Task-Based Core-Periphery Organization of Human Brain Dynamics
Publication Date
September 26, 2013
Journal
PLOS Computational Biology
Authors
Danielle S. Bassett, Nicholas F. Wymbs, M. Puck Rombach, Mason A. Porter, et al
Volume
9
Issue
9
Pages
e1003171
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003171
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003171
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24086116
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3784512
Europe PMC
http://europepmc.org/abstract/MED/24086116
Web of Science
000325080900002
Scopus
84883099746
Mendeley
http://www.mendeley.com/research/taskbased-coreperiphery-organization-human-brain-dynamics-1
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Mendeley | Further Information

{"title"=>"Task-Based Core-Periphery Organization of Human Brain Dynamics", "type"=>"journal", "authors"=>[{"first_name"=>"Danielle S.", "last_name"=>"Bassett", "scopus_author_id"=>"15047396100"}, {"first_name"=>"Nicholas F.", "last_name"=>"Wymbs", "scopus_author_id"=>"6507860353"}, {"first_name"=>"M. Puck", "last_name"=>"Rombach", "scopus_author_id"=>"55889143700"}, {"first_name"=>"Mason A.", "last_name"=>"Porter", "scopus_author_id"=>"7201468248"}, {"first_name"=>"Peter J.", "last_name"=>"Mucha", "scopus_author_id"=>"7004946624"}, {"first_name"=>"Scott T.", "last_name"=>"Grafton", "scopus_author_id"=>"35463856500"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84883099746", "pmid"=>"24086116", "arxiv"=>"arXiv:1210.3555v2", "pui"=>"369903903", "isbn"=>"1553-7358 (Electronic) 1553-734X (Linking)", "scopus"=>"2-s2.0-84883099746", "doi"=>"10.1371/journal.pcbi.1003171", "issn"=>"1553734X"}, "id"=>"a17d41a4-a7a2-3741-abe3-570add79b4df", "abstract"=>"In humans, the parts of the brain involved in vision are organized into distinct regions that are arranged into a hierarchy. Each of these regions contains neurons that are specialized for a particular role, such as responding to shape, color or motion. To actually ‘see’ an object, these different regions must communicate with each other and exchange information via connections between lower and higher levels of the hierarchy. However, it remains unclear how these connections work. A brain region called the primary visual cortex is the lowest level of the visual cortical hierarchy as it is the first area to receive information from the eye. This region then passes information to higher regions in the hierarchy including the frontal eye fields (FEF), which help to control visual attention and eye movements. In turn, the FEF is thought to provide ‘feedback’ to the primary visual cortex. Cocchi et al. examined how the FEF and primary visual cortex communicate with the rest of the brain by temporarily inhibiting the activity of these regions in human volunteers. The experiments show that inhibiting the primary visual cortex increased communication between this region and higher level visual areas. On the other hand, inhibiting the FEF reduced communication between this region and lower visual areas. Computer simulations revealed that inhibiting particular brain regions alters communication between visual regions by changing the timing of local neural activity. In the simulations, inhibiting the primary visual cortex slows down neural activity in that region, leading to better communication with higher regions, which already operate on slower timescales. By contrast, inhibition of the FEF reduces its influence on lower visual regions by increasing the difference in timescales of neural activity between these regions. The next step is to determine whether similar mechanisms regulate changes in the activity of neural networks outside of the visual system.", "link"=>"http://www.mendeley.com/research/taskbased-coreperiphery-organization-human-brain-dynamics-1", "reader_count"=>208, "reader_count_by_academic_status"=>{"Unspecified"=>7, "Professor > Associate Professor"=>13, "Librarian"=>2, "Student > Doctoral Student"=>6, "Researcher"=>43, "Student > Ph. D. Student"=>63, "Student > Postgraduate"=>8, "Student > Master"=>27, "Other"=>2, "Student > Bachelor"=>16, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>15}, "reader_count_by_user_role"=>{"Unspecified"=>7, "Professor > Associate Professor"=>13, "Librarian"=>2, "Student > Doctoral Student"=>6, "Researcher"=>43, "Student > Ph. D. Student"=>63, "Student > Postgraduate"=>8, "Student > Master"=>27, "Other"=>2, "Student > Bachelor"=>16, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>15}, "reader_count_by_subject_area"=>{"Unspecified"=>24, "Agricultural and Biological Sciences"=>36, "Philosophy"=>1, "Business, Management and Accounting"=>2, "Computer Science"=>20, "Engineering"=>16, "Mathematics"=>7, "Medicine and Dentistry"=>16, "Neuroscience"=>32, "Design"=>1, "Physics and Astronomy"=>10, "Psychology"=>41, "Social Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>16}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>10}, "Psychology"=>{"Psychology"=>41}, "Mathematics"=>{"Mathematics"=>7}, "Unspecified"=>{"Unspecified"=>24}, "Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>16}, "Neuroscience"=>{"Neuroscience"=>32}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>36}, "Computer Science"=>{"Computer Science"=>20}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Republic of Singapore"=>2, "United States"=>12, "Japan"=>1, "Finland"=>1, "Poland"=>1, "United Kingdom"=>4, "Germany"=>1, "Russia"=>1, "Spain"=>4}, "group_count"=>12}

