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Torres-González C, Ricardo-Garcell J, Alvarez-Núñez D, Galindo-Aldana G. Intellectual Development in Mexican Preterm Children at Risk of Perinatal Brain Damage: A Longitudinal Study. CHILDREN (BASEL, SWITZERLAND) 2024; 11:652. [PMID: 38929232 DOI: 10.3390/children11060652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/24/2024] [Accepted: 05/25/2024] [Indexed: 06/28/2024]
Abstract
Preterm birth accounts for about 10% of births worldwide. Studying risk factors for perinatal brain damage is essential, as findings suggest that almost 20% of disabilities are linked to risks in the early stages of development. This research aimed to study longitudinal changes in intelligence from 6 to 8 years of age in a sample of 39 preterm children with a history of risk of brain damage and a control group of 35 children born at term. The Wechsler Intelligence Scale (WISC-IV) was used to measure cognitive ability at six, seven, and eight years old. The results showed that the preterm group obtained significantly lower scores than the control group. The working memory indicator significantly affected the interaction between age and prematurity. We consider it crucial to expand the knowledge we have about the neurocognitive development of premature infants, both in specific cognitive domains and in age ranges, so that the information obtained can help predict the probability of presenting cognitive alterations from early stages. This, therefore, helps in implementing intervention strategies and programs based on scientific evidence, and their design is complemented by clinical experience and empirical and theoretical knowledge of the different professionals involved in infant cognitive intervention.
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Affiliation(s)
- Cynthia Torres-González
- Faculty of Administrative, Social, and Engineering Sciences, Universidad Autónoma de Baja California, State Hwy No. 3, Guadalupe Victoria, Mexicali 21720, Baja California, Mexico
| | - Josefina Ricardo-Garcell
- Neurodevelopmental Research Unit "Augusto Fernandez Guardiola", Institute of Neurobiology, Autonomous University of Mexico, Boulevard Juriquilla 3001, La Mesa, Juriquilla 76230, Querétaro, Mexico
| | - Daniel Alvarez-Núñez
- CETyS University, Calzada CETYS s/n. Col. Rivera, Mexicali 21259, Baja California, Mexico
| | - Gilberto Galindo-Aldana
- Faculty of Administrative, Social, and Engineering Sciences, Universidad Autónoma de Baja California, State Hwy No. 3, Guadalupe Victoria, Mexicali 21720, Baja California, Mexico
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2
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Fjell AM. Aging Brain from a Lifespan Perspective. Curr Top Behav Neurosci 2024. [PMID: 38797799 DOI: 10.1007/7854_2024_476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Research during the last two decades has shown that the brain undergoes continuous changes throughout life, with substantial heterogeneity in age trajectories between regions. Especially, temporal and prefrontal cortices show large changes, and these correlate modestly with changes in the corresponding cognitive abilities such as episodic memory and executive function. Changes seen in normal aging overlap with changes seen in neurodegenerative conditions such as Alzheimer's disease; differences between what reflects normal aging vs. a disease-related change are often blurry. This calls for a dimensional view on cognitive decline in aging, where clear-cut distinctions between normality and pathology cannot be always drawn. Although much progress has been made in describing typical patterns of age-related changes in the brain, identifying risk and protective factors, and mapping cognitive correlates, there are still limits to our knowledge that should be addressed by future research. We need more longitudinal studies following the same participants over longer time intervals with cognitive testing and brain imaging, and an increased focus on the representativeness vs. selection bias in neuroimaging research of aging.
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Affiliation(s)
- Anders Martin Fjell
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.
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3
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Driver C, Boyes A, Mohamed AZ, Levenstein JM, Parker M, Hermens DF. Understanding Wellbeing Profiles According to White Matter Structural Connectivity Sub-types in Early Adolescents: The First Hundred Brains Cohort from the Longitudinal Adolescent Brain Study. J Youth Adolesc 2024; 53:1029-1046. [PMID: 38217837 PMCID: PMC10980632 DOI: 10.1007/s10964-024-01939-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/04/2024] [Indexed: 01/15/2024]
Abstract
Wellbeing is protective against the emergence of psychopathology. Neurobiological markers associated with mental wellbeing during adolescence are important to understand. Limited research has examined neural networks (white matter tracts) and mental wellbeing in early adolescence specifically. A cross-sectional diffusion tensor imaging analysis approach was conducted, from the Longitudinal Adolescent Brain study, First Hundred Brains cohort (N = 99; 46.5% female; Mage = 13.01, SD = 0.55). Participants completed self-report measures including wellbeing, quality-of-life, and psychological distress. Potential neurobiological profiles using fractional anisotropy, axial, and radial diffusivity were determined via a whole brain voxel-wise approach, and hierarchical cluster analysis of fractional anisotropy values, obtained from 21 major white matter tracts. Three cluster groups with significantly different neurobiological profiles were distinguished. No significant differences were found between the three cluster groups and measures of wellbeing, but two left lateralized significant associations between white matter tracts and wellbeing measures were found. These results provide preliminary evidence for potential neurobiological markers of mental health and wellbeing in early adolescence and should be tracked longitudinally to provide more detailed and robust findings.
