Altered Functional Connectivity Following an Inflammatory White Matter Injury in the Newborn Rat: A High Spatial and Temporal Resolution Intrinsic Optical Imaging Study

• Published in: Frontiers in neuroscience Vol. 11; p. 358
• Main Authors: Guevara, Edgar, Pierre, Wyston C, Tessier, Camille, Akakpo, Luis, Londono, Irène, Lesage, Frédéric, Lodygensky, Gregory A
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 04.07.2017; Frontiers Media S.A
• Subjects: Adults; Blood; Brain injury; Brain research; Cognitive ability; Inflammation; Learning algorithms; Lipopolysaccharides; Neonates; Neural networks; Neuroimaging; Neuroscience; Neurosciences; optical imaging of intrinsic signals; prematurity; resting state functional connectivity; Seeds; Studies; Substantia alba; support vector machines; white matter injury; Canada; Montreal Quebec Canada; artificial neural networks; prematurity; support vector machines; inflammation; optical imaging of intrinsic signals; resting state functional connectivity; white matter injury
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2017.00358

Very preterm newborns have an increased risk of developing an inflammatory cerebral white matter injury that may lead to severe neuro-cognitive impairment. In this study we performed functional connectivity (fc) analysis using resting-state optical imaging of intrinsic signals (rs-OIS) to assess the impact of inflammation on resting-state networks (RSN) in a pre-clinical model of perinatal inflammatory brain injury. Lipopolysaccharide (LPS) or saline injections were administered in postnatal day (P3) rat pups and optical imaging of intrinsic signals were obtained 3 weeks later. (rs-OIS) fc seed-based analysis including spatial extent were performed. A support vector machine (SVM) was then used to classify rat pups in two categories using fc measures and an artificial neural network (ANN) was implemented to predict lesion size from those same fc measures. A significant decrease in the spatial extent of fc statistical maps was observed in the injured group, across contrasts and seeds ( = 0.0452 for HbO and = 0.0036 for HbR). Both machine learning techniques were applied successfully, yielding 92% accuracy in group classification and a significant correlation = 0.9431 in fractional lesion volume prediction ( = 0.0020). Our results suggest that fc is altered in the injured newborn brain, showing the long-standing effect of inflammation.