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Pierpont EI, Labounek R, Gupta A, Lund T, Orchard PJ, Dobyns WB, Bondy M, Paulson A, Metz A, Shanley R, Wozniak JR, Mueller BA, Loes D, Nascene D, Nestrasil I. Diffusion Tensor Imaging in Boys With Adrenoleukodystrophy: Identification of Cerebral Disease and Association With Neurocognitive Outcomes. Neurology 2024; 103:e209764. [PMID: 39151102 PMCID: PMC11329293 DOI: 10.1212/wnl.0000000000209764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Childhood cerebral adrenoleukodystrophy (C-ALD) is a severe inflammatory demyelinating disease that must be treated at an early stage to prevent permanent brain injury and neurocognitive decline. In standard clinical practice, C-ALD lesions are detected and characterized by a neuroradiologist reviewing anatomical MRI scans. We aimed to assess whether diffusion tensor imaging (DTI) is sensitive to the presence and severity of C-ALD lesions and to investigate associations with neurocognitive outcomes after hematopoietic cell therapy (HCT). METHODS In this retrospective cohort study, we analyzed high-resolution anatomical MRI, DTI, and neurocognitive assessments from boys with C-ALD undergoing HCT at the University of Minnesota between 2011 and 2021. Longitudinal DTI data were compared with an age-matched group of boys with ALD and no lesion (NL-ALD). DTI metrics were obtained for atlas-based regions of interest (ROIs) within 3 subdivisions of the corpus callosum (CC), corticospinal tract (CST), and total white matter (WM). Between-group baseline and slope differences in fractional anisotropy (FA) and axial (AD), radial (RD), and mean (MD) diffusivities were compared using analysis of covariance accounting for age, MRI severity (Loes score), and lesion location. RESULTS Among patients with NL-ALD (n = 14), stable or increasing FA, stable AD, and stable or decreasing RD and MD were generally observed during the 1-year study period across all ROIs. In comparison, patients with mild posterior lesions (Loes 1-2; n = 13) demonstrated lower baseline FA in the CC splenium (C-ALD 0.50 ± 0.08 vs NL-ALD 0.58 ± 0.04; pBH = 0.022 adjusted Benjamini-Hochberg p-value), lower baseline AD across ROIs (e.g., C-ALD 1.34 ± 0.03 ×10-9 m2/s in total WM vs NL-ALD 1.38 ± 0.04 ×10-9 m2/s; pBH = 0.005), lower baseline RD in CC body and CST, and lower baseline MD across ROIs except CC splenium. Longitudinal slopes in CC splenium showed high sensitivity and specificity in differentiating early C-ALD from NL-ALD. Among all patients with C-ALD (n = 38), baseline Loes scores and DTI metrics were associated with post-HCT neurocognitive functions, including processing speed (e.g., FA WM Spearman correlation coefficient R = 0.64) and visual-motor integration (e.g., FA WM R = 0.71). DISCUSSION DTI was sensitive to lesion presence and severity as well as clinical neurocognitive effects of C-ALD. DTI metrics quantify C-ALD even at an early stage.
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Affiliation(s)
- Elizabeth I Pierpont
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - René Labounek
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Ashish Gupta
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Troy Lund
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Paul J Orchard
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - William B Dobyns
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Monica Bondy
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Amy Paulson
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Andrew Metz
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Ryan Shanley
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Jeffrey R Wozniak
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Bryon A Mueller
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Daniel Loes
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - David Nascene
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
| | - Igor Nestrasil
- From the Departments of Pediatrics (E.I.P., R.L., A.G., T.L., P.J.O., W.B.D., M.B., A.P., I.N.), Neurology (A.M.), Psychiatry & Behavioral Sciences (J.R.W., B.A.M.), and Radiology (D.N.), University of Minnesota Medical School, Minneapolis; Biostatistical Design and Analysis Center (R.S.), Clinical and Translational Science Institute, University of Minnesota, Minneapolis; and Independent Neuroradiologist-Consultant (D.L.), Minneapolis, MN
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Vandecruys F, Vandermosten M, De Smedt B. The role of formal schooling in the development of children's reading and arithmetic white matter networks. Dev Sci 2024:e13557. [PMID: 39129483 DOI: 10.1111/desc.13557] [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: 04/20/2021] [Revised: 03/15/2024] [Accepted: 07/18/2024] [Indexed: 08/13/2024]
Abstract
Children's white matter development is driven by experience, yet it remains poorly understood how it is shaped by attending formal education. A small number of studies compared children before and after the start of formal schooling to understand this, yet they do not allow to separate maturational effects from schooling-related effects. A clever way to (quasi-)experimentally address this issue is the longitudinal school cut-off design, which compares children who are similar in age but differ in schooling (because they are born right before or after the cut-off date for school entry). We used for the first time such a longitudinal school cut-off design to experimentally investigate the effect of schooling on children's white matter networks. We compared "young" first graders (schooling group, n = 34; Mage = 68 months; 20 girls) and "old" preschoolers (non-schooling group, n = 33; Mage = 66 months; 18 girls) that were similar in age but differed in the amount of formal instruction they received. Our study revealed that changes in fractional anisotropy and mean diffusivity in five a priori selected white matter tracts during the transition from preschool to primary school were predominantly driven by age-related maturation. We did not find specific schooling effects on white matter, despite their strong presence for early reading and early arithmetic skills. The present study is the first to disentangle the effects of age-related maturation and schooling on white matter within a longitudinal cohort of 5-year-old preschoolers. RESEARCH HIGHLIGHTS: White matter tracts that have been associated with reading and arithmetic may be susceptible to experience-dependent neuroplasticity when children learn to read and calculate. This longitudinal study used the school cut-off design to isolate schooling-induced from coinciding maturational influences on children's white matter development. White matter changes during the transition from preschool to primary school are predominantly driven by age-related maturation and not by schooling effects. Strong effects of schooling on behavior were shown for early reading and early arithmetic, but not for verbal ability and spatial ability.
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Affiliation(s)
- Floor Vandecruys
- Parenting and Special Education Research Unit, KU Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Belgium
| | - Maaike Vandermosten
- Leuven Brain Institute, KU Leuven, Belgium
- Experimental ORL, Department of Neurosciences, KU Leuven, Belgium
| | - Bert De Smedt
- Parenting and Special Education Research Unit, KU Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Belgium
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Chan SY, Fitzgerald E, Ngoh ZM, Lee J, Chuah J, Chia JSM, Fortier MV, Tham EH, Zhou JH, Silveira PP, Meaney MJ, Tan AP. Examining the associations between microglia genetic capacity, early life exposures and white matter development at the level of the individual. Brain Behav Immun 2024; 119:781-791. [PMID: 38677627 DOI: 10.1016/j.bbi.2024.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/17/2024] [Accepted: 04/23/2024] [Indexed: 04/29/2024] Open
Abstract
There are inter-individual differences in susceptibility to the influence of early life experiences for which the underlying neurobiological mechanisms are poorly understood. Microglia play a role in environmental surveillance and may influence individual susceptibility to environmental factors. As an index of neurodevelopment, we estimated individual slopes of mean white matter fractional anisotropy (WM-FA) across three time-points (age 4.5, 6.0, and 7.5 years) for 351 participants. Individual variation in microglia reactivity was derived from an expression-based polygenic score(ePGS) comprised of Single Nucleotide Polymorphisms (SNPs) functionally related to the expression of microglia-enriched genes.A higher ePGS denotes an increased genetic capacity for the expression of microglia-related genes, and thus may confer a greater capacity to respond to the early environment and to influence brain development. We hypothesized that this ePGS would associate with the WM-FA index of neurodevelopment and moderate the influence of early environmental factors.Our findings show sex dependency, where a significant association between WM-FA and microglia ePGS was only obtained for females.We then examined associations with perinatal factors known to decrease (optimal birth outcomes and familial conditions) or increase (systemic inflammation) the risk for later mental health problems.In females, individuals with high microglia ePGS showed a negative association between systemic inflammation and WM-FA and a positive association between more advantageous environmental conditions and WM-FA. The microglia ePGS in females thus accounted for variations in the influence of the quality of the early environment on WM-FA.Finally, WM-FA slopes mediated the association of microglia ePGS with interpersonal problems and social hostility in females. Our findings suggest the genetic capacity for microglia function as a potential factor underlying differential susceptibility to early life exposuresthrough influences on neurodevelopment.
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Affiliation(s)
- Shi Yu Chan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Eamon Fitzgerald
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, 1010 Rue Sherbrooke O, QC H3A 2R7, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada
| | - Zhen Ming Ngoh
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Janice Lee
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Jasmine Chuah
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Joanne S M Chia
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore
| | - Marielle V Fortier
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, 100 Bukit Timah Rd, Singapore 229899, Singapore; Duke-NUS Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Elizabeth H Tham
- Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Khoo Teck Puat-National University Children's Medical Institute, National University Health System (NUHS), 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Juan H Zhou
- Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Patricia P Silveira
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, 1010 Rue Sherbrooke O, QC H3A 2R7, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, 6875 Bd LaSalle, QC H4H 1R3, Canada; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Brain - Body Initiative Program, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis North Tower, Singapore 138632, Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), 30 Medical Dr, Singapore 117609, Singapore; Yong Loo Lin School of Medicine, National University of Singapore (NUS), 10 Medical Dr, Singapore 117597, Singapore; Brain - Body Initiative Program, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis North Tower, Singapore 138632, Singapore; Department of Diagnostic Imaging, National University Health System, 1E Kent Ridge Rd, Singapore 119228, Singapore.
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Korbmacher M, van der Meer D, Beck D, Askeland-Gjerde DE, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100323. [PMID: 39132576 PMCID: PMC11313202 DOI: 10.1016/j.bpsgos.2024.100323] [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: 03/21/2024] [Revised: 03/24/2024] [Accepted: 04/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. Methods We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. Results Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. Conclusions We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Daniel E. Askeland-Gjerde
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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Lynch KM, Bodison SC, Cabeen RP, Toga AW, Voelker CC. The spatial organization of ascending auditory pathway microstructural maturation from infancy through adolescence using a novel fiber tracking approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.10.597798. [PMID: 38915661 PMCID: PMC11195149 DOI: 10.1101/2024.06.10.597798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Auditory perception is established through experience-dependent stimuli exposure during sensitive developmental periods; however, little is known regarding the structural development of the central auditory pathway in humans. The present study characterized the regional developmental trajectories of the ascending auditory pathway from the brainstem to the auditory cortex from infancy through adolescence using a novel diffusion MRI-based tractography approach and along-tract analyses. We used diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to quantify the magnitude and timing of auditory pathway microstructural maturation. We found spatially varying patterns of white matter maturation along the length of the tract, with inferior brainstem regions developing earlier than thalamocortical projections and left hemisphere tracts developing earlier than the right. These results help to characterize the processes that give rise to functional auditory processing and may provide a baseline for detecting abnormal development.
