Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease
ObjectiveTerm congenital heart disease (CHD) neonates display abnormalities of brain structure and maturation, which are possibly related to underlying patient factors, abnormal physiology and perioperative insults. Our primary goal was to delineate associations between clinical factors and postnata...
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.952355/full |
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| author | Jodie K. Votava-Smith Jenna Gaesser Anna Lonyai Harbison Vince Lee Vince Lee Nhu Tran Vidya Rajagopalan Sylvia del Castillo S. Ram Kumar Elizabeth Herrup Tracy Baust Jennifer A. Johnson George C. Gabriel William T. Reynolds Julia Wallace Benjamin Meyers Rafael Ceschin Rafael Ceschin Cecilia W. Lo Vanessa J. Schmithorst Ashok Panigrahy Ashok Panigrahy Ashok Panigrahy |
| author_facet | Jodie K. Votava-Smith Jenna Gaesser Anna Lonyai Harbison Vince Lee Vince Lee Nhu Tran Vidya Rajagopalan Sylvia del Castillo S. Ram Kumar Elizabeth Herrup Tracy Baust Jennifer A. Johnson George C. Gabriel William T. Reynolds Julia Wallace Benjamin Meyers Rafael Ceschin Rafael Ceschin Cecilia W. Lo Vanessa J. Schmithorst Ashok Panigrahy Ashok Panigrahy Ashok Panigrahy |
| author_sort | Jodie K. Votava-Smith |
| collection | DOAJ |
| description | ObjectiveTerm congenital heart disease (CHD) neonates display abnormalities of brain structure and maturation, which are possibly related to underlying patient factors, abnormal physiology and perioperative insults. Our primary goal was to delineate associations between clinical factors and postnatal brain microstructure in term CHD neonates using diffusion tensor imaging (DTI) magnetic resonance (MR) acquisition combined with complementary data-driven connectome and seed-based tractography quantitative analyses. Our secondary goal was to delineate associations between mild dysplastic structural brain abnormalities and connectome and seed-base tractography quantitative analyses. These mild dysplastic structural abnormalities have been derived from prior human infant CHD MR studies and neonatal mouse models of CHD that were collectively used to calculate to calculate a brain dysplasia score (BDS) that included assessment of subcortical structures including the olfactory bulb, the cerebellum and the hippocampus.MethodsNeonates undergoing cardiac surgery for CHD were prospectively recruited from two large centers. Both pre- and postoperative MR brain scans were obtained. DTI in 42 directions was segmented into 90 regions using a neonatal brain template and three weighted methods. Clinical data collection included 18 patient-specific and 9 preoperative variables associated with preoperative scan and 6 intraoperative (e.g., cardiopulmonary bypass and deep hypothermic circulatory arrest times) and 12 postoperative variables associated with postoperative scan. We compared patient specific and preoperative clinical factors to network topology and tractography alterations on a preoperative neonatal brain MRI, and intra and postoperative clinical factors to network topology alterations on postoperative neonatal brain MRI. A composite BDS was created to score abnormal findings involving the cerebellar hemispheres and vermis, supratentorial extra-axial fluid, olfactory bulbs and sulci, hippocampus, choroid plexus, corpus callosum, and brainstem. The neuroimaging outcomes of this study included (1) connectome metrics: cost (number of connections) and global/nodal efficiency (network integration); (2) seed based tractography methods of fractional anisotropy (FA), radial diffusivity, and axial diffusivity. Statistics consisted of multiple regression with false discovery rate correction (FDR) comparing the clinical risk factors and BDS (including subcortical components) as predictors/exposures and the global connectome metrics, nodal efficiency, and seed based- tractography (FA, radial diffusivity, and axial diffusivity) as neuroimaging outcome measures.ResultsA total of 133 term neonates with complex CHD were prospectively enrolled and 110 had analyzable DTI. Multiple patient-specific factors including d-transposition of the great arteries (d-TGA) physiology and severity of impairment of fetal cerebral substrate delivery (i.e., how much the CHD lesion alters typical fetal circulation such that the highest oxygen and nutrient rich blood from the placenta are not directed toward the fetal brain) were predictive of preoperative reduced cost (p < 0.0073) and reduced global/nodal efficiency (p < 0.03). Cardiopulmonary bypass time predicted postoperative reduced cost (p < 0.04) and multiple postoperative factors [extracorporeal membrane oxygenation (ECMO), seizures and cardiopulmonary resuscitation (CPR)] were predictive of postoperative reduced cost and reduced global/nodal efficiency (p < 0.05). Anthropometric measurements (weight, length, and head size) predicted tractography outcomes. Total BDS was not predictive of brain network topology. However, key subcortical components of the BDS score did predict key global and nodal network topology: abnormalities of the cerebellum predicted reduced cost (p < 0.0417) and of the hippocampus predicted reduced global efficiency (p < 0.0126). All three subcortical structures predicted unique alterations of nodal efficiency (p < 0.05), including hippocampal