Name: Modellierung des funktionalen Netzwerks für die räumliche Navigation im menschlichen Gehirn (Video) | JoVE Journal
Uploaded: 2023-10-13T13:20:00.000+00:00
Duration: 5 min 55 s
Description: Erfahren Sie die Modellierung funktionaler Netzwerke für räumliche Navigation mittels fMRI-Daten und graphentheoretischer Analyse. Diese Methode liefert neue Biomarker zur Früherkennung von Hirnerkrankungen wie Alzheimer.

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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 earlier paper16. Minimal preprocessed data were downloaded for the demonstration, which had finished following preprocessing steps: gradient distortion correction, motion correction, field map preprocessing, spatial distortion correction, spatial normalization to the Montreal Neurological Institute (MNI) space, intensity normalization, and bias field removal. Resting-state fMRI data from researchers' projects can also be used.
1. Data preprocessing
<ol>
	<li>Check data quality and exclude participants with missing retest data and excessive head motion (3 mm in translation and 3° in rotation). 
	NOTE: Five participants were removed, and 38 young adults (22-35 years old) were included in the main analyses.</li>
	<li>Open the graph theoretical network analysis (GRETNA) toolbox17 in MATLAB to perform further preprocessing steps. Click the batch of FC Matrix Construction. Select the path of the functional dataset to load the NIFTI documents and execute the following steps, as shown in the pipeline option in Figure 1:
	<ol>
		<li>Remove the first 10 images by double-clicking Time Point Number to Remove in Remove First Images and entering 10.</li>
		<li>Spatially smooth (full width at half-maximum [FWHM] = [4 4 4] by double-clicking FWHM (mm) in Spatially Smooth and entering [4 4 4]).</li>
		<li>Regress out covariates. Choose White matter signals, CSF signals, and Head Motion as TRUE. Select the appropriate mask according to the actual voxel size, for example, mask with 2 mm here, and choose Friston-24 parameters for Head Motion.</li>
		<li>Temporally filter. Input the value of TR according to the repetition time of the MRI scan (e.g., 720 ms here) and remove high-frequency and low-frequency noise by double-clicking Band (Hz) and entering [0.01 0.1]. 
		NOTE: Results with and without regression of whole brain signals are presented below. When using unpreprocessed data, well-established pipelines such as fMRI-prep18 and Data Processing Assistant for Resting-State fMRI (DPARSF)19 are also recommended.</li>
	</ol>
	</li>
</ol>
<img alt="r-fMRI preprocessing interface; tool for DICOM to NIfTI, normalization, dynamic correlation analysis." class="xfigimg" src="/files/ftp_upload/65150/65150fig01.jpg" data-altupdatedat="1788304762000" data-alt="Figure 1"> 
Figure 1: Rs-fMRI preprocess and functional network connectivity estimation. The settings of preprocess (removing first 10 images, spatially smoothing with FWHM of 4 mm, linear temporally detrending, regressing out white matter signals, cerebrospinal fluid (CSF) signals, and head motion with 24 parameters, filtering the band of 0.01-0.1 HZ) and the static correlation with fisher' Z transformed. Abbreviations: Rs-fMRI = resting-state functional magnetic resonance imaging; FWHM = full width at half-maximum; CSF = cerebrospinal fluid. <a href="https://www.jove.com/files/ftp_upload/65150/65150fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Network construction and analyses
NOTE: The general workflow for the construction and analyses of the navigation network are summarized into three main steps (Figure 2).
<img alt="Functional brain mapping, activation coordinates table, meta-analysis diagram, AICHA atlas use." class="xfigimg" src="/files/ftp_upload/65150/65150fig02v2.jpg" data-altupdatedat="1788304762000" data-alt="Figure 2"> 
Figure 2: General workflow for the construction and analyses of the navigation network. (A) Choose navigation as the term to be searched in Neurosynth database. (B) A list of activation coordinates can be generated. (C) Run a meta-analysis using functions from the Neurosynth to get several brain maps. (D,E) By incorporating the meta-analytic map and a whole-brain parcellation atlas (AICHA), nodes (ROI) can be generated. (F) The construction of a navigation network using the resulting navigation nodes and their functional connectivity (Connectivity Estimation and Network Analysis). Abbreviations: ROI = region of interest; AICHA = atlas of intrinsic connectivity of homotopic areas. <a href="https://www.jove.com/files/ftp_upload/65150/65150fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Network node definition
	<ol>
		<li>Download the latest Neurosynth database (neurosynth.org)20 by typing the command in Python: 
		    