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Affiliation(s)
- Kirsten M. Lynch
- Laboratory of Neuro Imaging (LONI), USC Mark and Mary Stevens Institute for Neuroimaging and Informatics, USC Keck School of Medicine, Los Angeles, CA, USA
| | - Stefanie C. Bodison
- Department of Occupational Therapy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Ryan P. Cabeen
- Laboratory of Neuro Imaging (LONI), USC Mark and Mary Stevens Institute for Neuroimaging and Informatics, USC Keck School of Medicine, Los Angeles, CA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), USC Mark and Mary Stevens Institute for Neuroimaging and Informatics, USC Keck School of Medicine, Los Angeles, CA, USA
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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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Farrher E, Grinberg F, Khechiashvili T, Neuner I, Konrad K, Shah NJ. Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study. Brain Sci 2024; 14:495. [PMID: 38790472 PMCID: PMC11119177 DOI: 10.3390/brainsci14050495] [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: 04/11/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen's d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen's d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.
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Affiliation(s)
- Ezequiel Farrher
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Farida Grinberg
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
| | - Tamara Khechiashvili
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
| | - Kerstin Konrad
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 3, INM-3, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
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8
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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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9
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Zhu AH, Nir TM, Javid S, Villalon-Reina JE, Rodrigue AL, Strike LT, de Zubicaray GI, McMahon KL, Wright MJ, Medland SE, Blangero J, Glahn DC, Kochunov P, Håberg AK, Thompson PM, Jahanshad N. Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581646. [PMID: 38463962 PMCID: PMC10925090 DOI: 10.1101/2024.02.22.581646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).
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Affiliation(s)
- Alyssa H Zhu
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Shayan Javid
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Amanda L Rodrigue
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lachlan T Strike
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Katie L McMahon
- Queensland University of Technology, Brisbane, QLD, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
- School of Psychology, `, Brisbane, QLD, Australia
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Peter Kochunov
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of MiDtT National Research Center, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Paul M Thompson
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
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10
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Vieites V, Ralph Y, Reeb-Sutherland B, Dick AS, Mattfeld AT, Pruden SM. Neurite density of the hippocampus is associated with trace eyeblink conditioning latency in 4- to 6-year-olds. Eur J Neurosci 2024; 59:358-369. [PMID: 38092417 PMCID: PMC10872972 DOI: 10.1111/ejn.16217] [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: 02/04/2022] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 02/06/2024]
Abstract
Limited options exist to evaluate the development of hippocampal function in young children. Research has established that trace eyeblink conditioning (EBC) relies on a functional hippocampus. Hence, we set out to investigate whether trace EBC is linked to hippocampal structure, potentially serving as a valuable indicator of hippocampal development. Our study explored potential associations between individual differences in hippocampal volume and neurite density with trace EBC performance in young children. We used onset latency of conditioned responses (CR) and percentage of conditioned responses (% CR) as measures of hippocampal-dependent associative learning. Using a sample of typically developing children aged 4 to 6 years (N = 30; 14 girls; M = 5.70 years), participants underwent T1- and diffusion-weighted MRI scans and completed a 15-min trace eyeblink conditioning task conducted outside the MRI. % CR and CR onset latency were calculated based on all trials involving tone-puff presentations and tone-alone trials. Findings revealed a connection between greater left hippocampal neurite density and delayed CR onset latency. Children with higher neurite density in the left hippocampus tended to blink closer to the onset of the unconditioned stimulus, indicating that structural variations in the hippocampus were associated with more precise timing of conditioned responses. No other relationships were observed between hippocampal volume, cerebellum volume or neurite density, hippocampal white matter connectivity and any EBC measures. Preliminary results suggest that trace EBC may serve as a straightforward yet innovative approach for studying hippocampal development in young children and populations with atypical development.
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Affiliation(s)
- Vanessa Vieites
- Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
| | - Yvonne Ralph
- Department of Psychology, University of Texas at Tyler, Tyler, Texas, USA
| | | | - Anthony Steven Dick
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Aaron T Mattfeld
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Shannon M Pruden
- Department of Psychology, Florida International University, Miami, Florida, USA
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11
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Porcu M, Cocco L, Cau R, Suri JS, Mannelli L, Manchia M, Puig J, Qi Y, Saba L. Correlation of Cognitive Reappraisal and the Microstructural Properties of the Forceps Minor: A Deductive Exploratory Diffusion Tensor Imaging Study. Brain Topogr 2024; 37:63-74. [PMID: 38062326 DOI: 10.1007/s10548-023-01020-4] [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: 07/15/2022] [Accepted: 10/29/2023] [Indexed: 01/07/2024]
Abstract
Cognitive reappraisal (CR) is a mechanism for emotion regulation, and the prefrontal cortex (PFC) plays a central role in the regulation of emotions. We tested the hypothesis of an association between CR function and microstructural properties of forceps minor (a commissural bundle within the PFC) in healthy subjects (HS). We analyzed a population of 65 young HS of a public dataset. The diffusion tensor imaging (DTI) sequence of every subject was analyzed to extract the derived shape (diameter and volume) and DTI metrics in terms of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the forceps minor. The CR subscale of the German version of the Emotion Regulation Questionnaire (ERQ) was used for CR assessment. The Shapiro-Wilk test was applied to test the assumption of normality in all these parameters, adopting a statistical threshold at p < 0.05. Whenever appropriate a non-parametric two-tailed partial correlation analysis was applied to test for correlations between the CR ERQ score and the derived shape and DTI metrics, including age and sex as confounders, adopting a statistical threshold at p < 0.05. The non-parametric two-tailed partial correlation analysis revealed a mildly significant correlation with FA (ρ = 0.303; p = 0.016), a weakly significant negative correlation with MD (ρ = - 0.269; p = 0.033), and a mildly significant negative correlation with RD (ρ = - 0.305; p = 0.015). These findings suggest a correlation between DTI microstructural properties of forceps minor and CR.
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Affiliation(s)
- Michele Porcu
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy.
- Department of Medical Imaging, Azienda Ospedaliera Universitaria di Cagliari, S.S: 554, Km 4,500, Monserrato, 09042, Cagliari, Italy.
| | - Luigi Cocco
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
| | - Riccardo Cau
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | | | - Mirko Manchia
- Unit of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Josep Puig
- Department of Radiology (IDI) and Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Yang Qi
- Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, China
| | - Luca Saba
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
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12
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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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13
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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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14
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Sullivan-Toole H, Jobson KR, Hoffman LJ, Stewart LC, Olson IR, Olino TM. Adolescents at risk for depression show increased white matter microstructure with age across diffuse areas of the brain. Dev Cogn Neurosci 2023; 64:101307. [PMID: 37813039 PMCID: PMC10570597 DOI: 10.1016/j.dcn.2023.101307] [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: 06/03/2023] [Revised: 08/22/2023] [Accepted: 09/23/2023] [Indexed: 10/11/2023] Open
Abstract
Maternal history of depression is a strong predictor of depression in offspring and linked to structural and functional alterations in the developing brain. However, very little work has examined differences in white matter in adolescents at familial risk for depression. In a sample aged 9-14 (n = 117), we used tract-based spatial statistics (TBSS) to examine differences in white matter microstructure between adolescents with (n = 42) and without (n = 75) maternal history of depression. Microstructure was indexed using fractional anisotropy (FA). Threshold-free cluster enhancement was applied and cluster maps were thresholded at whole-brain family-wise error < .05. There was no significant main effect of risk status on FA. However, there was a significant interaction between risk status and age, such that large and diffuse portions of the white matter skeleton showed relatively increased FA with age for youth with a maternal history of depression compared to those without. Most tracts identified by the interaction were robust to controlling for sex, youth internalizing, in-scanner motion, neighborhood SES, and intra-cranial volume, evidence that maternal depression is a unique predictor of white matter alterations in youth. Widespread increases in FA with age may correspond to a global pattern of accelerated brain maturation in youth at risk for depression.
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Affiliation(s)
| | - Katie R Jobson
- Department of Psychology and Neuroscience, Temple University, USA
| | - Linda J Hoffman
- Department of Psychology and Neuroscience, Temple University, USA
| | | | - Ingrid R Olson
- Department of Psychology and Neuroscience, Temple University, USA
| | - Thomas M Olino
- Department of Psychology and Neuroscience, Temple University, USA
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15
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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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16
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Dremmen MHG, Papp D, Hernandez-Tamames JA, Vernooij MW, White T. The Influence of Nonaerated Paranasal Sinuses on DTI Parameters of the Brain in 6- to 9-Year-Old Children. AJNR Am J Neuroradiol 2023; 44:1318-1324. [PMID: 37918939 PMCID: PMC10631535 DOI: 10.3174/ajnr.a8033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/20/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND AND PURPOSE DTI is prone to susceptibility artifacts. Air in the paranasal sinuses can cause field inhomogeneity, thus affecting measurements. Children often have mucus in their sinuses or no pneumatization of them. This study investigated the influence of lack of air in the paranasal sinuses on measurements of WM diffusion characteristics. MATERIALS AND METHODS The study was embedded in the Generation R Study, a prospective population-based birth cohort in Rotterdam (the Netherlands). Brain MR imaging studies (1070 children, 6-9 years of age) were evaluated for mucosal thickening of the paranasal sinuses. Nonaeration of the paranasal sinuses (modified Lund-Mackay score) was compared with that in a randomly selected control group. The relationship between nonaerated paranasal sinuses and fractional anisotropy and mean diffusivity in the DTI fiber tracts was evaluated using ANCOVA and independent t tests. RESULTS The prevalence of mucosal thickening was 10.2% (109/1070). The mean modified Lund-Mackay score was 6.87 (SD, 3.76). In 52.3% (57/109), ≥ 1 paranasal sinus was not pneumatized. The results are reported in effect sizes (Cohen's d). Lower mean fractional anisotropy values were found in the uncinate fasciculus (right uncinate fasciculus/right frontal sinus, d = -0.60), superior longitudinal fasciculus (right superior longitudinal fasciculus/right ethmoid sinus, d = -0.56; right superior longitudinal fasciculus/right sphenoid sinus, d = -2.09), and cingulate bundle (right cingulum bundle/right sphenoid sinus, d = -1.28; left cingulum bundle/left sphenoid sinus, d = -1.49). Higher mean diffusivity values were found in the forceps major/right and left sphenoid sinuses, d = 0.78. CONCLUSIONS Nonaeration of the paranasal sinuses is a common incidental finding on pediatric MR imaging brain scans. The amount of air in the paranasal sinuses can influence fractional anisotropy and, to a lesser degree, mean diffusivity values of WM tracts and should be considered in DTI studies in pediatric populations.