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Affiliation(s)
- Christina Driver
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia.
| | - Amanda Boyes
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
| | - Abdalla Z Mohamed
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
| | - Jacob M Levenstein
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
| | - Marcella Parker
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
| | - Daniel F Hermens
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
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4
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Cristi-Montero C, Johansen-Berg H, Salvan P. Multimodal neuroimaging correlates of physical-cognitive covariation in Chilean adolescents. The Cogni-Action Project. Dev Cogn Neurosci 2024; 66:101345. [PMID: 38277711 PMCID: PMC10832367 DOI: 10.1016/j.dcn.2024.101345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/19/2023] [Accepted: 01/13/2024] [Indexed: 01/28/2024] Open
Abstract
Health-related behaviours have been related to brain structural features. In developing settings, such as Latin America, high social inequality has been inversely associated with several health-related behaviours affecting brain development. Understanding the relationship between health behaviours and brain structure in such settings is particularly important during adolescence when critical habits are acquired and ingrained. In this cross-sectional study, we carry out a multimodal analysis identifying a brain region associated with health-related behaviours (i.e., adiposity, fitness, sleep problems and others) and cognitive/academic performance, independent of socioeconomic status in a large sample of Chilean adolescents. Our findings suggest that the relationship between health behaviours and cognitive/academic performance involves a particular brain phenotype that could play a mediator role. These findings fill a significant gap in the literature, which has largely focused on developed countries and raise the possibility of promoting healthy behaviours in adolescence as a means to influence brain structure and thereby cognitive/academic achievement, independently of socioeconomic factors. By highlighting the potential impact on brain structure and cognitive/academic achievement, policymakers could design interventions that are more effective in reducing health disparities in developing countries.
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Affiliation(s)
- Carlos Cristi-Montero
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom; IRyS Group, Physical Education School, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, Chile.
| | - Heidi Johansen-Berg
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom
| | - Piergiorgio Salvan
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom
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5
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von Werdt L, Binz TM, O’Gorman RT, Schmid A, Naef N, Rousson V, Kretschmar O, Liamlahi R, Latal B, Ehrler M. Stress Markers, Executive Functioning, and Resilience Among Early Adolescents With Complex Congenital Heart Disease. JAMA Netw Open 2024; 7:e2355373. [PMID: 38334997 PMCID: PMC10858402 DOI: 10.1001/jamanetworkopen.2023.55373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 12/18/2023] [Indexed: 02/10/2024] Open
Abstract
Importance Infants with complex congenital heart disease (cCHD) may experience prolonged and severe stress when undergoing open heart surgery. However, little is known about long-term stress and its role in neurodevelopmental impairments in this population. Objective To investigate potential differences between early adolescents aged 10 to 15 years with cCHD and healthy controls in physiological stress markers by hair analysis, executive function (EF) performance, and resilience. Design, Setting, and Participants This single-center, population-based case-control study was conducted at the University Children's Hospital Zurich, Switzerland. Patients with different types of cCHD who underwent cardiopulmonary bypass surgery during the first year of life and who did not have a genetic disorder were included in a prospective cohort study between 2004 and 2012. A total of 178 patients were eligible for assessment at ages 10 to 15 years. A control group of healthy term-born individuals was cross-sectionally recruited. Data assessment was between 2019 and 2021. Statistical analysis was performed from January to April 2023. Exposure Patients with cCHD who underwent infant open heart surgery. Main Outcomes and Measures Physiological stress markers were quantified by summing cortisol and cortisone concentrations measured with liquid chromatography with tandem mass spectrometry in a 3-centimeter hair strand. EFs were assessed with a neuropsychological test battery to produce an age-adjusted EF summary score. Resilience was assessed with a standardized self-report questionnaire. Results The study included 100 patients with cCHD and 104 controls between 10 and 15 years of age (mean [SD] age, 13.3 [1.3] years); 110 (53.9%) were male and 94 (46.1%) were female. When adjusting for age, sex, and parental education, patients had significantly higher sums of hair cortisol and cortisone concentrations (β, 0.28 [95% CI, 0.12 to 0.43]; P < .001) and lower EF scores (β, -0.36 [95% CI, -0.49 to -0.23]; P < .001) than controls. There was no group difference in self-reported resilience (β, -0.04 [95% CI, -0.23 to 0.12]; P = .63). A significant interaction effect between stress markers and EFs was found, indicating a stronger negative association in patients than controls (β, -0.65 [95% CI, -1.15 to -0.15]; P = .01). The contrast effects were not significant in patients (β, -0.21 [95% CI, -0.43 to -0.00]; P = .06) and controls (β, 0.09 [95% CI, -0.11 to 0.30]; P = .38). Conclusions and Relevance This case-control study provides evidence for altered physiological stress levels in adolescents with cCHD and an association with poorer EF. These results suggest that future studies are needed to better understand the neurobiological mechanisms and timing of alterations in the stress system and its role in neurodevelopment.
