Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry

Abstract

Noninvasive neuroimaging has revolutionized the study of the organization of the human brain and how its structure and function are altered in psychiatric disorders. A critical explanatory gap lies in our mechanistic understanding of how systems-level neuroimaging biomarkers emerge from underlying synaptic-level perturbations associated with a disease state. We describe an emerging computational psychiatry approach leveraging biophysically based computational models of large-scale brain dynamics and their potential integration with clinical and pharmacological neuroimaging. In particular, we focus on neural circuit models, which describe how patterns of functional connectivity observed in resting-state functional magnetic resonance imaging emerge from neural dynamics shaped by inter-areal interactions through underlying structural connectivity defining long-range projections. We highlight the importance of local circuit physiological dynamics, in combination with structural connectivity, in shaping the emergent functional connectivity. Furthermore, heterogeneity of local circuit properties across brain areas, which impacts large-scale dynamics, may be critical for modeling whole-brain phenomena and alterations in psychiatric disorders and pharmacological manipulation. Finally, we discuss important directions for future model development and biophysical extensions, which will expand their utility to link clinical neuroimaging to neurobiological mechanisms.

Keywords: Computational model; Functional connectivity; Neuroimaging; Resting-state; Schizophrenia; Transcriptomics.

Figure 1.

Computational circuit models of resting-state dynamics in the human brain and their application to study of functional dysconnectivity in schizophrenia. (A) Modeling framework. The neurodynamical model simulates dynamical activity of interconnected microcircuits, each representing a parcellated brain area. Structural connectivity, defining the strengths of long-range connections between local areas, is derived from diffusion magnetic resonance imaging tractography. The simulated blood oxygen level–dependent (BOLD) signal yields a model functional connectivity pattern, which can be compared with empirical functional connectivity patterns. The biophysical parameters of the model (e.g., local or long-range synaptic strengths) can then be fitted to optimize this correspondence. (B) Application of the model to study structural and functional dysconnectivity in schizophrenia. The graph-theoretic small-world index is calculated on the simulated functional connectivity, using distinct structural connectivity matrices for control subjects and patients with schizophrenia, as a function of the global coupling strength, and compared with empirically measured values (dotted lines). (C) Expansion of the model to incorporate hierarchical heterogeneity of local circuit properties, specifically stronger recurrent excitation (W) in association with cortical networks compared with sensory cortical networks. Elevated excitation-inhibition ratio increases preferential dysconnectivity in association networks in the model. The difference between association and sensory measures (A – S) of within-network connectivity (covariance) is plotted. (D) Empirical measures of within-network connectivity in schizophrenia reveal preferential increase in connectivity in association networks. DTI, diffusion tensor imaging; E-E, excitatory-to-excitatory; E-I, excitatory-to-inhibitory; HCS, healthy control subjects; n.s., not significant; SCZ, schizophrenia (patients). [(A) Modified with permission from Deco et al. (31); (B) modified with permission from Cabral et al. (37); (C) and (D) modified with permission from Yang et al. (35).]

Figure 2.

Heterogeneity of local circuit properties across human cortex shapes large-scale functional dynamics: insights from transcriptional mapping, computational modeling, and pharmacological neuroimaging. (A) (Top) Structural magnetic resonance imaging–derived T1-weighted (T1w)/T2-weighted (T2w) map over human cortex. The T1w/T2w map exhibits a hierarchical topography, with high values in primary sensorimotor areas and low values in higher-order association areas. (Bottom) The first principal component (PC1) of transcriptional expression levels across human cortex for brain-specific genes. PC1 is the spatial map that captures the maximal gene expression variance across cortical areas. (B) PC1 captures a dominant proportion of gene expression variance (left), and the T1w/T2w map is highly correlated with PC1 (right). (C) Computational modeling of hierarchical heterogeneity in the large-scale model. In contrast to a homogeneous model with uniform values for local circuit parameters across areas (top), in the heterogeneous model, values for local circuit parameters (e.g., recurrent excitatory strength) are scaled according to a heterogeneity map. The heterogeneity map shown is based on the T1w/T2w map to capture specialization along a sensory-association axis related to cortical hierarchy. (D) Hierarchical heterogeneity of local circuit parameters improves the fit to empirical functional connectivity, compared with the homogeneous model or the structural connectivity (SC) (left). The heterogeneous model improves the fit through a gradient of increasing recurrent excitatory strengths along cortical hierarchy (right). (E) (Top) Change in global brain connectivity (GBC) (i.e., mean functional connectivity) on resting-state functional magnetic resonance imaging induced by administration of lysergic acid diethylamide (LSD). (Bottom) Transcriptional expression levels for the gene HTR2A, which codes for the serotonergic 5-hydroxytryptamine 2A receptor. These maps are correlated (r_p = .50), with both exhibiting high values in occipital visual cortex. (F) Distribution across genes of the correlation between the transcriptional expression map and the LSD ΔGBC map. Among putative LSD-related receptor genes, HTR2A exhibits the highest similarity with the LSD ΔGBC map and is in the top 5% of all genes. E-E, excitatory-to-excitatory; E-I, excitatory-to-inhibitory; mRNA, messenger RNA. [(A) and (B) modified with permission from Burt et al. (46); (C) and (D) modified with permission from Demirtaş et al. (33); (E) and (F) modified with permission from Preller et al. (47).]