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Hostalet N, González A, Salgado-Pineda P, Gonzàlez-Colom R, Canales-Rodríguez EJ, Aguirre C, Guerrero-Pedraza A, Llanos-Torres M, Salvador R, Pomarol-Clotet E, Sevillano X, Martínez-Abadías N, Fatjó-Vilas M. Face-brain correlates as potential sex-specific biomarkers for schizophrenia and bipolar disorder. Psychiatry Res 2024; 339:116027. [PMID: 38954892 DOI: 10.1016/j.psychres.2024.116027] [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: 01/16/2024] [Revised: 05/13/2024] [Accepted: 06/10/2024] [Indexed: 07/04/2024]
Abstract
Given the shared ectodermal origin and integrated development of the face and the brain, facial biomarkers emerge as potential candidates to assess vulnerability for disorders in which neurodevelopment is compromised, such as schizophrenia (SZ) and bipolar disorder (BD). The sample comprised 188 individuals (67 SZ patients, 46 BD patients and 75 healthy controls (HC)). Using a landmark-based approach on 3D facial reconstructions, we quantified global and local facial shape differences between SZ/BD patients and HC using geometric morphometrics. We also assessed correlations between facial and brain cortical measures. All analyses were performed separately by sex. Diagnosis explained 4.1 % - 5.9 % of global facial shape variance in males and females with SZ, and 4.5 % - 4.1 % in BD. Regarding local facial shape, we detected 43.2 % of significantly different distances in males and 47.4 % in females with SZ as compared to HC, whereas in BD the percentages decreased to 35.8 % and 26.8 %, respectively. We detected that brain area and volume significantly explained 2.2 % and 2 % of facial shape variance in the male SZ - HC sample. Our results support facial shape as a neurodevelopmental marker for SZ and BD and reveal sex-specific pathophysiological mechanisms modulating the interplay between the brain and the face.
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Affiliation(s)
- Noemí Hostalet
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de Barcelona (UB), Spain; CIBERSAM, Biomedical Research Network in Mental Health, Instituto de Salud Carlos III, Madrid, Spain
| | - Alejandro González
- HER - Human-Environment Research Group, La Salle, Universitat Ramon Llull, Spain
| | - Pilar Salgado-Pineda
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM, Biomedical Research Network in Mental Health, Instituto de Salud Carlos III, Madrid, Spain
| | - Rubèn Gonzàlez-Colom
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de Barcelona (UB), Spain
| | - Erick J Canales-Rodríguez
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM, Biomedical Research Network in Mental Health, Instituto de Salud Carlos III, Madrid, Spain; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Candibel Aguirre
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; Consorci Sanitari de Terrassa (CST). Hospital de Dia de Salut Mental de Terrassa, Spain
| | - Amalia Guerrero-Pedraza
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; Hospital Benito Menni CASM, Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain
| | - María Llanos-Torres
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; Hospital Mare de Déu de la Mercè, Germanes Hospitalàries, Barcelona, Spain
| | - Raymond Salvador
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM, Biomedical Research Network in Mental Health, Instituto de Salud Carlos III, Madrid, Spain
| | - Edith Pomarol-Clotet
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; CIBERSAM, Biomedical Research Network in Mental Health, Instituto de Salud Carlos III, Madrid, Spain
| | - Xavier Sevillano
- HER - Human-Environment Research Group, La Salle, Universitat Ramon Llull, Spain
| | - Neus Martínez-Abadías
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de Barcelona (UB), Spain.
| | - Mar Fatjó-Vilas
- FIDMAG, Germanes Hospitalàries Research Foundation, Barcelona, Spain; Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Facultat de Biologia, Universitat de Barcelona (UB), Spain; CIBERSAM, Biomedical Research Network in Mental Health, Instituto de Salud Carlos III, Madrid, Spain.