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Affiliation(s)
- Marjolein H G Dremmen
- From the Department of Radiology and Nuclear Medicine (M.H.G.D., D.P., J.A.H.-T., M.W.V., T.W.), Erasmus University Medical Center, Rotterdam, the Netherlands
- The Generation R Study Group (M.H.G.D.), Erasmus Medical Center Sophia, Rotterdam, the Netherlands
| | - Dorottya Papp
- From the Department of Radiology and Nuclear Medicine (M.H.G.D., D.P., J.A.H.-T., M.W.V., T.W.), Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Juan A Hernandez-Tamames
- From the Department of Radiology and Nuclear Medicine (M.H.G.D., D.P., J.A.H.-T., M.W.V., T.W.), Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Meike W Vernooij
- From the Department of Radiology and Nuclear Medicine (M.H.G.D., D.P., J.A.H.-T., M.W.V., T.W.), Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology (M.W.V.), Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Tonya White
- From the Department of Radiology and Nuclear Medicine (M.H.G.D., D.P., J.A.H.-T., M.W.V., T.W.), Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Child and Adolescent Psychiatry (T.W.), Erasmus Medical Center Sophia, Rotterdam, the Netherlands
- Section on Social and Cognitive Developmental Neuroscience (T.W.), National Institute of Mental Health, Bethesda, Maryland
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17
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Huang X, Ming Y, Zhao W, Feng R, Zhou Y, Wu L, Wang J, Xiao J, Li L, Shan X, Cao J, Kang X, Chen H, Duan X. Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain. Mol Autism 2023; 14:41. [PMID: 37899464 PMCID: PMC10614412 DOI: 10.1186/s13229-023-00573-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/22/2023] [Indexed: 10/31/2023] Open
Abstract
OBJECTIVE There has been increasing evidence for atypical white matter (WM) microstructure in autistic people, but findings have been divergent. The development of autistic people in early childhood is clouded by the concurrently rapid brain growth, which might lead to the inconsistent findings of atypical WM microstructure in autism. Here, we aimed to reveal the developmental nature of autistic children and delineate atypical WM microstructure throughout early childhood while taking developmental considerations into account. METHOD In this study, diffusion tensor imaging was acquired from two independent cohorts, containing 91 autistic children and 100 typically developing children (TDC), aged 4-7 years. Developmental prediction modeling using support vector regression based on TDC participants was conducted to estimate the WM atypical development index of autistic children. Then, subgroups of autistic children were identified by using the k-means clustering method and were compared to each other on the basis of demographic information, WM atypical development index, and autistic trait by using two-sample t-test. Relationship of the WM atypical development index with age was estimated by using partial correlation. Furthermore, we performed threshold-free cluster enhancement-based two-sample t-test for the group comparison in WM microstructures of each subgroup of autistic children with the rematched subsets of TDC. RESULTS We clustered autistic children into two subgroups according to WM atypical development index. The two subgroups exhibited distinct developmental stages and age-dependent diversity. WM atypical development index was found negatively associated with age. Moreover, an inverse pattern of atypical WM microstructures and different clinical manifestations in the two stages, with subgroup 1 showing overgrowth with low level of autistic traits and subgroup 2 exhibiting delayed maturation with high level of autistic traits, were revealed. CONCLUSION This study illustrated age-dependent heterogeneity in early childhood autistic children and delineated developmental stage-specific difference that ranged from an overgrowth pattern to a delayed pattern. Trial registration This study has been registered at ClinicalTrials.gov (Identifier: NCT02807766) on June 21, 2016 ( https://clinicaltrials.gov/ct2/show/NCT02807766 ).
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Affiliation(s)
- Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yating Ming
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Weixing Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Rui Feng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yuanyue Zhou
- Department of Medical Psychology, The First Affiliated Hospital, Hainan Medical University, Haikou, 571199, Hainan, People's Republic of China
| | - Lijie Wu
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, 150086, People's Republic of China
| | - Jia Wang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, 150086, People's Republic of China
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Jing Cao
- Child Rehabilitation Unit, Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCM, Sichuan Bayi Rehabilitation Center, Chengdu, 611135, People's Republic of China
| | - Xiaodong Kang
- Child Rehabilitation Unit, Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCM, Sichuan Bayi Rehabilitation Center, Chengdu, 611135, People's Republic of China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
- MOE Key Lab for Neuro Information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
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18
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Schilling KG, Chad JA, Chamberland M, Nozais V, Rheault F, Archer D, Li M, Gao Y, Cai L, Del'Acqua F, Newton A, Moyer D, Gore JC, Lebel C, Landman BA. White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559330. [PMID: 37808645 PMCID: PMC10557619 DOI: 10.1101/2023.09.25.559330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan A Chad
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, ON, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Maxime Chamberland
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Francois Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, University of Sherbrooke, Canada
| | - Derek Archer
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Muwei Li
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Flavio Del'Acqua
- NatbrainLab, Department of Forensics and Neurodevelopmental Sciences, King's College London, London UK
| | - Allen Newton
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel Moyer
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Catherine Lebel
- Alberta Children's Hospital Research Institute (ACHRI), Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Bennett A Landman
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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19
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Wu CY, Huang SM, Lin YH, Hsieh HH, Chu LWL, Yang HC, Chiu SC, Peng SL. Reproducibility of diffusion tensor imaging-derived parameters: implications for the streptozotocin-induced type 1 diabetic rats. MAGMA (NEW YORK, N.Y.) 2023; 36:631-639. [PMID: 36378408 DOI: 10.1007/s10334-022-01048-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 10/13/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVE Diffusion tensor imaging (DTI) is a useful approach for studying neuronal integrity in animals. However, the test-retest reproducibility of DTI techniques in animals has not been discussed. Therefore, the first part of this work was to systematically elucidate the reliability of DTI-derived parameters in an animal study. Subsequently, we applied the DTI approach to an animal model of diabetes in a longitudinal manner. MATERIALS AND METHODS In Study 1, nine rats underwent two DTI sessions using the same scanner and protocols, with a gap of 4 weeks. The reliability of the DTI-derived parameters was evaluated in terms of sessions and raters. In Study 2, nine rats received a single intraperitoneal injection of 70 mg/kg streptozotocin (STZ) to develop diabetes. Longitudinal DTI scans were used to assess brain alterations before and 4 weeks after STZ administration. RESULTS In the test-retest evaluation, the inter-scan coefficient of variation (CoV) ranged from 3.04 to 3.73% and 2.12-2.59% for fractional anisotropy (FA) and mean diffusivity (MD), respectively, in different brain regions, suggesting excellent reproducibility. Moreover, rater-dependence had minimal effects on FA and MD quantification, with all inter-rater CoV values less than 4%. Following the onset of diabetes, FA in striatum and cortex were noted to be significantly lower relative to the period where they had not developed diabetes (both P < 0.05). However, when compared to the control group, a significant change in FA caused by diabetes was detected only in the striatum (P < 0.05), but not in the cortex. CONCLUSION These results demonstrate good inter-rater and inter-scan reliability of DTI in animal studies, and the longitudinal setting has a beneficial effect on detecting small changes in the brain due to diseases.
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Affiliation(s)
- Chun-Yi Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei Branch, Taipei, Taiwan
| | - Sheng-Min Huang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Yu-Hsin Lin
- Department of Pharmacy, Taipei Branch, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Hsin-Hua Hsieh
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei Branch, Taipei, Taiwan
| | - Lok Wang Lauren Chu
- Department of Biomedical Imaging and Radiological Science, China Medical University, No. 91 Hsueh-Shih Road, Taichung, 40402, Taiwan
| | - Hui-Chieh Yang
- Department of Biomedical Imaging and Radiological Science, China Medical University, No. 91 Hsueh-Shih Road, Taichung, 40402, Taiwan
| | - Shao-Chieh Chiu
- Center for Advanced Molecular Imaging and Translation, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Shin-Lei Peng
- Department of Biomedical Imaging and Radiological Science, China Medical University, No. 91 Hsueh-Shih Road, Taichung, 40402, Taiwan.
- Neuroscience and Brain Disease Center, China Medical University, No. 91 Hsueh-Shih Road, Taichung, 40402, Taiwan.
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20
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Wang M, Barker PB, Cascella NG, Coughlin JM, Nestadt G, Nucifora FC, Sedlak TW, Kelly A, Younes L, Geman D, Palaniyappan L, Sawa A, Yang K. Longitudinal changes in brain metabolites in healthy controls and patients with first episode psychosis: a 7-Tesla MRS study. Mol Psychiatry 2023; 28:2018-2029. [PMID: 36732587 PMCID: PMC10394114 DOI: 10.1038/s41380-023-01969-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 02/04/2023]
Abstract
Seven Tesla magnetic resonance spectroscopy (7T MRS) offers a precise measurement of metabolic levels in the human brain via a non-invasive approach. Studying longitudinal changes in brain metabolites could help evaluate the characteristics of disease over time. This approach may also shed light on how the age of study participants and duration of illness may influence these metabolites. This study used 7T MRS to investigate longitudinal patterns of brain metabolites in young adulthood in both healthy controls and patients. A four-year longitudinal cohort with 38 patients with first episode psychosis (onset within 2 years) and 48 healthy controls was used to examine 10 brain metabolites in 5 brain regions associated with the pathophysiology of psychosis in a comprehensive manner. Both patients and controls were found to have significant longitudinal reductions in glutamate in the anterior cingulate cortex (ACC). Only patients were found to have a significant decrease over time in γ-aminobutyric acid, N-acetyl aspartate, myo-inositol, total choline, and total creatine in the ACC. Together we highlight the ACC with dynamic changes in several metabolites in early-stage psychosis, in contrast to the other 4 brain regions that also are known to play roles in psychosis. Meanwhile, glutathione was uniquely found to have a near zero annual percentage change in both patients and controls in all 5 brain regions during a four-year follow-up in young adulthood. Given that a reduction of the glutathione in the ACC has been reported as a feature of treatment-refractory psychosis, this observation further supports the potential of glutathione as a biomarker for this subset of patients with psychosis.
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Affiliation(s)
- Min Wang
- Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Peter B Barker
- Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Nicola G Cascella
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jennifer M Coughlin
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gerald Nestadt
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Frederick C Nucifora
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas W Sedlak
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alexandra Kelly
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Donald Geman
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Lena Palaniyappan
- Robarts Research Institution, University of Western Ontario, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Akira Sawa
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Kun Yang
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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21
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Siffredi V, Liverani MC, Van De Ville D, Freitas LGA, Borradori Tolsa C, Hüppi PS, Ha-Vinh Leuchter R. Corpus callosum structural characteristics in very preterm children and adolescents: Developmental trajectory and relationship to cognitive functioning. Dev Cogn Neurosci 2023; 60:101211. [PMID: 36780739 PMCID: PMC9925611 DOI: 10.1016/j.dcn.2023.101211] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 02/02/2023] [Accepted: 02/04/2023] [Indexed: 02/08/2023] Open
Abstract
Previous studies suggest that structural alteration of the corpus callosum, i.e., the largest white matter commissural pathway, occurs after a preterm birth in the neonatal period and lasts across development. The present study aims to unravel corpus callosum structural characteristics across childhood and adolescence in very preterm (VPT) individuals, and their associations with general intellectual, executive and socio-emotional functioning. Neuropsychological assessments, T1-weighted and multi-shell diffusion MRI were collected in 79 VPT and 46 full term controls aged 6-14 years. Volumetric, diffusion tensor and neurite orientation dispersion and density imaging (NODDI) measures were extracted on 7 callosal portions using TractSeg. A multivariate data-driven approach (partial least squares correlation) and a cohort-based age normative modelling approach were used to explore associations between callosal characteristics and neuropsychological outcomes. The VPT and a full-term control groups showed similar trends of white-matter maturation over time, i.e., increase FA and reduced ODI, in all callosal segments, that was associated with increase in general intellectual functioning. However, using a cohort-based age-related normative modelling, findings show atypical pattern of callosal development in the VPT group, with reduced callosal maturation over time that was associated with poorer general intellectual and working memory functioning, as well as with lower gestational age.
