Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs
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{"title"=>"Probabilistic clustering of the human connectome identifies communities and hubs", "type"=>"journal", "authors"=>[{"first_name"=>"Max", "last_name"=>"Hinne", "scopus_author_id"=>"26867829100"}, {"first_name"=>"Matthias", "last_name"=>"Ekman", "scopus_author_id"=>"55386812800"}, {"first_name"=>"Ronald J.", "last_name"=>"Janssen", "scopus_author_id"=>"7202677544"}, {"first_name"=>"Tom", "last_name"=>"Heskes", "scopus_author_id"=>"7004495581"}, {"first_name"=>"Marcel A.J.", "last_name"=>"Van Gerven", "scopus_author_id"=>"8925642800"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84922387120", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0117179", "pmid"=>"25635390", "pui"=>"601957021", "scopus"=>"2-s2.0-84922387120", "isbn"=>"1932-6203"}, "id"=>"21d60df8-387a-3319-89dc-57ae92342b18", "abstract"=>"A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the 'rich club'; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures.", "link"=>"http://www.mendeley.com/research/probabilistic-clustering-human-connectome-identifies-communities-hubs", "reader_count"=>61, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>12, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>5, "Student > Master"=>12, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>12, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>5, "Student > Master"=>12, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>6, "Unspecified"=>8, "Mathematics"=>2, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>4, "Philosophy"=>1, "Neuroscience"=>8, "Physics and Astronomy"=>5, "Psychology"=>7, "Social Sciences"=>1, "Computer Science"=>12}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>6}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>8}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Psychology"=>{"Psychology"=>7}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>12}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>8}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Greece"=>1, "Netherlands"=>2, "Bangladesh"=>1, "United States"=>1, "United Kingdom"=>1, "Germany"=>3}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1876811"], "description"=>"<p><b>A.</b> The adjacency matrix <b>A</b>. <b>B.</b> The connection probability matrix <i>ρ</i>. <b>C.</b> The number of connections between clusters <b>ZAZ</b><sup><i>T</i></sup>. <b>D.</b> Visualization of the maximum a posteriori network structure and parcellation. The clusters are color coded to be able to compare the network with the adjacency matrix in <b>A.</b> Node sizes are scaled by their degree. <b>E.</b> Visualization of the expectation of network structure and parcellations. Colors are interpolated with the MAP estimate as point of reference (see text). To keep the visualization uncluttered, only the <i>m</i> most probable edges are shown, where <i>m</i> is the number of edges in the MAP estimate.</p>", "links"=>[], "tags"=>["parcellation", "brain regions", "groups brain regions", "connection profiles", "connectivity", "mri", "Human Connectome Identifies Communities", "model", "cluster"], "article_id"=>1298272, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Hinne", "Matthias Ekman", "Ronald J. Janssen", "Tom Heskes", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0117179.g002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Color_online_Maximum_a_posteriori_parcellations_for_one_participant_/1298272", "title"=>"(Color online) Maximum a posteriori parcellations for one participant.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-30 02:53:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1876817"], "description"=>"<p>The plots show, for the cluster each region is assigned to, the ratio of within-cluster streamlines versus the total number of streamlines connected to that cluster, for parcellations of individual participants (left) and group-parcellations (right). This reveals that in particular for the sIRM method, this ratio is small for a number of regions, indicating that these regions are not part of community-based clusters. Regions that consistently have a low ratio, are described in the text. Note that the nodes corresponding to each line are ordered differently.</p>", "links"=>[], "tags"=>["parcellation", "brain regions", "groups brain regions", "connection profiles", "connectivity", "mri", "Human Connectome Identifies Communities", "model", "cluster"], "article_id"=>1298278, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Hinne", "Matthias Ekman", "Ronald J. Janssen", "Tom Heskes", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0117179.g006", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_ratio_describing_how_community_like_the_clusters_are_that_each_region_is_assigned_to_/1298278", "title"=>"The ratio describing how community-like the clusters are that each region is assigned to.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-30 02:53:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1876813"], "description"=>"<p>(left) Cluster probability for the inferior frontal gyrus. The map shows that the highlighted region is likely to be assigned to regions in the frontal cortex, and to a much lesser degree to regions in the parietal cortex. The opposite pattern is shown for the postcentral gyrus (right).