Why Mental Health Treatments Need Algorithmic “Flight Simulators”
I would like to highlight a recent perspective article that my colleagues and I have recently published: https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(25)00167-3
Despite decades of research and billions in global spending, mental health treatment remains an incomplete puzzle. Nearly one-third of people with schizophrenia remain treatment-resistant, and fewer than 15% experience functional recovery. The lack of progress in psychiatric treatment underscores a pressing need for innovative solutions. While new therapies like xanomeline/trospium—a recently FDA-approved drug targeting muscarinic receptors rather than dopamine receptors—offer fresh hope, they only highlight a more profound issue in the field. Simply put: We still lack a framework for predicting how molecular interventions impact cognition and behavior.
In traditional psychiatry, treatments such as pills, therapy, and brain stimulation are often designed in isolation, targeting one specific aspect of the brain’s complex network. For example, a schizophrenia drug may target dopamine receptors, but it doesn’t predict how its effects will cascade through neural circuits to influence decision-making, emotion regulation, and belief systems. Similarly, cognitive therapies are based on the idea of flexible thinking but fail to account for how underlying molecular deficits may limit their effectiveness.
This disconnect is psychiatry’s fundamental challenge: treatments are designed for isolated components, while their effects unfold across all levels of the brain’s functioning, from molecular mechanisms to cognitive behaviors. This is where algorithmic psychiatry steps in, offering a computational “flight simulator” that can model how perturbations at any level—molecular, circuit, or cognitive—affect the entire system.
What is Algorithmic Psychiatry?
Algorithmic psychiatry combines data from behavioral tasks and neural signals (from EEG, fMRI, or intracranial recordings) to model the brain’s internal processes. These models focus on hidden variables, such as how the brain updates beliefs and expectations, as well as its ability to adjust predictions in response to new information. These variables are what drive psychiatric symptoms, and from a treatment perspective, recalibrating them is the key to success.
For example, imagine a treatment designed to enhance the brain’s ability to predict sensory inputs more accurately, helping to reduce hallucinations in schizophrenia. Another approach might focus on lowering overconfidence in rigid memories, which would improve cognitive flexibility and reduce symptoms of rigidity and paranoia. Together, these interventions aim to re-wire the brain’s internal algorithms, addressing the root causes of symptoms rather than just masking them.
The Promise of Multi-Level Interventions
In algorithmic psychiatry, success is not just about reducing symptoms—it’s about recalibrating the brain’s internal computational processes. It involves not just one intervention, but a combination of approaches that work across different levels of brain function. For instance, pairing a drug that enhances a neurochemical feature with targeted neurostimulation can enhance specific circuits, thereby boosting the drug’s effects. When combined with behavioral therapies that are timed appropriately, this multi-level approach has the potential to rewire the brain for recovery.
This approach goes beyond symptom control—it focuses on designing treatments that consider the entire biological, cognitive, and neural network. The idea is that by interacting with the brain’s “software” (its internal computations) and improving its “hardware” (its neurochemical and neural networks), we can create truly transformative treatments for mental health.
The Road Ahead for Algorithmic Psychiatry
The goal of algorithmic psychiatry is to create a precision psychiatry model, where treatments are individualized based on how each patient’s brain is wired. This model offers new hope for those suffering from chronic and treatment-resistant conditions like schizophrenia. Instead of simply targeting symptoms with broad drugs, this approach focuses on understanding and recalibrating the brain’s underlying computations.
While this vision of “flight simulator” models is still evolving, the potential for better-targeted treatments is already within reach. With advancements in computational neuroscience, machine learning, and neurostimulation, we are beginning to see the first real glimpses of how these multi-level, algorithmic treatments could revolutionize mental health care.
As we continue to refine these models and develop new technologies, we move closer to the promise of a psychiatry that is not just based on treating symptoms, but on re-wiring the brain for a more functional and flexible future.