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/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/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/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/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 n...

Functional specificity is a fundamental organization principle of the human brain, which plays a crucial role in shaping cognitive functions1. Abnormalities in the organization of functional specificity can reflect hallmark cognitive impairments and the associated pathological foundations of major brain disorders such as autism and Alzheimer&#39;s disease2,3. While conventional theories and research have tended to focus on single brain regions, such as the fusiform face area (FFA) for face recognition4 and parahippocampus place area (PPA)5 for scene processing, an increasing body of evidence suggests that complex cognitive functions, including spatial navigation and language, require coordinate activity across multiple brain regions6. Investigating the mechanisms underlying the interactions in support of complex cognitive functions is a critical scientific question that will help to shed light on the functional architecture and operation of the brain. Here, taking spatial navigation as an example, we present an integrative method for modeling the functional network for spatial navigation in the human brain.
Spatial navigation is a complex cognitive function, which involves the integration and manipulation of multiple cognitive components, such as visual-spatial coding, memory, and decision making7. With functional magnetic resonance imaging (fMRI), numerous studies have made significant advances in understanding the underlying cognitive processing and neural mechanisms. For instance, specific functions have been linked to different brain regions using various navigation tasks: scene processing&#160;is specifically associated with PPA, and transformation of navigation strategies is associated with the retrosplenial cortex (RSC)8,9. These studies provided important insights into the neural basis of spatial navigation. However, navigation is an internally dynamic and multimodal function, and the functions of single regions are not sufficient to explain large individual differences in spatial navigation10 that are commonly observed.
With the emergence of fMRI-based connectomics, researchers began to explore how some key brain regions interact with each other to support spatial navigation. For example, functional connectivity between the entorhinal and posterior cingulate cortices has been found to underpin navigation discrepancies in at-risk Alzheimer&#39;s disease11. In another study, we for the first time proposed a network approach by integrating connectome methods and almost all functionally relevant regions (nodes) for spatial navigation, and the results showed that topological properties of this network showed specific associations with navigation behaviors12. This study provides new insights into theories of how multiple brain regions interact with each other to support flexible navigation behaviors10,13.
The present work demonstrates an updated version of the integrative approach for modeling the functional network. Briefly, two updates were included: 1) While the nodes defined in the original study were identified based on an earlier and smaller database (55 studies with 2,765 activations, accessed in 2014), the present definition was based on the latest database (77 studies with 3,908 activations, accessed in 2022); 2) to increase functional homogeneity of each node, besides the original anatomical AAL (Anatomical Automatic Labeling) atlas14, we applied a new brain parcellation, which has a much finer resolution and higher functional homogeneity (see below). We expected that both updates would improve the modeling of the functional network. This updated protocol provides a detailed procedure for investigating the neural basis of spatial navigation from a network perspective and helps understand individual variations in navigation behaviors in health and disease. A similar procedure could also be used for network modeling for other cognitive constructs (e.g., language and memory).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Brain connectivity toolbox (BCT)</td>
			<td>Mikail Rubinov &#38; Olaf Sporns&#160;</td>
			<td>2019</td>
			<td>The Brain Connectivity Toolbox (brain-connectivity-toolbox.net) is a MATLAB toolbox for complex-network (graph) analysis of structural and functional brain-connectivity data sets.&#160;</td>
		</tr>
		<tr>
			<td>GRETNA</td>
			<td>Jinhui Wang et al.</td>
			<td>2</td>
			<td>GRETNA is a graph theoretical network analysis toolbox which allows researchers to perform comprehensive analysis on the topology of brain connectome by integrating the most of network measures studied in current neuroscience field.</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>2021a</td>
			<td>MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.</td>
		</tr>
		<tr>
			<td>Python</td>
			<td>Guido van Rossum et al.</td>
			<td>3.8.6</td>
			<td>Python is a programming language that lets you work more quickly and integrate your systems more effectively.</td>
		</tr>
		<tr>
			<td>Statistical Parametric Mapping (SPM)</td>
			<td>Karl Friston et.al&#160;</td>
			<td>12</td>
			<td>Statistical Parametric Mapping&#160;refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data.</td>
		</tr>
	</tbody>
</table>