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2
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Kurth F, Schijven D, van den Heuvel OA, Hoogman M, van Rooij D, Stein DJ, Buitelaar JK, Bölte S, Auzias G, Kushki A, Venkatasubramanian G, Rubia K, Bollmann S, Isaksson J, Jaspers‐Fayer F, Marsh R, Batistuzzo MC, Arnold PD, Bressan RA, Stewart SE, Gruner P, Sorensen L, Pan PM, Silk TJ, Gur RC, Cubillo AI, Haavik J, O'Gorman Tuura RL, Hartman CA, Calvo R, McGrath J, Calderoni S, Jackowski A, Chantiluke KC, Satterthwaite TD, Busatto GF, Nigg JT, Gur RE, Retico A, Tosetti M, Gallagher L, Szeszko PR, Neufeld J, Ortiz AE, Ghisleni C, Lazaro L, Hoekstra PJ, Anagnostou E, Hoekstra L, Simpson B, Plessen JK, Deruelle C, Soreni N, James A, Narayanaswamy J, Reddy JY, Fitzgerald J, Bellgrove MA, Salum GA, Janssen J, Muratori F, Vila M, Giral MG, Ameis SH, Bosco P, Remnélius KL, Huyser C, Pariente JC, Jalbrzikowski M, Rosa PG, O'Hearn KM, Ehrlich S, Mollon J, Zugman A, Christakou A, Arango C, Fisher SE, Kong X, Franke B, Medland SE, Thomopoulos SI, Jahanshad N, Glahn DC, Thompson PM, Francks C, Luders E. Large-scale analysis of structural brain asymmetries during neurodevelopment: Associations with age and sex in 4265 children and adolescents. Hum Brain Mapp 2024; 45:e26754. [PMID: 39046031 PMCID: PMC11267452 DOI: 10.1002/hbm.26754] [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] [Revised: 04/29/2024] [Accepted: 05/23/2024] [Indexed: 07/25/2024] Open
Abstract
Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females.
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Affiliation(s)
- F. Kurth
- School of PsychologyUniversity of AucklandAucklandNew Zealand
- Institute of Diagnostic and Interventional Radiology, Jena University HospitalJenaGermany
| | - D. Schijven
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
| | - O. A. van den Heuvel
- Department of PsychiatryAmsterdam University Medical CenterAmsterdamThe Netherlands
| | - M. Hoogman
- Department of PsychiatryRadboud University Medical CenterNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - D. van Rooij
- Donders Institute for Brain, Cognition and Behavior, Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenThe Netherlands
| | - D. J. Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - J. K. Buitelaar
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboudumcNijmegenThe Netherlands
| | - S. Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's HealthKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
- Curtin Autism Research Group, Curtin School of Allied HealthCurtin UniversityPerthAustralia
| | - G. Auzias
- Institut de neurosciences de la Timone UMR 7289, Aix‐Marseille Université & CNRSMarseilleFrance
| | - A. Kushki
- Holland Bloorview Kids Rehabilitation Hospital, Institute for Biomedical EngineeringUniversity of TorontoTorontoCanada
| | - G. Venkatasubramanian
- National Institute of Mental Health and Neuro Sciences (NIMHANS)BengaluruIndia
- Department of Psychiatry, Temerty Faculty of MedicineUniversity of TorontoTorontoCanada
| | - K. Rubia
- Institute of Psychiatry, Psychology & NeuroscienceKing's College LondonLondonUK
| | - S. Bollmann
- School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
| | - J. Isaksson
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's HealthKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
- Child and Adolescent Psychiatry Unit, Department of Medical SciencesUppsala UniversityUppsalaSweden
| | - F. Jaspers‐Fayer
- BC Children's Research Institute and the University of British ColumbiaVancouverCanada
| | - R. Marsh
- Department of PsychiatryColumbia University Irving Medical Center and the New York State Psychiatric InstituteNew YorkNew YorkUSA
| | - M. C. Batistuzzo
- Department & Institute of PsychiatryUniversity of Sao Paulo, Medical SchoolSao PauloBrazil
- Department of Methods and Techniques in PsychologyPontifical Catholic UniversitySao PauloBrazil
| | - P. D. Arnold
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain InstituteUniversity of CalgaryCalgaryCanada
| | - R. A. Bressan
- Federal University of São PauloSão PauloBrazil
- Instituto Ame Sua MenteSão PauloBrazil
| | - S. E. Stewart
- British Columbia Children's Hospital, British Columbia Mental Health and Substance Use ServicesUniversity of British ColumbiaVancouverCanada
| | - P. Gruner
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
| | - L. Sorensen
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - P. M. Pan
- Laboratório de Neurociências Integrativas (LINC), Departamento de PsiquiatriaUniversidade Federal de São Paulo (UNIFESP)São PauloBrazil
- Instituto Nacional de siquiatria do Desenvolvimento (INPD)São PauloBrazil
| | - T. J. Silk
- Centre for Social and Early Emotional Development and School of PsychologyDeakin UniversityGeelongAustralia
- Murdoch Children's Research InstituteMelbourneAustralia
