Millennium Problems in Psychiatry
An essay on foundational psychiatric questions and my research interests - and why the next decades of psychiatry will be paradigm-shifting.
I was listening to Sean Carroll’s Mindscape this morning and his conversation with Nicole Rust. They made an interesting observation - put simply, there are three key areas of brain research:
Understanding the brain (Neuroscience)
Building tools related to the brain (ML)
Treating brain disorders (Psychiatry and Neurology)
We’ve made phenomenal strides in areas 1 and 2, but lag behind in 3.
This got me thinking: Mathematics has Hilbert’s problems or Millennium Prize Problems (i.e. some of the complex and influential questions in mathematics). These problems inspire thinkers and act as grand objectives to focus effort - What are psychiatry’s equivalents?
I don’t profess to have the expertise nor the funding to be able to craft these problems - in fact, it’s arguably bad to think about such huge problems too much without having built up the requisite domain knowledge and ability. As a psychiatry-keen medical student and a rookie computational neuroscientist, I fit nicely into the ‘over-optimistic’ chunk on the Dunning-Kruger graph.
That being said, I’ve always found it fun and inspiring to think about the big problems, so one can figure out where the little problems we work on fit in. I’ll sprinkle in some of my current personal research interests too.
I propose two ‘millenium-esque’ questions on the epistemic and biological - with psychiatry’s core challenge being the inability to bridge the domains together, as we’ll discuss below. Endocrinology was flailing around until blood glucose measurement made the symptomatology of Diabetes Mellitus come together (imagine trying to relate numb feet to hypertension and sweet-tasting urine!) - and psychiatry is in the same boat. We lack both a unifying theory and helpful biomarkers to guide our clinical management.
1. The Epistemic: What are the most scientifically valid units of psychiatric phenomena?
Context: A common criticism of psychiatry is the weakness of the DSM-5, the diagnostic criteria largely responsible for our current clinical definitions of mental health (1). It has poor interrater reliability (different clinicians make different diagnoses with the same patient), construct validity (diagnoses are not related to aetiology), and significant comorbidity (diagnoses seem to overlap) (2).
The utility of a diagnosis in medicine comes from its ability to group presentations and thus treat them - to see how psychiatry struggles, one can simply look at the non-specific efficacy of antidepressants on the group known as ‘depression’ (3).
This is further compounded by issues in mental health scales, which are used to guide the development of novel drugs and interventions. Two popular ones used today, the Hamilton Depression Rating Scale (HDRS) and Beck Depression Inventory (BDI), were developed in the post-war era of the 1960s! Surely, with the strides in neuroscience we’ve made since then, there is room for a new scale…
Status Quo: This brings us to the attempts made for new classification systems; with the two most notable being the Research Domain Criteria (RDoC; 2010) and the Hierarchical Taxonomy of Psychopathology (HiTOP; 2017). The two systems make a convincing effort to solve the above - but have struggled to translate clinically, with issues in applicability and biological grounding (4). I haven’t met any clinicians who actively use these frameworks (only researchers), as they tend to be more focused on how a theoretical framework informs treatment.
Personal Directions: I see the difficulty with diagnostic categories as a reflection of the strong network-related interactions between systems. As described by Nicole - there is no simple causal gene → molecule → neuron → network → psychology → symptomatology chain in psychiatry, but instead, feedback loops that regulate every step. Hence, it is unsurprising that our human attempts to carve out linear, discrete categories have failed (barring biologically specific cases like Anti-NMDA receptor (NMDAR) encephalitis). The brain is dynamic and we can’t seek to constrain it.
Hence, I’ve been curious about the following ideas -
Mechanistic Interpretability in Mental Health is an interdisciplinary field that, as far as I’m aware, doesn’t exist yet. But I’ve always been excited by the premise that AI can learn features from high-dimensional spaces that humans can’t (ie the space of psychopathology). With a sufficiently advanced unsupervised learning approach, might they be able to uncover emergent structures from voice, language, and other features like EEG? I believe that for this world to be clinically translated, we need interpretability tools to constrain and define model learnings’ into human-digestible ideas.
Computational Psychiatry provides a new paradigm of atomic units within psychiatry that are formal and falsifiable, incorporating ideas from Bayesian statistics and ML that can be used to study dynamic systems (for instance, uniting the different symptoms of depression under Bayesian reasoning). Like other frameworks, also struggling with clinical translation, but I’ve found the work intellectually appealing!
