickness changes in anorexia nervosa could reflect the interaction of histamine with brain areas implicated in feeding behavior, anxiety and reward processing. Evidence from studies in patients with anorexia suggests altered histamine signaling may play a role in dysregulated appetite, stress response and emotional processing⁸. Future clinical trials targeting histaminergic pathways may provide valuable insights into treatments for psychiatric conditions characterized by altered neurotransmitter dynamics and associated structural brain changes^55,67.

Histaminergic gene expression follows a dynamic developmental trajectory, with HDC expression peaking during fetal development and early postnatal stages, reinforcing histamine’s role in early neural circuit formation. Interestingly, the protracted increase in HRH3 expression from childhood to adulthood mirrors the maturation of prefrontal cognitive control networks, which aligns well with the view that histamine plays a role in shaping executive function. Developmental analyses using regional data further demonstrate that histaminergic gene expression follows distinct regional time courses, with early maturation in sensorimotor regions and delayed expression in association cortices. This extended developmental trajectory aligns with the putative histamine’s involvement in cognitive flexibility and learning, functions that continue to mature into adulthood^2,7. Together, these findings suggest that histaminergic signaling might play a fundamental role in neurodevelopmental processes and cognitive maturation, making it a candidate for further longitudinal developmental studies in clinical populations examining, for example, whether functional genetic variation in genes of the histamine pathway might shape trajectories of brain development from early infancy into adulthood.

Although this work places particular emphasis on the histamine H₃ receptor—motivated by its role as a presynaptic auto- and heteroreceptor and by the availability of harmonized in vivo PET templates—the histaminergic system cannot be fully understood without considering the broader contributions of H₁ and H₂ receptors. Indeed, the modern study of brain histamine emerged from early anatomical and pharmacological efforts to identify histaminergic neurons and characterize H₁ receptor binding in the vertebrate brain, followed by the development of PET radiotracers such as [¹¹C]doxepin⁶⁸ that enabled the first in vivo mapping of histamine receptors in humans. This foundational body of work established histamine as a key modulator of arousal, attention and cortical excitability, providing an essential framework for subsequent molecular and systems-level investigations. Consistent with this historical literature, our transcriptomic analyses revealed that HRH1 and HRH2 expression is preferentially enriched in excitatory neuronal populations, particularly within frontal and limbic cortices. This pattern aligns with experimental evidence demonstrating that H₁ and H₂ receptor activation enhances neuronal excitability, modulates glutamatergic transmission and influences large-scale cortical state regulation. Although we were unable to directly validate these findings using in vivo H₁ or H₂ receptor PET data—due to the absence of publicly available, harmonized whole-brain templates—the observed cellular and regional expression profiles provide an important molecular substrate for interpreting previous clinical and pharmacological observations. Notably, alterations in H₁ receptor availability and function have been reported across a range of neuropsychiatric and neurological conditions³⁷, including depression⁶⁹, anorexia nervosa, schizophrenia⁶², aging and Alzheimer’s disease^70,71. PET studies using H₁ receptor ligands and pharmacological challenge paradigms have linked histaminergic dysregulation to changes in arousal, mood, cognitive performance and sedation-related side effects of psychotropic medications⁷². In anorexia nervosa in particular, reduced H₁ receptor binding has been associated with altered cortical and limbic processing, offering a potential mechanistic bridge between histamine signaling, interoceptive processing and affective regulation. Within this context, the association we observed between a principal component summarizing histaminergic gene expression and disorder-specific cortical alterations provides convergent, systems-level support for the relevance of histamine pathways in eating disorders, despite the current lack of direct multimodal H₁ receptor validation. By contrast, the H₃ receptor occupies a distinct regulatory position within the histaminergic system, acting primarily as a presynaptic modulator of histamine synthesis and release while also regulating the release of other neurotransmitters, including dopamine, norepinephrine and acetylcholine. The strong spatial correspondence we observed between HRH3 gene expression and H₃ receptor PET binding, across independent tracers and cohorts, supports the use of H₃ receptor imaging as a systems-level proxy for histaminergic innervation. Importantly, this validation strengthens confidence in the broader transcriptomic signature identified here, including components driven by HRH1 and HRH2, even in the absence of direct receptor-specific imaging for these targets. Finally, it is important to note that histamine H₂ receptor imaging remains largely unexplored in humans as no validated PET radiotracers are currently available for reliable whole-brain in vivo assessment. This represents a significant methodological gap in the field and limits the ability to directly test hypotheses regarding H₂-mediated mechanisms in cognition and psychiatric disorders. The development of selective, shareable H₁ and H₂ receptor PET ligands—together with prospective attention to data harmonization and open sharing—would therefore constitute a major advance, enabling future studies to extend integrative multimodal frameworks such as the one presented here and to more fully disentangle receptor-specific contributions to histaminergic function in health and disease.

