From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis

Abstract

As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term "mechanistic connectome." The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.

Keywords: Dynamic Neural Model; biophysical network model; brain network; computational modeling; connectome; dynamical causal model; fMRI; neural mass model.

FIGURE 1

Overview of DCM. In DCM, neural interactions among different brain regions (R1, R2, etc.) are described by a bilinear model. Effective connectivity includes both a baseline (a₂₁ a₃₁, etc.) and a modulatory component (e.g., b²₃₁) due to exogenous experimental inputs (u₁, u₂), whereas the matrix C represents the influence of external inputs on neural activity. The regional neural activity [x(t)] is converted to BOLD response [y(t)] via a biophysical hemodynamic model. With individual empirical fMRI data, DCM estimates effective connectivity (the matrices A and B) as well as the matrix C using Bayesian estimation technique.

FIGURE 2

Overview of BNM. BNM is large-scale whole brain network model containing up to hundreds of network nodes (i.e., brain regions). For a parcellation template, the connectivity of different brain regions is determined by structural connectivity (SC) informed from diffusion tensor imaging (DTI) tractography. The SC is scaled by a global coefficient to model synaptic efficiency or strengths among remote neural populations. The neuronal dynamics of each network node is described by first-order differential equation modeling the membrane potential of individual neurons. The regional neural activity [x(t)] is transformed into BOLD signal [y(t)] via a hemodynamic model from which simulated functional connectivity (FC) is computed. BNM fits simulated FC to empirical FC by optimizing the global scaling coefficient. After fitting, BNM can be applied to simulate fMRI response and study the relationship between SC and FC.

FIGURE 3

Overview of the MNMI framework. The neural activity [x(t)] is generated by the Wilson-Cowan network model (Wilson and Cowan, 1972) consisting of multiple brain regions (R1, R2, etc.). Each region contains one excitatory (E) and one inhibitory (I) neural populations coupled with reciprocal connections and receives spontaneous input (u). Different brain regions are connected via long-range fibers whose baseline strengths are determined by structural connectivity from diffusion MRI. The regional neural activity is converted to corresponding BOLD signal [y(t)] via a hemodynamic model (Friston et al., 2003). Both intra-regional recurrent excitation (W_EE) and inhibition (W_IE) weights and inter-regional connection strengths (W₁₂, W₂₁, etc.) as well as spontaneous input (u) are estimated using genetic algorithm to maximize the similarity between simulated and empirical FC. Adapted from Li et al. (2021) under the Creative Commons Attribution License (CC BY).

FIGURE 4

A hypothetic model of executive-limbic malfunction in MDD. MDD is mediated by increased recurrent excitation in the superior parietal cortex (SPC) and greater inhibition from the SPC to the dorsolateral prefrontal cortex (dlPFC), leading to increased SPC activity and decreased dlPFC response, which may underlie deficit cognitive control and biased attention for negative stimuli. In addition, the excitatory drive from the SPC to the amygdala is abnormally elevated in MDD. Combined with reduced recurrent inhibition, the amygdala shows hyperactivity which causes increased anxiety and biased processing of negative stimuli. Besides, the inhibitory drive from the SPC to the thalamus is reduced while the excitatory projection from the dlPFC to the hippocampus is abnormally decreased in MDD. The former change could result in abnormal brain oscillations and insomnia while the latter change could account for impaired memory function and biased memory for negative stimuli. The blue arrows indicate the change of the connection strengths in MDD from normal control. The pink UP/DOWN arrows next to the brain regions indicate the change in neural responses in MDD compared to normal control. Adapted from Li et al. (2021) under the Creative Commons Attribution License (CC BY).

FIGURE 5

A unified mechanistic pipeline for generative model-based neuroimaging analysis to treat brain disorders. In this analysis pipeline, individual subjects first undergo multiple neuroimaging scans/recordings such as MRI, EEG and MEG. The multi-modal neuroimaging data are then combined with data fusion and fed into the generative model to estimate individualized EC and other relevant physiological parameters such as neuromodulatory levels. The estimated model parameters are then fed back into the generative model to simulate existing treatment response and new treatment development as well as their side effects. Based on the simulation outcome, the generative model will predict optimal treatment strategy for the patient along with drug dose or stimulation parameters.