Systems Neuroscience as a Foundation for Psychiatric Drug Discovery

Why psychiatric pharma needs a neuroscience update

Jun 28, 2025

The recent Innovation in Psychosis Therapeutics Summit in Boston revealed a clear truth: while the field celebrates breakthroughs like muscarinic agents, it's equally clear that psychiatric drug development needs a neuroscience reboot. We've mastered molecules. What we lack is a model of the mind.

The molecular era of neuroscience has been productive. It gave us the tools to understand neurotransmitter systems, develop targeted receptor modulators, and generate narratives that we can explain to patients and families struggling to understand the burden of mental illness. The dopamine hypothesis, GABAergic interventions, and serotonergic medications established the scientific credibility of early biological psychiatry.

But as became clear throughout the summit discussions and pre-meeting workshops, the extreme focus on molecular details as the 'mechanism of action' misses a larger point. In contrast to other organ systems where knowledge of molecular and cell biology gives one a pretty reasonable understanding of organ function (Cytochrome P450 functioning in a hepatocyte tells you quite a bit about what the liver does in drug detoxification and actin sliding on myosin in a cardiomyocyte explains a lot of what the heart does), understanding action potentials and synaptic transmission tells us very little about how thinking works.

Think of it this way: studying individual brain cells and their chemical signals to understand mental illness is like trying to understand a movie by analyzing the pixels on your TV screen. You can learn a lot about how pixels work (their color values, brightness, refresh rates) but that won't tell you whether you're watching a comedy or a thriller, or why the plot doesn't make sense. The story emerges from how all those pixels work together in patterns over time.

This is psychiatry's fundamental challenge. We've become experts at the "pixels" (the molecular mechanisms, neurotransmitter systems, and individual brain cells). But mental illness isn't a problem with individual pixels. It's a problem with how the brain's software processes information, makes decisions, and builds our sense of reality.

Building the Brain's "Flight Simulator"

What we really need is something like a flight simulator for the brain—computational models that can show us how molecular changes ripple through neural circuits to affect thinking, emotion, and behavior. Just as pilots use flight simulators to understand how adjusting one control affects the entire aircraft's performance, we need brain simulators to predict how a new medication will affect a person's ability to think clearly, regulate emotions, or maintain stable beliefs about reality.

Take depression, for example. Molecular framing focuses on "low serotonin" or other types of "chemical imbalances." But computational models indicate that certain forms of depression have more to do with how the brain learns from rewards and punishments. Imagine your brain has a built-in prediction system that's supposed to help you learn from experience, when good things happen, it should update your expectations upward; when bad things happen, it should adjust appropriately. In depression, this system over-learns from negative experiences and under-learns from positive ones, creating a downward spiral of increasingly pessimistic predictions about the future.

Of course, this algorithm has a neural implementation—involving specific circuits, cell types, and neuromodulators—but the unit of analysis most relevant to symptoms and their relief is the algorithm itself, not the transmitter systems.

Understanding this algorithmic dysfunction opens up entirely new treatment possibilities. Instead of just trying to boost serotonin levels, we can target the specific computational processes that have gone awry.

Recent clinical trials are demonstrating exactly this approach. Researchers have used computational models to predict which patients with depression will respond to cognitive behavioral therapy by measuring how their brains process reward prediction errors during learning tasks (Rzepa et al., 2017). Other studies have shown that computational measures of effort-based decision-making can predict which patients will relapse after stopping antidepressants, identifying a persistent algorithmic dysfunction that outlasts mood symptoms (Berwian et al., 2020).

This isn't just about having more treatment options. It's about matching the right intervention to the right computational problem. Some patients might benefit most from medications that restore balanced reward learning. Others might need brain stimulation that resets dysfunctional prediction circuits. Still others might respond best to digital therapies that provide targeted algorithm retraining.

The Missing Piece: Systems Neuroscience

Here's what's been missing from the molecular-to-computational translation: systems neuroscience. Over the past two decades, this field has exploded with revolutionary tools and insights that completely change how we understand brain function. We can now record from hundreds of neurons simultaneously, manipulate specific cell types with optogenetics, trace connectivity patterns across entire brains, and interpret brain dynamics with unprecedented precision.

These advances have revealed something remarkable: the brain operates through large-scale computational principles that emerge from how circuits are organized and interact. We've discovered that the cortex implements hierarchical predictive processing—constantly generating predictions about incoming information and updating these predictions when they're wrong. We've learned that the dopaminergic system implements temporal difference learning in the brain. We've found that the hippocampus works like a sophisticated pattern-completion system, able to reconstruct entire memories from partial cues by leveraging the same mathematical principles that power modern AI memory networks.

I have been fortunate to establish my lab around the time many of the technical advances in systems neuroscience had come to the fore. Using these tools and working with many talented students and collaborators, we made a series of surprising observations that challenged a long held dogma: the thalamus, considered a major sensory relay station in the brain, plays critical roles in higher cognition. In my own lab, we've used these tools to understand how the thalamus regulates cortical state switching—an operation fundamental to cognitive flexibility and psychiatric dysfunction.

In fact, most of the thalamus in our brains as humans is unlikely to play much of a role in sensory processing. Instead, it dynamically regulates cortical dynamics and implements context-dependent gating of information flow. This discovery emerged from combining well-controlled animal behavior (building on years of work by pioneers in the field), optogenetic manipulations, and high-density neural recordings.

