Imagine your brain as an iPhone. In a healthy state, all your apps—email, maps, music, social media—run smoothly, even if they occasionally compete for resources. For example, your email app might want to check for new messages while your music app tries to play a song. The phone’s operating system ensures that these apps don’t interfere with each other, prioritizing one task at a time and maintaining overall functionality.

Now, imagine what happens when the operating system starts to fail. Apps crash, freeze, or behave unpredictably. They might run simultaneously, draining the battery and overloading the system, or they might shut down unexpectedly, leaving the phone unresponsive. The once-coordinated system becomes chaotic, and the phone becomes nearly unusable.

This analogy may capture something interesting about how the brain functions. Like the iPhone, the brain is not a singular entity but a coalition of distributed systems, each optimized for specific computational tasks. In health, these systems are harmonized by executive control mechanisms. But in conditions like mania or psychosis, this coordination can break down, revealing the tension between competing systems.

Understanding this framework has helped me make sense of patients and approach their care more effectively. It has also enhanced my ability to mentor other healthcare providers, offering them a new lens through which to view mental illness and treatment.

The Neuroscience of Distributed Systems

The brain is a coalition of distributed systems, each optimized for specific computational tasks. These systems operate in parallel, often with overlapping but distinct objectives, and their interactions give rise to coherent behavior and thought. Two key systems—reward-seeking and predictive—illustrate how these systems work together, even as their differing goals can create tension.

Reward-seeking systems are optimized to identify and pursue rewards, whether they are immediate (e.g., eating a delicious meal) or long-term (e.g., achieving a career goal). These systems rely on mechanisms like reinforcement learning to update strategies based on feedback. They drive goal-directed behavior, habit formation, and decision-making, but they can also prioritize short-term rewards over long-term stability, leading to conflicts with other systems.

Predictive systems, on the other hand, are optimized to build and maintain a stable model of the world. They use mechanisms like predictive coding to minimize uncertainty, allowing the brain to anticipate future events and adjust behavior accordingly. These systems underpin perception, attention, and belief formation, but they can also resist updating beliefs in light of new evidence, leading to rigidity or maladaptive behaviors.

These systems interact dynamically to produce behavior. For example, the value assigned to an action by reward-seeking systems can shape predictions about future outcomes, while predictions about the likelihood of rewards can influence which actions are pursued. However, their differing objectives can create tension. Reward-seeking systems may prioritize immediate gratification, while predictive systems emphasize long-term stability. Similarly, reward-seeking systems drive exploration (trying new strategies to maximize rewards), while predictive systems favor exploitation (relying on stable, predictable models).

Executive Control: Harmonizing the Coalition

Executive control mechanisms act as the brain’s “operating system,” integrating signals from reward-seeking and predictive systems and resolving conflicts. For example, executive control may suppress impulsive actions driven by reward-seeking systems in favor of actions that align with long-term goals. It may also update predictive models when new evidence contradicts prior beliefs, ensuring that behavior remains adaptive.

In healthy individuals, this coordination allows for flexible, goal-directed behavior. But in conditions like psychosis or mania, executive control is compromised, and the tension between systems becomes more apparent. For example, hyperactivity in reward-seeking systems may lead to impulsive behavior and excessive goal-directed activity, while predictive systems struggle to maintain stability. Aberrant predictive systems may result in hallucinations (overweighting prior beliefs) or delusions (failure to update beliefs in light of new evidence), while reward-seeking systems reinforce maladaptive behaviors.

Clinical Implications: Treating the Coalition

This framework has important implications for treatment. Rather than viewing the patient as a singular entity with a unified set of beliefs and behaviors, clinicians can recognize the multiplicity of systems at play. By identifying and targeting the system most responsive to treatment, they can adjust medications and therapeutic interventions more effectively.

For instance, a patient experiencing conflicting beliefs about their illness might benefit from interventions that strengthen executive control, such as cognitive-behavioral therapy (CBT) or mindfulness practices. Medications can be tailored to address the specific systems contributing to symptoms, whether they involve dopamine dysregulation, glutamate imbalances, or other mechanisms.

Philosophical and Psychological Perspectives

This idea aligns with both psychodynamic theory and modern neuroscience. Psychodynamic theorists have long emphasized the role of internal conflict in mental illness, often framing it as a struggle between conscious and subconscious forces. Neuroscience provides a complementary perspective, grounding these conflicts in the activity of distributed systems.

This framework also challenges traditional notions of the self. Rather than a singular, unified entity, the self emerges from the dynamic interplay of multiple systems, each with its own objectives and priorities. This perspective can reduce stigma by framing mental illness as a breakdown in coordination, rather than a fundamental flaw in the individual.

Conclusion: Embracing the Complexity of the Mind

The brain is not a monolithic entity but a coalition of distributed systems, each optimized for specific computational tasks. In health, these systems are harmonized by executive control. But in conditions like psychosis and mania, this coordination breaks down, revealing the tension between competing systems.

