The work addresses a longstanding problem in systems neuroscience: how can events occurring at the level of molecules and receptors shape the coordinated activity of billions of neurons? Many whole-brain models simplify the cortex by assigning similar physiological properties to every region. While this approach makes simulations easier to construct, it can overlook an important feature of real brains—the cortex is chemically and functionally heterogeneous. Different areas contain different concentrations of receptors, possess distinct cellular properties, and participate in different anatomical networks.

To investigate the consequences of this heterogeneity, the researchers built their model using The Virtual Brain, an open-source platform for simulating activity across the entire brain. The model combined the brain’s structural connectivity—the physical network of long-distance pathways linking cortical regions—with regional maps of muscarinic acetylcholine receptor density. These receptors are activated by acetylcholine, a neuromodulator involved in attention, arousal, learning, memory, and transitions between sleep and wakefulness.

The model represented 68 cortical regions, each with its own receptor-defined response characteristics. Rather than treating acetylcholine as a uniform influence across the cortex, the researchers allowed its effects to vary according to the local density of muscarinic receptors. In technical terms, receptor maps were used to alter the dynamical properties of individual regions within a network model, while anatomical connections determined how activity could spread between them. This created a simulation in which local chemistry and global communication were linked within the same computational framework.

The researchers then examined how the simulated cortex behaved across a range of brain states, from wakefulness to sleep. When regional receptor differences were included, the model produced stronger coordination between areas and more effective information flow than a control model in which all cortical regions were assigned identical properties. The result suggests that biological variation is not simply a source of noise or complexity. When it is spatially organised and aligned with the brain’s structural network, heterogeneity can help the cortex generate richer and more flexible patterns of activity.

The findings also reveal why neuromodulators cannot be expected to act in the same way everywhere. A chemical signal such as acetylcholine may increase or decrease the excitability of a region depending on which receptor populations are present and how they interact with local network dynamics. A high-receptor area may respond strongly to a change in neuromodulatory input, while a neighbouring region with a different receptor profile may respond more weakly or in another manner. The resulting pattern of activity depends not only on chemistry, but also on the pathways connecting those regions to the rest of the brain.

One of the most striking outcomes was the model’s ability to reproduce localised slow waves. In real brains, slow, sleep-like activity can sometimes appear in limited cortical areas while other regions remain in a more awake-like state. Such local sleep patterns have been associated with attentional lapses, sleep deprivation, and the presence of brain lesions. The simulated phenomenon emerged spontaneously, without requiring every region to enter the same global state at once, suggesting that regional differences in neuromodulatory sensitivity may contribute to the brain’s ability to occupy mixed or transitional states.

This behaviour is important because conventional descriptions of sleep and wakefulness often treat them as whole-brain conditions. In reality, brain states can be fragmented: some networks may become less responsive while others continue to support perception, attention, or internally directed thought. By incorporating receptor distributions into a model of the cortical connectome, the researchers provide a possible mechanism for this partial decoupling. Local chemistry may help determine which regions are vulnerable to state changes, while structural connections influence whether those changes remain local or spread through the network.

The study’s authors say this type of multiscale modelling could eventually help explain abnormal state transitions in conditions involving brain damage or impaired consciousness. Lesions can disrupt structural connectivity, while disease or injury may also alter receptor expression and neuromodulatory systems. A model that represents both factors could help researchers test how local damage changes global dynamics and why some brain regions continue to function while others display sleep-like activity. The work does not yet provide a clinical tool, but it offers a framework for generating predictions that can be tested with neuroimaging, electrophysiology, and pharmacological experiments.

More broadly, the findings challenge the idea that brain-wide dynamics can be understood from anatomy alone. The physical wiring of the cortex provides the routes along which activity can move, but receptor distributions help determine how each region responds when activity arrives. By combining molecular maps with structural connectivity, whole-brain simulations may become more biologically realistic without needing to model every neuron individually. The researchers suggest that this approach could improve future studies of attention, sleep, consciousness, and neuromodulation—and bring computational neuroscience closer to explaining how microscopic biology gives rise to the shifting states of the human brain.

Subject of Research: The influence of spatially structured muscarinic acetylcholine receptor density on large-scale human cortical dynamics.

Article Title: Spatially structured heterogeneity shapes large-scale cortical dynamics in a model of the human cortex

Web References:
https://www.pnas.org/doi/10.1073/pnas.2532072123
https://ebrains.eu/data-tools-services/tools/the-virtual-brain
https://ebrains.eu/impact/projects/ebrains-20
https://www.virtualbraintwin.eu

References: Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2532072123

Keywords

Neuroscience, human cortex, computational neuroscience, whole-brain modelling, The Virtual Brain, acetylcholine, muscarinic receptors, neuromodulation, brain connectivity, sleep, consciousness, cortical dynamics

Traditionally, antipsychotic medications function by blocking dopamine D2 receptors, aiming to reduce hallucinations and delusions. However, these treatments often come with cumbersome side effects and variable efficacy across patients, highlighting an urgent need for innovative therapies with different biological targets. Cobenfy diverges from this paradigm by modulating muscarinic acetylcholine receptors in the nervous system, which may underpin different facets of psychotic symptoms. This mechanistic innovation promises to address treatment-resistant cases and offers a fresh biological pathway to improve patient outcomes.