While this study offers a comprehensive characterization of the histaminergic system, several limitations should be considered. First, and foremost, our findings are correlational in nature and should be interpreted as hypothesis-generating for further experimental work rather than direct proofs. To achieve this goal, future work relying on drug challenges and longitudinal imaging genetics designs in both healthy controls and patients with neuropsychiatric disorders will be pivotal to clarify how histaminergic signaling trajectories shape brain maturation and psychiatric risk. One potential example would be pharmacological studies with H₃ receptor antagonists such as pitolisant, which are known as inducers of synaptic histamine release and would allow testing the effects of targeted release of histamine in the brain on behavior and cognition in both healthy and clinical populations⁷³. Second, the reliance on postmortem transcriptomic datasets, although valuable for spatial mapping, does not capture dynamic and diurnal fluctuations in histamine signaling that occur in vivo^74,75. The integration of PET imaging with gene expression data strengthens our findings, but PET measures of receptor binding provide indirect estimates of functional histaminergic activity rather than real-time neurotransmission⁷⁵. Future studies incorporating functional imaging techniques with drug challenges could offer a complementary assessment of histamine’s role in living human brains. Third, the relatively small sample sizes of several source datasets—especially those informing histaminergic gene expression—limit statistical power and may reduce the generalizability of the observed spatial and molecular associations. For example, the AHBA derives from six donors only, and data from the right hemisphere was collected for only four donors, which raises issues regarding the capacity to appropriately capture interindividual variability⁷⁶. The same applies regarding sex (only one donor is female) and age (24–57 years) representation. Although the AHBA remains as the sole most anatomically detailed of the transcriptomics of the human brain, it is clear there is much to be done on diversity and inclusion to ensure representativeness measures to current standards of human neuroscience. Fourth, it is important to note the resolution constraints of microarray and RNA sequencing data, which do not fully capture cellular heterogeneity within complex brain regions. Fine-grained single-cell and spatial transcriptomic approaches could refine our understanding of histaminergic gene expression at a more granular level using spatial single transcriptomics to dissect potential differences among, for example, cortical layers and regional microcircuitry^24,77. Given histamine’s widespread neuromodulatory effects, a deeper exploration of histamine’s interactions with other neurotransmitter systems may yield insights into cross-system regulation and homeostatic balance. This could be achieved by leveraging recent advances in multiscale large biophysical modeling approaches integrating transcriptomics, receptor binding, neurotransmitter dynamics and computational simulations to help bridge the gap between molecular findings and systems-level histaminergic function, ultimately providing a more mechanistic understanding of its role in cognition and neuropsychiatric disorders^78,79.