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Affiliation(s)
- Lilian von Werdt
- Child Development Center, University Children’s Hospital Zurich, Switzerland
- Children’s Research Centre, University Children’s Hospital Zurich, Switzerland
| | - Tina M. Binz
- Center for Forensic Hair Analytics, Zurich Institute of Forensic Medicine, University of Zurich, Switzerland
| | - Ruth Tuura O’Gorman
- Children’s Research Centre, University Children’s Hospital Zurich, Switzerland
- Center for MR Research, University Children’s Hospital Zurich, Switzerland
| | - Alenka Schmid
- Child Development Center, University Children’s Hospital Zurich, Switzerland
- Children’s Research Centre, University Children’s Hospital Zurich, Switzerland
| | - Nadja Naef
- Child Development Center, University Children’s Hospital Zurich, Switzerland
- Children’s Research Centre, University Children’s Hospital Zurich, Switzerland
| | - Valentin Rousson
- Division of Biostatistics, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland
| | - Oliver Kretschmar
- Children’s Research Centre, University Children’s Hospital Zurich, Switzerland
- Pediatric Cardiology, Pediatric Heart Center, Department of Surgery, University Children’s Hospital Zurich, Switzerland
| | - Rabia Liamlahi
- Child Development Center, University Children’s Hospital Zurich, Switzerland
- Children’s Research Centre, University Children’s Hospital Zurich, Switzerland
| | - Bea Latal
- Child Development Center, University Children’s Hospital Zurich, Switzerland
- Children’s Research Centre, University Children’s Hospital Zurich, Switzerland
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Switzerland
| | - Melanie Ehrler
- Child Development Center, University Children’s Hospital Zurich, Switzerland
- Children’s Research Centre, University Children’s Hospital Zurich, Switzerland
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Switzerland
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6
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Glazer S, Kim YJ, Fecher M, Billetdeaux KA, Gilliland EB, Wilde EA, Olshefski R, Yeates KO, Vannatta K, Hoskinson KR. Higher order neurocognition in pediatric brain tumor survivors: What can we learn from white matter microstructure? Pediatr Blood Cancer 2024; 71:e30787. [PMID: 38014868 DOI: 10.1002/pbc.30787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Pediatric brain tumor survivors (PBTS) experience neurocognitive late effects, including problems with working memory, processing speed, and other higher order skills. These skill domains are subserved by various white matter (WM) pathways, but not much is known about these brain-behavior links in PBTS. This study examined the anterior corona radiata (ACR), inferior fronto-occipital fasciculi (IFOF), and superior longitudinal fasciculi (SLF) by analyzing associations among diffusion metrics and neurocognition. PROCEDURE Thirteen PBTS and 10 healthy controls (HC), aged 9-14 years, completed performance-based measures of processing speed and executive function, and parents rated their child's day-to-day executive skills. Children underwent magnetic resonance imaging (MRI) with diffusion weighted imaging that yielded fractional anisotropy (FA) and mean diffusivity (MD) values. Independent samples t-tests assessed group differences on neurocognitive and imaging measures, and pooled within-group correlations examined relationships among measures across groups. RESULTS PBTS performed more poorly than HC on measures of processing speed, divided attention, and shifting (d = -1.08 to -1.44). WM microstructure differences were significant in MD values for the bilateral SLF and ACR, with PBTS showing higher diffusivity (d = 0.75 to 1.21). Better processing speed, divided attention, and shifting were associated with lower diffusivity in the IFOF, SLF, and ACR, but were not strongly correlated with FA. CONCLUSIONS PBTS demonstrate poorer neurocognitive functioning that is linked to differences in WM microstructure, as evidenced by higher diffusivity in the ACR, SLF, and IFOF. These findings support the use of MD in understanding alterations in WM microstructure in PTBS and shed light on potential functions of these pathways.
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Affiliation(s)
- Sandra Glazer
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Psychology, University of Cincinnati, Cincinnati, Ohio, USA
| | - Young Jin Kim
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Educational Psychology, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Madison Fecher
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Katherine A Billetdeaux
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Erin B Gilliland
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Randal Olshefski
- Section of Hematology/Oncology/Bone Marrow Transplantation, Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Keith Owen Yeates
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, Alberta Children's Hospital, Calgary, Alberta, Canada
| | - Kathryn Vannatta
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA
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7
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Bagautdinova J, Bourque J, Sydnor VJ, Cieslak M, Alexander-Bloch AF, Bertolero MA, Cook PA, Gur RE, Gur RC, Hu F, Larsen B, Moore TM, Radhakrishnan H, Roalf DR, Shinohara RT, Tapera TM, Zhao C, Sotiras A, Davatzikos C, Satterthwaite TD. Development of white matter fiber covariance networks supports executive function in youth. Cell Rep 2023; 42:113487. [PMID: 37995188 PMCID: PMC10795769 DOI: 10.1016/j.celrep.2023.113487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/05/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.