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1216445"], "description"=>"<p>Training sessions in the MRI scanner during the collection of blood-oxygen-level-dependent (BOLD) signals were interleaved with training sessions at home. Participants first practiced the sequences in the MRI scanner during a baseline training session <i>(top)</i>. Following every approximately 10 training sessions (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003171#pcbi.1003171.s009\" target=\"_blank\">Table S1</a>), participants returned for another scanning session. During each scanning session, a participant practiced each sequence for 50 trials. Participants trained at home between the scanning sessions <i>(bottom)</i>. During each home training session, participants practiced the sequences in a random order. (We determined a random order using the Mersenne Twister algorithm of Nishimura and Matsumoto <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003171#pcbi.1003171-Matsumoto1\" target=\"_blank\">[101]</a> as implemented in the random number generator rand.m of Matlab version 7.1). Each EXT sequence was practiced for 64 trials, each MOD sequence was practiced for 10 trials, and each MIN sequence was practiced for 1 trial.</p>", "links"=>[], "tags"=>[], "article_id"=>808699, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g009", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experiment_timeline_/808699", "title"=>"Experiment timeline.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216456", "https://ndownloader.figshare.com/files/1216457", "https://ndownloader.figshare.com/files/1216458", "https://ndownloader.figshare.com/files/1216459", "https://ndownloader.figshare.com/files/1216461", "https://ndownloader.figshare.com/files/1216462", "https://ndownloader.figshare.com/files/1216463", "https://ndownloader.figshare.com/files/1216464", "https://ndownloader.figshare.com/files/1216465", "https://ndownloader.figshare.com/files/1216466", "https://ndownloader.figshare.com/files/1216467", "https://ndownloader.figshare.com/files/1216468"], "description"=>"<div><p>As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal <i>core</i> that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal <i>periphery</i> that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.</p></div>", "links"=>[], "tags"=>["core-periphery"], "article_id"=>808710, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003171.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s010", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s011", "https://dx.doi.org/10.1371/journal.pcbi.1003171.s012"], "stats"=>{"downloads"=>51, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Task_Based_Core_Periphery_Organization_of_Human_Brain_Dynamics_/808710", "title"=>"Task-Based Core-Periphery Organization of Human Brain Dynamics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216438"], "description"=>"<p>Variance of the distribution of mean geometrical core scores over brain regions as a function of the number of trials completed after a scanning session. (See <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003171#pcbi-1003171-t001\" target=\"_blank\">Table 1</a> for the relationship between the number of trials practiced and training duration and intensity.) Error bars indicate the standard error of the mean over participants (where the data point from each participant is the mean geometrical core score over brain regions, scanning sessions, sequence types, and network layers).</p>", "links"=>[], "tags"=>["scores"], "article_id"=>808692, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Geometrical_core_scores_change_with_learning_/808692", "title"=>"Geometrical core scores change with learning.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216435"], "description"=>"<p>determined using fMRI signals during the performance of a simple motor learning task. <i>(A)</i> The core (cyan), bulk (gold), and periphery (maroon) nodes consist, respectively, of brain regions whose mean flexibility over individuals is less than, equal to, and greater than that expected in a null model (gray shaded region). We measure flexibility based on the allegiance of nodes to putative functional modules. Error bars indicate the standard error of the mean over individuals. <i>(B)</i> The anatomical distribution of regions in the core, bulk, and periphery appears to be spatially contiguous. The core primarily contains sensorimotor and visual processing areas, the periphery primarily contains multimodal association areas, and the bulk contains the remainder of the brain (and is therefore composed predominantly of frontal and temporal cortex).