Xiang-Zhen Kong was supported by the National Natural Science Foundation of China (32171031), STI 2030 - Major Project (2021ZD0200409), Fundamental Research Funds for the Central Universities (2021XZZX006), and Information Technology Center of Zhejiang University.

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 e...

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The authors declare that there is no conflict of interest.

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...

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&#39;s disease2,3. 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 for scene processing, an increasing body of evidence suggests that complex cognitive functions, including spatial navigation and language, require coordinate activity across multiple brain regions6. Investigating the mechanisms underlying the interactions in support of complex cognitive functions is a critical scientific question that will help to shed light on the functional architecture and operation of the brain. Here, taking spatial navigation as an example, we present an integrative method for modeling the functional network for spatial navigation in the human brain.
Spatial navigation is a complex cognitive function, which involves the integration and manipulation of multiple cognitive components, such as visual-spatial coding, memory, and decision making7. With functional magnetic resonance imaging (fMRI), numerous studies have made significant advances in understanding the underlying cognitive processing and neural mechanisms. For instance, specific functions have been linked to different brain regions using various navigation tasks: scene processing&#160;is specifically associated with PPA, and transformation of navigation strategies is associated with the retrosplenial cortex (RSC)8,9. These studies provided important insights into the neural basis of spatial navigation. However, navigation is an internally dynamic and multimodal function, and the functions of single regions are not sufficient to explain large individual differences in spatial navigation10 that are commonly observed.
With the emergence of fMRI-based connectomics, researchers began to explore how some key brain regions interact with each other to support spatial navigation. For example, functional connectivity between the entorhinal and posterior cingulate cortices has been found to underpin navigation discrepancies in at-risk Alzheimer&#39;s disease11. In another study, we for the first time proposed a network approach by integrating connectome methods and almost all functionally relevant regions (nodes) for spatial navigation, and the results showed that topological properties of this network showed specific associations with navigation behaviors12. This study provides new insights into theories of how multiple brain regions interact with each other to support flexible navigation behaviors10,13.
The present work demonstrates an updated version of the integrative approach for modeling the functional network. Briefly, two updates were included: 1) While the nodes defined in the original study were identified based on an earlier and smaller database (55 studies with 2,765 activations, accessed in 2014), the present definition was based on the latest database (77 studies with 3,908 activations, accessed in 2022); 2) to increase functional homogeneity of each node, besides the original anatomical AAL (Anatomical Automatic Labeling) atlas14, we applied a new brain parcellation, which has a much finer resolution and higher functional homogeneity (see below). We expected that both updates would improve the modeling of the functional network. This updated protocol provides a detailed procedure for investigating the neural basis of spatial navigation from a network perspective and helps understand individual variations in navigation behaviors in health and disease. A similar procedure could also be used for network modeling for other cognitive constructs (e.g., language and memory).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Brain connectivity toolbox (BCT)</td>
			<td>Mikail Rubinov &#38; Olaf Sporns&#160;</td>
			<td>2019</td>
			<td>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.&#160;</td>
		</tr>
		<tr>
			<td>GRETNA</td>
			<td>Jinhui Wang et al.</td>
			<td>2</td>
			<td>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.</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>2021a</td>
			<td>MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.</td>
		</tr>
		<tr>
			<td>Python</td>
			<td>Guido van Rossum et al.</td>
			<td>3.8.6</td>
			<td>Python is a programming language that lets you work more quickly and integrate your systems more effectively.</td>
		</tr>
		<tr>
			<td>Statistical Parametric Mapping (SPM)</td>
			<td>Karl Friston et.al&#160;</td>
			<td>12</td>
			<td>Statistical Parametric Mapping&#160;refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data.</td>
		</tr>
	</tbody>
</table>

modeling the functional network for spatial navigation in the human brain

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.

The navigation networks 
The present study identified 27 brain regions, which are associated with spatial navigation, by incorporating the latest meta-analysis neuroimaging database and the AICHA atlas. These regions consisted of the medial temporal and the parietal regions that have been commonly reported in navigation neuroimaging studies. The spatial distribution of these regions is shown in Figure 5A and Figure 5C. As a...

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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.

Spatial navigation is a complex function involving the integration and manipulation of multisensory information. Using different navigation tasks, many ...