The prefrontal cortex is a critical area in psychiatry because its neurons form coalitions that provide mental simulations, working memory and action plans. My lab among others discovered that inputs from the thalamus are critical for maintaining and switching prefrontal representations underlying these algorithmic processes. In essence, when you need to switch between different mental tasks, thalamic circuits provide the actual switching signals, determining the timing and specificity of cortical state changes.

This has profound implications for understanding cognitive deficits in disorders like schizophrenia. There is good neuroimaging evidence to suggest thalamic dysfunction in schizophrenia and we are in early stages trying to determine whether that may be related to the inability of patients to maintain accurate models of the world, revise their mental simulations when they are implemented or some combination of such processes. Close integration between animal and human work is key to making good progress.

Most importantly, this systems-level understanding opens new therapeutic possibilities. Rather than targeting broad neurotransmitter systems, we might develop interventions that specifically modulate thalamocortical dynamics. For instance, understanding how cholinergic signaling regulates thalamic gating could inform more precise pharmacological approaches. Similarly, targeted neuromodulation techniques could potentially restore proper state regulation in these circuits. However, translating these insights into clinical interventions will require careful validation of the computational models we develop in animals and their relevance to human psychiatric conditions.

Algorithmic Circuit Psychiatry: The Bridge We Need

This is where systems neuroscience becomes the essential bridge between cellular neuroscience and computational science. We can now connect specific molecular mechanisms to circuit dynamics to algorithmic functions—creating what I call "algorithmic circuit psychiatry."

The framework works like this: cellular neuroscience identifies the molecular players (receptors, channels, neurotransmitters), systems neuroscience reveals how these molecules shape circuit dynamics and information processing, and computational science provides the mathematical frameworks to understand what algorithms these circuits implement. Instead of having three separate fields talking past each other, we can trace a coherent path from molecules to circuits to algorithms to symptoms.

Designing the Next Generation of Trials

The clinical application of this framework involves a systematic approach: first, we decompose patient symptoms using computational methods, fitting their behavioral data into mathematical models and extracting specific algorithmic parameters. Next, we use precision neuroimaging to identify the neural circuit alterations underlying these computational dysfunctions. Finally, we leverage mechanistic models built from animal studies to predict which pharmacological and behavioral interventions will restore healthy circuit-algorithm function in each individual patient.

This approach could fundamentally transform psychiatric treatment by replacing trial-and-error prescribing with mechanistically-informed precision medicine. Mental illness is not caused by broken molecules, but by maladaptive computations implemented in circuit dynamics. The treatment of the future won't correct a chemical imbalance—it will recalibrate an algorithm.

Instead of cycling through different medications hoping something works, we could predict treatment response based on each patient's specific pattern of circuit-algorithm dysfunction. The computational parameters tell us what's broken, the neuroimaging reveals where it's broken, and the mechanistic models suggest how to fix it.

The Path Forward: Evolution, Not Revolution

What's most exciting about this moment is that we're not throwing out decades of neuroscience research. Instead, we're building on that solid molecular foundation to create more sophisticated, comprehensive approaches to psychiatric treatment.

This evolution is already transforming drug development in several ways. For smarter target identification, instead of hunting for individual molecules to drug, we can identify key bottlenecks in dysfunctional brain algorithms and ask what molecular interventions might restore healthy computational processes.

We're also developing better animal models. Instead of relying on crude behavioral measures that don't really capture human mental illness, we can focus on algorithmic functions that are conserved across species and ask whether potential treatments restore these core computational abilities.

This approach enables more meaningful biomarkers. Instead of simple blood tests or brain scans, we can develop assessments that capture how well someone's brain algorithms are functioning, providing much richer information for treatment selection and monitoring progress.

Finally, understanding how different interventions work across levels opens up possibilities for rational combination therapies. We might pair a medication that fixes a molecular problem with brain stimulation that resets dysfunctional circuits and cognitive training that helps retrain maladaptive algorithms.

An Invitation to the Future

The conversations following my Boston summit report suggest that the field is ready for this evolution. Researchers across academia and industry are recognizing that our most exciting recent advances have come from thinking about mental illness as a multi-level problem requiring multi-level solutions.

This isn't about abandoning the rigorous molecular research that brought us this far. It's about using that foundation to build something much more powerful—treatments that are informed by molecular mechanisms, guided by circuit-level insights, and targeted toward restoring the algorithms that generate healthy thinking and emotion.

We have the molecular foundation. Circuit-level insights are maturing rapidly. Computational frameworks are emerging from labs around the world. The clinical need remains as urgent as ever.

The pieces are finally in place for a new generation of psychiatric treatments—ones that don't just manage symptoms, but recalibrate the brain's computational machinery for healthy thinking, feeling, and action.

The time for integration is now.

If you're working at the interface of neuroscience, computation, or psychiatry, I'd love to hear your thoughts. The more minds we bring to this integration effort, the sooner patients benefit. Subscribe for updates as this conversation—and the science—continues to evolve.

This piece builds on ongoing conversations following my report from the 2025 Innovation in Psychosis Therapeutics Summit in Boston. For more on the algorithmic circuit psychiatry framework, see my earlier post here. For the full Boston summit report, see here.