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&#39; 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&#176; 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="Figure 1" class="xfigimg" src="/files/ftp_upload/65150/65150fig01.jpg" /> 
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&#39; 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.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2.&#160;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="Figure 2" class="xfigimg" src="/files/ftp_upload/65150/65150fig02v2.jpg" /> 
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.bdigitaluss.remotexs.co/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&#160;by typing the command in Python: 
		&#160; &#160; 
		&#62;import neurosynth as ns 
		&#62;ns.dataset.download (path=&#39;./&#39;, unpack = True) 
		&#160; &#160; 
		NOTE: The dataset archive (&#39;current_data.tar.gz&#39;) contains two files: &#39;database.txt&#39; and &#39;features.txt&#39;. 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: 
		&#160; &#160; 
		&#62; from neurosynth.base.dataset import Dataset 
		&#62; dataset = Dataset(&#39;data/database.txt&#39;) 
		&#62; dataset.add_features(&#39;data/features.txt&#39;) 
		&#160; &#160;</li>
		<li>Run a meta-analysis with the term of interest (i.e., &#39;navigation&#39;)by typing the command: 
		&#160; &#160; 
		&#62; ids = dataset.get_ids_by_features (&#39;navigation&#39;, threshold=0.01) 
		&#62; ma = meta.MetaAnalysis (dataset, ids) 
		&#62; ma.save_results(&#39;.&#39;, &#39;navigation&#39;) 
		&#160; &#160; 
		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: 
		&#160; &#160; 
		&#62;fslmaths navigation_0.01.nii.gz -bin navi_bin.nii.gz 
		&#62;fslmaths navi_bin.nii.gz -mul AICHA/AAL.nii.gz navi_label_aicha/aal.nii.gz 
		&#62;fslmaths navi_label_aicha/aal.nii.gz -thr n -uthr n label _n.nii.gz 
		&#62;cluster -i label _n.nii.gz -t 0.2 -o cluster_n.nii.gz 
		&#62;fslmaths cluster_n.nii.gz -thr m -uthr m cluster_n_m.nii.gz 
		&#62;fslmaths cluster_n_m.nii.gz -bin -mul x node_x.nii.gz 
		&#62;fslmaths node_1.nii.gz -add &#8230; -add node_x.nii.gz navi_AICHA/AAL_mask.nii.gz 
		&#160; &#160; 
		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: 
		&#160; &#160; 
		&#62;for i in np.arange(n)+1: 
		&#62;____region_list.append(i) 
		&#62;____size1_list.append(np.sum(img_dat==i)) 
		&#62;____size2_list.append(np.sum(aicha_img_dat==i)) 
		&#62;____pct_list.append(np.sum(img_dat==i)/np.sum(aicha_img_dat==i)) 
		&#160; &#160; 
		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&#39;s z scores for improving normality. 
		NOTE: The detailed operation is shown in Figure 1. The navigation network matrices of N &#215;&#160;N (N&#160;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&#39; 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&#160;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&#160;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&#160;&#60; ICC&#160;&#60; 0.4 is interpreted as indicative of a fair test-retest reliability and ICC&#160;&#62; 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="Figure 3" class="xfigimg" src="/files/ftp_upload/65150/65150fig03.jpg" /> 
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.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65150/65150fig04.jpg" /> 
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.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<ol>
	<li>Kanwisher, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+specificity+in+the+human+brain:+a+window+into+the+functional+architecture+of+the+mind.">Functional specificity in the human brain: a window into the functional architecture of the mind.</a> Proceedings of the National Academy of Sciences of the United States of America. 107 (25), 11163-11170 (2010).</li><li>Coughlan, G., Laczo, J., Hort, J., Minihane, A. M., Hornberger, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+navigation+deficits+-+overlooked+cognitive+marker+for+preclinical+Alzheimer+disease.">Spatial navigation deficits - overlooked cognitive marker for preclinical Alzheimer disease.</a> Nature Reviews Neurology. 14 (8), 496-506 (2018).</li><li>Gilbert, S. J., Meuwese, J. D., Towgood, K. J., Frith, C. D., Burgess, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Abnormal+functional+specialization+within+medial+prefrontal+cortex+in+high-functioning+autism:+a+multi-voxel+similarity+analysis.">Abnormal functional specialization within medial prefrontal cortex in high-functioning autism: a multi-voxel similarity analysis.</a> Brain. 132 (4), 869-878 (2009).</li><li>Kanwisher, N., McDermott, J., Chun, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+fusiform+face+area:+a+module+in+human+extrastriate+cortex+specialized+for+face+perception.">The fusiform face area: a module in human extrastriate cortex specialized for face perception.</a> Journal of Neuroscience. 17 (11), 4302-4311 (1997).</li><li>Epstein, R., Harris, A., Stanley, D., Kanwisher, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+parahippocampal+place+area:+recognition,+navigation,+or+encoding.">The parahippocampal place area: recognition, navigation, or encoding.</a> Neuron. 23 (1), 115-125 (1999).</li><li>Epstein, R. A., Patai, E. Z., Julian, J. B., Spiers, H. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cognitive+map+in+humans:+spatial+navigation+and+beyond.">The cognitive map in humans: spatial navigation and beyond.</a> Nature Neuroscience. 20 (11), 1504-1513 (2017).</li><li>Baumann, O., Mattingley, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extrahippocampal+contributions+to+spatial+navigation+in+humans:+A+review+of+the+neuroimaging+evidence.">Extrahippocampal contributions to spatial navigation in humans: A review of the neuroimaging evidence.</a> Hippocampus. 31 (7), 640-657 (2021).</li><li>Auger, S. D., Mullally, S. L., Maguire, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Retrosplenial+cortex+codes+for+permanent+landmarks.">Retrosplenial cortex codes for permanent landmarks.</a> PloS One. 7 (8), e43620 (2012).</li><li>Zhen, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+the+variability+of+scene-selective+regions:+Interindividual,+interhemispheric,+and+sex+differences.">Quantifying the variability of scene-selective regions: Interindividual, interhemispheric, and sex differences.</a> Human Brain Mapping. 38 (4), 2260-2275 (2017).</li><li>Ekstrom, A. D., Huffman, D. J., Starrett, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interacting+networks+of+brain+regions+underlie+human+spatial+navigation:+a+review+and+novel+synthesis+of+the+literature.">Interacting networks of brain regions underlie human spatial navigation: a review and novel synthesis of the literature.</a> Journal of Neurophysiology. 118 (6), 3328-3344 (2017).</li><li>Coughlan, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+connectivity+between+the+entorhinal+and+posterior+cingulate+cortices+underpins+navigation+discrepancies+in+at-risk+Alzheimer's+disease.">Functional connectivity between the entorhinal and posterior cingulate cortices underpins navigation discrepancies in at-risk Alzheimer's disease.