| | - R. C. Gur
- Department of Psychiatry, Section on Neurodevelopment and Psychosis and the Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - A. I. Cubillo
- Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - J. Haavik
- Department of BiomedicineUniversity of BergenBergenNorway
- Division of PsychiatryHaukeland University HospitalBergenNorway
| | - R. L. O'Gorman Tuura
- Center for MR Research, University Children's Hospital ZurichUniversity of ZurichZurichSwitzerland
| | - C. A. Hartman
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University of GroningenUniversity Medical Center GroningenGroningenThe Netherlands
| | - R. Calvo
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
- School of MedicineUniversity of BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM)BarcelonaSpain
- Institute d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - J. McGrath
- Department of PsychiatryTrinity College DublinDublinIreland
| | - S. Calderoni
- IRCCS Stella Maris FoundationPisaItaly
- Department of Clinical and Experimental MedicineUniversity of PisaPisaItaly
| | - A. Jackowski
- Department of PsychiatryUNIFESPSão PauloBrazil
- Department of EducationICT and Learning, Østfold University CollegeHaldenNorway
| | - K. C. Chantiluke
- Institute of Psychiatry, Psychology & NeuroscienceKing's College LondonLondonUK
| | - T. D. Satterthwaite
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain InstituteUniversity of Pennsylvania & Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - G. F. Busatto
- Department of Psychiatry, Faculty of MedicineUniversity of São PauloSão PauloBrazil
| | - J. T. Nigg
- Department of Psychiatry and Center for ADHD ResearchOregon Health & Science UniversityPortlandOregonUSA
| | - R. E. Gur
- Department of Psychiatry, The Penn‐CHOP Lifespan Brain InstituteUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - A. Retico
- Pisa DivisionNational Institute for Nuclear Physics (INFN)PisaItaly
| | | | - L. Gallagher
- Department of PsychiatryTrinity College DublinDublinIreland
- The Hospital for Sick childrenTorontoCanada
- The Centre for Addiction and Mental Health TorontoTorontoCanada
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - P. R. Szeszko
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mental Illness Research, Education and Clinical Center (MIRECC)James J. Peters VA Medical CenterNew YorkNew YorkUSA
| | - J. Neufeld
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's HealthKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
- Swedish Collegium for Advanced Study (SCAS)UppsalaSweden
| | - A. E. Ortiz
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
- Institute d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - C. Ghisleni
- Center for MR Research, University Children's Hospital ZurichUniversity of ZurichZurichSwitzerland
| | - L. Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
- School of MedicineUniversity of BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM)BarcelonaSpain
- Institute d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - P. J. Hoekstra
- Department of Child and Adolescent Psychiatry & Accare Child Study CenterUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - E. Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital, Department of Pediatrics, Temetry School of MedicineUniversity of TorontoTorontoCanada
| | - L. Hoekstra
- Karakter University Center for Child and Adolescent PsychiatryNijmegenThe Netherlands
- Donders Center for Cognitive NeuroimagingNijmegenThe Netherlands
- Radboud University Medical CenterNijmegenThe Netherlands
| | - B. Simpson
- New York State Psychiatric Institute/CUIMCNew YorkNew YorkUSA
| | - J. K. Plessen
- Division of Child and Adolescent Psychiatry, Department of PsychiatryUniversity Hospital LausanneLausanneSwitzerland
| | - C. Deruelle
- Institut de neurosciences de la Timone UMR 7289, Aix‐Marseille Université & CNRSMarseilleFrance
| | - N. Soreni
- Pediatric OCD Consultation ClinicSJHHamiltonCanada
- Department of Psychiatry and Behavioral Neurosciences and Offord Child StudiesMcMaster UniversityHamiltonCanada
| | - A. James
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - J. Narayanaswamy
- National Institute of Mental Health and Neuro Sciences (NIMHANS)BengaluruIndia
| | - J. Y. Reddy
- National Institute of Mental Health and Neuro Sciences (NIMHANS)BengaluruIndia
| | | | - M. A. Bellgrove
- School of Psychological Sciences and Turner Institute for Brain and Mental HealthMonash UniversityMelbourneAustralia
| | - G. A. Salum
- Graduate Program of Psychiatry and Behavioral SciencesUniversidade Federal do Rio Grande do Sul, Hospital de Clínicas de Porto AlegrePorto AlegreBrazil
- Child Mind InstituteNew YorkNew YorkUSA
| | - J. Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental HealthHospital General Universitario Gregorio Marañón, IiSGM, CIBERSAMMadridSpain
| | | | - M. Vila
