Invasive Computational Psychiatry

Abstract

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.

Keywords: Algorithmic targeting; Computational psychiatry; DBS; ECoG; Intracranial neuroscience; sEEG.

Figure 1.

A primer on computational psychiatry. (A) The influential Marr’s trilevel of analysis sets the theoretical foundation for computational psychiatry, which proposes that to study the brain, one must consider multiple levels of explanation including the goal (“why”), the algorithmic representation (“how”), and finally the physical implementation (e.g., neural substrates, “where”). (B) Cognitive models such as Bayesian and reinforcement learning, biophysical models such as dynamic causal modeling (DCM), and machine learning models such as support vector machine or deep learning, constitute the 3 main categories of models commonly used in neuropsychiatry research.

Figure 2.

Opportunities for invasive computational psychiatry. Computational psychiatry provides quantitative models that describe behavior and specify the underlying computations (1) and allows identifying regions and computations affected by disease status (2). The combination with existing intracranial approaches (3 and 4) opens the door to more detailed depictions of the neurophysiological basis of behavior (i.e., activity across frequency bands, fine temporal resolution) and its associated computations (5) and to the development of anatomically targeted neurostimulation paradigms (6). Currently, this methodological approach can be carried out opportunistically by leveraging the existence of psychiatric comorbidities in patients undergoing neurosurgical interventions or through the development of ad hoc interventions if sufficient evidence exists to support novel invasive clinical trials. DBS, deep brain stimulation; sEEG, stereotactic electroencephalography.