</p>", "links"=>[], "tags"=>["core-periphery"], "article_id"=>808689, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g003", "stats"=>{"downloads"=>3, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Temporal_core_periphery_organization_of_the_brain_/808689", "title"=>"Temporal core-periphery organization of the brain.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216433"], "description"=>"<p><i>(A)</i> Multilayer modularity, <i>(B)</i> number of communities, and <i>(C)</i> mean flexibility calculated as a function of the number of trials completed after a scanning session (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003171#pcbi-1003171-t001\" target=\"_blank\">Table 1</a> for the relationship between the number of trials practiced and training duration and intensity). We average the values for each diagnostic over the 100 multilayer modularity optimizations, and we average flexibility over the 112 brain regions (in addition to averaging over the 100 optimizations per subject). Error bars indicate the standard error of the mean over participants.</p>", "links"=>[], "tags"=>["diagnostics"], "article_id"=>808687, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dynamic_network_diagnostics_change_with_learning_/808687", "title"=>"Dynamic network diagnostics change with learning.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216432"], "description"=>"<p><i>(A)</i> Temporal Networks of the Human Brain. We parcellate the brain into anatomical regions that can be represented as nodes in a network, and we use the coherence between functional Magnetic Resonance Imaging (fMRI) time series of each pair of nodes over a time window to determine the weight of the network edge connecting those nodes. We determine these weights separately using approximately 10 non-overlapping time windows of 2–3 min duration and thereby construct temporal networks that represent the dynamical functional connectivity in the brain. <i>(B)</i> Cohesive Mesoscale Structures. <i>(top)</i> An example of a network with a modular organization in which high-degree nodes (brown) are often found in the center of modules or bridging distinct modules that are composed mostly of low-degree nodes (blue). <i>(bottom)</i> A network with a core-periphery organization in which nodes in the core (purple) are more densely connected with one another than nodes in the periphery are with one another (green).</p>", "links"=>[], "tags"=>[], "article_id"=>808686, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_organization_of_human_brain_dynamics_/808686", "title"=>"Network organization of human brain dynamics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216446"], "description"=>"<p>We report the number of trials (i.e., “depth”) of each sequence type (i.e., “intensity”) completed after each scanning session (i.e., “duration”) averaged over the 20 participants.</p>", "links"=>[], "tags"=>[], "article_id"=>808700, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_training_duration_intensity_and_depth_/808700", "title"=>"Relationship between training duration, intensity, and depth.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216443"], "description"=>"<p><i>(A)</i> Each trial began with the presentation of a sequence-identity cue that remained on screen for seconds. Each of the trained sequences was paired with a unique identity cue. A discrete sequence-production (DSP) event structure was used to guide sequence production. The onset of the initial DSP stimulus (thick square, colored red in the task) served as the imperative to produce the sequence. A correct key press led to the immediate presentation of the next DSP stimulus (and so on) until the -element sequence was correctly executed. Participants received a feedback “+” to signal that a sequence was completed and to wait (approximately – seconds) for the start of the next trial. This waiting period is called the “inter-trial interval” (ITI). At any point, if an incorrect key was hit, a participant would receive an error signal (not shown in the figure) and the DSP sequence would pause until the correct response was received. <i>(B)</i> There was a direct S-R mapping between a conventional keyboard or an MRI-compatible button box (see the lower left of the figure) and a participant's right hand, so the leftmost DSP stimulus cued the thumb and the rightmost stimulus cued the pinky finger. Note that the button location for the thumb was positioned to the lower left to achieve maximum comfort and ease of motion.