Modeling brain, symptom, and behavior in the winds of change

Abstract

Neuropsychopharmacology addresses pressing questions in the study of three intertwined complex systems: the brain, human behavior, and symptoms of illness. The field seeks to understand the perturbations that impinge upon those systems, either driving greater health or illness. In the pursuit of this aim, investigators often perform analyses that make certain assumptions about the nature of the systems that are being perturbed. Those assumptions can be encoded in powerful computational models that serve to bridge the wide gulf between a descriptive analysis and a formal theory of a system’s response. Here we review a set of three such models along a continuum of complexity, moving from a local treatment to a network treatment: one commonly applied form of the general linear model, impulse response models, and network control models. For each, we describe the model’s basic form, review its use in the field, and provide a frank assessment of its relative strengths and weaknesses. The discussion naturally motivates future efforts to interlink data analysis, computational modeling, and formal theory. Our goal is to inspire practitioners to consider the assumptions implicit in their analytical approach, align those assumptions to the complexity of the systems under study, and take advantage of exciting recent advances in modeling the relations between perturbations and system function.

Computational Psychiatry Needs Time and Context

“…The problems themselves can also have what we call meaningful heterogeneity. This is heterogeneity that arises due to the interdependence of the elements that constitute the problem, which makes it difficult to classify them at any one point in time and out of context (Lydon-Staley et al, 2021;Nelson et al, 2017). The three heuristics of this section are meant to build intuition about indicators and practical consequences of meaningful heterogeneity through a series of examples.…”

“…Hierarchical modeling (including frequentist mixed-effects models and hierarchical Bayesian models; see Box 2) offer a statistically principled approach to modeling between and within-subject effects and enjoy widespread use in computational neuroscience and psychiatry. Multilevel vector auto-regressive (VAR) models enable the estimation of some specific temporal effects, permitting examination (for example) of how various emotions predict themselves and other emotions over time (Lydon-Staley et al, 2021). This has allowed researchers to corroborate clinical insights, such as the idea that, among neurotic individuals, worry strengthens the duration of and transition between negative emotions (Bringmann et al, 2013).…”

A single-mode associates global patterns of brain network structure and behavior across the human lifespan

“…As neuroscience shifts towards data-driven approaches, data-fusion methods [114–116] such as CCA are likely to increase in popularity. Yet, the CCA approach used here is limited to two domains at a time.…”

Evolving Networks of Human Intelligence

“…The priorities and accompanying challenges reflect current developments in psychological theory formation. This is evidenced by their alignment with efforts in neighbouring fields such as neuropsychopharmacology (Lydon-Staley et al, 2020). Ultimately, progress on each of the priorities will contribute to an understanding of human's mesmerizing cognitive complexity.…”