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Affiliation(s)
- Vanessa Siffredi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals, Geneva, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland; Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland.
| | - Maria Chiara Liverani
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals, Geneva, Switzerland; SensoriMotor, Affective and Social Development Laboratory, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals, Geneva, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland; Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland
| | - Lorena G A Freitas
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals, Geneva, Switzerland; Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland; Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland
| | - Cristina Borradori Tolsa
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals, Geneva, Switzerland
| | - Petra Susan Hüppi
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals, Geneva, Switzerland
| | - Russia Ha-Vinh Leuchter
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals, Geneva, Switzerland
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22
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Kumpulainen V, Merisaari H, Silver E, Copeland A, Pulli EP, Lewis JD, Saukko E, Shulist SJ, Saunavaara J, Parkkola R, Lähdesmäki T, Karlsson L, Karlsson H, Tuulari JJ. Sex differences, asymmetry, and age-related white matter development in infants and 5-year-olds as assessed with tract-based spatial statistics. Hum Brain Mapp 2023; 44:2712-2725. [PMID: 36946076 PMCID: PMC10089102 DOI: 10.1002/hbm.26238] [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: 10/16/2022] [Revised: 01/24/2023] [Accepted: 02/08/2023] [Indexed: 03/23/2023] Open
Abstract
The rapid white matter (WM) maturation of first years of life is followed by slower yet long-lasting development, accompanied by learning of more elaborate skills. By the age of 5 years, behavioural and cognitive differences between females and males, and functions associated with brain lateralization such as language skills are appearing. Diffusion tensor imaging (DTI) can be used to quantify fractional anisotropy (FA) within the WM and increasing values correspond to advancing brain development. To investigate the normal features of WM development during early childhood, we gathered a DTI data set of 166 healthy infants (mean 3.8 wk, range 2-5 wk; 89 males; born on gestational week 36 or later) and 144 healthy children (mean 5.4 years, range 5.1-5.8 years; 76 males). The sex differences, lateralization patterns and age-dependent changes were examined using tract-based spatial statistics (TBSS). In 5-year-olds, females showed higher FA in wide-spread regions in the posterior and the temporal WM and more so in the right hemisphere, while sex differences were not detected in infants. Gestational age showed stronger association with FA values compared to age after birth in infants. Additionally, child age at scan associated positively with FA around the age of 5 years in the body of corpus callosum, the connections of which are important especially for sensory and motor functions. Lastly, asymmetry of WM microstructure was detected already in infants, yet significant changes in lateralization pattern seem to occur during early childhood, and in 5-year-olds the pattern already resembles adult-like WM asymmetry.
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Affiliation(s)
- Venla Kumpulainen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Eero Silver
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Anni Copeland
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Elmo P Pulli
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - John D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Ekaterina Saukko
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Satu J Shulist
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital and University of Turku, Turku, Finland
| | - Riitta Parkkola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Pediatric Neurology, Turku University Hospital, University of Turku, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Paediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital & University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital & University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital & University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
- Department of Psychiatry, University of Oxford, Oxford, UK
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23
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DiPiero M, Rodrigues PG, Gromala A, Dean DC. Applications of advanced diffusion MRI in early brain development: a comprehensive review. Brain Struct Funct 2023; 228:367-392. [PMID: 36585970 PMCID: PMC9974794 DOI: 10.1007/s00429-022-02605-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023]
Abstract
Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.
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Affiliation(s)
- Marissa DiPiero
- Department of Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Alyssa Gromala
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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24
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Dégeilh F, Leblanc É, Daneault V, Beauchamp MH, Bernier A. Longitudinal associations between mother-child attachment security in toddlerhood and white matter microstructure in late childhood: a preliminary investigation. Attach Hum Dev 2023; 25:291-310. [PMID: 36794390 DOI: 10.1080/14616734.2023.2172437] [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: 02/17/2023]
Abstract
Early childhood experiences are considered to influence the strength and effectiveness of neural connections and thus the development of brain connectivity. As one of the most pervasive and potent early relational experiences, parent-child attachment is a prime candidate to account for experience-driven differences in brain development. Yet, knowledge of the effects of parent-child attachment on brain structure in typically developing children is scarce and largely limited to grey matter, whereas caregiving influences on white matter (i.e. neural connections) have seldom been explored. This study examined whether normative variation in mother-child attachment security predicts white matter microstructure in late childhood and explored associations with cognitive-inhibition. Mother-child attachment security was assessed using home observations when children (N = 32, 20 girls) were 15 and 26 months old. White matter microstructure was assessed using diffusion magnetic resonance imaging when children were 10 years old. Child cognitive-inhibition was tested when children were 11 years old. Results revealed a negative association between mother-toddler attachment security and child white matter microstructure organization, which in turn related to better child cognitive-inhibition. While preliminary given the sample size, these findings add to the growing literature that suggests that rich and positive experiences are likely to decelerate brain development.
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Affiliation(s)
- Fanny Dégeilh
- Department of Psychology, University of Montreal, Quebec, Canada.,Sainte-Justine Research Center, Montreal, Quebec, Canada
| | - Élizabel Leblanc
- Department of Psychology, University of Montreal, Quebec, Canada
| | - Véronique Daneault
- Department of Psychology, University of Montreal, Quebec, Canada.,Functional Neuroimaging Unit, Montreal Geriatric University Institute, Quebec, Canada.,Center for Advanced Research in Sleep Medicine, Montreal Sacré-Coeur Hospital, Quebec, Canada
| | - Miriam H Beauchamp
- Department of Psychology, University of Montreal, Quebec, Canada.,Sainte-Justine Research Center, Montreal, Quebec, Canada
| | - Annie Bernier
- Department of Psychology, University of Montreal, Quebec, Canada
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25
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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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26
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Lawrence KE, Abaryan Z, Laltoo E, Hernandez LM, Gandal MJ, McCracken JT, Thompson PM. White matter microstructure shows sex differences in late childhood: Evidence from 6797 children. Hum Brain Mapp 2023; 44:535-548. [PMID: 36177528 PMCID: PMC9842921 DOI: 10.1002/hbm.26079] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/29/2022] [Accepted: 08/19/2022] [Indexed: 02/01/2023] Open
Abstract
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
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Affiliation(s)
- Katherine E. Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Leanna M. Hernandez
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael J. Gandal
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Human Genetics, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - James T. McCracken
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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27
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Guldner S, Sarvasmaa AS, Lemaître H, Massicotte J, Vulser H, Miranda R, Bezivin-Frère P, Filippi I, Penttilä J, Banaschewski T, Barker GJ, Bokde AL, Bromberg U, Büchel C, Conrod PJ, Desrivières S, Flor H, Frouin V, Gallinat J, Garavan H, Gowland P, Heinz A, Nees F, Papadopoulos-Orfanos D, Smolka MN, Schumann G, Artiges E, Martinot MLP, Martinot JL. Longitudinal associations between adolescent catch-up sleep, white-matter maturation and internalizing problems. Dev Cogn Neurosci 2023; 59:101193. [PMID: 36610292 PMCID: PMC9841167 DOI: 10.1016/j.dcn.2022.101193] [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: 10/25/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
Sleep is an important contributor for neural maturation and emotion regulation during adolescence, with long-term effects on a range of white matter tracts implicated in affective processing in at-risk populations. We investigated the effects of adolescent sleep patterns on longitudinal changes in white matter development and whether this is related to the emergence of emotional (internalizing) problems. Sleep patterns and internalizing problems were assessed using self-report questionnaires in adolescents recruited in the general population followed up from age 14-19 years (N = 111 White matter structure was measured using diffusion tensor imaging (DTI) and estimated using fractional anisotropy (FA). We found that longitudinal increases in time in bed (TIB) on weekends and increases in TIB-variability between weekdays to weekend, were associated with an increase in FA in various interhemispheric and cortico-striatal tracts. Extracted FA values from left superior longitudinal fasciculus mediated the relationship between increases in TIB on weekends and a decrease in internalizing problems. These results imply that while insufficient sleep might have potentially harmful effects on long-term white matter development and internalizing problems, longer sleep duration on weekends (catch-up sleep) might be a natural counteractive and protective strategy.
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Affiliation(s)
- Stella Guldner
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France; Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anna S Sarvasmaa
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France; National Institute for Health and Welfare, Department of Public Health Solutions, Mental Health Unit, Helsinki, Finland; Finnish Student Health Service, Helsinki, Finland
| | - Hervé Lemaître
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France; Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076 Bordeaux, France
| | - Jessica Massicotte
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France
| | - Hélène Vulser
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France
| | - Ruben Miranda
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France
| | - Pauline Bezivin-Frère
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France
| | - Irina Filippi
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France
| | - Jani Penttilä
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France; Department of Social and Health Care, Psychosocial Services Adolescent Outpatient Clinic, Lahti, Finland
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Arun Lw Bokde
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom; Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Uli Bromberg
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Büchel
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Patricia J Conrod
- Department of Psychiatry, CHU Sainte-Justine Research Center, University of Montreal, Montreal, QC, Canada
| | - Sylvane Desrivières
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Vincent Frouin
- Neurospin, Commissariat à l'Energie Atomique et aux Energies Alternatives, Saclay, France
| | - Jürgen Gallinat
- University Medical Centre Hamburg-Eppendorf, Clinic and Policlinic for Psychiatry and Psychotherapy; Hamburg, Germany
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre, University of Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University Shanghai; and Dept. of Psychiatry and Neuroscience, Charité University Medicine, Berlin, Germany
| | - Eric Artiges
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France; EPS Barthelemy Durand, Etampes, France
| | - Marie-Laure Paillère Martinot
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France; AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Jean-Luc Martinot
- Université Paris-Saclay; Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Trajectoires développementales Psychiatrie", Ecole Normale Supérieure Paris-Saclay, CNRS UMR 9010, Université Paris Cité, Centre Borelli, Gif sur Yvette, France.
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Chiang HL, Tseng WYI, Tseng WL, Tung YH, Hsu YC, Chen CL, Gau SSF. Atypical development in white matter microstructures in ADHD: A longitudinal diffusion imaging study. Asian J Psychiatr 2023; 79:103358. [PMID: 36481569 DOI: 10.1016/j.ajp.2022.103358] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/13/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022]
Abstract
BACKGROUND In cross-sectional studies, alterations in white matter microstructure are evident in children with attention-deficit/hyperactivity disorder (ADHD) but not so prominent in adults with ADHD compared to typically-developing controls (TDC). Moreover, the developmental trajectories of white matter microstructures in ADHD are unclear, given the limited longitudinal imaging studies that characterize developmental changes in ADHD vs. TDC. METHODS This longitudinal study acquired diffusion spectrum imaging (DSI) at two time points. The sample included 55 participants with ADHD and 61 TDC. The enrollment/first DSI age ranged from 7 to 18 years, with a five-year mean follow-up time. We examined time-by-diagnosis interaction on the generalized fractional anisotropy (GFA) of 45 white matter tracts, adjusting for confounding factors and correcting for multiple comparisons. We also tested whether the longitudinal changes of microstructures were associated with ADHD symptoms and attention performance in a computerized continuous performance test. RESULTS Participants with ADHD showed more rapid development of GFA in the arcuate fasciculus, superior longitudinal fasciculus, frontal aslant tract, cingulum, inferior fronto-occipital fasciculus (IFOF), frontostriatal tract connecting the prefrontal cortex (FS-PFC), thalamic radiation, corticospinal tract, and corpus callosum. Within participants with ADHD, more rapid GFA increases in cingulum and FS-PFC were associated with slower decreases in inattention symptoms. In addition, in all participants, more rapid GFA increases in cingulum and IFOF were associated with greater improvement in attention performance. CONCLUSION Our findings suggest atypical developmental trajectories of white matter tracts in ADHD, characterized by normalization and possible compensatory neuroplastic processes with age from childhood to early adulthood.