By embracing this framework, clinicians can develop more nuanced and effective treatments, tailored to the specific systems at play. Patients, too, can benefit from this perspective, which reframes mental illness as a disruption in coordination rather than a failure of the self. In doing so, we can move closer to a future where mental health is understood not as the absence of conflict, but as the ability to harmonize the brain’s many voices.

References

1. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
2. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127-138.
3. Maia, T. V., & Frank, M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14(2), 154-162.

Schizophrenia is a complex mental health condition characterized by disruptions in thought processes, perception, and behavior. Among its myriad symptoms, executive dysfunction—challenges in making decisions and adapting to new information—is particularly debilitating and poorly understood. The newly published paper, in the relatively new journalCell Reports Medicine, sheds light on this issue, uncovering a crucial neural biomarker that links the mediodorsal (MD) thalamus and prefrontal cortex (PFC) to difficulties in processing conflicting information.

The Challenge of Cognitive Rigidity

Executive function allows us to navigate uncertainty and make decisions based on incomplete or conflicting information. For most people, this ability is seamless, like choosing to revisit a favorite restaurant despite one bad experience. However, for individuals with schizophrenia, this cognitive flexibility often breaks down, leading to inflexible and maladaptive decision-making.

Our research aimed to uncover the biological underpinnings of this rigidity. We hypothesized that disruptions in communication between the dorsolateral prefrontal cortex (dlPFC) and the MD thalamus—a network critical for integrating and resolving conflicting information based on our previous animal work—might be responsible.

Translating Insights from Animals to Humans

Building on a previous study led by postdocs Arghya Mukherjee and Norman Lam, where they developed a decision making task for allocating attention based on ambiguous cues, we developed an analogous human task which we could correlate with neuroimaging readouts. Anna and Neil recruited 40 participants, both neurotypical individuals and patients with schizophrenia, to perform tasks that required selecting between a visual and auditory target based on ambiguous cues on single trials. Healthy participants maintained high performance even in challenging scenarios, but those with schizophrenia struggled as ambiguity increased, revealing an elevated susceptibility to sensory noise. This was qualitatively similar to what we had observed in mice with optogenetic suppression of the MD thalamus!

A Novel Biomarker for Schizophrenia

In a subset of patients, resting state functional MRI data was available, so we were able to examine functional connectivity (the pearson correlation of BOLD signals across time) in various brain networks and examine if any correlated with the behavioral measures we observed. Remarkably we found that a network involving the right MD and dlPFC showed a significant correlation with the behavioral ambiguity threshold derived from the aforementioned task. This correlation generalized to an entirely new dataset involving the ability of patients to perform a working memory task in the presence of a conflicting input. All told, we think that this neural readout may represent a potential biomarker for conflict-related executive dysfunction in schizophrenia. Optimistically, it provides a measurement that may track certain forms of cognitive flexibility, paving the way for precision diagnostics and targeted interventions.

Toward Better Treatments

One of the most exciting implications of this study is its potential for patient stratification and the prediction of treatment efficacy. By combining the data from these types of measurements with other phenotyping efforts in the field, we might be able to identify subtypes of schizophrenia spectrum disorder that responds to certain antipsychotics, cognitive remediation or non-invasive neurostimulation. This aligns with our broader goal of bridging neuroscience research and clinical application to better serve patients.

Looking Ahead

Our next steps include validating these results in a larger and more diverse cohort. Additionally, we aim to expand the scope of our tasks to include more hierarchical decision-making scenarios that mirror everyday challenges. This will further refine our understanding of how thalamocortical disruptions impact real-world cognition in schizophrenia.

This study was a true collaborative effort between our two groups—a remarkable interdisciplinary effort among people who come from diverse intellectual backgrounds yet share similar goals of making a difference for science and humanity. I am extremely grateful to play a small part in this remarkable scientific story.

I had the unique opportunity to hear a talk by Steve Brannan, one of the key figures behind the development of KarXT (sold as Cobenfy in the U.S.). This happened just a single day before the FDA approved Cobenfy for the treatment of schizophrenia. Steve (Moss) and I talked to Brannan for about 30 minutes after the symposium and it was quite fascinating to learn about some of the challenges their team had to overcome during the process of drug development, clinical trials and finally FDA ruling.

As many readers may already know, Brannan served as the principal investigator in the pivotal clinical trials that brought KarXT from concept to clinic, such as EMERGENT-1, EMERGENT-2 and EMERGENT-3, showing the drug’s potential to address some of the unmet needs in schizophrenia treatment.

This development is especially exciting because KarXT represents a significant shift in how we approach schizophrenia pharmacotherapy. It leverages a different cellular mechanism of action compared to traditional antipsychotics, which may be related to its potential utility in areas where previous medications have stalled.

The Unique Mechanism and Composition of KarXT

KarXT is unique among antipsychotics because it targets muscarinic acetylcholine receptors rather than the dopamine receptors that have been the primary focus of antipsychotic treatments for decades. This two-drug combination consists of xanomeline and trospium.