Spatial navigation is a complex process that involves a distributed network in the brain. Our work presents an integrative approach for modeling a functional network for spatial navigation using FMRI data. The optimal network model provides new insights for understanding how brain regions within the network interact.The network model could also better capture variability in behaviors in both health and disease. Our method can also be applied to studies or other complex functions. For instance, a similar procedure could be used for modeling brain network for language and memory.To begin, check the data quality and exclude participants with missing retest data and excessive head motion. Then open the graph theoretical network analysis or GRETNA toolbox in MATLAB. Click the tab FC Matrix Construction"and select the path of the functional dataset to load the nifty documents.Execute the steps as displayed in the pipeline option. Next, to perform network node definition, download the latest Neurosynth database by typing the following command. Then generate a new dataset instance from database.txt and add features to this data by typing this command. Run a meta analysis of the term of interest such as navigation by typing the command. Next, define clusters of interest by incorporating the meta analytic map and the whole brain parcelization atlas such as AICHA or AAL by typing this command from FSL.Then type the following scripts in Python to check the size of each region in the map. After that, integrate all brain regions into a template by FSL Maths in FSL. For network connectivity estimation, click on FC Matrix Construction in the GRETNA software and click Static Correlation.Upload the node obtained from network node definition as an atlas to calculate the static correlation of rs-fMRI signals of each pair of regions and transfer them into Fisher's Z scores. To get a positive and weighted network with the steps displayed in the pipeline option, click on Network Analysis. Then add the network matrices into the brain connectivity matrix window and choose an output directory for preparation.Add a small world, global efficiency, clustering coefficient, shortest path length, degree centrality, and local efficiency to the GRETNA network metric analysis pipeline. Select positive in the sign of matrix. Select network sparsity in the thresholding method and enter a set of threshold sequences.Choose the network type as weighted. Set the random network number as 1, 000 and click on Run. To determine the optimal number of modules in the network, first calculate the averaging navigation network.Then divide the resulting average network into 2, 3, 4, and 5 modules using the function, spectral cluster, in MATLAB. Then align the module divisions using the script, procrustes_alignment. m, and calculate the portion of nodes divided into the same module in rest one and rest two.Select the number of modules with the highest repeatability. To perform the network analysis, examine the similarity of these network metrics between two networks with different types of strategies for no definition like NaviNet AICHA and NaviNet AAL. Check the test retest reliability of these network metrics using the function ICC in MATLAB.In this study, 27 brain regions associated with spatial navigation were identified using the AICHA atlas. These regions consisted of the medial temporal and parietal regions that have been reported in navigation neuroimaging studies. As a comparison, 20 regions from the AAL atlas were included.It showed a large overlap and similar community distribution between the two sets including similar ventral and dorsal modules in both networks. Further, five of the six metrics, except for the clustering coefficient, showed significant correlations between the two networks. The similarity values increased with the sparsity threshold for almost all metrics, suggesting that the network level analysis could reflect stable individual differences, independent of node definition choices, and that the sparsity threshold of 0.30 to 0.40 would result in better generalizability in navigation network analysis.Moreover, evaluation of the rest retest reliability of the topological measures of the navigation networks indicated that the majority of the network's metrics showed fair to good reliability in AICHA, while the AAL network showed relatively higher reliability. In addition, including global signal regression in FMRI data pre-processing could result in high reliability. These results suggested that the clustering coefficient and small world are the most reliable among these metrics.With this approach, researchers can investigate the developmental trajectory of functionally specific networks. Network properties also provide new biomarkers for guiding early identification of brain disorders such as Alzheimer's disease.

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743 Görüntüleme.  Zhejiang University. Uzamsal navigasyon, beyinde dağıtılmış bir ağ içeren karmaşık bir süreçtir. Çalışmamız, FMRI verilerini kullanarak uzamsal navigasyon için işlevsel bir ağı modellemek için bütünleştirici bir yaklaşım sunmaktadır. En uygun ağ modeli, ağ içindeki beyin bölgelerinin nasıl etkileşime girdiğini anlamak için yeni bilgiler sağlar.Ağ modeli ayrıca hem sağlık hem de hastalıktaki davranışlardaki değişkenliği daha iyi yakalayabilir. Yöntemimiz çalı?...