</a> Neurobiology of Aging. 90, 110-118 (2020).</li><li>Kong, X. Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+navigation+network:+the+intrinsic+functional+organization+and+behavioral+relevance.">Human navigation network: the intrinsic functional organization and behavioral relevance.</a> Brain Structure &amp; Function. 222 (2), 749-764 (2017).</li><li>Weisberg, S. M., Ekstrom, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hippocampal+volume+and+navigational+ability:+The+map+(ping)+is+not+to+scale.">Hippocampal volume and navigational ability: The map (ping) is not to scale.</a> Neuroscience &amp; Biobehavioral Reviews. 126, 102-112 (2021).</li><li>Tzourio-Mazoyer, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+anatomical+labeling+of+activations+in+SPM+using+a+macroscopic+anatomical+parcellation+of+the+MNI+MRI+single-subject+brain.">Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.</a> Neuroimage. 15 (1), 273-289 (2002).</li><li>Van Essen, D. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+WU-Minn+human+connectome+project:+an+overview.">The WU-Minn human connectome project: an overview.</a> Neuroimage. 80, 62-79 (2013).</li><li>Smith, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resting-state+fMRI+in+the+human+connectome+project.">Resting-state fMRI in the human connectome project.</a> Neuroimage. 80, 144-168 (2013).</li><li>Wang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GRETNA:+a+graph+theoretical+network+analysis+toolbox+for+imaging+connectomics.">GRETNA: a graph theoretical network analysis toolbox for imaging connectomics.</a> Frontiers in Human Neuroscience. 9, 386 (2015).</li><li>Esteban, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=fMRIPrep:+a+robust+preprocessing+pipeline+for+functional+MRI.">fMRIPrep: a robust preprocessing pipeline for functional MRI.</a> Nature Methods. 16 (1), 111-116 (2019).</li><li>Yan, C., Zang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DPARSF:+a+MATLAB+toolbox+for&amp;#34;+pipeline&amp;#34;+data+analysis+of+resting-state+fMRI.">DPARSF: a MATLAB toolbox for&amp;#34; pipeline&amp;#34; data analysis of resting-state fMRI.</a> Frontiers in Systems Neuroscience. 4, 13 (2010).</li><li>Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., Wager, T. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Large-scale+automated+synthesis+of+human+functional+neuroimaging+data.">Large-scale automated synthesis of human functional neuroimaging data.</a> Nature Methods. 8 (8), 665-670 (2011).</li><li>Joliot, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AICHA:+An+atlas+of+intrinsic+connectivity+of+homotopic+areas.">AICHA: An atlas of intrinsic connectivity of homotopic areas.</a> Journal of Neuroscience Methods. 254, 46-59 (2015).</li><li>Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., Bandettini, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+impact+of+global+signal+regression+on+resting+state+correlations:+are+anti-correlated+networks+introduced.">The impact of global signal regression on resting state correlations: are anti-correlated networks introduced.</a> Neuroimage. 44 (3), 893-905 (2009).</li><li>Fox, M. D., Zhang, D., Snyder, A. Z., Raichle, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+global+signal+and+observed+anticorrelated+resting+state+brain+networks.">The global signal and observed anticorrelated resting state brain networks.</a> Journal of Neurophysiology. 101 (6), 3270-3283 (2009).</li><li>Xiang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Graph-based+network+analysis+of+resting-state+fMRI:+test-retest+reliability+of+binarized+and+weighted+networks.">Graph-based network analysis of resting-state fMRI: test-retest reliability of binarized and weighted networks.</a> Brain Imaging and Behavior. 14, 1361-1372 (2020).</li><li>Rubinov, M., Sporns, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complex+network+measures+of+brain+connectivity:+uses+and+interpretations.">Complex network measures of brain connectivity: uses and interpretations.</a> Neuroimage. 52 (3), 1059-1069 (2010).</li><li>Maslov, S., Sneppen, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Specificity+and+stability+in+topology+of+protein+networks.">Specificity and stability in topology of protein networks.</a> Science. 296 (5569), 910-913 (2002).</li><li>Shrout, P. E., Fleiss, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intraclass+correlations:+uses+in+assessing+rater+reliability.">Intraclass correlations: uses in assessing rater reliability.</a> Psychological Bulletin. 86 (2), 420 (1979).</li><li>McGraw, K. O., Wong, S. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Forming+inferences+about+some+intraclass+correlation+coefficients.">Forming inferences about some intraclass correlation coefficients.</a> Psychological Methods. 1 (1), 30 (1996).</li><li>Andellini, M., Cannat&#224;, V., Gazzellini, S., Bernardi, B., Napolitano, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Test-retest+reliability+of+graph+metrics+of+resting+state+MRI+functional+brain+networks:+A+review.">Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review.</a> Journal of Neuroscience Methods. 253, 183-192 (2015).</li><li>Cao, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Test-retest+reliability+of+fMRI-based+graph+theoretical+properties+during+working+memory,+emotion+processing,+and+resting+state.">Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state.</a> Neuroimage. 84, 888-900 (2014).</li><li>Rousson, V., Gasser, T., Seifert, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+intrarater,+interrater+and+test-retest+reliability+of+continuous+measurements.">Assessing intrarater, interrater and test-retest reliability of continuous measurements.</a> Statistics in medicine. 21 (22), 3431-3446 (2002).</li><li>Bullmore, E., Sporns, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complex+brain+networks:+graph+theoretical+analysis+of+structural+and+functional+systems.">Complex brain networks: graph theoretical analysis of structural and functional systems.</a> Nature Reviews Neuroscience. 10 (3), 186-198 (2009).</li><li>Patai, E. Z., Spiers, H. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+versatile+wayfinder:+prefrontal+contributions+to+spatial+navigation.">The versatile wayfinder: prefrontal contributions to spatial navigation.</a> Trends in Cognitive Sciences. 25 (6), 520-533 (2021).</li><li>Wegman, J., Janzen, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+encoding+of+objects+relevant+for+navigation+and+resting+state+correlations+with+navigational+ability.">Neural encoding of objects relevant for navigation and resting state correlations with navigational ability.</a> Journal of Cognitive Neuroscience. 23 (12), 3841-3854 (2011).</li><li>Braun, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Test-retest+reliability+of+resting-state+connectivity+network+characteristics+using+fMRI+and+graph+theoretical+measures.">Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures.</a> Neuroimage. 59 (2), 1404-1412 (2012).</li></ol>