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
| | - M. Garcia Giral
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
| | - S. H. Ameis
- Campbell Family Mental Health Research InstituteCentre for Addiction and Mental HealthTorontoCanada
- Temerty Faculty of Medicine, Department of PsychiatryUniversity of TorontoTorontoCanada
| | - P. Bosco
- IRCCS Stella Maris FoundationPisaItaly
| | - K. Lundin Remnélius
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's HealthKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - C. Huyser
- Academic Center Child and Youth PsychiatryLevvelAmsterdamThe Netherlands
- Department of Child and Adolescent PsychiatryAmsterdamUMCAmsterdamThe Netherlands
| | - J. C. Pariente
- Magnetic Resonance Image Core FacilityInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - M. Jalbrzikowski
- Department of Psychiatry and Behavioral SciencesBoston Children's HospitalBostonMassachusettsUSA
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - P. G. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de Sao PauloSao PauloBrazil
| | - K. M. O'Hearn
- Atrium Health Wake Forest Baptist Medical CenterWinston‐SalemNorth CarolinaUSA
| | - S. Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences & Department of Child and Adolescent PsychiatryFaculty of Medicine, TU DresdenDresdenGermany
| | - J. Mollon
- Boston Children's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - A. Zugman
- National Institutes of Health/National Institute of Mental HealthBethesdaMarylandUSA
| | - A. Christakou
- Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
- Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language SciencesUniversity of ReadingReadingUK
| | - C. Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, School of MedicineUniversidad Complutense, CIBERSAMMadridSpain
| | - S. E. Fisher
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - X. Kong
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
- Department of Psychiatry of Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina
| | - B. Franke
- Department of PsychiatryRadboud University Medical CenterNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - S. E. Medland
- QIMR Berghofer Medical Research InstituteHerstonAustralia
| | - S. I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - N. Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - D. C. Glahn
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's HospitalBostonMassachusettsUSA
| | - P. M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - C. Francks
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - E. Luders
- School of PsychologyUniversity of AucklandAucklandNew Zealand
- Swedish Collegium for Advanced Study (SCAS)UppsalaSweden
- Department of Women's and Children's HealthUppsala UniversityUppsalaSweden
- Laboratory of Neuro Imaging, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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3
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Yang X, Liu N, Sun H, Li X, Li H, Gong Q, Lui S. Sex-related cortical asymmetry in antipsychotic-naïve first-episode schizophrenia. Cereb Cortex 2024; 34:bhae173. [PMID: 38706137 DOI: 10.1093/cercor/bhae173] [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: 08/23/2023] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 05/07/2024] Open
Abstract
Schizophrenia has been considered to exhibit sex-related clinical differences that might be associated with distinctly abnormal brain asymmetries between sexes. One hundred and thirty-two antipsychotic-naïve first-episode patients with schizophrenia and 150 healthy participants were recruited in this study to investigate whether cortical asymmetry would exhibit sex-related abnormalities in schizophrenia. After a 1-yr follow-up, patients were rescanned to obtain the effect of antipsychotic treatment on cortical asymmetry. Male patients were found to show increased lateralization index while female patients were found to exhibit decreased lateralization index in widespread regions when compared with healthy participants of the corresponding sex. Specifically, the cortical asymmetry of male and female patients showed contrary trends in the cingulate, orbitofrontal, parietal, temporal, occipital, and insular cortices. This result suggested male patients showed a leftward shift of asymmetry while female patients showed a rightward shift of asymmetry in these above regions that related to language, vision, emotion, and cognition. Notably, abnormal lateralization indices remained stable after antipsychotic treatment. The contrary trends in asymmetry between female and male patients with schizophrenia together with the persistent abnormalities after antipsychotic treatment suggested the altered brain asymmetries in schizophrenia might be sex-related disturbances, intrinsic, and resistant to the effect of antipsychotic therapy.
