{"id":14960,"date":"2024-01-16T13:53:54","date_gmt":"2024-01-16T12:53:54","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&p=14960"},"modified":"2024-01-16T13:53:54","modified_gmt":"2024-01-16T12:53:54","slug":"altered-longitudinal-structural-connectome-in-pediatric-mild-traumatic-brain-injury-an-a-cap-study","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/altered-longitudinal-structural-connectome-in-pediatric-mild-traumatic-brain-injury-an-a-cap-study\/","title":{"rendered":"Altered longitudinal structural connectome in pediatric mild traumatic brain injury: an A-CAP study"},"content":{"rendered":"\n

Ashley Ware, Adrian Onicas et al<\/h2>\n\n\n\n
<\/div>\n\n\n\n

Advanced diffusion-weighted imaging techniques have increased understanding of the neuropathology of paediatric mild traumatic brain injury (i.e. concussion). Most studies have examined discrete white-matter pathways, which may not capture the characteristically subtle, diffuse and heterogenous effects of paediatric concussion on brain microstructure. This study compared the structural connectome of children with concussion to those with mild orthopaedic injury to determine whether network metrics and their trajectories across time post-injury differentiate paediatric concussion from mild traumatic injury more generally. Data were drawn from of a large study of outcomes in paediatric concussion. Children aged 8\u201316.99 years were recruited from five paediatric emergency departments within 48\u2005h of sustaining a concussion (n<\/em>\u00a0= 360; 56% male) or mild orthopaedic injury (n<\/em>\u00a0= 196; 62% male). A reliable change score was used to classify children with concussion into two groups: concussion with or without persistent symptoms. Children completed 3\u2005T MRI at post-acute (2\u201333 days) and\/or chronic (3 or 6 months, via random assignment) post-injury follow-ups. Diffusion-weighted images were used to calculate the diffusion tensor, conduct deterministic whole-brain fibre tractography and compute connectivity matrices in native (diffusion) space for 90 supratentorial regions. Weighted adjacency matrices were constructed using average fractional anisotropy and used to calculate global and local (regional) graph theory metrics. Linear mixed effects modelling was performed to compare groups, correcting for multiple comparisons. Groups did not differ in global network metrics. However, the clustering coefficient, betweenness centrality and efficiency of the insula, cingulate, parietal, occipital and subcortical regions differed among groups, with differences moderated by time (days) post-injury, biological sex and age at time of injury. Post-acute differences were minimal, whereas more robust alterations emerged at 3 and especially 6 months in children with concussion with persistent symptoms, albeit differently by sex and age. In the largest neuroimaging study to date, post-acute regional network metrics distinguished concussion from mild orthopaedic injury and predicted symptom recovery 1-month post-injury. Regional network parameters alterations were more robust and widespread at chronic timepoints than post-acutely after concussion. Results suggest that increased regional and local subnetwork segregation (modularity) and inefficiency occurs across time after concussion, emerging after post-concussive symptom resolve in most children. These differences persist up to 6 months after concussion, especially in children who showed persistent symptoms. While prognostic, the small to modest effect size of group differences and the moderating effects of sex likely would preclude effective clinical application in individual patients.<\/p>\n\n\n\n

<\/div>\n\n\n\n\t\n \n \n\t\t\t\n\n\t\t\t\t\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n \n \n","protected":false},"excerpt":{"rendered":"