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 network analysis are the same as those in general network studies of the whole brain, node definition is the most critical step of this protocol. This step makes use of a large-scale meta-analysis of functional activation related to spatial navigation to localize the most important brain regions for navigation behaviors. Thus, we can model the functionally meaningful network, which helps understand the neural basis of the complex processing from a network perspective. Note that prefrontal regions were missing in the node definition results while an increasing number of navigation studies have suggested critical roles of these regions33. This could be due to the lack of activations of these regions in navigation-related studies within the database, which resulted in limited data for the meta-analysis. When more data are available for localizing these navigation-related prefrontal regions, it would be an interesting question to investigate their roles in the navigation network in future studies. Researchers could also apply this protocol to study other cognitive functions when localizing individual brain regions is possible. Field knowledge is needed for identifying regions of interest to ensure close associations with the specific function.
In this protocol, we focused on the spatial navigation network and showed a high cover of various brain regions reported in spatial navigation studies. Given the absence of a universally agreed-upon definition of brain regions that support spatial navigation, the demonstration used two sets of regions, one generated by incorporating the largest meta-analysis and the AICHA atlas, and the other with the AAL atlas. The network topological properties based on the two definitions generally showed high similarity, which supports the effectiveness of the functional-specific network modeling to a certain extent.
We noted that the similarity strength increased with the sparsity thresholds used in the network analyses, and the results suggested that a sparsity threshold of 0.30-0.40 would be a proper choice as all the network metrics showed the highest similarity with these thresholds. With such thresholds, the network metrics also showed fair to good test-retest reliability particularly for the shortest path length and small-worldness in the case where global signal regression was included in the data preprocessing. These results largely support the use of these metrics in studies of individual differences and related brain disorders.
Due to a lack of proper behavioral data, we could not present behavioral correlates of the network metrics with spatial navigation in this protocol. Based on a few previous studies on brain-behavior associations of functional connectivity metrics of navigation-related regions12,34, we expected that the network modeling with this protocol would show a specific association with spatial navigation. Large-scale samples with both neuroimaging and behavioral data are needed to further investigate these associations. In addition, while the test-retest reliability results were not very high, the strength was comparable with those reported by previous fMRI studies35.
Future studies can apply this protocol to better understand the neural basis of spatial navigation from a network perspective and explore its variations in humans. For instance, researchers can make use of this protocol to investigate the development and aging trajectory of navigation networks, and in clinical practice, the network properties provide important biomarkers for guiding early identification and diagnosis of brain disorders such as Alzheimer&#39;s disease. Besides, future studies could also apply a similar protocol to construct the network models for other cognitive constructs.