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Affiliation(s)
- Huey-Ling Chiang
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan; Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Wen-Yih Isaac Tseng
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Wan-Ling Tseng
- Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Yu-Hung Tung
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Yung-Chin Hsu
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chang-Le Chen
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Susan Shur-Fen Gau
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan; Graduate Institute of Clinical Medicine, and Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan.
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Vargas TG, Mittal VA. The Critical Roles of Early Development, Stress, and Environment in the Course of Psychosis. ANNUAL REVIEW OF DEVELOPMENTAL PSYCHOLOGY 2022; 4:423-445. [PMID: 36712999 PMCID: PMC9879333 DOI: 10.1146/annurev-devpsych-121020-032354] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Psychotic disorders are highly debilitating with poor prognoses and courses of chronic illness. In recent decades, conceptual models have shaped understanding, informed treatment, and guided research questions. However, these models have classically focused on the adolescent and early adulthood stages immediately preceding onset while conceptualizing early infancy through all of childhood as a unitary premorbid period. In addition, models have paid limited attention to differential effects of types of stress; contextual factors such as local, regional, and country-level characteristics or sociocultural contexts; and the timing of the stressor or environmental risk. This review discusses emerging research suggesting that (a) considering effects specific to neurodevelopmental stages prior to adolescence is highly informative, (b) understanding specific stressors and levels of environmental exposures (i.e., systemic or contextual features) is necessary, and (c) exploring the dynamic interplay between development, levels and types of stressors, and environments can shed new light, informing a specified neurodevelopmental and multifaceted diathesis-stress model.
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Affiliation(s)
- T G Vargas
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
| | - V A Mittal
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Departments of Psychiatry and Medical Social Sciences, Institute for Innovations in Developmental Sciences, and Institute for Policy Research, Northwestern University, Evanston, Illinois, USA
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Dick AS, Ralph Y, Farrant K, Reeb-Sutherland B, Pruden S, Mattfeld AT. Volumetric development of hippocampal subfields and hippocampal white matter connectivity: Relationship with episodic memory. Dev Psychobiol 2022; 64:e22333. [PMID: 36426794 DOI: 10.1002/dev.22333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 08/22/2022] [Accepted: 09/02/2022] [Indexed: 01/27/2023]
Abstract
The hippocampus is a complex structure composed of distinct subfields. It has been central to understanding neural foundations of episodic memory. In the current cross-sectional study, using a large sample of 830, 3- to 21-year-olds from a unique, publicly available dataset we examined the following questions: (1) Is there elevated grey matter volume of the hippocampus and subfields in late compared to early development? (2) How does hippocampal volume compare with the rest of the cerebral cortex at different developmental stages? and (3) What is the relation between hippocampal volume and connectivity with episodic memory performance? We found hippocampal subfield volumes exhibited a nonlinear relation with age and showed a lag in volumetric change with age when compared to adjacent cortical regions (e.g., entorhinal cortex). We also observed a significant reduction in cortical volume across older cohorts, while hippocampal volume showed the opposite pattern. In addition to age-related differences in gray matter volume, dentate gyrus/cornu ammonis 3 volume was significantly related to episodic memory. We did not, however, find any associations with episodic memory performance and connectivity through the uncinate fasciculus, fornix, or cingulum. The results are discussed in the context of current research and theories of hippocampal development and its relation to episodic memory.
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Affiliation(s)
- Anthony Steven Dick
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Yvonne Ralph
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Kristafor Farrant
- Department of Psychology, Florida International University, Miami, Florida, USA
| | | | - Shannon Pruden
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Aaron T Mattfeld
- Department of Psychology, Florida International University, Miami, Florida, USA
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van Genderen JG, Chia C, Van den Hof M, Mutsaerts HJMM, Reneman L, Pajkrt D, Schrantee A. Brain Differences in Adolescents Living With Perinatally Acquired HIV Compared With Adoption Status Matched Controls: A Cross-sectional Study. Neurology 2022; 99:e1676-e1684. [PMID: 35940898 PMCID: PMC9559945 DOI: 10.1212/wnl.0000000000200946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 05/20/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Despite effective combination antiretroviral therapy (cART), adolescents with perinatally acquired HIV (PHIV) exhibit cognitive impairment, of which structural changes could be the underlying pathophysiologic mechanism. Prior MRI studies found lower brain volumes, higher white matter (WM) hyperintensity (WMH) volume, lower WM integrity, and differences in cerebral blood flow (CBF). However, these findings may be confounded by adoption status, as a large portion of adolescents with PHIV have been adopted. Adoption has been associated with malnutrition and neglect, which, in turn, may have affected brain development. We investigated the long-term effects of PHIV on the brain, while minimizing the confounding effect of adoption status. METHODS We determined whole-brain gray matter (GM) and WM volume with 3D T1-weighted scans; total WMH volume with fluid-attenuated inversion recovery; CBF in the following regions of interest (ROIs): WM, GM, and subcortical GM with arterial spin labeling; and whole-brain WM microstructural markers: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) with diffusion tensor imaging in cART-treated adolescents with PHIV visiting our outpatient clinic in Amsterdam and controls matched for age, sex, ethnic origin, socioeconomic status, and adoption status. We assessed differences in neuroimaging parameters between adolescents with PHIV and controls using linear regression models adjusted for age and sex and applied multiple comparison correction. RESULTS Thirty-five adolescents with PHIV and 38 controls were included with a median age of 14.9 (interquartile range [IQR]: 10.7-18.5) and 15.6 (IQR: 11.1-17.6) years, respectively, with a similar rate of adoption. We found a lower overall FA (beta = -0.012; p < 0.014, -2.4%), a higher MD (beta = 0.014, p = 0.014, 1.3%), and a higher RD (beta = 0.02, p = 0.014, 3.3%) in adolescents with PHIV vs adoption-matched controls, but no differences in AD. We found comparable GM, WM, and WMH volume and CBF in ROIs between adolescents with PHIV and controls. We did not find an association between cognitive profiles and WM microstructural markers in adolescents with PHIV. DISCUSSION Irrespective of adoption status, adolescents with PHIV exhibited subtle lower WM integrity. Our findings may point toward early-acquired WM microstructural alterations associated with HIV.
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Affiliation(s)
- Jason G van Genderen
- From the Department of Pediatric Infectious Diseases (J.G.G., C.C., M.V.H., D.P.), Emma Children's Hospital, Amsterdam UMC, Location Academic Medical Center, the Netherlands; Department of Radiology and Nuclear Medicine (H.J.M.M.M.), Amsterdam University Medical Centers, Location VU Medical Center, University of Amsterdam, the Netherlands; and Department of Radiology and Nuclear Medicine (L.R., A.S.), Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, the Netherlands.
| | - Cecilia Chia
- From the Department of Pediatric Infectious Diseases (J.G.G., C.C., M.V.H., D.P.), Emma Children's Hospital, Amsterdam UMC, Location Academic Medical Center, the Netherlands; Department of Radiology and Nuclear Medicine (H.J.M.M.M.), Amsterdam University Medical Centers, Location VU Medical Center, University of Amsterdam, the Netherlands; and Department of Radiology and Nuclear Medicine (L.R., A.S.), Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, the Netherlands
| | - Malon Van den Hof
- From the Department of Pediatric Infectious Diseases (J.G.G., C.C., M.V.H., D.P.), Emma Children's Hospital, Amsterdam UMC, Location Academic Medical Center, the Netherlands; Department of Radiology and Nuclear Medicine (H.J.M.M.M.), Amsterdam University Medical Centers, Location VU Medical Center, University of Amsterdam, the Netherlands; and Department of Radiology and Nuclear Medicine (L.R., A.S.), Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, the Netherlands
| | - Henk J M M Mutsaerts
- From the Department of Pediatric Infectious Diseases (J.G.G., C.C., M.V.H., D.P.), Emma Children's Hospital, Amsterdam UMC, Location Academic Medical Center, the Netherlands; Department of Radiology and Nuclear Medicine (H.J.M.M.M.), Amsterdam University Medical Centers, Location VU Medical Center, University of Amsterdam, the Netherlands; and Department of Radiology and Nuclear Medicine (L.R., A.S.), Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, the Netherlands
| | - Liesbeth Reneman
- From the Department of Pediatric Infectious Diseases (J.G.G., C.C., M.V.H., D.P.), Emma Children's Hospital, Amsterdam UMC, Location Academic Medical Center, the Netherlands; Department of Radiology and Nuclear Medicine (H.J.M.M.M.), Amsterdam University Medical Centers, Location VU Medical Center, University of Amsterdam, the Netherlands; and Department of Radiology and Nuclear Medicine (L.R., A.S.), Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, the Netherlands
| | - Dasja Pajkrt
- From the Department of Pediatric Infectious Diseases (J.G.G., C.C., M.V.H., D.P.), Emma Children's Hospital, Amsterdam UMC, Location Academic Medical Center, the Netherlands; Department of Radiology and Nuclear Medicine (H.J.M.M.M.), Amsterdam University Medical Centers, Location VU Medical Center, University of Amsterdam, the Netherlands; and Department of Radiology and Nuclear Medicine (L.R., A.S.), Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, the Netherlands
| | - Anouk Schrantee
- From the Department of Pediatric Infectious Diseases (J.G.G., C.C., M.V.H., D.P.), Emma Children's Hospital, Amsterdam UMC, Location Academic Medical Center, the Netherlands; Department of Radiology and Nuclear Medicine (H.J.M.M.M.), Amsterdam University Medical Centers, Location VU Medical Center, University of Amsterdam, the Netherlands; and Department of Radiology and Nuclear Medicine (L.R., A.S.), Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, the Netherlands
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Freschl J, Azizi LA, Balboa L, Kaldy Z, Blaser E. The development of peak alpha frequency from infancy to adolescence and its role in visual temporal processing: A meta-analysis. Dev Cogn Neurosci 2022; 57:101146. [PMID: 35973361 PMCID: PMC9399966 DOI: 10.1016/j.dcn.2022.101146] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/21/2022] [Accepted: 08/08/2022] [Indexed: 01/19/2023] Open
Abstract
While it has been shown that alpha frequency increases over development (Stroganova et al., 1999), a precise trajectory has not yet been specified, making it challenging to constrain theories linking alpha rhythms to perceptual development. We conducted a comprehensive review of studies measuring resting-state occipital peak alpha frequency (PAF, the frequency exhibiting maximum power) from birth to 18 years of age. From 889 potentially relevant studies, we identified 40 reporting PAF (109 samples; 3882 subjects). A nonlinear regression revealed that PAF increases quickly in early childhood (from 6.1 Hz at 6 months to 8.4 Hz at 5 years) and levels off in adolescence (9.7 Hz at 13 years), with an asymptote at 10.1 Hz. We found no effect of resting state procedure (eyes-open versus eyes-closed) or biological sex. PAF has been implicated as a clock on visual temporal processing, with faster frequencies associated with higher visual temporal resolution. Psychophysical studies have shown that temporal resolution reaches adult levels by 5 years of age (Freschl et al., 2019, 2020). The fact that PAF reaches the adult range of 8-12 Hz by that age strengthens the link between PAF and temporal resolution.