Overview of analytical approach

Briefly, our pipeline followed a structured approach to analyzing the expression of genes related to the histamine system in the human brain, focusing on both adult and developmental stages. The analysis focused on key genes involved in neuronal histamine metabolism, signaling and interactions with other neurotransmitter systems. We examined HDC, the sole enzyme responsible for histamine synthesis, and HNMT, the primary enzyme for histamine degradation in the brain. In addition, we included AOC1, MAOB and ALDH7A1, which contribute to alternative histamine degradation pathways. We also analyzed the four known histamine receptors (H₁, H₂, H₃ and H₄ receptors), which mediate histamine’s diverse neuromodulatory effects. H₁ and H₂ receptors regulate excitatory neurotransmission, cognition and synaptic plasticity, while H₃ receptor acts as a presynaptic autoreceptor, modulating histamine release and influencing dopamine, serotonin and acetylcholine systems. H₄ receptor, although primarily linked to immune function, has emerging relevance in neuroinflammation and brain disorders^80,81,82.

In the adult analysis, we used microarray gene expression data from the AHBA⁸³ to map regional variation in gene expression in neuroimaging space. We used the abagen toolbox⁸⁴ to assign these data to specific brain regions on the basis of the DK atlas⁸⁵, which was selected for several practical and conceptual reasons. Critically, the AHBA does not provide a native, donor-consistent anatomical parcellation in neuroimaging space. Instead, it consists of sparsely and irregularly distributed tissue samples whose spatial locations and coverage vary substantially across donors. As such, mapping AHBA microarray expression data to a standardized brain atlas is necessary to enable regional aggregation, cross-donor normalization and spatial alignment with in vivo neuroimaging data. The DK atlas offers a widely used, anatomically grounded parcellation with 83 cortical and subcortical regions defined in standard neuroimaging space. Its native implementation within the abagen toolbox facilitates reproducible preprocessing choices, including probe selection, donor-wise normalization and region-level aggregation, while also allowing robust handling of inter-hemispheric asymmetries and uneven sampling density across donors. Importantly, the use of the DK atlas ensures spatial compatibility across all multimodal datasets integrated in this study. PET receptor density templates derived from the neuromaps framework, meta-analytic functional maps from Neurosynth, and MEG-derived oscillatory power maps were either natively available or most reliably parcellated in DK space. This common anatomical framework enables direct spatial correspondence across transcriptomic, molecular imaging and functional datasets without requiring additional interpolation or cross-atlas registration steps, thereby improving reproducibility and interpretability. While higher-resolution parcellations (for example, Glasser and Schaefer atlases) offer finer spatial granularity, they are optimized primarily for functional connectivity and can be more susceptible to noise, partial volume effects and sampling bias when applied to sparse postmortem transcriptomic data. Given our goal of integrating gene expression with molecular imaging and large-scale functional decoding, the DK atlas provided the most balanced and tractable solution.

Next we applied PCA to extract dominant patterns in their expression. Since we relied on gene expression to infer regional variation in the molecular architecture of the histamine system in the human brain, which does not necessarily reflect protein levels, we used PET data from the regional variation in H₃ receptor binding in normative samples of healthy participants⁸⁶ to examine spatial correlations with the expression of candidate genes and the scores of the first principal component summarizing gene expression. While a bulk of data exists from efforts mapping human brain H₁ receptors mostly using [¹¹C]doxepin^27,87, we could not access any suitable template data to be incorporated in our analyses. Therefore, while we discuss findings using this tracer, its omission from our current pipeline mostly reflects restraints on data sharing that made it impossible to test how well our transcriptomics signature would align against a normative signature of the H₁ receptor protein as assessed by PET. This similarly explains its absence from Supplementary Table 1, where we include only tracers for which we had accessible template data. Since histamine also modulates the release of other neurotransmitters, we used PET templates of the binding of multiple receptors in the living human brain to assess the spatial correlation between regional scores of the first PCA of gene expression and parcellated data of the chemoarchitecture of the human brain²⁸. To establish functional relevance, we examined the spatial correlation between the first principal component and meta-analytical maps of terms pertaining to different cognitive functions and behavioral domains using data from the Neurosynth repository⁸⁸. Finally, we also investigated the potential relevance to neuropsychiatric disorders by assessing spatial correlations between regional PCA1 scores and structural brain changes using ENIGMA consortium data⁸⁹. In the developmental analysis, we used data from the BrainSpan Atlas⁹⁰ to examine histaminergic gene expression across different stages of brain development. We applied PCA to summarize developmental trajectories, capturing global and regional dominant trends in histaminergic gene expression over time (Fig. 1).