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

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 comparison, we also visualized an earlier definition of the spatial navigation regions in Figure 5B and Figure 5D. Twenty regions from the AAL atlas were included as a comparison. These two sets of regions showed a large overlap.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65150/65150fig05v2.jpg" /> 
Figure 5: The modular average navigation networks. (A) Modularity of NaviNet_AICHA in REST1. (B) Modularity of NaviNet_AAL in REST1. (C,D) represent the modularity of NaviNet_AICHA and NaviNet_AAL in REST1 regressing out the whole brain signals respectively. Different node colors indicate different modules identified in each network. Two modules are shown in NaviNet_AICHA and NaviNet_AAL, which includes a medial temporal module and a parietal module. Abbreviations: AICHA = atlas of intrinsic connectivity of homotopic areas; AAL = anatomical automatic labeling. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Interestingly, these two networks showed similar community distribution (Figure 5). Specifically, modularity and repeatability analyses showed two modules in both NaviNet_AICHA and NaviNet_AAL (one ventral module including the medial temporal regions and one dorsal module including the parietal regions) (Table 1). The ventral and dorsal modules were similar between NaviNet_AICHA and NaviNet_AAL, although the number of nodes was larger in the former, given the finer brain parcellation of the AICHAI atlas. These results were independent of the strategies used for dealing with global signals in the preprocessing procedure (Figure 5). In addition, similar community distributions were observed in the REST2 dataset (Supplementary Figure S1).
Similarity of the topological properties of two navigation networks 
Next, we examined the similarity of each network measure between the two networks. The purpose of the similarity analyses was two-fold: (1) to evaluate the generalizability of the results when using different definition strategies and (2) to determine an optimal network threshold for the network analyses.
In general, five of the six metrics, except for the clustering coefficient, showed significant correlations between the two networks with the majority of the network sparsity thresholds used in the network analyses (Figure 6). The similarity values increased quickly with the sparsity threshold for all metrics, except for the mean node degree, which showed an excellent similarity value with either threshold. The small-world metric showed the highest similarity at a threshold between 0.30 and 0.40, where other metrics also showed the highest similarity. These results suggest that the network-level analyses could reflect stable individual differences independent of node definition choices, and that the sparsity threshold of 0.30-0.40 would result in better generalizability in navigation network analyses. See Supplementary Figure S2 for more similarities with REST2.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65150/65150fig06.jpg" /> 
Figure 6: Similarity of topological properties of two networks. The results of REST 1 (A) without and (B) with regressing out whole brain signals are shown. The Pearson correlation coefficient on the y-axis indicates the similarity of topological properties of the two networks. The sparsity threshold ranges from 0.05 to 0.5. The asterisks in the figures indicate the level of significance, where *p &#60; 0.05, ** p &#60; 0.01, *** p &#60; 0.001. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The test-retest reliability 
We also evaluated the test-retest reliability of the topological measures of the navigation networks. Various sparsity thresholds between 0.05 and 0.50 were used when calculating these network measures to remove potential spurious connectivity in the networks (see Supplementary Table S1 for details). Here, we mainly reported results with a threshold of 0.40, given the similarity results above. The majority of the network metrics showed fair to good reliability (ICC &#62; 0.2) both in the network NaviNet_AICHA and NaviNet_AAL, while the NaviNet_AAL showed relatively higher reliability than the NaviNet_AICHA. In addition, we found that including global signal regression in fMRI data preprocessing could result in a higher reliability (Figure 7). The clustering coefficient, shortest&#160;path length, and small-world in the NaviNet_AAL network showed the highest test-retest reliability, while the clustering coefficient and small-world in NaviNet_AICHA also showed higher test-retest reliability than other measures. These results suggest that the clustering coefficient and small-world are the most reliable among these metrics.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65150/65150fig07.jpg" /> 
Figure 7: Test-retest reliability of topological properties of navigation networks. (A) Test-retest reliability for data without regressing out the whole brain signals. (B) Test-retest reliability for data regressing out the whole brain signals. Abbreviations: ICC = intra-class correlation coefficient; Cc = clustering coefficient; Lp = shortest path length; Sw = small-worldness; Nd = mean nodal degree; Eg = global efficiency; Eloc = local efficiency; AICHA = atlas of intrinsic connectivity of homotopic areas; AAL = anatomical automatic labeling; cNGS = data without regressing out the whole brain signals; cWGS = regressing out the whole brain signals. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2" rowspan="2"></td>
			<td colspan="4">module numbers</td>
		</tr>
		<tr>
			<td>2</td>
			<td>3</td>
			<td>4</td>
			<td>5</td>
		</tr>
		<tr>
			<td rowspan="2">NaviNet_AICHA</td>
			<td>cNGS</td>
			<td>1</td>
			<td>1</td>
			<td>0.96</td>
			<td>0.67</td>
		</tr>
		<tr>
			<td>cWGS</td>
			<td>1</td>
			<td>0.96</td>
			<td>0.78</td>
			<td>0.89</td>
		</tr>
		<tr>
			<td rowspan="2">NaviNet_AAL</td>
			<td>cNGS</td>
			<td>1</td>
			<td>0.95</td>
			<td>0.95</td>
			<td>0.65</td>
		</tr>
		<tr>
			<td>cWGS</td>
			<td>1</td>
			<td>0.95</td>
			<td>0.95</td>
			<td>0.95</td>
		</tr>
	</tbody>
</table>
Table 1: The repeatability of module partition between REST 1 and REST 2. The first row indicates the number of modules. cNGS represents the rs-fMRI without regressing out the global signals and cWGS represents the rs-fMRI regressed out the global signals. A larger number indicates a higher repeatability, and two modules are chosen for NaviNet_AICHA and NaviNet_AAL in the present text. Abbreviations: AICHA = atlas of intrinsic connectivity of homotopic areas; AAL = anatomical automatic labeling.
Supplementary Figure S1: The modular average navigation networks in REST 2. (A,B) The modularity of NaviNet_AICHA and NaviNet_AAL in the REST 2 dataset without regressing out the whole brain signals. (C,D) The modularity of NaviNet_AICHA and NaviNet_AAL in the REST 2 dataset with the whole brain signals regression. Different node colors indicate different modules identified in each network. Both two modules are shown in NaviNet_AICHA and NaviNet_AAL. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150_Supplementary Figure 1.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure S2: Similarity of topological properties of two networks in REST 2. The results of REST 2 without/with regressing out whole brain signals are shown (A and B, respectively). The Pearson correlation coefficient on the y-axis indicates the similarity of topological properties of the two networks. The sparsity threshold ranges from 0.05 to 0.5. The asterisks in the figures indicate the level of significance, where * p &#60; 0.05, ** p &#60; 0.01, *** p &#60; 0.001. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150_Supplementary Figure 2.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Table S1: Test-retest reliability of topological properties of navigation networks with different sparsity thresholds. The values indicate the intra-class correlation coefficient of NaviNet_AICHA and NaviNet_AAL with different sparsity thresholds. Abbreviations: Cc = clustering coefficient; Lp = shortest path length; Sw = small woldness; Nd = mean nodal degree; Eg = global efficiency; Eloc = local efficiency; cNGS = data without regressing out the whole brain signals; cWGS = regressing out the whole brain signals. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/65150/65150_Supplementary Table 1.xlsx" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Modeling the Functional Network for Spatial Navigation in the Human Brain at JoVE.com