modeling the functional network for spatial navigation in the human brain

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

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

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

Research

JoVE Journal

Neuroscience

949 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

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

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful rest."1,2,3 It is thought the default mode network corresponds to self-referential or "internal mentation".2,3
It has been hypothesized that, in humans, activity within the default mode network is correlated with certain pathologies (for instance, hyper-activation has been linked to schizophrenia 4,5,6 and autism spectrum disorders 7 whilst hypo-activation of the network has been linked to Alzheimer's and other neurodegenerative diseases 8). As such, noninvasive modulation of this network may represent a potential therapeutic intervention for a number of neurological and psychiatric pathologies linked to abnormal network activation. One possible tool to effect this modulation is Transcranial Magnetic Stimulation: a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.9
In order to explore the default mode network's propensity towards and tolerance of modulation, we will be combining TMS (to the left inferior parietal lobe) with functional magnetic resonance imaging (fMRI). Through this article, we will examine the protocol and considerations necessary to successfully combine these two neuroscientific tools.

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.

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful ...

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 ...

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other neurological diseases at an increasing speed. However, little is known about the molecular mechanisms that underlie these disorders. The genetic, molecular, and behavioral toolbox of Drosophila melanogaster makes this model organism particularly useful to characterize new disease genes and mechanisms in a high-throughput manner. Nevertheless, high-throughput screens require efficient and reliable assays that, ideally, are cost-effective and allow for the automatized quantification of traits relevant to these disorders. The island assay is a cost-effective and easily set-up method to evaluate Drosophila locomotor behavior. In this assay, flies are thrown onto a platform from a fixed height. This induces an innate motor response that enables the flies to escape from the platform within seconds. At present, quantitative analyses of filmed island assays are done manually, which is a laborious undertaking, particularly when performing large screens.
This manuscript describes the &#34;Drosophila Island Assay&#34; and &#34;Island Assay Analysis&#34; algorithms for&#160;high-throughput, automated data processing and quantification of island assay data. In the setup, a simple webcam connected to a laptop collects an image series of the platform while the assay is performed. The &#34;Drosophila Island Assay&#34; algorithm developed for the open-source software Fiji processes these image series and quantifies, for each experimental condition, the number of flies on the platform over time. The &#34;Island Assay Analysis&#34; script, compatible with the free software R, was developed to automatically process the obtained data and to calculate whether treatments/genotypes are statistically different. This greatly improves the efficiency of the island assay and makes it a powerful readout for basic locomotion and flight behavior. It can thus be applied to large screens investigating fly locomotor ability, Drosophila models of movement disorders, and drug efficacy.

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

The island assay is a relatively new, cost-effective assay that can be used to evaluate the basic locomotor behavior of Drosophila melanogaster. This manuscript describes algorithms for automatic data processing and objective quantification of island assay data, making this assay a sensitive, high-throughput readout for large genetic or pharmacological screens.

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other ...

Recording Synaptic Plasticity in Acute Hippocampal Slices Maintained in a Small-volume Recycling-, Perfusion-, and Submersion-type Chamber System

Even though experiments on brain slices have been in use since 1951, problems remain that reduce the probability of achieving a stable and successful analysis of synaptic transmission modulation when performing field potential or intracellular recordings. This manuscript describes methodological aspects that might be helpful in improving experimental conditions for the maintenance of acute brain slices and for recording field excitatory postsynaptic potentials in a commercially available submersion chamber with an outflow-carbogenation unit. The outflow-carbogenation helps to stabilize the oxygen level in experiments that rely on the recycling of a small buffer reservoir to enhance the cost-efficiency of drug experiments. In addition, the manuscript presents representative experiments that examine the effects of different carbogenation modes and stimulation paradigms on the activity-dependent synaptic plasticity of synaptic transmission.

