Gaussian process linking functions for mind, brain, and behavior

Abstract

The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., electroencephalogram [EEG], functional MRI [fMRI]) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models. Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain–behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proved prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, nonparametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model’s performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fitted to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data and reveals interesting latent cognitive dynamics, the topology of which can be contrasted with several aspects of the experiment.

Understanding neural signals of post-decisional performance monitoring: An integrative review

“…A complementary approach to link both aspects would be through model-based EEG analysis, as was previously successfully employed for perceptual decisions ( Berberyan et al, 2021 ; Philiastides and Sajda, 2006 ; van Vugt et al, 2012 ) and associated confidence judgments ( Pereira et al, 2020 ). Potential approaches could be to include single-trial Pe amplitudes as regressors when estimating parameters of post-decisional evidence accumulation models (for a related approach, see: Desender et al, 2019b ), or to jointly model behavioral data and single-trial Pe amplitudes ( Bahg et al, 2020 ).…”

Integration of structural brain networks is related to openness to experience: A diffusion MRI study with CSD-based tractography

“…The mean of the resulting probabilistic distribution represents the most probable description of the data ( Rasmussen and Williams, 2016 ). Consequently, GPR is one of the most versatile non-linear methods in the field of machine learning and recently has been widely used in neuroscience and psychology ( Caywood et al, 2017 ; Bahg et al, 2020 ). We compared results from GPR with an exponential kernel, with the linear regression model results to see if a non-linear approach can better describe our data compared with a linear model.…”

Nonlinear expression patterns and multiple shifts in gene network interactions underlie robust phenotypic change in Drosophila melanogaster selected for night sleep duration

“…they implicitly assume genes do not interact in any way. Gaussian Processes can however be extended to include covariance between two or more sets of observations, a formulation that seems to be underexploited in the biological literature (but see Velten et al (2020) and Bahg et al (2020) ). The different dependent variables y i are sometimes called channels or tasks, and the resulting model is called a multi-task or multi-channel Gaussian Process.…”

“…they implicitly assume genes do not interact in any way. Gaussian Processes can however be extended to include covariance between two or more sets of observations, a formulation that seems to be underexploited in the biological literature (but see Velten et al (2022 ) and Bahg et al . ( 2020 )).…”