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 ...

modeling-functional-network-for-spatial-navigation-human

Universidad San Sebastian

Research

JoVE Journal

Neuroscience

959 Views.  Zhejiang University. 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. 

Video: Modeling the Functional Network for Spatial Navigation in the Human Brain

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

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments1. MazeSuite can be used to design and edit adapted virtual 3D environments, track a participants' behavioral performance within the virtual environment and synchronize with external devices for physiological and neuroimaging measures, including electroencephalogram and eye tracking.
Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables continuous, noninvasive, and portable monitoring of changes in cerebral blood oxygenation related to human brain functions2-7. Over the last decade fNIR is used to effectively monitor cognitive tasks such as attention, working memory and problem solving7-11. fNIR can be implemented in the form of a wearable and minimally intrusive device; it has the capacity to monitor brain activity in ecologically valid environments. 
Cognitive functions assessed through task performance involve patterns of brain activation of the prefrontal cortex (PFC) that vary from the initial novel task performance, after practice and during retention12. Using positron emission tomography (PET), Van Horn and colleagues found that regional cerebral blood flow was activated in the right frontal lobe during the encoding (i.e., initial na&iuml;ve performance) of spatial navigation of virtual mazes while there was little to no activation of the frontal regions after practice and during retention tests. Furthermore, the effects of contextual interference, a learning phenomenon related to organization of practice, are evident when individuals acquire multiple tasks under different practice schedules13,14. High contextual interference (random practice schedule) is created when the tasks to be learned are presented in a non-sequential, unpredictable order. Low contextual interference (blocked practice schedule) is created when the tasks to be learned are presented in a predictable order.
Our goal here is twofold: first to illustrate the experimental protocol design process and the use of MazeSuite, and second, to demonstrate the setup and deployment of the fNIR brain activity monitoring system using Cognitive Optical Brain Imaging (COBI) Studio software15. To illustrate our goals, a subsample from a study is reported to show the use of both MazeSuite and COBI Studio in a single experiment. The study involves the assessment of cognitive activity of the PFC during the acquisition and learning of computer maze tasks for blocked and random orders. Two right-handed adults (one male, one female) performed 315 acquisition, 30 retention and 20 transfer trials across four days. Design, implementation, data acquisition and analysis phases of the study were explained with the intention to provide a guideline for future studies.

