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Examining network dynamics after traumatic brain injury using the extended unified SEM approach

The current study uses effective connectivity modeling to examine how individuals with traumatic brain injury (TBI) learn a new task. We make use of recent advancements in connectivity modeling (extended unified structural equation modeling, euSEM) and a novel iterative grouping procedure (Group Ite... Full description

Journal Title: Brain Imaging and Behavior 2014, Vol.8(3), pp.435-445
Main Author: Hillary, F.
Other Authors: Medaglia, J. , Gates, K. , Molenaar, P. , Good, D.
Format: Electronic Article Electronic Article
Language: English
Subjects:
TBI
ID: ISSN: 1931-7557 ; E-ISSN: 1931-7565 ; DOI: 10.1007/s11682-012-9205-0
Link: http://dx.doi.org/10.1007/s11682-012-9205-0
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recordid: springer_jour10.1007/s11682-012-9205-0
title: Examining network dynamics after traumatic brain injury using the extended unified SEM approach
format: Article
creator:
  • Hillary, F.
  • Medaglia, J.
  • Gates, K.
  • Molenaar, P.
  • Good, D.
subjects:
  • fMRI
  • TBI
  • Brain injury
  • Rehabilitation
  • Working memory
  • Cognitive control
ispartof: Brain Imaging and Behavior, 2014, Vol.8(3), pp.435-445
description: The current study uses effective connectivity modeling to examine how individuals with traumatic brain injury (TBI) learn a new task. We make use of recent advancements in connectivity modeling (extended unified structural equation modeling, euSEM) and a novel iterative grouping procedure (Group Iterative Multiple Model Estimation, GIMME) in order to examine network flexibility after injury. The study enrolled 12 individuals sustaining moderate and severe TBI to examine the influence of task practice on connections between 8 network nodes (bilateral prefrontal cortex, anterior cingulate, inferior parietal lobule, and Crus I in the cerebellum). The data demonstrate alterations in networks from pre to post practice and differences in the models based upon distinct learning trajectories observed within the TBI sample. For example, better learning in the TBI sample was associated with diminished connectivity within frontal systems and increased frontal to parietal connectivity. These findings reveal the potential for using connectivity modeling and the euSEM to examine dynamic networks during task engagement and may ultimately be informative regarding when networks are moving in and out of periods of neural efficiency.
language: eng
source:
identifier: ISSN: 1931-7557 ; E-ISSN: 1931-7565 ; DOI: 10.1007/s11682-012-9205-0
fulltext: fulltext
issn:
  • 1931-7565
  • 19317565
  • 1931-7557
  • 19317557
url: Link


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titleExamining network dynamics after traumatic brain injury using the extended unified SEM approach
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descriptionThe current study uses effective connectivity modeling to examine how individuals with traumatic brain injury (TBI) learn a new task. We make use of recent advancements in connectivity modeling (extended unified structural equation modeling, euSEM) and a novel iterative grouping procedure (Group Iterative Multiple Model Estimation, GIMME) in order to examine network flexibility after injury. The study enrolled 12 individuals sustaining moderate and severe TBI to examine the influence of task practice on connections between 8 network nodes (bilateral prefrontal cortex, anterior cingulate, inferior parietal lobule, and Crus I in the cerebellum). The data demonstrate alterations in networks from pre to post practice and differences in the models based upon distinct learning trajectories observed within the TBI sample. For example, better learning in the TBI sample was associated with diminished connectivity within frontal systems and increased frontal to parietal connectivity. These findings reveal the potential for using connectivity modeling and the euSEM to examine dynamic networks during task engagement and may ultimately be informative regarding when networks are moving in and out of periods of neural efficiency.
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titleExamining network dynamics after traumatic brain injury using the extended unified SEM approach
descriptionThe current study uses effective connectivity modeling to examine how individuals with traumatic brain injury (TBI) learn a new task. We make use of recent advancements in connectivity modeling (extended unified structural equation modeling, euSEM) and a novel iterative grouping procedure (Group Iterative Multiple Model Estimation, GIMME) in order to examine network flexibility after injury. The study enrolled 12 individuals sustaining moderate and severe TBI to examine the influence of task practice on connections between 8 network nodes (bilateral prefrontal cortex, anterior cingulate, inferior parietal lobule, and Crus I in the cerebellum). The data demonstrate alterations in networks from pre to post practice and differences in the models based upon distinct learning trajectories observed within the TBI sample. For example, better learning in the TBI sample was associated with diminished connectivity within frontal systems and increased frontal to parietal connectivity. These findings reveal the potential for using connectivity modeling and the euSEM to examine dynamic networks during task engagement and may ultimately be informative regarding when networks are moving in and out of periods of neural efficiency.
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abstractThe current study uses effective connectivity modeling to examine how individuals with traumatic brain injury (TBI) learn a new task. We make use of recent advancements in connectivity modeling (extended unified structural equation modeling, euSEM) and a novel iterative grouping procedure (Group Iterative Multiple Model Estimation, GIMME) in order to examine network flexibility after injury. The study enrolled 12 individuals sustaining moderate and severe TBI to examine the influence of task practice on connections between 8 network nodes (bilateral prefrontal cortex, anterior cingulate, inferior parietal lobule, and Crus I in the cerebellum). The data demonstrate alterations in networks from pre to post practice and differences in the models based upon distinct learning trajectories observed within the TBI sample. For example, better learning in the TBI sample was associated with diminished connectivity within frontal systems and increased frontal to parietal connectivity. These findings reveal the potential for using connectivity modeling and the euSEM to examine dynamic networks during task engagement and may ultimately be informative regarding when networks are moving in and out of periods of neural efficiency.
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