		&gt;import neurosynth as ns 
		&gt;ns.dataset.download (path='./', unpack = True) 
		    
		NOTE: The dataset archive ('current_data.tar.gz') contains two files: 'database.txt' and 'features.txt'. These contain all the activation coordinates from neuroimaging articles and meta-analysis tags that occur at a high frequency in that article, respectively.</li>
		<li>Generate a new Dataset instance from the database.txt and add featuresto these data by typing the command: 
		    
		&gt; from neurosynth.base.dataset import Dataset 
		&gt; dataset = Dataset('data/database.txt') 
		&gt; dataset.add_features('data/features.txt') 
		   </li>
		<li>Run a meta-analysis with the term of interest (i.e., 'navigation')by typing the command: 
		    
		&gt; ids = dataset.get_ids_by_features ('navigation', threshold=0.01) 
		&gt; ma = meta.MetaAnalysis (dataset, ids) 
		&gt; ma.save_results('.', 'navigation') 
		    
		NOTE: The meta-analysis results in several brain maps in NIFTI format. A false discovery rate (FDR) threshold of 0.01 was applied to control the false positive rate. Filed knowledge is needed at this step for ensuring that the commonly-reported regions are included in the meta-analytic map. Similar steps can be applied to run meta-analyses for other cognitive functions such as language and memory.</li>
		<li>Define clusters of interest by incorporating the meta-analytic map and a whole-brain parcellation atlas by typing the command from FSL: 
		    
		&gt;fslmaths navigation_0.01.nii.gz -bin navi_bin.nii.gz 
		&gt;fslmaths navi_bin.nii.gz -mul AICHA/AAL.nii.gz navi_label_aicha/aal.nii.gz 
		&gt;fslmaths navi_label_aicha/aal.nii.gz -thr n -uthr n label _n.nii.gz 
		&gt;cluster -i label _n.nii.gz -t 0.2 -o cluster_n.nii.gz 
		&gt;fslmaths cluster_n.nii.gz -thr m -uthr m cluster_n_m.nii.gz 
		&gt;fslmaths cluster_n_m.nii.gz -bin -mul x node_x.nii.gz 
		&gt;fslmaths node_1.nii.gz -add … -add node_x.nii.gz navi_AICHA/AAL_mask.nii.gz 
		    
		NOTE: Two atlas were used here: AAL and AICHA. The AAL is the atlas that was used in the original study for the node definition12. This atlas was created based on the anatomical profiles. The atlas of intrinsic connectivity of homotopic areas (AICHA)21 has a much finer resolution and higher functional homogeneity. We defined the regions of interest using each of the atlas.</li>
		<li>Type scripts in Python for checking the size of each region in the map: 
		    
		&gt;for i in np.arange(n)+1: 
		&gt;____region_list.append(i) 
		&gt;____size1_list.append(np.sum(img_dat==i)) 
		&gt;____size2_list.append(np.sum(aicha_img_dat==i)) 
		&gt;____pct_list.append(np.sum(img_dat==i)/np.sum(aicha_img_dat==i)) 
		    