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.

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments1. MazeSuite can be used to design and edit adapted ...

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

Behavior

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

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when studying the brain, as the functional role that a brain area fulfills may be largely defined by its connections to other brain areas. In this report, we describe a novel, non-invasive approach for relating brain structure and function using magnetic resonance imaging (MRI). This approach, a combination of structural imaging of long-range fiber connections and functional imaging data, is illustrated in two distinct cognitive domains, visual attention and face perception. Structural imaging is performed with diffusion-weighted imaging (DWI) and fiber tractography, which track the diffusion of water molecules along white-matter fiber tracts in the brain (Figure 1). By visualizing these fiber tracts, we are able to investigate the long-range connective architecture of the brain. The results compare favorably with one of the most widely-used techniques in DWI, diffusion tensor imaging (DTI). DTI is unable to resolve complex configurations of fiber tracts, limiting its utility for constructing detailed, anatomically-informed models of brain function. In contrast, our analyses reproduce known neuroanatomy with precision and accuracy. This advantage is partly due to data acquisition procedures: while many DTI protocols measure diffusion in a small number of directions (e.g., 6 or 12), we employ a diffusion spectrum imaging (DSI)1, 2 protocol which assesses diffusion in 257 directions and at a range of magnetic gradient strengths. Moreover, DSI data allow us to use more sophisticated methods for reconstructing acquired data. In two experiments (visual attention and face perception), tractography reveals that co-active areas of the human brain are anatomically connected, supporting extant hypotheses that they form functional networks. DWI allows us to create a "circuit diagram" and reproduce it on an individual-subject basis, for the purpose of monitoring task-relevant brain activity in networks of interest.

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.

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when ...

3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache

A growing body of research, generated primarily from MRI-based studies, shows that migraine appears to occur, and possibly endure, due to the alteration of specific neural processes in the central nervous system. However, information is lacking on the molecular impact of these changes, especially on the endogenous opioid system during migraine headaches, and neuronavigation through these changes has never been done. This study aimed to investigate, using a novel 3D immersive and interactive neuronavigation (3D-IIN) approach, the endogenous &#181;-opioid transmission in the brain during a migraine headache attack in vivo. This is arguably one of the most central neuromechanisms associated with pain regulation, affecting multiple elements of the pain experience and analgesia. A 36 year-old female, who has been suffering with migraine for 10 years, was scanned in the typical headache (ictal) and nonheadache (interictal) migraine phases using Positron Emission Tomography (PET) with the selective radiotracer [11C]carfentanil, which allowed us to measure &#181;-opioid receptor availability in the brain (non-displaceable binding potential - &#181;OR BPND). The short-life radiotracer was produced by a cyclotron and chemical synthesis apparatus on campus located in close proximity to the imaging facility. Both PET scans, interictal and ictal, were scheduled during separate mid-late follicular phases of the patient's menstrual cycle. During the ictal PET session her spontaneous headache attack reached severe intensity levels; progressing to nausea and vomiting at the end of the scan session. There were reductions in &#181;OR BPND in the pain-modulatory regions of the endogenous &#181;-opioid system during the ictal phase, including the cingulate cortex, nucleus accumbens (NAcc), thalamus (Thal), and periaqueductal gray matter (PAG); indicating that &#181;ORs were already occupied by endogenous opioids released in response to the ongoing pain. To our knowledge, this is the first time that changes in &#181;OR BPND during a migraine headache attack have been neuronavigated using a novel 3D approach. This method allows for interactive research and educational exploration of a migraine attack in an actual patient's neuroimaging dataset.

3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache

In this study, the authors report for the first time a novel 3D-Immersive &#38; Interactive Neuronavigation (3D-IIN) through the impact of a spontaneous migraine headache attack in the &#956;-opioid system of a patient's brain in vivo.

A growing body of research, generated primarily from MRI-based studies, shows that migraine appears to occur, and possibly endure, due to the alteration ...

Medicine

Game-Based Exploration: Unlocking Human Spatial Navigation Abilities

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

Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. This capacity is known to rely on the sequential firing of place cells within the hippocampus. SN is an important behavior to investigate as this process deteriorates with age, especially in neurodegenerative disorders. However, the investigation of SN is limited by the lack of sophisticated behavioral techniques to assess this hippocampal-dependent task. Therefore, the goal of this protocol was to develop a novel, real-world approach to studying SN in humans. Specifically, an active virtual SN task was developed using a cross-platform game engine. During the encoding phase, participants navigated their way through a virtual city to locate landmarks. During the remembering phase, participants remembered where these reward locations were and delivered items to these locations. Time to find each location was captured and episodic memory was assessed by a free recall phase, including aspects of place, order, item, and association. Movement behavior (x, y, and z coordinates) was assessed through an asset available in the game engine. Importantly, results from this task demonstrate that it accurately captures both spatial learning and memory abilities as well as episodic memory. Further, findings indicate that this task is sensitive to exercise, which improves hippocampal functioning. Overall, the findings suggest a novel way to track human hippocampal functioning over the course of time, with this behavior being sensitive to physical activity training paradigms.