Data sources

Single-nucleus RNA sequencing data

We used single-nucleus transcriptomes from 49,495 nuclei across multiple human cortical areas available as part of the Allen Human Brain Cell Atlas. The methods underlying the generation of this dataset have been described in detail elsewhere⁹¹. Briefly, individual layers of cortex were dissected from tissues covering the middle temporal gyrus, anterior cingulate cortex (also known as the ventral division of medial prefrontal cortex, A24), primary visual cortex, primary motor cortex, primary somatosensory cortex and primary auditory cortex derived from the human brain. Nuclei were dissociated and sorted using the neuronal marker NeuN. These regions represent the full extent of currently available human single-nucleus transcriptomic data with layer-specific dissection and standardized NeuN-based neuronal sorting in this resource. They were selected by the original dataset authors to maximize anatomical and functional diversity across sensory, motor, association and paralimbic cortices rather than by the present study. Nuclei were sampled from postmortem and neurosurgical (middle temporal gyrus only) donor brains, and expression was profiled with SMART-Seq v4 RNA sequencing. Normalized gene expression was downloaded from ref. ⁹². To summarize gene expression in the main brain-cell types, we averaged expression of candidate genes across cells of the same cluster⁹¹.

Regional microarray expression data

Gene expression data from the AHBA were obtained from six adult postmortem brains aged 24–57 years. Genetic probes were reannotated following standardized methods outlined in ref. ⁹³ to increase accuracy, and probes deemed unreliable were excluded. For each gene, the most stable probe was selected on the basis of pooled donor correlations, resulting in a final dataset of 15,633 probes across the brain. Microarray samples were spatially assigned to the 83 regions of the DK atlas⁹⁴ using the abagen toolbox^84,95, with regional assignment based on corrected Montreal National Institute Space coordinates. Assignments were constrained by hemisphere and cortical/subcortical boundaries to improve anatomical accuracy. Gene expression values were normalized using a robust sigmoid function and rescaled to a unit interval for cross-region and cross-donor comparability. Due to limited availability of samples from the right hemisphere, analyses were restricted to 41 left-hemisphere regions spanning 34 cortical and 7 subcortical divisions.

Large-scale networks

We assigned each of the DK atlas regions to 7 Yeo resting large-scale networks at rest⁹⁶ and von Economo cytoarchitectonic classes⁹⁷ following the procedure described in ref. ⁹⁸. We then calculated mean PCA1 scores within each of these networks and classes to obtain a summary measure.

Developmental brain regional gene expression

The BrainSpan Atlas of the Developing Human Brain includes microarray and RNA-seq data from 42 neurotypical postmortem brains spanning key developmental stages, from 8 weeks post-conception to 40 years of age. Details of the methods underlying the generation of the dataset have been described elsewhere⁹⁰. Briefly, microarray samples (GeneChip Human Exon 1.0 ST Array platform) measure the expression of transcript variants across over 50,000 genes and probe sets. Normalized gene expression data were downloaded for our candidate genes from https://www.brainspan.org/. For this analysis, microarray data were prioritized due to the extensive temporal and spatial coverage. Samples were organized into developmental stages: fetal (8–20 weeks post-conception), infancy (birth to 1 year), childhood (1–10 years), adolescence (11–20 years) and adulthood (21+ years). We averaged normalized gene expression across donors for the same region and developmental age before conducting further analyses.

