When Tremor Treatment Accidentally Illuminates Psychosis

A groundbreaking study by Mackenzie et al. (2025, bioRxiv) has provided some of the strongest evidence yet for a circuit-level understanding of belief formation and revision. The researchers weren’t studying schizophrenia at all—they were investigating patients receiving focused ultrasound treatment for essential tremor. But when post-surgical brain swelling accidentally affected the mediodorsal (MD) thalamus, something remarkable happened: patients developed a specific pattern of overconfident decision-making that mirrors core features of delusional thinking.

Using a sophisticated behavioral task that probes how people balance exploiting known information versus exploring new possibilities, the researchers found that MD disruption led to a precise computational deficit: patients lost their capacity for adaptive doubt. They became overly confident in their existing beliefs and stopped seeking information that might challenge those beliefs—the very cognitive pattern we see in psychotic disorders.

The Neurobiology of Certainty Gone Wrong

This finding connects directly to my clinical experience treating patients with schizophrenia. In my practice, I’ve observed that the challenge isn’t simply that patients hold false beliefs—it’s that they hold beliefs with pathological certainty. The traditional psychiatric focus on the content of delusions may be missing the more fundamental issue: a breakdown in confidence calibration.

The MD thalamus appears to act as a critical “confidence regulator” in the brain’s decision-making networks. When functioning normally, it helps determine how much we should trust our own predictions versus remaining open to new information. This circuit-level understanding aligns with emerging theoretical frameworks about how the brain coordinates distributed computations for flexible cognition (Scott et al., 2024).

Consider the implications: if the thalamus normally helps us maintain appropriate uncertainty about our beliefs, then thalamic dysfunction could explain why patients with schizophrenia often exhibit such rigid certainty in their delusional beliefs. They haven’t simply acquired false information—they’ve lost the neural capacity to doubt what they think they know.

From Confidence to Delusions: A Circuit-Based Understanding

The Mackenzie study reveals something crucial about the computational nature of belief updating. When MD-prefrontal circuits were disrupted, patients didn’t simply become perseverative or confused. Instead, they showed a specific pattern:

• Increased reward sensitivity: Greater influence of learned values on choices
• Eliminated exploration bonus: Loss of information-seeking behavior
• Overexploitation: Excessive reliance on existing knowledge
• Reduced directed exploration: Failure to investigate uncertain but potentially informative options

This behavioral signature maps remarkably well onto what we observe clinically in psychotic disorders. Patients with delusions often show:

• Pathological certainty in false beliefs despite contradictory evidence
• Reduced information-seeking that might challenge their beliefs
• Overreliance on internal models rather than external feedback
• Failure to update beliefs when environmental contingencies change

The convergence is striking and suggests we may be looking at the same underlying computational dysfunction from different angles—one measured in the laboratory, the other observed in the clinic.

The Promise of Circuit-Based Psychiatry

This research opens exciting possibilities for precision approaches to treating schizophrenia. Rather than the broad neurochemical interventions we currently rely on, we might be able to target specific computational dysfunctions in thalamocortical circuits.

The study’s anatomical precision is particularly encouraging. The behavioral effects correlated specifically with disruption of the lateral (parvocellular) MD, which connects primarily to dorsolateral prefrontal cortex and frontal pole—regions critical for cognitive flexibility and belief updating. This anatomical specificity suggests that focused neuromodulation approaches could potentially restore more adaptive confidence calibration without affecting other brain functions.

Clinical Implications for Treatment

In my practice, I’ve been developing approaches that integrate computational insights with traditional psychiatric care. The MD thalamus findings suggest several potential therapeutic directions:

1. Targeted Neuromodulation: Technologies like focused ultrasound or deep brain stimulation could potentially modulate MD activity to restore appropriate exploration-exploitation balance.
2. Confidence Calibration Training: Cognitive interventions could be designed specifically to help patients develop more accurate metacognitive awareness of their own uncertainty.
3. Precision Diagnostics: Computational tasks like the restless bandit could help identify specific cognitive profiles and guide personalized treatment approaches.
4. Early Intervention: Understanding confidence miscalibration as a core deficit could lead to earlier detection and intervention before full psychotic episodes develop.