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Affiliation(s)
- Julie Freschl
- University of Massachusetts Boston, Boston, MA, USA; Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA.
| | | | | | - Zsuzsa Kaldy
- University of Massachusetts Boston, Boston, MA, USA
| | - Erik Blaser
- University of Massachusetts Boston, Boston, MA, USA
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Kumpulainen V, Merisaari H, Copeland A, Silver E, Pulli EP, Lewis JD, Saukko E, Saunavaara J, Karlsson L, Karlsson H, Tuulari JJ. Effect of number of diffusion-encoding directions in diffusion metrics of 5-year-olds using tract-based spatial statistical analysis. Eur J Neurosci 2022; 56:4843-4868. [PMID: 35904522 PMCID: PMC9545012 DOI: 10.1111/ejn.15785] [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: 09/19/2021] [Revised: 06/21/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022]
Abstract
Methodological aspects and effects of different imaging parameters on DTI (diffusion tensor imaging) results and their reproducibility have been recently studied comprehensively in adult populations. Although MR imaging of children's brains has become common, less interest has been focussed on researching whether adult-based optimised parameters and pre-processing protocols can be reliably applied to paediatric populations. Furthermore, DTI scalar values of preschool aged children are rarely reported. We gathered a DTI dataset from 5-year-old children (N = 49) to study the effect of the number of diffusion-encoding directions on the reliability of resultant scalar values with TBSS (tract-based spatial statistics) method. Additionally, the potential effect of within-scan head motion on DTI scalars was evaluated. Reducing the number of diffusion-encoding directions deteriorated both the accuracy and the precision of all DTI scalar values. To obtain reliable scalar values, a minimum of 18 directions for TBSS was required. For TBSS fractional anisotropy values, the intraclass correlation coefficient with two-way random-effects model (ICC[2,1]) for the subsets of 6 to 66 directions ranged between 0.136 [95%CI 0.0767;0.227] and 0.639 [0.542;0.740], whereas the corresponding values for subsets of 18 to 66 directions were 0.868 [0.815;0.913] and 0.995 [0.993;0.997]. Following the exclusion of motion-corrupted volumes, minor residual motion did not associate with the scalar values. A minimum of 18 diffusion directions is recommended to result in reliable DTI scalar results with TBSS. We suggest gathering extra directions in paediatric DTI to enable exclusion of volumes with motion artefacts and simultaneously preserve the overall data quality.
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Affiliation(s)
- Venla Kumpulainen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical MedicineUniversity of TurkuTurkuFinland
| | - Harri Merisaari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical MedicineUniversity of TurkuTurkuFinland
- Department of RadiologyTurku University HospitalTurkuFinland
| | - Anni Copeland
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical MedicineUniversity of TurkuTurkuFinland
| | - Eero Silver
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical MedicineUniversity of TurkuTurkuFinland
| | - Elmo P. Pulli
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical MedicineUniversity of TurkuTurkuFinland
| | - John D. Lewis
- Montreal Neurological InstituteMcGill UniversityMontrealQuebecCanada
| | | | - Jani Saunavaara
- Department of Medical PhysicsTurku University Hospital and University of TurkuTurkuFinland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical MedicineUniversity of TurkuTurkuFinland
- Department of Paediatrics and Adolescent MedicineTurku University Hospital and University of TurkuTurkuFinland
- Department of PsychiatryTurku University Hospital and University of TurkuTurkuFinland
- Centre for Population Health ResearchTurku University Hospital and University of TurkuTurkuFinland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical MedicineUniversity of TurkuTurkuFinland
- Department of PsychiatryTurku University Hospital and University of TurkuTurkuFinland
- Centre for Population Health ResearchTurku University Hospital and University of TurkuTurkuFinland
| | - Jetro J. Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical MedicineUniversity of TurkuTurkuFinland
- Department of PsychiatryTurku University Hospital and University of TurkuTurkuFinland
- Turku Collegium for Science and MedicineUniversity of TurkuTurkuFinland
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Subramaniapillai S, Suri S, Barth C, Maximov II, Voldsbekk I, van der Meer D, Gurholt TP, Beck D, Draganski B, Andreassen OA, Ebmeier KP, Westlye LT, de Lange AG. Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort. Hum Brain Mapp 2022; 43:3759-3774. [PMID: 35460147 PMCID: PMC9294301 DOI: 10.1002/hbm.25882] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/24/2022] [Accepted: 04/05/2022] [Indexed: 12/13/2022] Open
Abstract
Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.
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Affiliation(s)
- Sivaniya Subramaniapillai
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Department of Psychology, Faculty of ScienceMcGill UniversityMontrealQuebecCanada
- Department of PsychologyUniversity of OsloOsloNorway
| | - Sana Suri
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Claudia Barth
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
| | - Ivan I. Maximov
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Irene Voldsbekk
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
| | - Dani Beck
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
| | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | | | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Department of PsychologyUniversity of OsloOsloNorway
- Department of PsychiatryUniversity of OxfordOxfordUK
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Yang J, Sun H, Yang X, Jin B, Shen J, Hu L. Value of MRS combined with DTI in evaluating brain development of infants aged from 2 months to 2 years old. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2022; 47:910-919. [PMID: 36039588 PMCID: PMC10930286 DOI: 10.11817/j.issn.1672-7347.2022.220135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Many neuropsychiatric diseases are related to the abnormal development of brain tissue in infants. This study aims to analyze the changes in the parameters of magnetic resonance spectroscopy (MRS) and diffusion tensor imaging (DTI) in brain development of infants aged from 2 months to 2 years old, and to explore the value of MRS combined with DTI in evaluating brain development of infants aged from 2 months to 2 years old. METHODS A total of 116 normal infants, who received whole brain MRS and DTI examinations after delivery in Children Hospital of Shanxi Province from September 2020 to May 2021, were selected and were divided into a group A (n=7, at the age of 2-6 months), a group B (n=28, at the age of 7-12 months), a group C (n=41, at the age of 13-18 months), and a group D (n=40, at the age of 19-24 months). After collecting the MRS and DTI data, statistical analysis was performed to compare DTI parameters and MRS metabolic products ratio. RESULTS There were significant differences in the DTI parameters of frontal lobe, temporal lobe, occipital lobe, hind limb of internal capsule, fore limb of internal capsule, knee of corpus callosum, splenium of corpus callosum, and optic radiation among the 4 groups (P<0.05 or P<0.01). The values of fractional anisotropy (FA) showed an upward trend from the group A to the group D, while the values of apparent diffusion coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD) showed a downward trend, and the changes of parameters tended to slow down with age. In the left or right lentiform nucleus, the ratio of choline (Cho)/creatine (Cr) was decreased from the group A to the group D, and the group D was significantly lower than the group A and B (all P<0.01). The ratio of Cho/N-acetyl aspartate (NAA) was decreased from the group A to the group D, and the group D was significantly lower than the group A, B, and C (left lentiform nucleus, P<0.05 or P<0.01) or the group A, B (right lentiform nucleus, both P<0.01). The ratio of glutamine/glutamate (Glx)/Cr was decreased from the group A to the group D, and the group D was significantly lower than the group A, B and C (P<0.05 or P<0.01). The ratio of myo-inositol (mI)/Cr was increased from the group A to the group D, and the group D was significantly higher than the group A, B, and C (P<0.05 or P<0.01). The ratio of NAA/Cr was increased from the group A to the group D, and the group B, C, and D were significantly higher than the group A (P<0.05 or P<0.01). The ratios of mI/Cr and NAA/Cr in different brain regions from the group A to the group D showed an upward trend, and the ratios of Cho/Cr, Cho/NAA, and Glx/Cr showed a downward trend. The variation of each parameter tends to decrease with age. CONCLUSIONS MRS and DTI can detect the brain development of infants aged from 2 months to 2 years old, and provide a basis for predicting brain diseases.
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Affiliation(s)
- Jie Yang
- Department of MRI, Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan 030013.
| | - Huimiao Sun
- Department of MRI, Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan 030013.
| | - Xiaoyan Yang
- Department of MRI, Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan 030013
| | - Bo Jin
- Department of MRI, Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan 030013
| | - Jiaxin Shen
- Department of Hospital-Acquired Infection Control, Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan 030013, China
| | - Lei Hu
- Department of MRI, Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan 030013
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Grier MD, Yacoub E, Adriany G, Lagore RL, Harel N, Zhang RY, Lenglet C, Uğurbil K, Zimmermann J, Heilbronner SR. Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain. Neuroimage 2022; 255:119200. [PMID: 35427769 PMCID: PMC9446284 DOI: 10.1016/j.neuroimage.2022.119200] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/08/2022] [Accepted: 04/07/2022] [Indexed: 11/26/2022] Open
Abstract
Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields.
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Affiliation(s)
- Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Gregor Adriany
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Russell L Lagore
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Ru-Yuan Zhang
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States.
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van Duijvenvoorde ACK, Whitmore LB, Westhoff B, Mills KL. A methodological perspective on learning in the developing brain. NPJ SCIENCE OF LEARNING 2022; 7:12. [PMID: 35654860 PMCID: PMC9163171 DOI: 10.1038/s41539-022-00127-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 05/05/2022] [Indexed: 05/07/2023]
Abstract
The brain undergoes profound development across childhood and adolescence, including continuous changes in brain morphology, connectivity, and functioning that are, in part, dependent on one's experiences. These neurobiological changes are accompanied by significant changes in children's and adolescents' cognitive learning. By drawing from studies in the domains of reading, reinforcement learning, and learning difficulties, we present a brief overview of methodological approaches and research designs that bridge brain- and behavioral research on learning. We argue that ultimately these methods and designs may help to unravel questions such as why learning interventions work, what learning computations change across development, and how learning difficulties are distinct between individuals.
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Affiliation(s)
- Anna C K van Duijvenvoorde
- Institute of Psychology, Leiden University, Leiden, The Netherlands.