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Affiliation(s)
- Xiyue Yang
- Department of Radiology and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Huaxi MR Research Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
| | - Naici Liu
- Department of Radiology and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Huaxi MR Research Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
| | - Hui Sun
- Department of Radiology and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Huaxi MR Research Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
| | - Xing Li
- Department of Radiology and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Huaxi MR Research Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
| | - Hongwei Li
- Department of Radiology and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Huaxi MR Research Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, No. 190 Jiannan Road East, Youxian District, Mianyang, Sichuan Province 621000, China
| | - Qiyong Gong
- Department of Radiology and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Huaxi MR Research Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, 699 Jinyuan Xi Road, Jimei District, Xiamen, Fujian 361021, China
| | - Su Lui
- Department of Radiology and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Huaxi MR Research Center, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Wuhou District, Chengdu, Sichuan Province 610041, China
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Garcia KE, Wang X, Santiago SE, Bakshi S, Barnes AP, Kroenke CD. Longitudinal MRI of the developing ferret brain reveals regional variations in timing and rate of growth. Cereb Cortex 2024; 34:bhae172. [PMID: 38679479 PMCID: PMC11056283 DOI: 10.1093/cercor/bhae172] [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: 12/11/2023] [Revised: 03/22/2024] [Accepted: 04/04/2024] [Indexed: 05/01/2024] Open
Abstract
Normative ferret brain development was characterized using magnetic resonance imaging. Brain growth was longitudinally monitored in 10 ferrets (equal numbers of males and females) from postnatal day 8 (P8) through P38 in 6-d increments. Template T2-weighted images were constructed at each age, and these were manually segmented into 12 to 14 brain regions. A logistic growth model was used to fit data from whole brain volumes and 8 of the individual regions in both males and females. More protracted growth was found in males, which results in larger brains; however, sex differences were not apparent when results were corrected for body weight. Additionally, surface models of the developing cortical plate were registered to one another using the anatomically-constrained Multimodal Surface Matching algorithm. This, in turn, enabled local logistic growth parameters to be mapped across the cortical surface. A close similarity was observed between surface area expansion timing and previous reports of the transverse neurogenic gradient in ferrets. Regional variation in the extent of surface area expansion and the maximum expansion rate was also revealed. This characterization of normative brain growth over the period of cerebral cortex folding may serve as a reference for ferret studies of brain development.
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Affiliation(s)
- Kara E Garcia
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Evansville, IN 47715, United States
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Xiaojie Wang
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR 97006, United States
| | - Sarah E Santiago
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR 97239, United States
| | - Stuti Bakshi
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR 97006, United States
| | - Anthony P Barnes
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR 97239, United States
| | - Christopher D Kroenke
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR 97006, United States
- Oregon Health and Science Advanced Imaging Research Center, Portland, OR 97239, United States
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5
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Masse O, Brumfield O, Ahmad E, Velasco-Annis C, Zhang J, Rollins CK, Connolly S, Barnewolt C, Shamshirsaz AA, Qaderi S, Javinani A, Warfield SK, Yang E, Gholipour A, Feldman HA, Grant PE, Mulliken JB, Pierotich L, Estroff J. Divergent growth of the transient brain compartments in fetuses with nonsyndromic isolated clefts involving the primary and secondary palate. Cereb Cortex 2024; 34:bhae024. [PMID: 38365268 PMCID: PMC10872676 DOI: 10.1093/cercor/bhae024] [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: 11/03/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 02/18/2024] Open
Abstract
Cleft lip/palate is a common orofacial malformation that often leads to speech/language difficulties as well as developmental delays in affected children, despite surgical repair. Our understanding of brain development in these children is limited. This study aimed to analyze prenatal brain development in fetuses with cleft lip/palate and controls. We examined in utero MRIs of 30 controls and 42 cleft lip/palate fetal cases and measured regional brain volumes. Cleft lip/palate was categorized into groups A (cleft lip or alveolus) and B (any combination of clefts involving the primary and secondary palates). Using a repeated-measures regression model with relative brain hemisphere volumes (%), and after adjusting for multiple comparisons, we did not identify significant differences in regional brain growth between group A and controls. Group B clefts had significantly slower weekly cerebellar growth compared with controls. We also observed divergent brain growth in transient brain structures (cortical plate, subplate, ganglionic eminence) within group B clefts, depending on severity (unilateral or bilateral) and defect location (hemisphere ipsilateral or contralateral to the defect). Further research is needed to explore the association between regional fetal brain growth and cleft lip/palate severity, with the potential to inform early neurodevelopmental biomarkers and personalized diagnostics.