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Affiliation(s)
- Joëlle Bagautdinova
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Josiane Bourque
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Maxwell A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hamsanandini Radhakrishnan
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russel T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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8
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2023; 6:1257. [PMID: 38087047 PMCID: PMC10716168 DOI: 10.1038/s42003-023-05647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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9
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Dall'Aglio L, Xu B, Tiemeier H, Muetzel RL. Longitudinal Associations Between White Matter Microstructure and Psychiatric Symptoms in Youth. J Am Acad Child Adolesc Psychiatry 2023; 62:1326-1339. [PMID: 37400062 DOI: 10.1016/j.jaac.2023.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 04/03/2023] [Accepted: 06/14/2023] [Indexed: 07/05/2023]
Abstract
OBJECTIVE Associations between psychiatric problems and white matter (WM) microstructure have been reported in youth. Yet, a deeper understanding of this relation has been hampered by a dearth of well-powered longitudinal studies and a lack of explicit examination of the bidirectional associations between brain and behavior. We investigated the temporal directionality of WM microstructure and psychiatric symptom associations in youth. METHOD In this observational study, we leveraged the world's largest single- and multi-site cohorts of neurodevelopment: the Generation R (GenR) and Adolescent Brain Cognitive Development Studies (ABCD) (total n scans = 11,400; total N = 5,700). We assessed psychiatric symptoms with the Child Behavioral Checklist as broad-band internalizing and externalizing scales, and as syndrome scales (eg, Anxious/Depressed). We quantified WM with diffusion tensor imaging (DTI), globally and at a tract level. We used cross-lagged panel models to test bidirectional associations of global and specific measures of psychopathology and WM microstructure, meta-analyzed results across cohorts, and used linear mixed-effects models for validation. RESULTS We did not identify any longitudinal associations of global WM microstructure with internalizing or externalizing problems across cohorts (confirmatory analyses) before, and after multiple testing corrections. We observed similar findings for longitudinal associations between tract-based microstructure with internalizing and externalizing symptoms, and for global WM microstructure with specific syndromes (exploratory analyses). Some cross-sectional associations surpassed multiple testing corrections in ABCD, but not in GenR. CONCLUSION Uni- or bi-directionality of longitudinal associations between WM and psychiatric symptoms were not robustly identified. We have proposed several explanations for these findings, including interindividual differences, the use of longitudinal approaches, and smaller effects than expected. STUDY REGISTRATION INFORMATION Bidirectionality Brain Function and Psychiatric Symptoms; https://doi.org/10.17605/OSF.IO/PNY92.
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Affiliation(s)
- Lorenza Dall'Aglio
- Erasmus MC, University Medical Center Rotterdam-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Bing Xu
- Erasmus MC, University Medical Center Rotterdam-Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Erasmus MC, University Medical Center Rotterdam-Sophia Children's Hospital, Rotterdam, the Netherlands; Harvard T. Chan School of Public Health, Boston, Massachusetts
| | - Ryan L Muetzel
- Erasmus MC, University Medical Center Rotterdam-Sophia Children's Hospital, Rotterdam, the Netherlands.
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10
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Anderson J, Calhoun VD, Pearlson GD, Hawkins KA, Stevens MC. Age-related, multivariate associations between white matter microstructure and behavioral performance in three executive function domains. Dev Cogn Neurosci 2023; 64:101318. [PMID: 37875033 PMCID: PMC10618425 DOI: 10.1016/j.dcn.2023.101318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 10/04/2023] [Accepted: 10/20/2023] [Indexed: 10/26/2023] Open
Abstract
The executive function (EF) domains of working memory (WM), response inhibition (RI), and set shifting (SS) show maturational gains and are linked to neuroimaging-measured brain changes. This study explored ways in which maturation-linked differences in EF abilities are systematically associated with white matter microstructural differences from adolescence into young adulthood. Diffusion tensor imaging (DTI) and nine neurocognitive tests were collected from 120 healthy subjects ages 12-24. Analyses across the white matter skeleton were performed, focusing on fractional anisotropy (FA). Data were 'fused' using a multivariate technique (CCA+jICA), producing four independent components (ICs) depicting white matter FA values that covaried with test performance. Correlations between age and IC loading coefficients identified three EF-DTI profiles that may change developmentally. In one, SS performance was linked to greater reliance on the FA of ventral brain tracts, and less on dorsal tracts with age. In another, white matter microstructure was related to a pattern of strong WM and weak SS that became more pronounced with age. A final IC revealed that younger individuals with low RI and high WM/SS skills typically matured out of this cognitive imbalance, underscored by white matter changes with age. These novel multivariate results begin to emphasize the complexity of brain structure-cognition relationships in adolescents and young adults.
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Affiliation(s)
- Jacey Anderson
- Olin Neuropsychiatry Research Center, Institute of Living, 200 Retreat Ave, Hartford, CT 06106, USA.
| | - Vince D Calhoun
- Department of Psychology, Georgia State University, 33 Gilmer St SE , Atlanta, GA 30303, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, 200 Retreat Ave, Hartford, CT 06106, USA; Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Keith A Hawkins
- Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Michael C Stevens
- Olin Neuropsychiatry Research Center, Institute of Living, 200 Retreat Ave, Hartford, CT 06106, USA; Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
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11
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Peters A, Zeytinoglu S, Leerkes EM, Isbell E. Component-specific developmental trajectories of ERP indices of cognitive control in early childhood. Dev Cogn Neurosci 2023; 64:101319. [PMID: 37907010 PMCID: PMC10632416 DOI: 10.1016/j.dcn.2023.101319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/25/2023] [Accepted: 10/22/2023] [Indexed: 11/02/2023] Open
Abstract
Early childhood is characterized by robust developmental changes in cognitive control. However, our understanding of intra-individual change in neural indices of cognitive control during this period remains limited. Here, we examined developmental changes in event-related potential (ERP) indices of cognitive control from preschool through first grade, in a large and diverse sample of children (N = 257). We recorded ERPs during a visual Go/No-Go task. N2 and P3b mean amplitudes were extracted from the observed waveforms (Go and No-Go) and the difference wave (No-Go minus Go, or ∆). Latent growth curve modeling revealed that while N2 Go and No-Go amplitudes showed no linear change, P3b Go and No-Go amplitudes displayed linear decreases in magnitude (became less positive) over time. ∆N2 amplitude demonstrated a linear increase in magnitude (became more negative) over time whereas ∆P3b amplitude was more positive in kindergarten compared to preschool. Younger age in preschool predicted greater rates of change in ∆N2 amplitude, and higher maternal education predicted larger initial P3b Go and No-Go amplitudes in preschool. Our findings suggest that observed waveforms and difference waves are not interchangeable for indexing neurodevelopment, and the developmental trajectories of different ERP indices of cognitive control are component-specific in early childhood.