Beyond Schizophrenia: A New Framework for Mental Health

The implications extend beyond schizophrenia to other conditions where belief updating goes awry:

• Depression: May involve underconfidence leading to learned helplessness
• Anxiety disorders: Could reflect miscalibrated threat assessments
• Substance use disorders: Might involve overconfidence in drug-related beliefs
• Obsessive-compulsive disorder: May reflect inability to achieve confidence in safety

This represents a fundamental shift from thinking about psychiatric symptoms as categorical disease states toward understanding them as specific computational dysfunctions in learning and decision-making algorithms.

The Clinical Reality: From Lab to Bedside

As someone who treats patients with schizophrenia daily, I’m acutely aware of the challenges in translating neuroscience findings into clinical practice. However, this study is particularly compelling because it provides causal evidence in humans—not just correlational findings from neuroimaging studies.

The patients in the Mackenzie study didn’t lose their ability to learn or make decisions entirely. They maintained overall task performance while showing specific deficits in belief updating and uncertainty management. This selectivity suggests that interventions targeting MD-prefrontal circuits might improve cognitive flexibility without causing global cognitive impairment.

Looking Forward: A Personal Perspective

For me, this research represents something I’ve been working toward throughout my career: a true bridge between basic neuroscience and clinical psychiatry. The fact that these insights emerged from a completely different clinical context—tremor treatment—underscores how interconnected our understanding of brain function really is.

In my clinical work, I’ve seen how traditional approaches to schizophrenia, while helpful, often fall short of fully restoring cognitive flexibility and adaptive functioning. Understanding the neural basis of confidence calibration offers hope for more targeted, effective interventions.

The convergence between this human lesion study and years of animal research on thalamic function (including work from our lab and others) gives me confidence that we’re identifying fundamental principles of brain organization rather than isolated curiosities. When different methodologies and species point toward the same underlying mechanisms, it usually means we’re onto something important.

The Road Ahead

Several critical questions remain:

1. Reversibility: Can confidence calibration deficits be restored through targeted interventions?
2. Early detection: Could computational tasks identify at-risk individuals before psychotic episodes?
3. Personalized medicine: How can we match specific circuit dysfunctions to optimal treatments?
4. Combination approaches: How might neuromodulation combine with cognitive and pharmacological interventions?

As we move forward, the goal isn’t to replace current treatments but to enhance them with circuit-based insights. The patients I treat deserve approaches grounded in rigorous understanding of how their brains actually work, not just symptomatic management.

Conclusion: When Certainty Becomes the Enemy

The patients in the Mackenzie study teach us something profound about the nature of adaptive cognition: the capacity to doubt ourselves, when doubt is warranted, may be one of our most important mental faculties. When that capacity is lost—whether through thalamic dysfunction, psychiatric illness, or other causes—we become trapped by our own certainty.

This has broader implications beyond psychiatry. In an era of polarization and “alternative facts,” understanding the neural basis of belief formation and revision is more important than ever. The same circuits that go awry in schizophrenia may also be involved in more everyday forms of rigid thinking and confirmation bias.

For my patients with schizophrenia, this research offers something precious: hope for treatments based on understanding rather than trial and error. Instead of simply suppressing symptoms with broad-acting medications, we may soon be able to restore the specific cognitive functions—like appropriate confidence calibration—that enable adaptive functioning in a complex, uncertain world.

The brain’s confidence problem is solvable. And that gives me confidence that better treatments are within reach.

This research builds on extensive work linking thalamic circuits to cognitive flexibility and psychiatric disorders, offering new insights into the computational basis of belief updating and its therapeutic implications.

References:

• Mackenzie, G., et al. (2025). Focused ultrasound neuromodulation of mediodorsal thalamus disrupts decision flexibility during reward learning. bioRxiv.
• Scott, D.N., Mukherjee, A., Nassar, M.R., & Halassa, M.M. (2024). Thalamocortical architectures for flexible cognition and efficient learning. Trends in Cognitive Sciences, 28(7), 639-652.

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.