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

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.

Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. ...

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

The purpose of this study is to show the methodology for the examination of the white matter connections of the supplementary motor area (SMA) complex (pre-SMA and SMA proper) using a combination of fiber dissection techniques on cadaveric specimens and magnetic resonance (MR) tractography. The protocol will also describe the procedure for a white matter dissection of a human brain, diffusion tensor tractography imaging, and three-dimensional documentation. The fiber dissections on human brains and the 3D documentation were performed at the University of Minnesota, Microsurgery and Neuroanatomy Laboratory, Department of Neurosurgery. Five postmortem human brain specimens and two whole heads were prepared in accordance with Klingler's method. Brain hemispheres were dissected step by step from lateral to medial and medial to lateral under an operating microscope, and 3D images were captured at every stage. All dissection results were supported by diffusion tensor imaging. Investigations on the connections in line with Meynert's fiber tract classification, including association fibers (short, superior longitudinal fasciculus I and frontal aslant tracts), projection fibers (corticospinal, claustrocortical, cingulum, and frontostriatal tracts), and commissural fibers (callosal fibers) were also conducted.

The purpose of this study is to show each step of the fiber dissection technique on human cadaveric brains, the 3D documentation of these dissections, and the diffusion tensor imaging of the anatomically dissected fiber pathways.

The purpose of this study is to show the methodology for the examination of the white matter connections of the supplementary motor area (SMA) complex ...

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and microscopic neuronal spikes, propagating along axons and dendrites. Both scales complement different aspects of human cognitive and behavioral functions. At the macroscopic level, MRI has been the current standard imaging technology, in which the smallest spatial resolution, voxel size, is 0.1&#8211;1 mm3. Also, at the microscopic level, previous physiological studies were aware of nonuniform neuronal architectures within such voxels. This study develops a powerful way to accurately embed microscopic data into a macroscopic map by interfacing biological scientific research with technological advancements in 3D scanning technology. Since 3D scanning technology has mostly been used for engineering and industrial design until now, it is repurposed for the first time to embed microconnectomes into the whole brain while preserving natural spiking in living brain cells. In order to achieve this purpose, first, we constructed a scanning protocol to obtain accurate 3D images from living bio-organisms inherently challenging to image due to moist and reflective surfaces. Second, we trained to keep speed to prevent the degradation of living brain tissue, which is a key factor in retaining better conditions and recording more natural neuronal spikes from active neurons in the brain tissue. Two cortical surface images, independently extracted from two different imaging modules, namely MRI and 3D scanner surface images, surprisingly show a distance error of only 50 &#956;m as mode value of the histogram. This accuracy is comparable in scale to the microscopic resolution of intercellular distances; also, it is stable among different individual mice. This new protocol, the 3D novel embedding overlapping (3D-NEO) protocol, bridges macroscopic and microscopic levels derived by this integrative protocol and accelerates new scientific findings to study comprehensive connectivity architectures (i.e., microconnectome).

This article introduces an experimental protocol using 3D scanning technology bridging two spatial scales: the macroscopic spatial scale of whole-brain anatomy imaged by MRI at &#62;100 &#956;m and the microscopic spatial scale of neuronal distributions using immunohistochemistry staining and a multielectrode array system and other methods (~10 &#956;m).

The human brain, being a multiscale system, has both macroscopic electrical signals, globally flowing along thick white-matter fiber bundles, and ...

A Visuospatial Planning Task Coupled with Eye-Tracker and Electroencephalogram Systems

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since planning is compromised in several neuropsychiatric disorders, the implementation of proper clinical and experimental tests to examine planning is critical. Due to the nature of the deployment of planning, in which several cognitive domains participate, the assessment of planning and the design of behavioral paradigms coupled with neuroimaging methods are current challenges in cognitive neuroscience. A planning task was evaluated in combination with an electroencephalogram (EEG) system and eye movement recordings in 27 healthy adult participants. Planning can be separated into two stages: a mental planning stage in which a sequence of steps is internally represented and an execution stage in which motor action is used to achieve a previously planned goal. Our protocol included a planning task and a control task. The planning task involved solving 36 maze trials, each representing a zoo map. The task had four periods: i) planning, where the subjects were instructed to plan a path to visit the locations of four animals according to a set of rules; ii) maintenance, where the subjects had to retain the planned path in their working memory; iii) execution, where the subjects used eye movements to trace the previously planned path as indicated by the eye-tracker system; and iv) response, where the subjects reported the order of the visited animals. The control task had a similar structure, but the cognitive planning component was removed by modifying the task goal. The spatial and temporal patterns of the EEG revealed that planning induces a gradual and lasting rise in frontal-midline theta activity (FM&#952;) over time. The source of this activity was identified within the prefrontal cortex by source analyses. Our results suggested that the experimental paradigm combining EEG and eye-tracker systems was optimal for evaluating cognitive planning.

