Modeling the Functional Network for Spatial Navigation in the Human Brain

Fengxiang Zhang; Chenghui Zhang; Yi Pu; Xiang-Zhen Kong

doi:10.3791/65150

JoVE Journal
Neuroscience

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JoVE Journal Neuroscience

Modeling the Functional Network for Spatial Navigation in the Human Brain

ヒト脳における空間ナビゲーションのための機能的ネットワークのモデル化

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05:55 min

October 13, 2023

DOI: 10.3791/65150-v

Fengxiang Zhang*¹, Chenghui Zhang*¹, Yi Pu², Xiang-Zhen Kong^1,3

¹Department of Psychology and Behavioral Sciences,Zhejiang University, ²Department of Neuroscience,Max Planck Institute for Empirical Aesthetics, ³Department of Psychiatry of Sir Run Run Shaw Hospital,Zhejiang University School of Medicine

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Overview

This study presents an integrative approach to modeling the functional network involved in spatial navigation using resting-state fMRI data. By employing network modeling and graph-theoretical techniques, the research aims to enhance the understanding of interactions among brain regions key to navigation, with implications for health and disease.

Key Study Components

Area of Science

Neuroscience
Functional neuroimaging
Network analysis

Background

Spatial navigation involves a complex brain network.
The study identifies crucial brain regions associated with navigation.
Utilizes a large-scale neuroimaging meta-analytic database for data comparison.
Aim to explore variability in behaviors linked to navigation in health and disease contexts.

Purpose of Study

To investigate the functional brain network for spatial navigation.
To improve the capture of individual behavior variability.
To provide methodological frameworks applicable to other cognitive functions.

Methods Used

The study uses resting-state functional magnetic resonance imaging (fMRI) data.
Key biological models include identified brain regions associated with spatial navigation using the AICHA and AAL atlases.
Steps include data quality checks, network node definition, and network connectivity estimation using GRETNA toolbox in MATLAB.
Multiple metrics are analyzed to assess network properties.
Evaluation of test-retest reliability for network metrics is performed.

Main Results

Identified 27 brain regions related to spatial navigation with both AICHA and AAL atlases showing overlap.
Higher reliability in AAL network metrics than AICHA, emphasizing the importance of global signal regression.
Five of six network metrics displayed significant correlations between the two atlases.
Findings suggest that clustering coefficient and small-world properties are particularly reliable metrics.

Conclusions

This integrative method allows deeper investigation of navigation networks in the brain.
The study's findings provide new biomarkers for identifying brain disorders.
Implications extend to understanding neuronal mechanisms in health and potential early interventions for diseases like Alzheimer's.

Frequently Asked Questions

What are the advantages of using fMRI for this study?

fMRI allows for non-invasive examination of brain activity and connectivity. It provides insights into the functional interactions among brain regions involved in spatial navigation.

How are the main biological models implemented?

The models consist of regions identified using fMRI data analyzed against standard atlases like AICHA and AAL. The analysis includes excluding participants with confounding factors.

What types of outcomes are obtained from this analysis?

The analysis yields metrics related to network properties, such as clustering coefficients and small-worldness, which reflect the organization's stability of the navigation network.

How can this method be adapted for other functions?

This methodological framework can be applied to study networks involved in other cognitive functions by following similar analysis steps with tailored datasets.

What are the key limitations of the study?

Limitations may include the reliance on specific atlas definitions which can affect network integrity, as well as potential variability due to participant demographics.

How reliable are the network metrics used in this study?

The majority of network metrics demonstrated fair to good reliability, particularly with global signal regression applied during data preprocessing.

この論文は、人間の脳における空間ナビゲーションのための機能的ネットワークを調査するための統合的アプローチを提示します。このアプローチには、大規模なニューロイメージングメタ分析データベース、安静時機能磁気共鳴画像法、ネットワークモデリング、グラフ理論技術が組み込まれています。

空間ナビゲーションは、脳内の分散ネットワークが関与する複雑なプロセスです。本研究は、FMRIデータを用いた空間ナビゲーションのための機能ネットワークをモデル化するための統合的アプローチを提示する。最適なネットワークモデルは、ネットワーク内の脳領域がどのように相互作用するかを理解するための新しい洞察を提供します。

また、ネットワークモデルは、健康と病気の両方における行動のばらつきをより適切に捉えることができます。私たちの方法は、研究やその他の複雑な機能にも適用できます。例えば、言語と記憶の脳内ネットワークをモデル化するために、同様の手順を用いることができる。

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