abnormalities predicting widespread reduced nodal efficiency in all lobes of the brain, cerebellar abnormalities predicting increased prefrontal nodal efficiency, and olfactory bulb abnormalities predicting posterior parietal-occipital nodal efficiency.ConclusionPatient-specific (d-TGA anatomy, preoperative impairment of fetal cerebral substrate delivery) and postoperative (e.g., seizures, need for ECMO, or CPR) clinical factors were most predictive of diffuse postnatal microstructural dysmaturation in term CHD neonates. Anthropometric measurements (weight, length, and head size) predicted tractography outcomes. In contrast, subcortical components (cerebellum, hippocampus, olfactory) of a structurally based BDS (derived from CHD mouse mutants), predicted more localized and regional postnatal microstructural differences. Collectively, these findings suggest that brain DTI connectome and seed-based tractography are complementary techniques which may facilitate deciphering the mechanistic relative contribution of clinical and genetic risk factors related to poor neurodevelopmental outcomes in CHD. |
| first_indexed | 2024-04-11T06:51:15Z |
| format | Article |
| id | doaj.art-7b940e0985d74517948688f5516069fd |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-11T06:51:15Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-7b940e0985d74517948688f5516069fd2022-12-22T04:39:10ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-11-011610.3389/fnins.2022.952355952355Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart diseaseJodie K. Votava-Smith0Jenna Gaesser1Anna Lonyai Harbison2Vince Lee3Vince Lee4Nhu Tran5Vidya Rajagopalan6Sylvia del Castillo7S. Ram Kumar8Elizabeth Herrup9Tracy Baust10Jennifer A. Johnson11George C. Gabriel12William T. Reynolds13Julia Wallace14Benjamin Meyers15Rafael Ceschin16Rafael Ceschin17Cecilia W. Lo18Vanessa J. Schmithorst19Ashok Panigrahy20Ashok Panigrahy21Ashok Panigrahy22Division of Cardiology, Department of Pediatrics, Children’s Hospital Los Angeles, Keck School of Medicine of USC, Los Angeles, CA, United StatesDepartment of Neurology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United StatesStanford Children’s Health, Palo Alto, CA, United StatesDepartment of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United StatesDepartment of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, United StatesDivision of Neonatology, Department of Pediatrics, Keck School of Medicine of USC, Children’s Hospital Los Angeles, Fetal and Neonatal Institute, Los Angeles, CA, United StatesDepartment of Radiology, Children’s Hospital Los Angeles, Keck School of Medicine of USC, Los Angeles, CA, United StatesDepartment of Anesthesiology Critical Care Medicine Anesthesiology, Children’s Hospital Los Angeles, Keck School of Medicine of USC, Los Angeles, CA, United StatesDivision of Cardiothoracic Surgery, Department of Surgery, Children’s Hospital Los Angeles, Keck School of Medicine of USC, Los Angeles, CA, United States0Division of Pediatric Cardiac Intensive Care, Department of Critical Care, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States0Division of Pediatric Cardiac Intensive Care, Department of Critical Care, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States1Division of Pediatric Cardiology, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States2Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United StatesDepartment of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United StatesDepartment of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United StatesDepartment of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States3Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States2Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United StatesDepartment of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United StatesDepartment of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, United States3Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United StatesObjectiveTerm congenital heart disease (CHD) neonates display abnormalities of brain structure and maturation, which are possibly related to underlying patient factors, abnormal physiology and perioperative insults. Our primary goal was to delineate associations between clinical factors and postnatal brain microstructure in term CHD neonates using diffusion tensor imaging (DTI) magnetic resonance (MR) acquisition combined with complementary data-driven connectome and seed-based tractography quantitative analyses. Our secondary goal was to delineate associations between mild dysplastic structural brain abnormalities and connectome and seed-base tractography quantitative analyses. These mild dysplastic structural abnormalities have been derived from prior human infant CHD MR studies and neonatal mouse models of CHD that were collectively used to calculate to calculate a brain dysplasia score (BDS) that included assessment of subcortical structures including the olfactory bulb, the cerebellum and the hippocampus.MethodsNeonates undergoing cardiac surgery for CHD were prospectively recruited from two large centers. Both pre- and postoperative MR brain scans were obtained. DTI in 42 directions was segmented into 90 regions using a neonatal brain template and three weighted methods. Clinical data collection included 18 patient-specific and 9 preoperative