What does success look like? A new language where how we talk about mental health is grounded in a dynamical, systems-based theory, that has strong explanatory power both in aetiology and treatment.
2. The Biological: How can we translate our neuroscientific understanding to psychiatric management?
Context: For the last 50 years, we’ve made huge strides in neuroimaging, neurophysiology, molecular genetics, and circuit-level understanding of the brain (5). Yet, almost every paper I read in the translational field tends to start with something along the lines of “There are currently no clinically translated biomarkers in psychiatry”.
Biomarkers are useful in disease diagnosis, prognosis, or prediction. In psychiatry, diagnosis has limited utility due to the lack of ground truth - many studies seek to use DSM-V diagnoses or scales as labels for data to compare differences in groups but this approach suffers due to the invalidity of psychiatric categories (see previous section).
There’s also the twin cousin of the ‘biomarker’ story, where it is described that “all psychiatric treatments have been serendipitous rather than hypothesis-driven”. A brief walkthrough of the history of psychiatry and you’ll find a host of stories describing how lithium, tricyclic antidepressants, first-generation antipsychotics all ‘happened to work’.
Status Quo: There is hope in the field that we’re on the precipice of major breakthroughs, and there are some examples of translational advances. For instance, Transcranial Magnetic Stimulation is a relatively new intervention for Treatment Resistant Depression that was borne out of fMRI studies implicating the dorsolateral prefrontal cortex as a key causal node - as such, stimulating this area leads to clinical intervention (6).
However, the field at large is struggling. I’m interested in the Default Mode Network, a resting state network of brain regions related to rumination and self-introspective thought, and thought to be overactivated in depression (7). However, despite the explosion of interest in the field from every measure under the sun (EEG/MEG, dMRI/fMRI, PET), there hasn’t been a clear clinical use case. In particular, it struggles most from methodology heterogeneity - unlike blood-based biomarkers, whose definitions tend to be the same, the DMN is a loose concept in the same way AI is considered by the public.
Personal Directions:
Real-time biomarkers are a broad idea, but one that excites me greatly. Taking the Diabetes example again; in medicine, we have HbA1c which monitors long-term (~3mth) progress, but it is the continuous glucose monitors that can give us actionable information day-to-day, which seems particularly important for psychiatric conditions. In this vein, two particular areas I’m curious about are:
EEG-based network measures of depression, related to what we’re trying to achieve at Resonait.
Blood-based markers of bipolar disorder, given the neurobiological relations to circadian rhythms, HPA axis, and stress response.
Explainable AI is distinct from the mechanistic interpretability described above, although it is a similar concept. Here, I’m more interested in how deterministic ML models (ie an EEG tool that predicts treatment response) can be understood by clinicians not as black boxes with particular sensitivities/specificities, but tools where one can have an intuition of how the outputs might change based on input.
What does success look like? Biomarkers that enable deep, patient-based understanding of psychiatric conditions, and can inform monitoring, prognosis, and treatment selection.
There are so many more challenges in psychiatry than those listed here, and I’ve also narrowed the questions to focus on the domains of my interest - biological and technological innovations - as opposed to more public health approaches (8). I’ve always believed that technological breakthroughs motivate societal shifts, and are an ’easier’ problem to work on.
Psychiatry is in for some huge paradigmatic shifts in the next decades; when else is a better time to contribute to a field?
(1) Shrinks, by Lieberman provides a great overview of the history. These systems have had their benefits, but we need something better!
(2) Fried (2022) provides further citations for these problems and argues for studying mental health as systems rather than syndromes.
(3) Too much literature here to synthesise (probably will be another post), but see Rush et al. (2006) for one of the earliest papers in this field.
(4) See Ross et al. (2019) and Haeffel et al. (2022) for more nuanced critiques.
(5) Google (2024) mapped a 1mm3 chunk of the human temporal cortex down to every synapse, which is around ~1.4PB of electron microscopy data… Wow.
(6) More to come on TMS in a follow-up blog post.
(7) More to come on the DMN as well.
(8) For an example, see Jon Haidt’s After Babel on the movement to ban social media.
Much thanks to Alice Park, Guillaume Troadec, Naveen Golyala, and Alexey Guzey for reading drafts.