Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex.

Antonio Ulloa,Barry Horwitz

doi:10.3389/fninf.2016.00032

Abstract

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were “non-task-specific” (NS) neurons that served as noise generators to “task-specific” neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors.

Highlights

Large-scale neural network models aim to shed light on the mechanisms used by the brain to accomplish goal-directed behavioral tasks
We demonstrated how to embed a previously or newly constructed large-scale neural models (LSNM) that performs one or more specific cognitive tasks into a structural connectome model of the human cerebral cortex that is part of The Virtual Brain (TVB) software framework
The final result of the current paper is a full cerebral cortex model that performs both a visual delayed match-to-sample (DMS) task as well as a control passive viewing task in a part of the brain, and generates inherent activity in the remaining parts of the brain not directly engaged by the task

Summary

Introduction

Large-scale neural network models aim to shed light on the mechanisms used by the brain to accomplish goal-directed behavioral tasks. Examples can be found for resting state data (Honey et al, 2007; Alstott et al, 2009; Cabral et al, 2011, 2012; van Dellen et al, 2013), and for taskbased data (Tagamets and Horwitz, 1998; Horwitz and Tagamets, 1999; Corchs and Deco, 2002; Deco et al, 2004; Husain et al, 2004; Horwitz et al, 2005; Robinson et al, 2005; Ulloa et al, 2008; Peters et al, 2010; Bojak et al, 2011; Banerjee et al, 2012a; Furtinger et al, 2014) Such a modeling framework requires three submodels: a structural model of the anatomical links between brain regions that provides the interregional connection weights; one or more neural models at each node for generating the neural activity; and a forward model that transforms a combination of the neural activity into a neuroimaging signal

Methods

Results

Conclusion