</p>", "links"=>[], "tags"=>["parcellation", "brain regions", "groups brain regions", "connection profiles", "connectivity", "mri", "Human Connectome Identifies Communities", "model", "cluster"], "article_id"=>1298274, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Hinne", "Matthias Ekman", "Ronald J. Janssen", "Tom Heskes", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0117179.g004", "stats"=>{"downloads"=>1, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cluster_probability_map_to_visualize_the_uncertainty_of_the_resulting_cluster_assignments_/1298274", "title"=>"Cluster probability map to visualize the uncertainty of the resulting cluster assignments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-30 02:53:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1876804"], "description"=>"<p><b>A</b>. The streamline infinite relational model combines a forward model for streamline data <b>S</b> with an infinite relational model that allows estimation of the cluster assignment matrix <b>Z</b> as well as the connection probability matrix <i>ρ</i>. Hyperparameters {<i>ξ</i>, <i>α</i>, <i>β</i>, <i>δ</i><sub><i>T</i></sub>, <i>δ</i><sub><i>F</i></sub>} complete the model. <b>B</b>. The probabilistic model supports both profile-based clustering as well as community-based clustering. The top row shows simulated connection probability matrices that correspond to profile-based clustering (left) and community-based clustering (right). Example networks that correspond to these probabilities are shown at the bottom.</p>", "links"=>[], "tags"=>["parcellation", "brain regions", "groups brain regions", "connection profiles", "connectivity", "mri", "Human Connectome Identifies Communities", "model", "cluster"], "article_id"=>1298268, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Hinne", "Matthias Ekman", "Ronald J. Janssen", "Tom Heskes", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0117179.g001", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probabilistic_model_for_connectivity_based_parcellation_/1298268", "title"=>"Probabilistic model for connectivity-based parcellation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-30 02:53:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1876830", "https://ndownloader.figshare.com/files/1876831", "https://ndownloader.figshare.com/files/1876832", "https://ndownloader.figshare.com/files/1876833", "https://ndownloader.figshare.com/files/1876834", "https://ndownloader.figshare.com/files/1876835", "https://ndownloader.figshare.com/files/1876836", "https://ndownloader.figshare.com/files/1876837", "https://ndownloader.figshare.com/files/1876838"], "description"=>"<div><p>A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the ‘rich club’; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures.</p></div>", "links"=>[], "tags"=>["parcellation", "brain regions", "groups brain regions", "connection profiles", "connectivity", "mri", "Human Connectome Identifies Communities", "model", "cluster"], "article_id"=>1298291, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Hinne", "Matthias Ekman", "Ronald J. Janssen", "Tom Heskes", "Marcel A. J. van Gerven"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117179.s001", "https://dx.doi.org/10.1371/journal.pone.0117179.s002", "https://dx.doi.org/10.1371/journal.pone.0117179.s003", "https://dx.doi.org/10.1371/journal.pone.0117179.s004", "https://dx.doi.org/10.1371/journal.pone.0117179.s005", "https://dx.doi.org/10.1371/journal.pone.0117179.s006", "https://dx.doi.org/10.1371/journal.pone.0117179.s007", "https://dx.doi.org/10.1371/journal.pone.0117179.s008", "https://dx.doi.org/10.1371/journal.pone.0117179.s009"], "stats"=>{"downloads"=>9, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probabilistic_Clustering_of_the_Human_Connectome_Identifies_Communities_and_Hubs_/1298291", "title"=>"Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-01-30 02:53:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1876815"], "description"=>"<p><b>A.</b> Mean adjusted mutual information (AMI, see main text) for pairwise comparisons between parcellations of all participants. <b>B.</b> Mean AMI for each of the pairs of parcellations that were created by randomly splitting the participant group into halves and obtaining a parcellation for each half.</p>", "links"=>[], "tags"=>["parcellation", "brain regions", "groups brain regions", "connection profiles", "connectivity", "mri", "Human Connectome Identifies Communities", "model", "cluster"], "article_id"=>1298276, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Hinne", "Matthias Ekman", "Ronald J. Janssen", "Tom Heskes", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0117179.g005", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_reproducibility_of_parcellations_/1298276", "title"=>"The reproducibility of parcellations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-30 02:53:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1876812"], "description"=>"<p>The coloring corresponds to the colors used in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117179#pone.0117179.g002\" target=\"_blank\">Fig. 2</a>.</p>", "links"=>[], "tags"=>["parcellation", "brain regions", "groups brain regions", "connection profiles", "connectivity", "mri", "Human Connectome Identifies Communities", "model", "cluster"], "article_id"=>1298273, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Hinne", "Matthias Ekman", "Ronald J. Janssen", "Tom Heskes", "Marcel A. J. van Gerven"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0117179.g003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Color_online_The_maximum_a_posteriori_parcellation_for_one_participant_see_Fig_2_projected_onto_the_inated_cortical_surface_and_subcortical_areas_/1298273", "title"=>"(Color online) The maximum a posteriori parcellation for one participant (see Fig. 2), projected onto the inated cortical surface and subcortical areas.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-01-30 02:53:25"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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