• Xanomeline is a muscarinic receptor agonist, meaning it stimulates these receptors, which are involved in various central nervous system processes, including cognition, mood, and motor control.
• Trospium, on the other hand, is a peripherally acting muscarinic antagonist. Its role is to block the effects of xanomeline in the peripheral nervous system, which helps to minimize side effects such as gastrointestinal and cardiovascular issues that are often associated with muscarinic activation outside the brain.

This combination allows xanomeline to act primarily in the central nervous system, where it is believed to modulate circuits involved in the symptoms of schizophrenia. The ability to target the muscarinic system with minimal peripheral side effects is what sets KarXT apart from previous attempts to develop muscarinic-based therapies.

Targeting Negative and Cognitive Symptoms

One of the most exciting aspects of KarXT is its potential to target negative and cognitive symptoms, areas where current antipsychotic medications have largely fallen short. Negative symptoms—such as emotional blunting, anhedonia, and reduced social engagement—are often more disabling for patients, but they’ve proven notoriously difficult to treat. Cognitive deficits, such as poor attention, working memory issues, and difficulties with executive function, also represent significant barriers to a better quality of life for patients.

While positive symptoms like hallucinations can be fairly well controlled with dopaminergic medications, these negative and cognitive domains have been less responsive to treatment. In secondary analyses of the KarXT trials, some promising data has emerged regarding its efficacy in improving these difficult-to-treat symptoms. Perhaps this is related to KarXT’s unique pharmacology?

Caution: Not Without Side Effects

As with any new medication, there are side effects to consider. In clinical trials, some of the common side effects associated with KarXT included gastrointestinal issues like nausea, vomiting, and constipation. These muscarinic-related side effects are different from those associated with traditional antipsychotics, but they can still impact patient adherence and quality of life.

It’s crucial that clinicians remain vigilant when prescribing this drug, balancing the potential benefits with these side effects, particularly in patients who may already have sensitivities or complications due to other medications.

The Future of Psychiatric Treatment and the Role of KarXT

Looking ahead, KarXT’s approval opens up exciting new possibilities not just as a standalone treatment but also within the context of polypharmacy, which is often necessary in treating patients. Could KarXT be used to augment other antipsychotics, especially in patients who experience partial responses to dopaminergic treatments?

KarXT’s muscarinic mechanism may complement the dopaminergic treatments already in use, targeting specific symptom clusters more effectively when used together. It’s also possible that KarXT could lead to a deeper understanding of the neurobiology of schizophrenia itself. By engaging muscarinic receptors in patients, we may gain a better understanding of how the cholinergic system may contribute to symptoms, brain readouts or computational processes in individual patients, potentially leading to more targeted therapies in the future.

This idea of tailoring treatment more precisely mirrors the principles of algorithmic psychiatry, which I explored in an earlier post titled A Blueprint for Algorithmic Psychiatry: Revolutionizing Mental Health Treatment. In that piece, I discussed how the future of mental health care might lie in predictive models that leverage latent features and neurobiological data. KarXT’s novel mechanism may well fit into such frameworks, offering ways to ‘nudge’ the system in an interpretable manner.

Conclusion

KarXT is not a magic bullet, but it represents a fresh addition to the treatment of schizophrenia. Its distinct mechanism of action, combined with encouraging data on negative and cognitive symptom improvement, makes it a valuable new option for patients and clinicians alike. Of course, as with any new treatment, there are side effects and limitations to consider, but the approval of KarXT opens the door to a more nuanced, personalized approach to managing schizophrenia.

As we learn more about how this medication fits into the broader landscape of psychiatric treatment, there is hope that KarXT’s muscarinic mechanism could inspire the development of other innovative treatments, broadening our ability to treat this complex disorder in all its dimensions.

References

1. Kaul, I., Sawchak, S., Correll, C. U., Kakar, R., Breier, A., Zhu, H., Miller, A. C., Paul, S. M., & Brannan, S. K. (2024). Efficacy and safety of the muscarinic receptor agonist KarXT (xanomeline-trospium) in schizophrenia (EMERGENT-2) in the USA: results from a randomised, double-blind, placebo-controlled, flexible-dose phase 3 trial. Lancet (London, England), 403(10422), 160–170. https://doi.org/10.1016/S0140-6736(23)02190-6
2. Kaul, I., Sawchak, S., Walling, D. P., Tamminga, C. A., Breier, A., Zhu, H., Miller, A. C., Paul, S. M., & Brannan, S. K. (2024). Efficacy and Safety of Xanomeline-Trospium Chloride in Schizophrenia: A Randomized Clinical Trial. JAMA psychiatry, 81(8), 749–756. https://doi.org/10.1001/jamapsychiatry.2024.0785
3. Brannan, S., Presentation at the 7th Neuropsychiatric Drug Development Summit, Boston, September 2024.
4. FDA Approves KarXT for Schizophrenia Treatment. (2024). NPR– https://www.npr.org/2024/09/26/nx-s1-5123694/for-the-first-time-in-decades-the-fda-has-approved-a-new-type-of-schizophrenia-drug
5. Previous blog post on algorithmic psychiatry. https://michaelhalassa.com/a-blueprint-for-algorithmic-psychiatry-revolutionizing-mental-health-treatment/.