- Leiden Institute for Brain & Cognition, Leiden University, Leiden, The Netherlands.
| | - Lucy B Whitmore
- Department of Psychology, University of Oregon, Eugene, OR, USA
| | - Bianca Westhoff
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain & Cognition, Leiden University, Leiden, The Netherlands
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, Eugene, OR, USA
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
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Kar P, Reynolds JE, Gibbard WB, McMorris C, Tortorelli C, Lebel C. Trajectories of brain white matter development in young children with prenatal alcohol exposure. Hum Brain Mapp 2022; 43:4145-4157. [PMID: 35596624 PMCID: PMC9374879 DOI: 10.1002/hbm.25944] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/08/2022] [Accepted: 04/16/2022] [Indexed: 12/22/2022] Open
Abstract
Prenatal alcohol exposure (PAE) is associated with alterations to brain white matter microstructure. Previous studies of PAE have demonstrated different findings in young children compared to older children and adolescents, suggesting altered developmental trajectories and highlighting the need for longitudinal research. 122 datasets in 54 children with PAE (27 males) and 196 datasets in 89 children without PAE (45 males) were included in this analysis. Children underwent diffusion tensor imaging between 2 and 8 years of age, returning approximately every 6 months. Mean fractional anisotropy (FA) and mean diffusivity (MD) were obtained for 10 major brain white matter tracts and examined for age-related changes using linear mixed effects models with age, sex, group (PAE vs. control) and an age-by-group interaction. Children with PAE had slower decreases of MD over time in the genu of the corpus callosum, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus. No significant age-by-group interactions were noted for FA. These findings show slower white matter development in young children with PAE than in unexposed controls. This connects previous cross-sectional findings of lower MD in young children with PAE to findings of higher MD in older children and adolescents with PAE, and further helps to understand brain development in children with PAE. This deviation from typical development trajectories may reflect altered brain plasticity, which has implications for cognitive and behavioral learning in children with PAE.
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Affiliation(s)
- Preeti Kar
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Jess E Reynolds
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - William Ben Gibbard
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada
| | - Carly McMorris
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Werklund School of Education, University of Calgary, Calgary, Alberta, Canada
| | | | - Catherine Lebel
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada
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Cox CS, Juranek J, Kosmach S, Pedroza C, Thakur N, Dempsey A, Rennie K, Scott MC, Jackson M, Kumar A, Aertker B, Caplan H, Triolo F, Savitz SI. Autologous cellular therapy for cerebral palsy: a randomized, crossover trial. Brain Commun 2022; 4:fcac131. [PMID: 35702731 PMCID: PMC9188321 DOI: 10.1093/braincomms/fcac131] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/24/2022] [Accepted: 05/17/2022] [Indexed: 11/14/2022] Open
Abstract
We examined an autologous mononuclear-cell-therapy-based approach to treat cerebral palsy using autologous umbilical cord blood or bone-marrow-derived mononuclear cells. The primary objective was to determine if autologous cells are safe to administer in children with cerebral palsy. The secondary objectives were to determine if there was improvement in motor function of patients 12 months after infusion using the Gross Motor Function Measure and to evaluate impact of treatment on corticospinal tract microstructure as determined by radial diffusivity measurement. This Phase 1/2a trial was a randomized, blinded, placebo-controlled, crossover study in children aged 2-10 years of age with cerebral palsy enrolled between November 2013 and November 2016. Participants were randomized to 2:1 treatment:placebo. Treatment was either autologous bone-marrow-derived mononuclear cells or autologous umbilical cord blood. All participants who enrolled and completed their baseline visit planned to return for follow-up visits at 6 months, 12 months and 24 months after the baseline visit. At the 12-month post-treatment visit, participants who originally received the placebo received either bone-marrow-derived mononuclear cell or umbilical cord blood treatment. Twenty participants were included; 7 initially randomized to placebo, and 13 randomized to treatment. Five participants randomized to placebo received bone-marrow-derived mononuclear cells, and 2 received umbilical cord blood at the 12-month visit. None of the participants experienced adverse events related to the stem cell infusion. Cell infusion at the doses used in our study did not dramatically alter motor function. We observed concordant bilateral changes in radial diffusivity in 10 of 15 cases where each corticospinal tract could be reconstructed in each hemisphere. In 60% of these cases (6/10), concordant decreases in bilateral corticospinal tract radial diffusivity occurred post-treatment. In addition, 100% of unilateral corticospinal tract cases (3/3) exhibited decreased corticospinal tract radial diffusivity post-treatment. In our discordant cases (n = 5), directionality of changes in corticospinal tract radial diffusivity appeared to coincide with handedness. There was a significant improvement in corticospinal tract radial diffusivity that appears related to handedness. Connectivity strength increased in either or both pathways (corticio-striatal and thalamo-cortical) in each participant at 12 months post-treatment. These data suggest that both stem cell infusions are safe. There may be an improvement in myelination in some groups of patients that correlate with small improvements in the Gross Motor Function Measure scales. A larger autologous cord blood trial is impractical at current rates of blood banking. Either increased private banking or matched units would be required to perform a larger-scale trial.
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Affiliation(s)
- Charles S. Cox
- Department of Pediatric Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
- Program in Pediatric Regenerative Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Jenifer Juranek
- Department of Pediatric Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
- Program in Pediatric Regenerative Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Steven Kosmach
- Department of Pediatric Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Claudia Pedroza
- Department of Pediatrics, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Nivedita Thakur
- Department of Pediatrics, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Allison Dempsey
- Department of Pediatrics, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Kimberly Rennie
- Department of Pediatrics, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
- Department of Neuropsychology, NeuroBehavioral Health, Milwaukee, WI, USA
| | - Michael C. Scott
- Department of Pediatric Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Margaret Jackson
- Department of Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Akshita Kumar
- Department of Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Benjamin Aertker
- Department of Neurology, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Henry Caplan
- Department of Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Fabio Triolo
- Department of Pediatric Surgery, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
- Program in Pediatric Regenerative Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Sean I. Savitz
- Department of Neurology, McGovern Medical School at The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
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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] [MESH Headings] [Grants] [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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41
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Hu Y, Zeydabadinezhad M, Li L, Guo Y. A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development. J Am Stat Assoc 2022; 117:1134-1148. [PMID: 36204347 PMCID: PMC9531911 DOI: 10.1080/01621459.2022.2055559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Secondly, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Thirdly, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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Affiliation(s)
- Yingtian Hu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322
| | | | - Longchuan Li
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322
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Fiber tracing and microstructural characterization among audiovisual integration brain regions in neonates compared with young adults. Neuroimage 2022; 254:119141. [PMID: 35342006 DOI: 10.1016/j.neuroimage.2022.119141] [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: 06/09/2021] [Revised: 02/23/2022] [Accepted: 03/21/2022] [Indexed: 11/23/2022] Open
Abstract
Audiovisual integration has been related with cognitive-processing and behavioral advantages, as well as with various socio-cognitive disorders. While some studies have identified brain regions instantiating this ability shortly after birth, little is known about the structural pathways connecting them. The goal of the present study was to reconstruct fiber tracts linking AVI regions in the newborn in-vivo brain and assess their adult-likeness by comparing them with analogous fiber tracts of young adults. We performed probabilistic tractography and compared connective probabilities between a sample of term-born neonates (N = 311; the Developing Human Connectome Project (dHCP, http://www.developingconnectome.org) and young adults (N = 311 The Human Connectome Project; https://www.humanconnectome.org/) by means of a classification algorithm. Furthermore, we computed Dice coefficients to assess between-group spatial similarity of the reconstructed fibers and used diffusion metrics to characterize neonates' AVI brain network in terms of microstructural properties, interhemispheric differences and the association with perinatal covariates and biological sex. Overall, our results indicate that the AVI fiber bundles were successfully reconstructed in a vast majority of neonates, similarly to adults. Connective probability distributional similarities and spatial overlaps of AVI fibers between the two groups differed across the reconstructed fibers. There was a rank-order correspondence of the fibers' connective strengths across the groups. Additionally, the study revealed patterns of diffusion metrics in line with early white matter developmental trajectories and a developmental advantage for females. Altogether, these findings deliver evidence of meaningful structural connections among AVI regions in the newborn in-vivo brain.
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Thompson DK, Yang JYM, Chen J, Kelly CE, Adamson CL, Alexander B, Gilchrist C, Matthews LG, Lee KJ, Hunt RW, Cheong JLY, Spencer-Smith M, Neil JJ, Seal ML, Inder TE, Doyle LW, Anderson PJ. Brain White Matter Development Over the First 13 Years in Very Preterm and Typically Developing Children Based on the T 1-w/ T 2-w Ratio. Neurology 2022; 98:e924-e937. [PMID: 34937788 PMCID: PMC8901175 DOI: 10.1212/wnl.0000000000013250] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 12/13/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES To investigate brain regional white matter development in full-term (FT) and very preterm (VP) children at term equivalent and 7 and 13 years of age based on the ratio of T 1- and T 2-weighted MRI (T 1-w/T 2-w), including (1) whether longitudinal changes differ between birth groups or sexes, (2) associations with perinatal risk factors in VP children, and (3) relationships with neurodevelopmental outcomes at 13 years. METHODS Prospective longitudinal cohort study of VP (born <30 weeks' gestation or <1,250 g) and FT infants born between 2001 and 2004 and followed up at term equivalent and 7 and 13 years of age, including MRI studies and neurodevelopmental assessments. T 1-w/T 2-w images were parcellated into 48 white matter regions of interest. RESULTS Of 224 VP participants and 76 FT participants, 197 VP and 55 FT participants had useable T 1-w/T 2-w data from at least one timepoint. T 1-w/T 2-w values increased between term equivalent and 13 years of age, with little evidence that longitudinal changes varied between birth groups or sexes. VP birth, neonatal brain abnormalities, being small for gestational age, and postnatal infection were associated with reduced regional T 1-w/T 2-w values in childhood and adolescence. Increased T 1-w/T 2-w values across the white matter at 13 years were associated with better motor and working memory function for all children. Within the FT group only, larger increases in T 1-w/T 2-w values from term equivalent to 7 years were associated with poorer attention and executive function, and higher T 1-w/T 2-w values at 7 years were associated with poorer mathematics performance. DISCUSSION VP birth and multiple known perinatal risk factors are associated with long-term reductions in the T 1-w/T 2-w ratio in white matter regions in childhood and adolescence, which may relate to alterations in microstructure and myelin content. Increased T 1-w/T 2-w ratio at 13 years appeared to be associated with better motor and working memory function and there appeared to be developmental differences between VP and FT children in the associations for attention, executive functioning, and mathematics performance.