In: Brain Communications, 2023.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-14960","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"\nAltered longitudinal structural connectome in pediatric mild traumatic brain injury: an A-CAP study - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/altered-longitudinal-structural-connectome-in-pediatric-mild-traumatic-brain-injury-an-a-cap-study\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Altered longitudinal structural connectome in pediatric mild traumatic brain injury: an A-CAP study\" \/>\n<meta property=\"og:description\" content=\"In: Brain Communications, 2023.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/altered-longitudinal-structural-connectome-in-pediatric-mild-traumatic-brain-injury-an-a-cap-study\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/altered-longitudinal-structural-connectome-in-pediatric-mild-traumatic-brain-injury-an-a-cap-study\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/altered-longitudinal-structural-connectome-in-pediatric-mild-traumatic-brain-injury-an-a-cap-study\\\/\",\"name\":\"Altered longitudinal structural connectome in pediatric mild traumatic brain injury: an A-CAP study - 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Most studies have examined discrete white-matter pathways, which may not capture the characteristically subtle, diffuse and heterogenous effects of paediatric concussion on brain microstructure. This study compared the structural connectome of children with concussion to those with mild orthopaedic injury to determine whether network metrics and their trajectories across time post-injury differentiate paediatric concussion from mild traumatic injury more generally. Data were drawn from of a large study of outcomes in paediatric concussion. Children aged 8\u201316.99 years were recruited from five paediatric emergency departments within 48\u2005h of sustaining a concussion (<em>n<\/em>\u00a0= 360; 56% male) or mild orthopaedic injury (<em>n<\/em>\u00a0= 196; 62% male). A reliable change score was used to classify children with concussion into two groups: concussion with or without persistent symptoms. Children completed 3\u2005T MRI at post-acute (2\u201333 days) and\/or chronic (3 or 6 months, via random assignment) post-injury follow-ups. Diffusion-weighted images were used to calculate the diffusion tensor, conduct deterministic whole-brain fibre tractography and compute connectivity matrices in native (diffusion) space for 90 supratentorial regions. Weighted adjacency matrices were constructed using average fractional anisotropy and used to calculate global and local (regional) graph theory metrics. Linear mixed effects modelling was performed to compare groups, correcting for multiple comparisons. Groups did not differ in global network metrics. However, the clustering coefficient, betweenness centrality and efficiency of the insula, cingulate, parietal, occipital and subcortical regions differed among groups, with differences moderated by time (days) post-injury, biological sex and age at time of injury. Post-acute differences were minimal, whereas more robust alterations emerged at 3 and especially 6 months in children with concussion with persistent symptoms, albeit differently by sex and age. In the largest neuroimaging study to date, post-acute regional network metrics distinguished concussion from mild orthopaedic injury and predicted symptom recovery 1-month post-injury. Regional network parameters alterations were more robust and widespread at chronic timepoints than post-acutely after concussion. Results suggest that increased regional and local subnetwork segregation (modularity) and inefficiency occurs across time after concussion, emerging after post-concussive symptom resolve in most children. These differences persist up to 6 months after concussion, especially in children who showed persistent symptoms. While prognostic, the small to modest effect size of group differences and the moderating effects of sex likely would preclude effective clinical application in individual patients.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Advanced diffusion-weighted imaging techniques have increased understanding of the neuropathology of paediatric mild traumatic brain injury (i.e. concussion). Most studies have examined discrete white-matter pathways, which may not capture the characteristically subtle, diffuse and heterogenous effects of paediatric concussion on brain microstructure. This study compared the structural connectome of children with concussion to those with mild orthopaedic injury to determine whether network metrics and their trajectories across time post-injury differentiate paediatric concussion from mild traumatic injury more generally. Data were drawn from of a large study of outcomes in paediatric concussion. Children aged 8\u201316.99 years were recruited from five paediatric emergency departments within 48\u2005h of sustaining a concussion (<em>n<\/em>\u00a0= 360; 56% male) or mild orthopaedic injury (<em>n<\/em>\u00a0= 196; 62% male). A reliable change score was used to classify children with concussion into two groups: concussion with or without persistent symptoms. Children completed 3\u2005T MRI at post-acute (2\u201333 days) and\/or chronic (3 or 6 months, via random assignment) post-injury follow-ups. Diffusion-weighted images were used to calculate the diffusion tensor, conduct deterministic whole-brain fibre tractography and compute connectivity matrices in native (diffusion) space for 90 supratentorial regions. Weighted adjacency matrices were constructed using average fractional anisotropy and used to calculate global and local (regional) graph theory metrics. Linear mixed effects modelling was performed to compare groups, correcting for multiple comparisons. Groups did not differ in global network metrics. However, the clustering coefficient, betweenness centrality and efficiency of the insula, cingulate, parietal, occipital and subcortical regions differed among groups, with differences moderated by time (days) post-injury, biological sex and age at time of injury. Post-acute differences were minimal, whereas more robust alterations emerged at 3 and especially 6 months in children with concussion with persistent symptoms, albeit differently by sex and age. In the largest neuroimaging study to date, post-acute regional network metrics distinguished concussion from mild orthopaedic injury and predicted symptom recovery 1-month post-injury. Regional network parameters alterations were more robust and widespread at chronic timepoints than post-acutely after concussion. Results suggest that increased regional and local subnetwork segregation (modularity) and inefficiency occurs across time after concussion, emerging after post-concussive symptom resolve in most children. These differences persist up to 6 months after concussion, especially in children who showed persistent symptoms. While prognostic, the small to modest effect size of group differences and the moderating effects of sex likely would preclude effective clinical application in individual patients.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-J4MdEZ","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/academic.oup.com\/braincomms\/article\/5\/3\/fcad173\/7187292","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14960","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":2,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14960\/revisions"}],"predecessor-version":[{"id":14962,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14960\/revisions\/14962"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=14960"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=14960"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=14960"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}