Differential Development of Human Brain White Matter Tracts
Publication Date
August 31, 2011
Journal
PLOS ONE
Authors
Davide Imperati, Stan Colcombe, Clare Kelly, Adriana Di Martino, et al
Volume
6
Issue
8
Pages
e23437
DOI
https://dx.plos.org/10.1371/journal.pone.0023437
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0023437
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/21909351
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166135
Europe PMC
http://europepmc.org/abstract/MED/21909351
Web of Science
000294680800011
Scopus
80052318134
Mendeley
http://www.mendeley.com/research/differential-development-human-brain-white-matter-tracts-3
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Mendeley | Further Information

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CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/373659", "https://ndownloader.figshare.com/files/373700", "https://ndownloader.figshare.com/files/373718"], "description"=>"<div><p>Neuroscience is increasingly focusing on developmental factors related to human structural and functional connectivity. Unfortunately, to date, diffusion-based imaging approaches have only contributed modestly to these broad objectives, despite the promise of diffusion-based tractography. Here, we report a novel data-driven approach to detect similarities and differences among white matter tracts with respect to their developmental trajectories, using 64-direction diffusion tensor imaging. Specifically, using a cross-sectional sample comprising 144 healthy individuals (7 to 48 years old), we applied k-means cluster analysis to separate white matter voxels based on their age-related trajectories of fractional anisotropy. Optimal solutions included 5-, 9- and 14-clusters. Our results recapitulate well-established tracts (e.g., internal and external capsule, optic radiations, corpus callosum, cingulum bundle, cerebral peduncles) and subdivisions within tracts (e.g., corpus callosum, internal capsule). For all but one tract identified, age-related trajectories were curvilinear (i.e., inverted ‘U-shape’), with age-related increases during childhood and adolescence followed by decreases in middle adulthood. Identification of peaks in the trajectories suggests that age-related losses in fractional anisotropy occur as early as 23 years of age, with mean onset at 30 years of age. Our findings demonstrate that data-driven analytic techniques may be fruitfully applied to extant diffusion tensor imaging datasets in normative and neuropsychiatric samples.</p> </div>", "links"=>[], "tags"=>["differential", "tracts"], "article_id"=>133800, "categories"=>["Neuroscience", "Cell Biology"], "users"=>["Davide Imperati", "Stan Colcombe", "Clare Kelly", "Adriana Di Martino", "Juan Zhou", "F. Xavier Castellanos", "Michael P. Milham"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0023437.s001", "https://dx.doi.org/10.1371/journal.pone.0023437.s002", "https://dx.doi.org/10.1371/journal.pone.0023437.s003"], "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Differential_Development_of_Human_Brain_White_Matter_Tracts/133800", "title"=>"Differential Development of Human Brain White Matter Tracts", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-08-31 01:03:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/740624"], "description"=>"<p>Atlas-defined tracts are presented in terms of their %voxels located in each of the clusters from the 14-cluster solution. For each tract, the bold-faced number indicates the cluster containing the largest percentage of that tract's voxels.</p>", "links"=>[], "tags"=>["icbm-81", "dti", "atlas", "tracts"], "article_id"=>410993, "categories"=>["Neuroscience", "Cell Biology"], "users"=>["Davide Imperati", "Stan Colcombe", "Clare Kelly", "Adriana Di Martino", "Juan Zhou", "F. Xavier Castellanos", "Michael P. Milham"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0023437.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Division_of_ICBM_81_DTI_Atlas_Tracts_Across_Clusters_/410993", "title"=>"Division of ICBM-81 DTI Atlas Tracts Across Clusters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-08-31 00:16:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/740528"], "description"=>"<p>Permutation-based non-parametric testing, using a general linear model containing linear and quadratic terms, revealed significant age-related changes in skeletonized white matter throughout the brain (<i>p</i><0.05, corrected). We employed an F-test to determine optimal fit (linear vs. quadratic). Tracts for which the optimal fit was the inverted quadratic (‘U-shaped’) developmental trajectory are shown in blue, and those for which the optimal fit was the linear trajectory are shown in red.</p>", "links"=>[], "tags"=>["trajectory"], "article_id"=>410905, "categories"=>["Neuroscience", "Cell Biology"], "users"=>["Davide Imperati", "Stan Colcombe", "Clare Kelly", "Adriana Di Martino", "Juan Zhou", "F. Xavier Castellanos", "Michael P. Milham"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0023437.