		NOTE: The integer n in the script indicates the total number of regions within the AICHA and AAL parcellation (384 and 128, respectively). To avoid the effects of spurious clusters, it is suggested that clusters with relatively small sizes (e.g., 100 voxels) could be removed. The AICHA atlas used here is generated using functional connectivity data, with each region showing homogeneity of functional temporal activity within itself.</li>
	</ol>
	</li>
	<li>Network connectivity estimation 
	NOTE: The GRETNA toolbox is used for connectivity estimation and network analysis.
	<ol>
		<li>Click the batch of FC Matrix Construction. Load the preprocessed rs-fMRI data by selecting the path of the functional dataset. Click the static correlation option. Upload the node obtained in the previous step 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 for improving normality. 
		NOTE: The detailed operation is shown in Figure 1. The navigation network matrices of N × N (N represents the number of nodes) for each participant would be obtained in .txt format.</li>
		<li>Get a positive and weighted network with the following steps, as shown in Figure 3.
		<ol>
			<li>Click the batch of Network Analysis. Add the network matrixes into the Brain Connectivity Matrix window and choose one output directory for preparation.</li>
			<li>For the pipeline option of Network Configuration, select positive in the Sign of matrix, which will set negative connections in the function connection matrix to 0 and eliminate ambiguous connections22. Choose the network type as weighted to get the undirected weighted network. 
			NOTE: Besides the weighted networks, one could also binarize the networks to create binary networks for subsequent analyses (with different approaches), but the weighted one is often considered to show higher reliability23,24.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Network analysis
	<ol>
		<li>Add small world, global efficiency, clustering coefficient, shortest path length, degree centrality, and local efficiency to the GRETNA network metric analysis pipeline, as shown in Figure 3. 
		NOTE: Small world and global efficiency are two global network metrics. Specifically, the network with small-worldness can maximize the efficiency of information transfer at a comparatively low wiring cost. Global efficiency reflects the transmission efficiency parallel information in the transportation network. For nodal network metrics, the degree centrality measures the number of links connected to a node. The shortest path length, as its name, is a basis for measuring integration. The clustering coefficient indicates the degree to which the nodes' neighbors are interrelated with each other. Local efficiency is the efficiency of communication with the node and its neighbors (the detailed formula and usage are shown in these papers) 17,25. Brain connectivity toolbox (BCT)25 and other toolboxes can also be used for the calculation of the network metrics.</li>
		<li>Select Network Sparsity in the thresholding method to exclude the confounding effects of spurious connections, and enter a set of threshold sequences (i.e., 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5 is used here) to further determine the appropriate threshold according to the statistical results. 
		NOTE: The ratio of edges to the maximum number of edges in a network with the sample number of nodes is known as the sparsity threshold. A sparsity threshold guarantees that different individuals have the same number of edges. We chose to explore different thresholds for validation, which could provide helpful data for choosing an optimal threshold in future studies.</li>
		<li>Set the random network number as 1,000 to generate random networks using a Markov wiring algorithm26. Click Run to run the pipeline in GRETNA after all steps are set up. 
		NOTE: Similar to genuine brain networks, the random networks maintain the same number of nodes, edges, and degree distribution. To determine whether they are considerably non-randomly topologically constructed, they will be compared to brain networks. After running the pipeline, a group of scores for the network metrics for each of the thresholds would be obtained for further statistical analyses.</li>
		<li>Determine the optimal number of modules in the network in four steps.
		<ol>
			<li>Calculate the averaging navigation network. Click the bath of Metric Comparison and choose Connection. Load the network matrixes obtained above and choose the Averaged (Functional) operation. Select an output direction to preserve the averaged network matrix; see Figure 4 for more details.</li>
			<li>Divide the average network obtained from the above step into 2, 3, 4, and 5 modules using the function spectralcluster in MATLAB.</li>
			<li>Calculate the proportion of nodes divided into the same module in REST 1 and REST 2 after aligning the module divisions using the script procrustes_alignment.m. Use the proportion as the index of repeatability of the module partition.</li>
			<li>Select the number of modules with the highest repeatability.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Statistical analyses 
	NOTE: The following analyses are mainly for validation and would not be necessary when applying this protocol to individual variation studies.
	<ol>
		<li>Examine the similarity of these network metrics between two networks with different types of strategies for node definition (i.e., the new one generated in the present study, termed as NaviNet_AICHA and the earlier one from Kong et al., termed as NaviNet_AAL)12. Calculate the Pearson correlation using the function corrcoef in MATLAB and repeat the analyses for each sparsity threshold. 
		NOTE: After extracting the network metrics, one can conduct any statistical analyses they are interested in.</li>
		<li>Check the test-retest reliability of these network metrics using the function ICC in MATLAB27,28, which implements the calculation of the Intraclass Correlation Coefficient. 
		NOTE: The original uncorrected p values were reported in the representative results section. 0.2 &lt; ICC &lt; 0.4 is interpreted as indicative of a fair test-retest reliability and ICC &gt; 0.4 is interpreted as moderate to good test-retest reliability29,30. Negative ICC scores were set to zero, given the fact that the presence of negative ICCs is meaningless and difficult to interpret31.</li>
	</ol>
	</li>
</ol>
<img alt="Network metric analysis interface for brain connectivity matrix; features global, nodal metrics; weighted." class="xfigimg" src="/files/ftp_upload/65150/65150fig03.jpg" data-altupdatedat="1788304762000" data-alt="Figure 3"> 
Figure 3: Network metrics analysis. This analysis defines the weighted positive networks with 10 thresholds. Calculate two global network metrics of small word and efficiency, four nodal network metrics of clustering coefficient, shortest path length, efficiency, and degree centrality. <a href="https://www.jove.com/files/ftp_upload/65150/65150fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Connectional metric comparison setup with network matrix selection for connectivity analysis." class="xfigimg" src="/files/ftp_upload/65150/65150fig04.jpg" data-altupdatedat="1788304762000" data-alt="Figure 4"> 
Figure 4: The calculation of average navigation networks. The averaged (functional) operation helps to calculate the average networks of all participants. <a href="https://www.jove.com/files/ftp_upload/65150/65150fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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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><tbody><tr><td>Brain connectivity toolbox (BCT)</td><td>Mikail Rubinov &amp;amp; Olaf Sporns&amp;nbsp;</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.&amp;nbsp;</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&amp;nbsp;</td><td>12</td><td>Statistical Parametric Mapping&amp;nbsp;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

