Engineering the Mind: Actionable Technical Requirements for Innovation in Clinical Neuroscience

Abstract

With significant investments in neuroscience over the past years, our understanding of the brain, its link to behavior, and related disorders has greatly improved. However, a notable gap persists in applying this knowledge to practical clinical interventions, particularly in psychiatry. One key area where this gap is evident is in the limited use of functional neuroimaging in routine psychiatric practice. This stems, at least in part, from a lack of established causal links and neurobiological mechanisms for specific psychiatric disorders or their associated symptoms based on functional neuroimaging. There is a growing consensus in the scientific community that complexity science, dynamical systems theory (DST) in particular, may be key to bridging this gap. Building upon this view, we ask the question which technical prerequisites are required to successfully employ a dynamical systems approach in neuroscience. Specifically, we identify three technical prerequisites for a successful application of DST in neuroscience. In short, three challenges emerge: 1) Temporal incongruities between interventions and their outcomes, manifesting in both scale and lag. 2) The lack of quantitative metrics for assessing the dimensionality of neural activity and behavior. 3) Ambiguity surrounding the state variables in models of complex systems. Addressing these challenges is crucial for initiating a transformative shift in paradigms, potentially leading to more effective neuropsychiatric interventions. We propose that DST-based approaches, particularly those incorporating generative machine learning models could provide solutions to these challenges.