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Affiliation(s)
- Deanne K Thompson
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia.
| | - Joseph Y M Yang
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Jian Chen
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Claire E Kelly
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Christopher L Adamson
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Bonnie Alexander
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Courtney Gilchrist
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Lillian G Matthews
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Katherine J Lee
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Rodney W Hunt
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Jeanie L Y Cheong
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Megan Spencer-Smith
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Jeffrey J Neil
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Marc L Seal
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Terrie E Inder
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Lex W Doyle
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
| | - Peter J Anderson
- From the Victorian Infant Brain Study (VIBeS) (D.T., C.K.), Developmental Imaging (J. Chen, C.L.A., M.S.), and Clinical Epidemiology and Biostatistics Unit (K.J.L.), Murdoch Children's Research Institute; Department of Neurosurgery (J.Y.-M.Y., B.A.) and Neonatal Medicine (R.H.), The Royal Children's Hospital, Parkville; Neurodevelopment in Health and Disease Program (C.G.), School of Health and Biomedical Sciences, RMIT University, Bundoora; Turner Institute for Brain and Mental Health (L.M., M.S.-S., P.A.), Monash University, Clayton; Neonatal Services (J. Cheong), The Royal Women's Hospital, Parkville, Melbourne, Australia; Department of Pediatric Neurology (J.N.), Washington University School of Medicine, St. Louis, MO; Department of Pediatric Newborn Medicine (T.I.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Department of Obstetrics and Gynaecology (L.D.), The University of Melbourne, Parkville, Australia
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Laczkovics C, Nenning KH, Wittek T, Schmidbauer V, Schwarzenberg J, Maurer ES, Wagner G, Seidel S, Philipp J, Prayer D, Kasprian G, Karwautz A. White matter integrity is disrupted in adolescents with acute anorexia nervosa: A diffusion tensor imaging study. Psychiatry Res Neuroimaging 2022; 320:111427. [PMID: 34952446 DOI: 10.1016/j.pscychresns.2021.111427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 12/09/2021] [Accepted: 12/10/2021] [Indexed: 11/30/2022]
Abstract
Anorexia nervosa (AN) is a highly debilitating mental illness with multifactorial etiology. It oftentimes begins in adolescence, therefore understanding the pathophysiology in this period is important. Few studies investigated the possible impact of the acute state of illness on white matter (WM) tissue properties in the developing adolescent brain. The present study expands our understanding of the implications of AN and starvation on WM integrity. 67 acutely ill adolescent patients suffering from AN restricting type were compared with 32 healthy controls using diffusion tensor imaging assessing fractional anisotropy (FA) and mean diffusivity (MD). We found widespread alterations in the vast majority of the WM regions with significantly decreased FA and increased MD in the AN group. In this highly selective sample in the acute stage of AN, the alterations are likely to be the consequence of starvation. Still, we cannot rule out that some of the affected regions might play a key role in AN-specific psychopathology.
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Affiliation(s)
- Clarissa Laczkovics
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria.
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Tanja Wittek
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Victor Schmidbauer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Julia Schwarzenberg
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Elisabeth Sophie Maurer
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Gudrun Wagner
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Stefan Seidel
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Julia Philipp
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Daniela Prayer
- Department of Neurology, Medical University of Vienna, Austria
| | - Gregor Kasprian
- Department of Neurology, Medical University of Vienna, Austria
| | - Andreas Karwautz
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
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45
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The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children. Neurosci Bull 2022; 38:607-621. [PMID: 35092576 DOI: 10.1007/s12264-022-00820-1] [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: 06/02/2021] [Accepted: 10/22/2021] [Indexed: 10/19/2022] Open
Abstract
School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).
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Bouyeure A, Bekha D, Patil S, Hertz-Pannier L, Noulhiane M. OUP accepted manuscript. Cereb Cortex Commun 2022; 3:tgac004. [PMID: 35261977 PMCID: PMC8895309 DOI: 10.1093/texcom/tgac004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 01/06/2022] [Accepted: 01/08/2022] [Indexed: 11/13/2022] Open
Abstract
The structure-function relationship between white matter microstructure and episodic memory (EM) has been poorly studied in the developing brain, particularly in early childhood. Previous studies in adolescents and adults have shown that episodic memory recall is associated with prefrontal-limbic white matter microstructure. It is unknown whether this association is also observed during early ontogeny. Here, we investigated the association between prefrontal-limbic tract microstructure and EM performance in a cross-sectional sample of children aged 4 to 12 years. We used a multivariate partial least squares correlation approach to extract tract-specific latent variables representing shared information between age and diffusion parameters describing tract microstructure. Individual projections onto these latent variables describe patterns of interindividual differences in tract maturation that can be interpreted as scores of white matter tract microstructural maturity. Using these estimates of microstructural maturity, we showed that maturity scores of the uncinate fasciculus and dorsal cingulum bundle correlated with distinct measures of EM recall. Furthermore, the association between tract maturity scores and EM recall was comparable between younger and older children. Our results provide new evidence on the relation between white matter maturity and EM performance during development.
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Affiliation(s)
- Antoine Bouyeure
- UNIACT, NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- UMR1141, Inserm, Université de Paris, 75019 Paris, France
| | - Dhaif Bekha
- UNIACT, NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- UMR1141, Inserm, Université de Paris, 75019 Paris, France
| | - Sandesh Patil
- UNIACT, NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- UMR1141, Inserm, Université de Paris, 75019 Paris, France
| | - Lucie Hertz-Pannier
- UNIACT, NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- UMR1141, Inserm, Université de Paris, 75019 Paris, France
| | - Marion Noulhiane
- UNIACT, NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- UMR1141, Inserm, Université de Paris, 75019 Paris, France
- Corresponding author: UNIACT, NeuroSpin, CEA, 91191 Gif-sur-Yvette, France.
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Rahmani F, Wang Q, McKay NS, Keefe S, Hantler N, Hornbeck R, Wang Y, Hassenstab J, Schindler S, Xiong C, Morris JC, Benzinger TL, Raji CA. Sex-Specific Patterns of Body Mass Index Relationship with White Matter Connectivity. J Alzheimers Dis 2022; 86:1831-1848. [PMID: 35180116 PMCID: PMC9108572 DOI: 10.3233/jad-215329] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/31/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Obesity is an increasingly recognized modifiable risk factor for Alzheimer's disease (AD). Increased body mass index (BMI) is related to distinct changes in white matter (WM) fiber density and connectivity. OBJECTIVE We investigated whether sex differentially affects the relationship between BMI and WM structural connectivity. METHODS A cross-sectional sample of 231 cognitively normal participants were enrolled from the Knight Alzheimer Disease Research Center. Connectome analyses were done with diffusion data reconstructed using q-space diffeomorphic reconstruction to obtain the spin distribution function and tracts were selected using a deterministic fiber tracking algorithm. RESULTS We identified an inverse relationship between higher BMI and lower connectivity in the associational fibers of the temporal lobe in overweight and obese men. Normal to overweight women showed a significant positive association between BMI and connectivity in a wide array of WM fibers, an association that reversed in obese and morbidly obese women. Interaction analyses revealed that with increasing BMI, women showed higher WM connectivity in the bilateral frontoparietal and parahippocampal parts of the cingulum, while men showed lower connectivity in right sided corticostriatal and corticopontine tracts. Subgroup analyses demonstrated comparable results in participants with and without positron emission tomography or cerebrospinal fluid evidence of brain amyloidosis, indicating that the relationship between BMI and structural connectivity in men and women is independent of AD biomarker status. CONCLUSION BMI influences structural connectivity of WM differently in men and women across BMI categories and this relationship does not vary as a function of preclinical AD.
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Affiliation(s)
- Farzaneh Rahmani
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Qing Wang
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nicole S. McKay
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Sarah Keefe
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nancy Hantler
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Russ Hornbeck
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Yong Wang
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Jason Hassenstab
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Suzanne Schindler
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - John C. Morris
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, MO, USA
| | - Cyrus A. Raji
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
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48
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Ettehadi N, Zhang X, Wang Y, Semanek D, Guo J, Posner J, Laine AF. Automatic Volumetric Quality Assessment of Diffusion MR Images via Convolutional Neural Network Classifiers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2756-2760. [PMID: 34891820 DOI: 10.1109/embc46164.2021.9630834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Diffusion Tensor Imaging (DTI) is widely used to find brain biomarkers for various stages of brain structural and neuronal development. Processing DTI data requires a detailed Quality Assessment (QA) to detect artifactual volumes amongst a large pool of data. Since large cohorts of brain DTI data are often used in different studies, manual QA of such images is very labor-intensive. In this paper, a deep learning-based tool is developed for quick automatic QA of 3D raw diffusion MR images. We propose a 2-step framework to automate the process of binary (i.e., 'good' vs 'poor') quality classification of diffusion MR images. In the first step, using two separately trained 3D convolutional neural networks with different input sizes, quality labels for individual Regions of Interest (ROIs) sampled from whole DTI volumes are predicted. In the second step, two distinct novel voting systems are designed and fine-tuned to predict the quality label of whole brain DTI volumes using the individual ROI labels predicted in the previous step. Our results demonstrate the validity and practicality of our tool. Specifically, using a balanced dataset of 6,940 manually-labeled 3D DTI volumes from 85 unique subjects for training, validation, and testing, our model achieves 100% accuracy via one voting system, and 98% accuracy via another voting system on the same test set.
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Palmer CE, Pecheva D, Iversen JR, Hagler DJ, Sugrue L, Nedelec P, Fan CC, Thompson WK, Jernigan TL, Dale AM. Microstructural development from 9 to 14 years: Evidence from the ABCD Study. Dev Cogn Neurosci 2021; 53:101044. [PMID: 34896850 PMCID: PMC8671104 DOI: 10.1016/j.dcn.2021.101044] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/23/2021] [Accepted: 12/02/2021] [Indexed: 02/03/2023] Open
Abstract
During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.
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Affiliation(s)
- Clare E. Palmer
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA,Corresponding author.
| | - Diliana Pecheva
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA,Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - John R. Iversen
- Institute for Neural Computation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Donald J. Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA,Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Leo Sugrue
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Pierre Nedelec
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA
| | - Wesley K. Thompson
- Division of Biostatistics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Terry L. Jernigan
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA,Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA,Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA,Department of Psychiatry, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA,Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA,Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA,Department of Neuroscience, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
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Effects of tDCS on Language Recovery in Post-Stroke Aphasia: A Pilot Study Investigating Clinical Parameters and White Matter Change with Diffusion Imaging. Brain Sci 2021; 11:brainsci11101277. [PMID: 34679342 PMCID: PMC8534035 DOI: 10.3390/brainsci11101277] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 11/16/2022] Open
Abstract
Objectives: In this pilot study we investigated the effects of transcranial direct current stimulation (tDCS) on language recovery in the subacute stage of post-stroke aphasia using clinical parameters and diffusion imaging with constrained spherical deconvolution-based tractography. Methods: The study included 21 patients with subacute post-stroke aphasia. Patients were randomly classified into two groups with a ratio of 2:1 to receive real tDCS or sham tDCS as placebo control. Patients received 10 sessions (5/week) bi-hemispheric tDCS treatments over the left affected Broca's area (anodal electrode) and over the right unaffected Broca's area (cathodal stimulation). Aphasia score was assessed clinically using the language section of the Hemispheric Stroke Scale (HSS) before and after treatment sessions. Diffusion imaging and tractography were performed for seven patients of the real group, both before and after the 10th session. Dissection of language-related white matter tracts was achieved, and diffusion measures were extracted. A paired Student's t-test was used to compare the clinical recovery and diffusion measures of the dissected tracts both pre- and post- treatment. The partial correlation between changes in diffusion measures and the language improvements was calculated. Results: At baseline assessment, there were no significant differences between groups in demographic and clinical HSS language score. No significant clinical recovery in HSS was evident in the sham group. However, significant improvements in the different components of HSS were only observed in patients receiving real tDCS. Associated significant increase in the fractional anisotropy of the right uncinate fasciculus and a significant reduction in the mean diffusivity of the right frontal aslant tract were reported. A significant positive correlation was found between the changes in the right uncinate fasciculus and fluency improvement. Conclusions: Aphasia recovery after bi-hemispheric transcranial direct current stimulation was associated with contralesional right-sided white matter changes at the subacute stage. These changes probably reflect neuroplasticity that could contribute to the recovery. Both the right uncinate fasciculus and right frontal aslant tract seem to be involved in aphasia recovery.
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