Modeling the Functional Network for Spatial Navigation in the Human Brain

Summary

This paper presents an integrative approach to investigating the functional network for spatial navigation in the human brain. This approach incorporates a large-scale neuroimaging meta-analytic database, resting-state functional magnetic resonance imaging, and network modeling and graph-theoretical techniques.

Abstract

Spatial navigation is a complex function involving the integration and manipulation of multisensory information. Using different navigation tasks, many promising results have been achieved on the specific functions of various brain regions (e.g., hippocampus, entorhinal cortex, and parahippocampal place area). Recently, it has been suggested that a non-aggregate network process involving multiple interacting brain regions may better characterize the neural basis of this complex function. This paper presents an integrative approach for constructing and analyzing the functionally-specific network for spatial navigation in the human brain. Briefly, this integrative approach consists of three major steps: 1) to identify brain regions important for spatial navigation (nodes definition); 2) to estimate functional connectivity between each pair of these regions and construct the connectivity matrix (network construction); 3) to investigate the topological properties (e.g., modularity and small worldness) of the resulting network (network analysis). The presented approach, from a network perspective, could help us better understand how our brain supports flexible navigation in complex and dynamic environments, and the revealed topological properties of the network can also provide important biomarkers for guiding early identification and diagnosis of Alzheimer's disease in clinical practice.

Introduction

Functional specificity is a fundamental organization principle of the human brain, which plays a crucial role in shaping cognitive functions¹. Abnormalities in the organization of functional specificity can reflect hallmark cognitive impairments and the associated pathological foundations of major brain disorders such as autism and Alzheimer's disease^2,3. While conventional theories and research have tended to focus on single brain regions, such as the fusiform face area (FFA) for face recognition⁴ and parahippocampus place area (PPA)⁵....

Protocol

NOTE: All the software used here is shown in the Table of Materials. The data used in this study for demonstration purposes were from the Human Connectome Project (HCP: http://www. humanconnectome.org)¹⁵. All experimental procedures were approved by the Institutional Review Board (IRB) at Washington University. Imaging data in the HCP dataset were acquired using a modified 3T Siemens Skyra scanner with a 32-channel head coil. Other image acquisition parameters are detailed in an earlier paper¹⁶. Minimal preprocessed data were downloaded for the demonstration, which had finished following preprocessing st....

Discussion

Network neuroscience is expected to help in understanding how the brain network supports human cognitive functions³². This protocol demonstrates an integrative approach to studying the functional network for spatial navigation in the human brain, which can also inspire network modeling for other cognitive constructs (e.g., language).

This approach consisted of three main steps: node definition, network construction, and network analysis. While network construction and n.......

Materials

Name	Company	Catalog Number	Comments
Brain connectivity toolbox (BCT)	Mikail Rubinov & Olaf Sporns	2019	The Brain Connectivity Toolbox (brain-connectivity-toolbox.net) is a MATLAB toolbox for complex-network (graph) analysis of structural and functional brain-connectivity data sets.
GRETNA	Jinhui Wang et al.	2	GRETNA is a graph theoretical network analysis toolbox which allows researchers to perform comprehensive analysis on the topology of brain connectome by integrating the most of network measures studied in current neuroscience field.
MATLAB	MathWorks	2021a	MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.
Python	Guido van Rossum et al.	3.8.6	Python is a programming language that lets you work more quickly and integrate your systems more effectively.
Statistical Parametric Mapping (SPM)	Karl Friston et.al	12	Statistical Parametric Mapping refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data.