</p>", "links"=>[], "tags"=>["stimulus-response"], "article_id"=>808697, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g008", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Trial_structure_and_stimulus_response_S_R_mapping_/808697", "title"=>"Trial structure and stimulus-response (S-R) mapping.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216442"], "description"=>"<p>The relationship between temporal and geometrical core-periphery organization and their associations with learning are present in individual subjects. We represent this relationship using spirals in a plane; data points in this plane represent brain regions located at the polar coordinates (, ), where is the flexibility of the region, is the skewness of flexibility over all regions, and is the learning parameter (see the <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003171#s4\" target=\"_blank\">Materials and Methods</a>) that describes each individual's relative improvement between sessions. The skewness predicts individual differences in learning; the Spearman rank correlation is and . Poor learners (straighter spirals) tend to have a low skewness (short spirals), whereas good learners (curvier spirals) tend to have high skewness (long spirals). Color indicates flexibility: blue nodes have lower flexibility, and brown nodes have higher flexibility.</p>", "links"=>[], "tags"=>[], "article_id"=>808696, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g007", "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Core_periphery_organization_of_brain_dynamics_during_learning_/808696", "title"=>"Core-periphery organization of brain dynamics during learning.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216440"], "description"=>"<p>A strong negative correlation exists between flexibility and the geometrical core score for networks constructed from blocks of <i>(A)</i> extensively, <i>(B)</i> moderately, and <i>(C)</i> minimally trained sequences on scanning session 1 (day 1; circles), session 2 (after approximately 2 weeks of training; squares), session 3 (after approximately 4 weeks of training; diamonds), and session 4 (after approximately 6 weeks of training; stars). This negative correlation indicates that the temporal core-periphery organization is mimicked in the geometrical core-periphery organization and therefore that the core of dynamically stiff regions also exhibits dense connectivity. We show temporal core nodes in cyan, temporal bulk nodes in gold, and temporal periphery nodes in maroon. The darkness of data points indicates scanning session; darker colors indicate earlier scans, so the darkest colors indicate scan 1 and the lightest ones indicate scan 4. The grayscale lines indicate the best linear fits; again, darker colors indicate earlier scans, so session 1 is in gray and session 4 is in light gray. The Pearson correlation between the flexibility (averaged over 100 multilayer modularity optimizations, 20 participants, and 4 scanning sessions) and the geometrical core score (averaged over 20 participants and 4 scanning sessions) is significant for the EXT (, ), MOD (, ), and MIN (, ) data.</p>", "links"=>[], "tags"=>["temporal", "geometrical", "core-periphery"], "article_id"=>808694, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g006", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_temporal_and_geometrical_core_periphery_organizations_/808694", "title"=>"Relationship between temporal and geometrical core-periphery organizations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1216437"], "description"=>"<p><i>(A)</i> Core quality (5) in the parameter plane for a typical participant (3), scanning session (1), sequence type (EXT), and experimental block (1). <i>(B)</i> Distribution of the and values that maximize the -score. We compute this distribution over all network layers, participants, scanning sessions, and sequence types. The parameter is much more localized (its standard deviation is 0.05) than the parameter (its standard deviation is 0.26). <i>(C)</i> Mean core shape. We plot the ordered vector of values. We have set the values of and to the mean values of those that maximize the -score for all network layers, participants, scanning sessions, and sequence types.</p>", "links"=>[], "tags"=>["core-periphery"], "article_id"=>808691, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Science Policy", "Ecology"], "users"=>["Danielle S. Bassett", "Nicholas F. Wymbs", "M. Puck Rombach", "Mason A. Porter", "Peter J. Mucha", "Scott T. Grafton"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003171.g004", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Geometrical_core_periphery_organization_in_brain_networks_/808691", "title"=>"Geometrical core-periphery organization in brain networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-26 01:36:00"}

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Relative Metric

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