The study of cognitive planning combining EEG and eye-tracking systems provides a multimodal approach to investigate the neural mechanisms that mediate cognitive control and goal-directed behavior in humans. Here, we describe a protocol for investigating the role of brain oscillations and eye movements in planning performance.

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since ...

Multimodal Large-Scale Neural Recordings and Analysis on HD-MEA 

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

Large-scale neuronal networks and their complex distributed microcircuits are essential to generate perception, cognition, and behavior that emerge from patterns of spatiotemporal neuronal activity. These dynamic patterns emerging from functional groups of interconnected neuronal ensembles facilitate precise computations for processing and coding multiscale neural information, thereby driving higher brain functions. To probe the computational principles of neural dynamics underlying this complexity and investigate the multiscale impact of biological processes in health and disease, large-scale simultaneous recordings have become instrumental. Here, a high-density microelectrode array (HD-MEA) is employed to study two modalities of neural dynamics - hippocampal and olfactory bulb circuits from ex-vivo mouse brain slices and neuronal networks from in-vitro cell cultures of human induced pluripotent stem cells (iPSCs). The HD-MEA platform, with 4096 microelectrodes, enables non-invasive, multi-site, label-free recordings of extracellular firing patterns from thousands of neuronal ensembles simultaneously at high spatiotemporal resolution. This approach allows the characterization of several electrophysiological network-wide features, including single/-multi-unit spiking activity patterns and local field potential oscillations. To scrutinize these multidimensional neural data, we have developed several computational tools incorporating machine learning algorithms, automatic event detection and classification, graph theory, and other advanced analyses. By supplementing these computational pipelines with this platform, we provide a methodology for studying the large, multiscale, and multimodal dynamics from cell assemblies to networks. This can potentially advance our understanding of complex brain functions and cognitive processes in health and disease. Commitment to open science and insights into large-scale computational neural dynamics could enhance brain-inspired modeling, neuromorphic computing, and neural learning algorithms. Furthermore, understanding the underlying mechanisms of impaired large-scale neural computations and their interconnected microcircuit dynamics could lead to the identification of specific biomarkers, paving the way for more accurate diagnostic tools and targeted therapies for neurological disorders.

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

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.

Large-scale neuronal networks and their complex distributed microcircuits are essential to generate perception, cognition, and behavior that emerge from ...

Motor Maps

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
One principle of brain organization is the topographic mapping of information. Especially in sensory and motor cortices, adjacent regions of the brain tend to represent information from adjacent parts of the body, resulting in maps of the body expressed on the surface of the brain. The primary sensory and motor maps in the brain surround a prominent sulcus known as the central sulcus. The cortex anterior to the central sulcus is known as the precentral gyrus and contains the primary motor cortex, while the cortex posterior to the central sulcus is known as the postcentral gyrus and contains the primary sensory cortex (Figure 1).
<img alt="Figure 1" src="/files/ftp_upload/10175/10175fig1.jpg" /> 
Figure 1: Sensory and motor maps around the central sulcus. The primary motor cortex, which contains a motor map of the body&#39;s effectors, is anterior to the central sulcus, in the precentral gyrus of the frontal lobe. The primary somesthetic (sensory) cortex, which receives touch, pain, and temperature information from the external parts of the body, is located posterior to the central sulcus, in the postcentral gyrus of the parietal lobe.
In this experiment, functional neuroimaging is used to demonstrate the motor map in the precentral gyrus. This map is often called the motor homunculus, which is Latin for &#34;little man,&#34; because it is as if there is a little version of one&#39;s self represented in this part of a person&#39;s brain. One interesting property of this map is that more cortical space is devoted to body parts requiring finer control, such as the hands and mouth, which results in disproportionate representation of those appendages in the cortex. Also, because of the anatomy of the motor system, the neurons that control the right side of the body are in the left primary motor cortex, and vice versa. Therefore, when a participant in the experiment is asked to move their right hand or foot, an increased activation on their left precentral gyrus is expected.
In this experiment, participants are asked to alternately move their hands and feet, on the left and right sides, while their brain activity is measured with fMRI. Since the fMRI signal relies on changes in blood oxygenation, which are slow in comparison to the movements the participants make, the periods of movement are separated with periods of stillness to ensure that the various conditions can be distinguished from each other and from the resting baseline. To achieve precise timing of the movements, participants are instructed on when to begin and end each movement with a visual cue. The methods in this video are similar to those used by several fMRI studies that have demonstrated somatotopy in primary motor cortex.1,2

Studying Brain Activation and Motor Maps Using fMRI

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
One principle of brain organization is the topographic ...

Modeling the Functional Network for Spatial Navigation in the Human Brain

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

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

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

3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache

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

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

A Visuospatial Planning Task Coupled with Eye-Tracker and Electroencephalogram Systems

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

Motor Maps