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Affiliation(s)
- Amanda Peters
- Department of Psychological Sciences, University of California Merced, Merced, CA 95343, USA.
| | - Selin Zeytinoglu
- Human Development and Quantitative Methodology Department, University of Maryland College Park, College Park, MD 20742, USA
| | - Esther M Leerkes
- Department of Human Development and Family Studies, University of North Carolina Greensboro, Greensboro, NC 27412, USA
| | - Elif Isbell
- Department of Psychological Sciences, University of California Merced, Merced, CA 95343, USA
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12
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De Benedictis A, Rossi-Espagnet MC, de Palma L, Sarubbo S, Marras CE. Structural networking of the developing brain: from maturation to neurosurgical implications. Front Neuroanat 2023; 17:1242757. [PMID: 38099209 PMCID: PMC10719860 DOI: 10.3389/fnana.2023.1242757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/09/2023] [Indexed: 12/17/2023] Open
Abstract
Modern neuroscience agrees that neurological processing emerges from the multimodal interaction among multiple cortical and subcortical neuronal hubs, connected at short and long distance by white matter, to form a largely integrated and dynamic network, called the brain "connectome." The final architecture of these circuits results from a complex, continuous, and highly protracted development process of several axonal pathways that constitute the anatomical substrate of neuronal interactions. Awareness of the network organization of the central nervous system is crucial not only to understand the basis of children's neurological development, but also it may be of special interest to improve the quality of neurosurgical treatments of many pediatric diseases. Although there are a flourishing number of neuroimaging studies of the connectome, a comprehensive vision linking this research to neurosurgical practice is still lacking in the current pediatric literature. The goal of this review is to contribute to bridging this gap. In the first part, we summarize the main current knowledge concerning brain network maturation and its involvement in different aspects of normal neurocognitive development as well as in the pathophysiology of specific diseases. The final section is devoted to identifying possible implications of this knowledge in the neurosurgical field, especially in epilepsy and tumor surgery, and to discuss promising perspectives for future investigations.
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Affiliation(s)
| | | | - Luca de Palma
- Clinical and Experimental Neurology, Bambino Gesù Children’s Hospital, Rome, Italy
| | - Silvio Sarubbo
- Department of Neurosurgery, Santa Chiara Hospital, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
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13
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Green R, Meredith LR, Mewton L, Squeglia LM. Adolescent Neurodevelopment Within the Context of Impulsivity and Substance Use. CURRENT ADDICTION REPORTS 2023; 10:166-177. [PMID: 38009082 PMCID: PMC10671920 DOI: 10.1007/s40429-023-00485-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2023] [Indexed: 11/28/2023]
Abstract
Purpose of Review The aim of the present review is to provide an update on recent studies examining adolescent neurodevelopment in the context of impulsivity and substance use. We provide a review of the neurodevelopmental changes in brain structure and function related to impulsivity, substance use, and their intersection. Recent Findings When examining brain structure, smaller gray matter volume coupled with lower white matter integrity is associated with greater impulsivity across three components: trait impulsivity, choice impulsivity, and response inhibition. Altered functional connectivity in networks including the inhibitory control network and reward processing network confers risk for greater impulsivity and substance use. Summary Across brain structure and function, there is evidence to suggest that overlapping areas involved in the rise in impulsivity during adolescence contribute to early substance use initiation and escalation. These overlapping neurodevelopmental correlates have promising implications for prevention and early intervention efforts for adolescent substance use.
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Affiliation(s)
- ReJoyce Green
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Lindsay R. Meredith
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Louise Mewton
- Matilda Centre for Mental Health and Substance Use, University of Sydney, Sydney, NSW, Australia
| | - Lindsay M. Squeglia
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
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14
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Gibb BE, Owens M, Brick LAD. Attentional biases for sad faces in offspring of mothers with a history of major depression: trajectories of change from childhood to adolescence. J Child Psychol Psychiatry 2023; 64:859-867. [PMID: 36549842 PMCID: PMC10182244 DOI: 10.1111/jcpp.13740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/30/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Theorists have proposed that the way children process social-emotional information may serve as a mechanism of risk for the intergenerational transmission of depression. There is growing evidence that infants and children of mothers with a history of major depressive disorder (MDD) during the child's life exhibit attentional avoidance of sad faces, which has been proposed as an early emerging emotion regulation strategy. In contrast, there is clear evidence that at-risk and depressed adolescents and adults exhibit difficulty disengaging attention from sad faces. METHODS Seeking to link these two literatures, the current U.S.-based study used eye tracking within the context of an accelerated longitudinal design to assess attentional biases in 8-14-year-old offspring of mothers with a history MDD during the child's life (n = 123) or no history of MDD (n = 119) every six months for two years, allowing us to map trajectories of attention from age 8 to 16. RESULTS Mother MDD history moderated age-based changes in children's gaze duration to sad (t[240] = 2.44, p = .02), but not happy (t[240] = 0.11, p = .91) or angry (t[240] = 0.67, p = .50), faces. Consistent our hypotheses, offspring of mothers with MDD exhibited significantly less attention to sad faces than offspring of never depressed mothers before age 8.5 but significantly more attention to sad faces after age 14.5, which was due to an increase in gaze duration to sad faces from childhood to adolescence among offspring of mothers with MDD (t[122] = 5.44, p < .001) but not among offspring of never depressed mothers (t[118] = 1.49, p = .14). CONCLUSIONS It appears that the form, and perhaps function, of attentional bias may shift across development in at-risk youth. To the extent that this is true, it has significant implications not only for theories of the intergenerational transmission of depression risk but also for prevention and early intervention efforts designed to reduce this risk.