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 c...

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 int

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Functional Mapping with Simultaneous MEG and EEG

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments. Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables noninvasive and portable monitoring of cerebral blood oxygenation changes. This paper summarizes collective use of MazeSuite and fNIR within a cognitive processing learning paradigm.

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

We describe a novel approach for simultaneous analysis of brain function and structure using magnetic resonance imaging (MRI). We assess brain structure with high-resolution diffusion-weighted imaging and white-matter fiber tractography. Unlike standard structural MRI, these techniques allow us to directly relate anatomical connectivity to functional properties of brain networks.

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Monitoring brain activity during upright motor tasks is of great value when investigating the neural source of movement disorders. Here, we demonstrate a protocol that combines functional near infrared spectroscopy with continuous monitoring of muscle and kinematic activity during 4 types of motor tasks.

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Here, we present a novel, brief, and active spatial navigation task that assesses both spatial navigation and episodic memory ability. Importantly, spatial navigation and episodic memory were associated with one another, and this task demonstrated sensitivity to exercise.

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Here, we employ HD-MEA to delve into computational dynamics of large-scale neuronal ensembles, particularly in hippocampal, olfactory bulb circuits, and human neuronal networks. Capturing spatiotemporal activity, combined with computational tools, provides insights into neuronal ensemble complexity. The method enhances understanding of brain functions, potentially identifying biomarkers and treatments for neurological disorders.

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

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Localizing Function-specific Targets for Transcranial Magnetic Stimulation in the Absence of Navigation Equipment

This study explores the effects of a configurable soft pneumatic robot on enhancing whole-brain network topology post-stroke. Graph theory analysis indicates significant improvements in clustering coefficient, path length, and global efficiency. Findings highlight the potential of programmable robotic protocols to modulate neuroplasticity and optimize functional recovery in stroke rehabilitation.

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Here, we describe a standardized protocol for motor mapping using nTMS combined with diffusion tensor imaging (DTI)-based reconstruction of the corticospinal tract (CST). The protocol is reproducible, clinically feasible, and readily integrable into routine clinical workflows, providing a robust and valuable framework for motor pathway assessment, neuroplasticity research, and rehabilitation planning.

A Standardized Protocol for Functional Motor Mapping Using Navigated Transcranial Magnetic Stimulation

In this article, we examine the methodology and considerations relevant to the combination of TMS and fMRI to examine the effects of brain stimulation on the default network.

Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network

Erfahren Sie die Modellierung funktionaler Netzwerke für räumliche Navigation mittels fMRI-Daten und graphentheoretischer Analyse. Diese Methode liefert neue Biomarker zur Früherkennung von Hirnerkrankungen wie Alzheimer.

Modeling the Functional Network for Spatial Navigation in the Human Brain

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