The study, led by Michael Halassa, a neuroscience professor at Tufts University School of Medicine, leveraged electronic health records from 49 individuals diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder featuring psychotic episodes. All participants were administered Cobenfy alongside their pre-existing antipsychotic regimens after conventional therapies failed to adequately control symptoms. This real-world observational study went beyond controlled clinical trials to explore nuanced, individualized responses in diverse patient populations, aiming for a precision psychiatry approach.

Employing rigorous statistical analyses on two separate patient cohorts, the investigation unearthed distinctive clinical response patterns to the xanomeline-trospium combination, highlighting that psychosis is not a monolithic entity but rather a cluster of biologically distinct subtypes. This finding echoes a shifting paradigm in psychiatry that recognizes the heterogeneity of psychotic disorders, emphasizing the necessity for tailored treatments based on underlying biological and symptomatic profiles rather than a universal pharmacological solution.

One of the study’s pivotal discoveries centered on the subgroup of patients exhibiting pronounced negative symptoms, such as social withdrawal, diminished motivation, and speech reduction. These individuals experienced significant symptomatic relief, including improved mood and enhanced social engagement, after receiving Cobenfy added to their standard antipsychotic drugs. This subgroup’s response indicates that targeting muscarinic receptors might directly influence cognitive and emotional deficits that traditional dopamine-centric drugs often miss.

Conversely, patients presenting predominantly manic or aggressive symptoms associated with bipolar disorder showed minimal benefit from the new treatment, underscoring the drug’s selective efficacy contingent on symptom profiles. Additionally, individuals with intellectual disabilities demonstrated limited improvements, though this observation requires cautious interpretation given the relatively small sample size. The variability in symptom response further asserts the complexity of psychotic disorders and signals limitations of conventional diagnostic categories.

The study also highlighted mixed outcomes for patients suffering from hallucinations, where some experienced moderate improvements while others showed negligible change. This inconsistency suggests that the pathways mediating perceptual disturbances are multifaceted and may require combinatory or adjunctive treatments involving different neurotransmitter systems. These nuanced insights underscore the necessity for comprehensive symptom mapping and longitudinal monitoring in psychiatric care to optimize therapeutic regimens.

Halassa emphasizes that these preliminary findings mark an important move toward precision psychiatry, a developing field that incorporates genetic, cognitive, and biological markers to customize treatments. This approach parallels advances in oncology and immunology, where tailoring therapy based on molecular profiles has transformed prognoses. For psychosis, recognizing distinct subgroups could reduce the prevailing trial-and-error method in medication management, thereby shortening the path to effective recovery and mitigating patient and family burden.

To actualize this vision, Halassa advocates for rigorous clinical trials designed to test various medications across well-characterized patient subsets, tracking symptom trajectories with high granularity over time. This research strategy aims to identify reliable predictive markers of treatment response, improving both therapeutic decisions and clinical outcomes. Such methodology contrasts with conventional clinical trials that often treat schizophrenia as a uniform disorder, potentially diluting therapeutic signals.

Furthermore, Halassa highlights the critical need for clinicians to meticulously document symptom-specific responses rather than solely global improvement scores. Capturing precise data on which symptoms relieve under given medications will create an invaluable dataset to decipher complex treatment dynamics. This data-driven strategy could revolutionize psychosis care by providing actionable insights for clinicians, enabling proactive rather than reactive management.

The implications of this study resonate beyond psychiatry, reinforcing the paradigm that mental illnesses are multifactorial and biologically intricate conditions necessitating equally sophisticated treatment frameworks. As neuroscience tools and computational models become increasingly integrated with clinical research, the hope is to not only improve symptom control but also address the cognitive and functional impairments that impair quality of life in these patients.

In conclusion, this research represents a beacon of hope for individuals living with psychotic disorders and their families. It paves the way for intelligent therapeutics tailored to individual biological and clinical profiles, promises a departure from ineffective one-size-fits-all approaches, and sets a new benchmark in psychiatric innovation. While further validation through expansive, controlled studies remains essential, the initial evidence reveals that the future of psychosis treatment lies in embracing its complexity with precision medicine tools.

Subject of Research: People

Article Title: Preliminary real-world predictors of response to muscarinic targeting in psychosis

News Publication Date: 6-Nov-2025

Web References:
10.1038/s44220-025-00529-w

References:
Halassa, M. et al. (2025). Preliminary real-world predictors of response to muscarinic targeting in psychosis. Nature Mental Health.

Keywords:
Schizophrenia, Psychosis, Precision psychiatry, Muscarinic receptors, Xanomeline, Trospium chloride, Bipolar disorder, Negative symptoms, Antipsychotics, Treatment response