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Affiliation(s)
- Brandon E. Gibb
- Department of Psychology, Binghamton University, State University of New York
| | - Max Owens
- Department of Psychology, University of South Florida St. Petersburg campus
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15
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Bagautdinova J, Bourque J, Sydnor VJ, Cieslak M, Alexander-Bloch AF, Bertolero MA, Cook PA, Gur RC, Gur RE, Larsen B, Moore TM, Radhakrishnan H, Roalf DR, Shinohara RT, Tapera TM, Zhao C, Sotiras A, Davatzikos C, Satterthwaite TD. Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.09.527696. [PMID: 36798354 PMCID: PMC9934602 DOI: 10.1101/2023.02.09.527696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.
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Affiliation(s)
- Joëlle Bagautdinova
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Josiane Bourque
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matt Cieslak
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Max A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Phil A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hamsi Radhakrishnan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russel T Shinohara
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St.Louis, 63130 MO, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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16
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Baran B, Trang Huong Nguyen Q, Mylonas D, Santangelo SL, Manoach DS. Increased resting-state thalamocortical functional connectivity in children and young adults with autism spectrum disorder. Autism Res 2023; 16:271-279. [PMID: 36546577 PMCID: PMC10619334 DOI: 10.1002/aur.2875] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022]
Abstract
There is converging evidence that abnormal thalamocortical interactions contribute to attention deficits and sensory sensitivities in autism spectrum disorder (ASD). However, previous functional MRI studies of thalamocortical connectivity in ASD have produced inconsistent findings in terms of both the direction (hyper vs. hypoconnectivity) and location of group differences. This may reflect, in part, the confounding effects of head motion during scans. In the present study, we investigated resting-state thalamocortical functional connectivity in 8-25 year-olds with ASD and their typically developing (TD) peers. We used pre-scan training, on-line motion correction, and rigorous data quality assurance protocols to minimize motion confounds. ASD participants showed increased thalamic connectivity with temporal cortex relative to TD. Both groups showed similar age-related decreases in thalamic connectivity with occipital cortex, consistent with a process of circuit refinement. Findings of thalamocortical hyperconnectivity in ASD are consistent with other evidence that decreased thalamic inhibition leads to increase and less filtered sensory information reaching the cortex where it disrupts attention and contributes to sensory sensitivity. This literature motivates studies of mechanisms, functional consequences, and treatment of thalamocortical circuit dysfunction in ASD.
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Affiliation(s)
- Bengi Baran
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA
| | | | - Dimitrios Mylonas
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Susan L. Santangelo
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Maine Medical Center Research Institute, Scarborough, ME
- Tufts University School of Medicine, Department of Psychiatry, Boston, MA
| | - Dara S. Manoach
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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17
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Liang W, Yu Q, Wang W, Dhollander T, Suluba E, Li Z, Xu F, Hu Y, Tang Y, Liu S. A comparative study of the superior longitudinal fasciculus subdivisions between neonates and young adults. Brain Struct Funct 2022; 227:2713-2730. [PMID: 36114859 PMCID: PMC9618541 DOI: 10.1007/s00429-022-02565-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 09/03/2022] [Indexed: 12/04/2022]
Abstract
The superior longitudinal fasciculus (SLF) is a complex associative tract comprising three distinct subdivisions in the frontoparietal cortex, each of which has its own anatomical connectivity and functional roles. However, many studies on white matter development, hampered by limitations of data quality and tractography methods, treated the SLF as a single entity. The exact anatomical trajectory and developmental status of each sub-bundle of the human SLF in neonates remain poorly understood. Here, we compared the morphological and microstructural characteristics of each branch of the SLF at two ages using diffusion MRI data from 40 healthy neonates and 40 adults. A multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) algorithm was used to ensure the successful separation of the three SLF branches (SLF I, SLF II and SLF III). Then, between-group differences in the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics were investigated in all the SLF branches. Meanwhile, Mahalanobis distances based on all the diffusion metrics were computed to quantify the maturation of neonatal SLF branches, considering the adult brain as the reference. The SLF branches, excluding SLF II, had similar fibre morphology and connectivity between the neonatal and adult groups. The Mahalanobis distance values further supported the notion of heterogeneous maturation among SLF branches. The greatest Mahalanobis distance was observed in SLF II, possibly indicating that it was the least mature. Our findings provide a new anatomical basis for the early diagnosis and treatment of diseases caused by abnormal neonatal SLF development.