This protocol describes the stabilization of the oxygen level in a small volume of recycled buffer and methodological aspects of recording activity-dependent synaptic plasticity in submerged acute hippocampal slices.

Even though experiments on brain slices have been in use since 1951, problems remain that reduce the probability of achieving a stable and successful ...

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of sciatic nerve injury rodent models. In this protocol, we describe a novel kinematic analysis method that uses a three-dimensional (3D) motion capture apparatus for functional evaluations using a rat sciatic nerve crush injury model. First, the rat is familiarized with treadmill walking. Markers are then attached to the designated bone landmarks and the rat is made to walk on the treadmill at the desired speed. Meanwhile, the posterior limb movements of the rat are recorded using four cameras. Depending on the software used, marker tracings are created using both automatic and manual modes and the desired data are produced after subtle adjustments. This method of kinematic analysis, which uses a 3D motion capture apparatus, offers numerous advantages, including superior precision and accuracy. Many more parameters can be investigated during the comprehensive functional evaluations. This method has several shortcomings that require consideration: The system is expensive, can be complicated to operate, and may produce data deviations due to skin shifting. Nevertheless, kinematic analysis using a 3D motion capture apparatus is useful for performing functional anterior and posterior limb evaluations. In the future, this method may become increasingly useful for generating accurate assessments of various traumas and diseases.

We introduce a kinematic analysis method that uses a three-dimensional motion capture apparatus containing four cameras and data processing software for performing functional evaluations during fundamental research involving rodent models.

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of ...

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

High interindividual variability in the recovery of upper limb (UL) function after stroke means it is difficult to predict an individual&#39;s potential for recovery based on clinical assessments alone. The functional integrity of the corticospinal tract is an important prognostic biomarker for recovery of UL function, particularly for those with severe initial UL impairment. This article presents a protocol for evaluating corticospinal tract function within 1 week of stroke. This protocol can be used to select and stratify patients in trials of interventions designed to improve UL motor recovery and outcomes after stroke. The protocol also forms part of the PREP2 algorithm, which predicts UL function for individual patients 3 months poststroke. The algorithm sequentially combines a UL strength assessment, age, transcranial magnetic stimulation, and stroke severity, within a few days of the stroke. The benefits of using PREP2 in clinical practice are described elsewhere. This article focuses on the use of a UL strength assessment and transcranial magnetic stimulation to evaluate corticospinal tract function.

This protocol is for evaluating corticospinal tract function within 1 week of stroke. It can be used to select and stratify patients in trials of interventions designed to improve upper limb motor recovery and outcomes and in clinical practice for predicting upper limb functional outcomes 3 months after stroke.

High interindividual variability in the recovery of upper limb (UL) function after stroke means it is difficult to predict an individual&#39;s potential ...

Generation of Human Neurons and Oligodendrocytes from Pluripotent Stem Cells for Modeling Neuron-Oligodendrocyte Interactions

In Alzheimer&#8217;s disease (AD) and other neurodegenerative disorders, oligodendroglial failure is a common early pathological feature, but how it contributes to disease development and progression, particularly in the gray matter of the brain, remains largely unknown. The dysfunction of oligodendrocyte lineage cells is hallmarked by deficiencies in myelination and impaired self-renewal of oligodendrocyte precursor cells (OPCs). These two defects are caused at least in part by the disruption of interactions between neuron and oligodendrocytes along the buildup of pathology. OPCs give rise to myelinating oligodendrocytes during CNS development. In the mature brain cortex, OPCs are the major proliferative cells (comprising ~5% of total brain cells) and control new myelin formation in a neural activity-dependent manner. Such neuron-to-oligodendrocyte communications are significantly understudied, especially in the context of neurodegenerative conditions such as AD, due to the lack of appropriate tools. In recent years, our group and others have made significant progress to improve currently available protocols to generate functional neurons and oligodendrocytes individually from human pluripotent stem cells. In this manuscript, we describe our optimized procedures, including the establishment of a co-culture system to model the neuron-oligodendrocyte connections. Our illustrative results suggest an unexpected contribution from OPCs/oligodendrocytes to the brain amyloidosis and synapse integrity and highlight the utility of this methodology for AD research. This reductionist approach is a powerful tool to dissect the specific hetero-cellular interactions out of the inherent complexity inside the brain. The protocols we describe here are expected to facilitate future studies on oligodendroglial defects in the pathogenesis of neurodegeneration.