PET templates

To integrate histaminergic gene expression with neurochemical profiles, we analyzed PET-derived maps of neurotransmitter receptor densities sourced from the neuromaps toolbox (https://netneurolab.github.io/neuromaps/). These templates included receptor binding measures for serotonin, dopamine, acetylcholine, GABA, glutamate and cannabinoid systems²⁸. Datatsets from tracers for receptors of the histamine system were available for the H₃ only: [¹¹C]GSK189254 (ref. ⁹⁹) and [¹¹C]MK-8278 (ref. ¹⁰⁰). These datasets provide in vivo regional maps of histamine H₃ receptor availability in healthy adults and were selected because the H₃ receptor acts as a key presynaptic autoreceptor regulating histamine synthesis and release, making it a particularly informative marker of histaminergic innervation at the systems level. Importantly, the inclusion of these PET templates allowed us to validate the spatial correspondence between postmortem histaminergic gene expression patterns and receptor distributions measured in the living human brain, thereby strengthening the biological plausibility of our multimodal findings. At present, histamine H₁ receptor PET datasets are not available in a shareable form that allows reliable parcellation and integration with large-scale multimodal neuroimaging frameworks such as those used here. Neurotransmitter receptor densities were averaged within DK atlas regions to ensure alignment with the transcriptomic data (Supplementary Table 2).

Magnetoencephalography oscillatory neural dynamics maps

To examine whether our summary measure of histaminergic gene expression spatially align with the cortical distribution of oscillatory dynamics across different frequency ranges, we used parcellated data from magnetoencephalography maps acquired in healthy volunteers as part of the Human Connectome Project as in ref. ¹⁰¹. Briefly, the dataset pertains to 6 minutes of resting-state data acquired with the eyes open. Complete MEG acquisition protocols can be found in the HCP S1200 Release Manual¹⁰². For each participant, the power spectrum was calculated at the vertex level across six different frequency bands—delta (2–4 Hz), theta (5–7 Hz), alpha (8–12 Hz), beta (15–29 Hz), low gamma (30–59 Hz) and high gamma (60–90 Hz)—using the open-source software Brainstorm. This was implemented using the Welch’s method with overlapping windows of length 4 seconds with 50% overlap. Average power at each frequency band was then calculated for each vertex (that is, source). Source-level power data were then parcellated into the 68 cortical regions of the DK atlas for each frequency band to ensure alignment with the transcriptomics data.

Neurosynth meta-analytical data

Probabilistic measures of the association between voxels and terms were obtained from the Neurosynth repository, a meta-analytical tool that synthesizes results from more than 15,000 published functional MRI (fMRI) studies by searching for high-frequency key words (such as pain and attention) that are published alongside fMRI voxel coordinates (https://github.com/neurosynth/neurosynth, using the volumetric association test maps)⁸⁸. This measure of association is the probability that a given term is reported in the study if there is activation observed at a given voxel. Note that the tool does not distinguish between areas that are activated or deactivated in relation to the term of interest, or the degree of activation, only that certain brain areas are frequently mentioned in conjunction with certain words. Although more than 1,000 terms are reported in Neurosynth, we followed the approach taken in ref. ⁹⁸ and focused primarily on cognitive function, therefore limiting the terms of interest to cognitive and behavioral terms. These terms were selected from the Cognitive Atlas, a public ontology of cognitive science that includes a comprehensive list of neurocognitive terms and has previously been used in conjunction with Neurosynth. We used 123 terms, ranging from umbrella terms (attention and emotion) to specific cognitive processes (visual attention and episodic memory), behaviors (eating and sleep) and emotional states (fear and anxiety). The coordinates reported by Neurosynth were parcellated into regions of the DK atlas. The probabilistic measure reported by Neurosynth can be interpreted as a quantitative representation of how regional fluctuations in activity are related to psychological processes. For simplicity, we refer to these probabilities as functional associations throughout this paper. The full list of terms is shown in Supplementary Table 3.