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Affiliation(s)
- Wenjia Liang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Qiaowen Yu
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Wenjun Wang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Thijs Dhollander
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Emmanuel Suluba
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Zhuoran Li
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Feifei Xu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Yang Hu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Yuchun Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China.
| | - Shuwei Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China.
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Kjelkenes R, Wolfers T, Alnæs D, Norbom LB, Voldsbekk I, Holm M, Dahl A, Berthet P, Tamnes CK, Marquand AF, Westlye LT. Deviations from normative brain white and gray matter structure are associated with psychopathology in youth. Dev Cogn Neurosci 2022; 58:101173. [PMID: 36332329 PMCID: PMC9637865 DOI: 10.1016/j.dcn.2022.101173] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/10/2022] [Accepted: 10/31/2022] [Indexed: 11/30/2022] Open
Abstract
Combining imaging modalities and metrics that are sensitive to various aspects of brain structure and maturation may help identify individuals that show deviations in relation to same-aged peers, and thus benefit early-risk-assessment for mental disorders. We used one timepoint multimodal brain imaging, cognitive, and questionnaire data from 1280 eight- to twenty-one-year-olds from the Philadelphia Neurodevelopmental Cohort. We estimated age-related gray and white matter properties and estimated individual deviation scores using normative modeling. Next, we tested for associations between the estimated deviation scores, and with psychopathology domain scores and cognition. More negative deviations in DTI-based fractional anisotropy (FA) and the first principal eigenvalue of the diffusion tensor (L1) were associated with higher scores on psychosis positive and prodromal symptoms and general psychopathology. A more negative deviation in cortical thickness (CT) was associated with a higher general psychopathology score. Negative deviations in global FA, surface area, L1 and CT were also associated with poorer cognitive performance. No robust associations were found between the deviation scores based on CT and DTI. The low correlations between the different multimodal magnetic resonance imaging-based deviation scores suggest that psychopathological burden in adolescence can be mapped onto partly distinct neurobiological features.
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Affiliation(s)
- Rikka Kjelkenes
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,Corresponding authors at: Department of Psychology, University of Oslo, Norway.
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,Oslo New University College, Oslo, Norway
| | - Linn B. Norbom
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,PROMENTA Research Center, Department of Psychology, University of Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Madelene Holm
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Andreas Dahl
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Christian K. Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,PROMENTA Research Center, Department of Psychology, University of Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands,Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands,Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, UK
| | - Lars T. Westlye
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway,Corresponding authors at: Department of Psychology, University of Oslo, Norway.
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19
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Kawabe Y, Tanaka T, Isonishi A, Nakahara K, Tatsumi K, Wanaka A. Characterization of Glial Populations in the Aging and Remyelinating Mouse Corpus Callosum. Neurochem Res 2022; 47:2826-2838. [PMID: 35859078 DOI: 10.1007/s11064-022-03676-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/15/2022] [Accepted: 07/04/2022] [Indexed: 11/28/2022]
Abstract
Cells in the white matter of the adult brain have a characteristic distribution pattern in which several cells are contiguously connected to each other, making a linear array (LA) resembling pearls-on-a-string parallel to the axon axis. We have been interested in how this pattern of cell distribution changes during aging and remyelination after demyelination. In the present study, with a multiplex staining method, semi-quantitative analysis of the localization of oligodendrocyte lineage cells (oligodendrocyte progenitors, premyelinating oligodendrocytes, and mature oligodendrocytes), astrocytes, and microglia in 8-week-old (young adult) and 32-week-old (aged) corpus callosum showed that young adult cells still include immature oligodendrocytes and that LAs contain a higher proportion of microglia than isolated cells. In aged mice, premyelinating oligodendrocytes were decreased, but microglia continued to be present in the LAs. These results suggest that the presence of microglia is important for the characteristic cell localization pattern of LAs. In a cuprizone-induced demyelination model, we observed re-formation of LAs after completion of cuprizone treatment, concurrent with remyelination. These re-formed LAs again contained more microglia than the isolated cells. This finding supports the hypothesis that microglia contribute to the formation and maintenance of LAs. In addition, regardless of the distribution of cells (LAs or isolated cells), astrocytes were found to be more abundant than in the normal corpus callosum at 24 weeks after cuprizone treatment when remyelination is completed. This suggests that astrocytes are involved in maintaining the functions of remyelinated white matter.