variables associated with preoperative scan and 6 intraoperative (e.g., cardiopulmonary bypass and deep hypothermic circulatory arrest times) and 12 postoperative variables associated with postoperative scan. We compared patient specific and preoperative clinical factors to network topology and tractography alterations on a preoperative neonatal brain MRI, and intra and postoperative clinical factors to network topology alterations on postoperative neonatal brain MRI. A composite BDS was created to score abnormal findings involving the cerebellar hemispheres and vermis, supratentorial extra-axial fluid, olfactory bulbs and sulci, hippocampus, choroid plexus, corpus callosum, and brainstem. The neuroimaging outcomes of this study included (1) connectome metrics: cost (number of connections) and global/nodal efficiency (network integration); (2) seed based tractography methods of fractional anisotropy (FA), radial diffusivity, and axial diffusivity. Statistics consisted of multiple regression with false discovery rate correction (FDR) comparing the clinical risk factors and BDS (including subcortical components) as predictors/exposures and the global connectome metrics, nodal efficiency, and seed based- tractography (FA, radial diffusivity, and axial diffusivity) as neuroimaging outcome measures.ResultsA total of 133 term neonates with complex CHD were prospectively enrolled and 110 had analyzable DTI. Multiple patient-specific factors including d-transposition of the great arteries (d-TGA) physiology and severity of impairment of fetal cerebral substrate delivery (i.e., how much the CHD lesion alters typical fetal circulation such that the highest oxygen and nutrient rich blood from the placenta are not directed toward the fetal brain) were predictive of preoperative reduced cost (p < 0.0073) and reduced global/nodal efficiency (p < 0.03). Cardiopulmonary bypass time predicted postoperative reduced cost (p < 0.04) and multiple postoperative factors [extracorporeal membrane oxygenation (ECMO), seizures and cardiopulmonary resuscitation (CPR)] were predictive of postoperative reduced cost and reduced global/nodal efficiency (p < 0.05). Anthropometric measurements (weight, length, and head size) predicted tractography outcomes. Total BDS was not predictive of brain network topology. However, key subcortical components of the BDS score did predict key global and nodal network topology: abnormalities of the cerebellum predicted reduced cost (p < 0.0417) and of the hippocampus predicted reduced global efficiency (p < 0.0126). All three subcortical structures predicted unique alterations of nodal efficiency (p < 0.05), including hippocampal abnormalities predicting widespread reduced nodal efficiency in all lobes of the brain, cerebellar abnormalities predicting increased prefrontal nodal efficiency, and olfactory bulb abnormalities predicting posterior parietal-occipital nodal efficiency.ConclusionPatient-specific (d-TGA anatomy, preoperative impairment of fetal cerebral substrate delivery) and postoperative (e.g., seizures, need for ECMO, or CPR) clinical factors were most predictive of diffuse postnatal microstructural dysmaturation in term CHD neonates. Anthropometric measurements (weight, length, and head size) predicted tractography outcomes. In contrast, subcortical components (cerebellum, hippocampus, olfactory) of a structurally based BDS (derived from CHD mouse mutants), predicted more localized and regional postnatal microstructural differences. Collectively, these findings suggest that brain DTI connectome and seed-based tractography are complementary techniques which may facilitate deciphering the mechanistic relative contribution of clinical and genetic risk factors related to poor neurodevelopmental outcomes in CHD.https://www.frontiersin.org/articles/10.3389/fnins.2022.952355/fullcongenital heart diseasediffusion tensor imagingconnectome analysisseed-based tractographysubcortical brain dysmaturationmagnetic resonance imaging |
| spellingShingle | Jodie K. Votava-Smith Jenna Gaesser Anna Lonyai Harbison Vince Lee Vince Lee Nhu Tran Vidya Rajagopalan Sylvia del Castillo S. Ram Kumar Elizabeth Herrup Tracy Baust Jennifer A. Johnson George C. Gabriel William T. Reynolds Julia Wallace Benjamin Meyers Rafael Ceschin Rafael Ceschin Cecilia W. Lo Vanessa J. Schmithorst Ashok Panigrahy Ashok Panigrahy Ashok Panigrahy Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease Frontiers in Neuroscience congenital heart disease diffusion tensor imaging connectome analysis seed-based tractography subcortical brain dysmaturation magnetic resonance imaging |
| title | Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease |
| title_full | Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease |
| title_fullStr | Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease |
| title_full_unstemmed | Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease |
| title_short | Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease |
| title_sort | clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease |
| topic | congenital heart disease diffusion tensor imaging connectome analysis seed-based tractography subcortical brain dysmaturation magnetic resonance imaging |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.952355/full |
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