ENIGMA structural brain changes in neuropsychiatric disorders

Meta-analytical data on the effect size of case–control structural differences in cortical thickness and surface area were obtained from the ENIGMA consortium for seven psychiatric disorders (ADHD, autism spectrum disorder, anorexia nervosa, bipolar disorder, MDD, obsessive–compulsive disorder and schizophrenia). These data are distributed as part of the ENIGMA toolbox^89,103. A summary of the sample sizes and sociodemographic characteristics is provided in Supplementary Table 4.

Statistical analysis

All statistical analyses were implemented in Python and MATLAB using the assistance of OpenAI ChatGPT to help with debugging code errors when those occurred. Spatial correlations between maps were assessed with Spearman rank correlations using spin-based permutation testing (Vasa method¹⁰⁴) to account for spatial autocorrelation. Gene expression was summarized using PCA implemented in Python, selecting the optimal number of components to retain using parallel simulations. For the developmental analyses, we used permutation tests shuffling samples across developmental age to assess the statistical significance of histaminergic gene expression trends over time. Corrections for multiple comparisons were conducted using the FDR to limit type I error rates. Plotting and data visualization were performed using Python’s Matplotlib (https://matplotlib.org/) and Seaborn (https://seaborn.pydata.org/) libraries and the ENIGMA toolbox⁸⁹.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

All analyses were conducted using custom scripts in Python and MATLAB, built on openly available toolboxes and resources. Mapping of Allen Human Brain Atlas microarray data to the Desikan–Killiany atlas was performed using abagen (https://abagen.readthedocs.io; https://github.com/rmarkello/abagen). Molecular PET templates were obtained and integrated via neuromaps (https://neuromaps-main.readthedocs.io; https://github.com/netneurolab/neuromaps). ENIGMA case–control structural effect-size maps were accessed and harmonized using the ENIGMA Toolbox (https://enigma-toolbox.readthedocs.io; https://github.com/MICA-MNI/ENIGMA). Meta-analytic functional decoding used Neurosynth association maps (https://neurosynth.org). MEG power spectra were processed in Brainstorm (https://neuroimage.usc.edu/brainstorm). Spatial autocorrelation-preserving permutation testing employed the spin-test implementation described by Vasa et al. (https://github.com/spin-test/spin-test). Principal component analysis and statistical analyses were implemented in Python using NumPy (https://numpy.org), SciPy (https://scipy.org) and scikit-learn (https://scikit-learn.org). Visualizations were produced with Matplotlib (https://matplotlib.org) and Seaborn (https://seaborn.pydata.org).

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Authors and Affiliations

1. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

   Daniel Martins, Mattia Veronese, Daniel van Wamelen, Adam Hampshire, Federico Turkheimer & Steven CR Williams

2. Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal

   Daniel Martins

3. RISE-HEALTH (Neurosciences thematic line), Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal

   Daniel Martins

4. Department of Biomedical Engineering, University of Padova, Padua, Italy

   Mattia Veronese

5. King’s College Hospital NHS Foundation Trust, London, UK

   Daniel van Wamelen

6. Department of Neuropsychiatric Disorders, Netherlands Institute of Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands

   Ling Shan

7. Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

   Oliver Howes

Contributions

D.M. contributed to the conceptualization of the study, data curation and formal analysis and led the investigation. D.M. also prepared the original draft of the paper and contributed to reviewing and editing subsequent versions. M.V. contributed to data curation and paper review and editing. D.v.W. contributed to paper review and editing. L.S. contributed to paper review and editing. O.H. contributed to data curation and paper review and editing. A.H. contributed to paper review and editing. F.T. contributed to paper review and editing, and secured funding for the project. S.C.R.W. contributed to paper review and editing, secured funding, and provided supervision.

Corresponding author

Correspondence to Daniel Martins.

Competing interests

The authors declare no competing interest.

Peer review information

Nature Mental Health thanks Alex Doruyter, Maria Beatrice Passani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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