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Affiliation(s)
- Yoshie Kawabe
- Department of Anatomy and Neuroscience, Nara Medical University, Faculty of Medicine, 840 Shijo-cho, Kashihara City, Nara, 634-8521, Japan
| | - Tatsuhide Tanaka
- Department of Anatomy and Neuroscience, Nara Medical University, Faculty of Medicine, 840 Shijo-cho, Kashihara City, Nara, 634-8521, Japan
| | - Ayami Isonishi
- Department of Anatomy and Neuroscience, Nara Medical University, Faculty of Medicine, 840 Shijo-cho, Kashihara City, Nara, 634-8521, Japan
| | - Kazuki Nakahara
- Department of Anatomy and Neuroscience, Nara Medical University, Faculty of Medicine, 840 Shijo-cho, Kashihara City, Nara, 634-8521, Japan
| | - Kouko Tatsumi
- Department of Anatomy and Neuroscience, Nara Medical University, Faculty of Medicine, 840 Shijo-cho, Kashihara City, Nara, 634-8521, Japan.
| | - Akio Wanaka
- Department of Anatomy and Neuroscience, Nara Medical University, Faculty of Medicine, 840 Shijo-cho, Kashihara City, Nara, 634-8521, Japan
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20
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Raja R, Na X, Moore A, Otoo R, Glasier CM, Badger TM, Ou X. Associations Between White Matter Microstructures and Cognitive Functioning in 8-Year-Old Children: A Track-Weighted Imaging Study. J Child Neurol 2022; 37:471-490. [PMID: 35254148 PMCID: PMC9149064 DOI: 10.1177/08830738221083487] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE Quantitative tractography using diffusion-weighted magnetic resonance imaging data is widely used in characterizing white matter microstructure throughout childhood, but more studies are still needed to investigate comprehensive brain-behavior relationships between tract-specific white matter measures and multiple cognitive functions in children. METHODS In this study, we analyzed diffusion-weighted MRI data of 71 healthy 8-year-old children utilizing white matter tract-specific quantitative measures derived from diffusion-weighted MRI tractography based on a novel track-weighted imaging approach. Track density imaging, average path length map and 4 track-weighted diffusion tensor imaging measures including: mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for 63 white matter tracts. The track-weighted imaging measures were then correlated with a comprehensive set of neuropsychological test scores in different cognitive domains including intelligence, language, memory, academic skills, and executive functions to identify tract-specific brain-behavior relationships. RESULTS Significant correlations (P < .05, false discovery rate corrected; r = 0.27-0.57) were found in multiple white matter tracts, with a total of 40 correlations identified between various track-weighted imaging measures including average path length map, track-weighted imaging-fractional anisotropy, and neuropsychological test scores and subscales. Specifically, track-weighted imaging measures indicative of better white matter connectivity and/or microstructural development significantly correlated with higher IQ and better language abilities. CONCLUSION Our findings demonstrate the ability of track-weighted imaging measures in establishing associations between white matter and cognitive functioning in healthy children and can serve as a reference for normal brain/cognition relationships in young school-age children and further aid in identifying imaging biomarkers predictive of adverse neurodevelopmental outcomes.
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Affiliation(s)
- Rajikha Raja
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Xiaoxu Na
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Alexandra Moore
- College of Medicine, University of Arkansas for Medical Sciences
| | - Raymond Otoo
- College of Medicine, University of Arkansas for Medical Sciences
| | - Charles M. Glasier
- Department of Radiology, University of Arkansas for Medical Sciences,Department of Pediatrics, University of Arkansas for Medical Sciences
| | - Thomas M. Badger
- Department of Pediatrics, University of Arkansas for Medical Sciences,Arkansas Children’s Nutrition Center
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences,Department of Pediatrics, University of Arkansas for Medical Sciences
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21
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Nyvold O, Nygaard E, Augusti EM, Tamnes CK. Unity or diversity of executive functioning in children and adolescents with post-traumatic stress symptoms? A systematic review and meta-analysis. Child Neuropsychol 2021; 28:374-393. [PMID: 34553675 DOI: 10.1080/09297049.2021.1979950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
For some children, psychological reactions to a traumatic event develop into severe or persistent post-traumatic stress symptoms (PTSS) or the clinical condition of post-traumatic stress disorder (PTSD). Cognitive problems in children with PTSS have been reported, but it is not clear which specific functions are affected. Executive functions is a domain of particular interest, given its importance for academic performance and social and emotional functioning. A systematic literature search was performed, and 12 studies with 55 comparisons of executive functions in children with PTSS and healthy controls were eligible for meta-analysis. A subset of the studies also included a comparison group of children with traumatic experienced but without PTSS. Overall, across all tasks and measures, children with PTSS showed lower executive functioning than healthy controls (SMD = -0.57). The effect sizes between the subdomains complex tasks, verbal fluency, inhibition, shifting and working memory were not significantly different from each other, but was largest for verbal fluency (SMD = -1.45). Analyses comparing children with traumatic experiences with and without PTSS similarly showed overall lower executive functioning in the PTSS group (SMD = -0.34) and no significant differences in effect sizes between subdomains. The results have implications for assessment and clinical work with youth exposed to traumatic events. We should be aware of the poor executive functioning that may be an issue for some children with a history of trauma and subsequent development of PTSS, and the impact this could have on everyday functioning.
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Affiliation(s)
- Otelie Nyvold
- Nic Waals Institute, Lovisenberg Hospital, Oslo, Norway
| | - Egil Nygaard
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Else-Marie Augusti
- Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway.,NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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22
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Ehrler M, von Rhein M, Schlosser L, Brugger P, Greutmann M, Kretschmar O, Latal B, Tuura O'Gorman R. Microstructural alterations of the corticospinal tract are associated with poor motor function in patients with severe congenital heart disease. NEUROIMAGE: CLINICAL 2021; 32:102885. [PMID: 34911191 PMCID: PMC8628013 DOI: 10.1016/j.nicl.2021.102885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/08/2021] [Accepted: 11/16/2021] [Indexed: 10/25/2022] Open
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