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_Based_Trajectory_Analyses_/410905", "title"=>"Model-Based Trajectory Analyses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-08-31 00:15:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/740412"], "description"=>"<p>The trajectories for the 2-, 5-, 9- and 14-cluster solutions (from top to bottom) are depicted as absolute FA values (left column; ordinate shows FA absolute values), FA difference from initial trajectory value (middle column; ordinate shows relative FA change in absolute units), and percent change in FA relative to initial value (right column; ordinate shows percent change in FA). Abscissas show age in years. Initial FA trajectory value and percent change in FA were not significantly related across any of the clusters.</p>", "links"=>[], "tags"=>["trajectories", "changes", "skeletonized", "fractional", "anisotropy"], "article_id"=>410776, "categories"=>["Neuroscience", "Cell Biology"], "users"=>["Davide Imperati", "Stan Colcombe", "Clare Kelly", "Adriana Di Martino", "Juan Zhou", "F. Xavier Castellanos", "Michael P. Milham"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0023437.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Absolute_Trajectories_and_Relative_Changes_of_Skeletonized_Fractional_Anisotropy_FA_Values_/410776", "title"=>"Absolute Trajectories and Relative Changes of Skeletonized Fractional Anisotropy (FA) Values.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-08-31 00:12:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/740661"], "description"=>"<p>In the second column, cluster components are presented in terms of the relative contributions of ICBM-81 Atlas defined tracts. Columns 3–10 provide measures of age-related increases in FA during development, as well as age-related decreases during adulthood and trajectory curvature (second-order parameter from quadratic model).</p>", "links"=>[], "tags"=>["fractional", "anisotropy", "trajectory"], "article_id"=>411030, "categories"=>["Neuroscience", "Cell Biology"], "users"=>["Davide Imperati", "Stan Colcombe", "Clare Kelly", "Adriana Di Martino", "Juan Zhou", "F. Xavier Castellanos", "Michael P. Milham"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0023437.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_14_Cluster_Solution_Fractional_Anisotropy_FA_Trajectory_Characteristics_/411030", "title"=>"14-Cluster Solution Fractional Anisotropy (FA) Trajectory Characteristics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-08-31 00:17:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/740308"], "description"=>"<p><i>Panel A (Skeletonized Fractional Anisotropy [FA])</i>: The 5-, 9- and 14- cluster solutions identified by the Davies-Bouldin cluster validation index as optimal are depicted here, along with the 2-cluster solution, for reference. Cluster analysis recapitulated well-established white matter tracts, as well as key distinctions within white matter tracts. Notable examples include separation of the corpus callosum (CC) into three major divisions (splenium, body, genu) and differentiation of anterior and posterior limb of the internal capsule. Across all solutions, the cerebral peduncles and anterior limb of the internal capsule demonstrated consistent clustering patterns, likely reflecting fronto-pontine pathways. Tract Marker Key: [A: splenium (CC), B: body (CC), C: genu (CC), D: corticospinal tract (marked at interface with cerebral peduncle), E: anterior limb of the internal capsule, F: posterior limb of the internal capsule, G: forceps minor, H: optic radiations, I: superior longitudinal fasciculus, J: superior corona radiata, K: body (CC), L: cingulum bundle]. <i>Panel B (Trajectories and Atlas-Based Projections)</i>: For each of the optimal cluster solutions (5-, 9- and 14-clusters), we depict the mean trajectory across voxels in each of the clusters; all trajectories are baselined with respect to the initial trajectory value to facilitate visual comparison. Values on the ordinates represent change in FA from the initial value obtained for each trajectory for each cluster. Age in years is shown on the abscissas. Across clusters, the mean age at which peak FA was reached was 30.1 years. To facilitate visualization, we provide ICBM-81 atlas-based tract projections, with each tract color-coded based upon the dominant cluster to which its voxels were assigned (note: clusters 11 and 12 were not dominant in any atlas-based tract). Immediately to the left of each anatomic projection is the overlay of the trajectories for each solution.</p>", "links"=>[], "tags"=>["neuroscience", "neurological disorders", "radiology and medical imaging"], "article_id"=>410674, "categories"=>["Neuroscience", "Cell Biology"], "users"=>["Davide Imperati", "Stan Colcombe", "Clare Kelly", "Adriana Di Martino", "Juan Zhou", "F. Xavier Castellanos", "Michael P. Milham"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0023437.g001", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimal_Cluster_Solutions_/410674", "title"=>"Optimal Cluster Solutions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-08-31 00:11:14"}

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Relative Metric

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