The neuron-glial interactions in neurodegeneration are not well understood due to inadequate tools and methods. Here, we describe optimized protocols to obtain induced neurons, oligodendrocyte precursor cells, and oligodendrocytes from human pluripotent stem cells and provide examples of the values of these methods in understanding cell-type-specific contributions in Alzheimer&#8217;s disease.

In Alzheimer&#8217;s disease (AD) and other neurodegenerative disorders, oligodendroglial failure is a common early pathological feature, but how it ...

A Metric Test for Assessing Spatial Working Memory in Adult Rats Following Traumatic Brain Injury

Impairments to sensory, short-term, and long-term memory are common side effects after traumatic brain injury (TBI). Due to the ethical limitations of human studies, animal models provide suitable alternatives to test treatment methods, and to study the mechanisms and related complications of the condition. Experimental rodent models have historically been the most widely used due to their accessibility, low cost, reproducibility, and validated approaches. A metric test, which tests the ability to recall the placement of two objects at various distances and angles from one another, is a technique to study impairment in spatial working memory (SWM) after TBI. The significant advantages of metric tasks include the possibility of dynamic observation, low cost, reproducibility, relative ease of implementation, and low stress environment. Here, we present a metric test protocol to measure impairment of SWM in adult rats after TBI. This test provides a feasible way to evaluate physiology and pathophysiology of brain function more effectively.

Traumatic brain injury (TBI) is commonly associated with memory impairment. Here, we present a protocol to assess spatial working memory after TBI via a metric task. A metric test is a useful tool to study spatial working memory impairment after TBI.

Impairments to sensory, short-term, and long-term memory are common side effects after traumatic brain injury (TBI). Due to the ethical limitations of ...

Generation of Human Brain Organoids for Mitochondrial Disease Modeling

Mitochondrial diseases represent the largest class of inborn errors of metabolism and are currently incurable. These diseases cause neurodevelopmental defects whose underlying mechanisms remain to be elucidated. A major roadblock is the lack of effective models recapitulating the early-onset neuronal impairment seen in the patients. Advances in the technology of induced pluripotent stem cells (iPSCs) enable the generation of three-dimensional (3D) brain organoids that can be used to investigate the impact of diseases on the development and organization of the nervous system. Researchers, including these authors, have recently introduced human brain organoids to model mitochondrial disorders. This paper reports a detailed protocol for the robust generation of human iPSC-derived brain organoids and their use in mitochondrial bioenergetic profiling and imaging analyses. These experiments will allow the use of brain organoids to investigate metabolic and developmental dysfunctions and may provide crucial information to dissect the neuronal pathology of mitochondrial diseases.

We describe a detailed protocol for the generation of human induced pluripotent stem cell-derived brain organoids and their use in modeling mitochondrial diseases.

Mitochondrial diseases represent the largest class of inborn errors of metabolism and are currently incurable. These diseases cause neurodevelopmental ...

Modeling the Functional Network for Spatial Navigation in the Human Brain

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

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

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

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

Recording Synaptic Plasticity in Acute Hippocampal Slices Maintained in a Small-volume Recycling-, Perfusion-, and Submersion-type Chamber System

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Generation of Human Neurons and Oligodendrocytes from Pluripotent Stem Cells for Modeling Neuron-Oligodendrocyte Interactions

A Metric Test for Assessing Spatial Working Memory in Adult Rats Following Traumatic Brain Injury

Generation of Human Brain Organoids for Mitochondrial Disease Modeling