@article{206,
  keywords = {brain injury, electroencephalography, functional connectivity, progression, theoretical model},
  author = {Rober Boshra and Kyle Ruiter and Kiret Dhindsa and Ranil Sonnadara and James Reilly and John Connolly},
  title = {On the time-course of functional connectivity: theory of a dynamic progression of concussion effects.},
  abstract = {<p>The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.</p>
},
  year = {2020},
  journal = {Brain communications},
  volume = {2},
  pages = {fcaa063},
  month = {12/2020},
  issn = {2632-1297},
  doi = {10.1093/braincomms/fcaa063},
  language = {eng},
}