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Affiliation(s)
- Olivia Masse
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Brumfield
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Esha Ahmad
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Jennings Zhang
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Susan Connolly
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Carol Barnewolt
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Alireza A Shamshirsaz
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Shohra Qaderi
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Ali Javinani
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Simon K Warfield
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Patricia E Grant
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - John B Mulliken
- Department of Plastic and Oral Surgery, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Lana Pierotich
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Judy Estroff
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
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6
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Steger C, Moatti C, Payette K, De Silvestro A, Nguyen TD, Coraj S, Yakoub N, Natalucci G, Kottke R, Tuura R, Knirsch W, Jakab A. Characterization of dynamic patterns of human fetal to neonatal brain asymmetry with deformation-based morphometry. Front Neurosci 2023; 17:1252850. [PMID: 38130698 PMCID: PMC10734644 DOI: 10.3389/fnins.2023.1252850] [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: 07/04/2023] [Accepted: 11/03/2023] [Indexed: 12/23/2023] Open
Abstract
Introduction Despite established knowledge on the morphological and functional asymmetries in the human brain, the understanding of how brain asymmetry patterns change during late fetal to neonatal life remains incomplete. The goal of this study was to characterize the dynamic patterns of inter-hemispheric brain asymmetry over this critically important developmental stage using longitudinally acquired MRI scans. Methods Super-resolution reconstructed T2-weighted MRI of 20 neurotypically developing participants were used, and for each participant fetal and neonatal MRI was acquired. To quantify brain morphological changes, deformation-based morphometry (DBM) on the longitudinal MRI scans was utilized. Two registration frameworks were evaluated and used in our study: (A) fetal to neonatal image registration and (B) registration through a mid-time template. Developmental changes of cerebral asymmetry were characterized as (A) the inter-hemispheric differences of the Jacobian determinant (JD) of fetal to neonatal morphometry change and the (B) time-dependent change of the JD capturing left-right differences at fetal or neonatal time points. Left-right and fetal-neonatal differences were statistically tested using multivariate linear models, corrected for participants' age and sex and using threshold-free cluster enhancement. Results Fetal to neonatal morphometry changes demonstrated asymmetry in the temporal pole, and left-right asymmetry differences between fetal and neonatal timepoints revealed temporal changes in the temporal pole, likely to go from right dominant in fetal to a bilateral morphology in neonatal timepoint. Furthermore, the analysis revealed right-dominant subcortical gray matter in neonates and three clusters of increased JD values in the left hemisphere from fetal to neonatal timepoints. Discussion While these findings provide evidence that morphological asymmetry gradually emerges during development, discrepancies between registration frameworks require careful considerations when using DBM for longitudinal data of early brain development.
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Affiliation(s)
- Céline Steger
- Center for MR Research, University Children’s Hospital Zurich, University of Zurich, Zürich, Switzerland
- Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
- Pediatric Heart Center, Division of Pediatric Cardiology, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Charles Moatti
- Center for MR Research, University Children’s Hospital Zurich, University of Zurich, Zürich, Switzerland
- Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Kelly Payette
- Center for MR Research, University Children’s Hospital Zurich, University of Zurich, Zürich, Switzerland
- Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alexandra De Silvestro
- Center for MR Research, University Children’s Hospital Zurich, University of Zurich, Zürich, Switzerland
- Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
- Pediatric Heart Center, Division of Pediatric Cardiology, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Thi Dao Nguyen
- Newborn Research, Department of Neonatology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
| | - Seline Coraj
- Larsson-Rosenquist Foundation Center for Neurodevelopment, Growth and Nutrition of the Newborn, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Ninib Yakoub
- Larsson-Rosenquist Foundation Center for Neurodevelopment, Growth and Nutrition of the Newborn, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Giancarlo Natalucci
- Newborn Research, Department of Neonatology, University of Zurich and University Hospital Zurich, Zurich, Switzerland
- Larsson-Rosenquist Foundation Center for Neurodevelopment, Growth and Nutrition of the Newborn, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Raimund Kottke
- Department of Diagnostic Imaging, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Ruth Tuura
- Center for MR Research, University Children’s Hospital Zurich, University of Zurich, Zürich, Switzerland
- Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Walter Knirsch
- Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
- Pediatric Heart Center, Division of Pediatric Cardiology, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Andras Jakab
- Center for MR Research, University Children’s Hospital Zurich, University of Zurich, Zürich, Switzerland
- Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology, Zurich, Switzerland
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7
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Turk E, van den Heuvel MI, Sleurs C, Billiet T, Uyttebroeck A, Sunaert S, Mennes M, Van den Bergh BRH. Maternal anxiety during pregnancy is associated with weaker prefrontal functional connectivity in adult offspring. Brain Imaging Behav 2023; 17:595-607. [PMID: 37380807 PMCID: PMC10733226 DOI: 10.1007/s11682-023-00787-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/18/2023] [Indexed: 06/30/2023]
Abstract
BACKGROUND The connectome, constituting a unique fingerprint of a person's brain, may be influenced by its prenatal environment, potentially affecting later-life resilience and mental health. METHODS We conducted a prospective resting-state functional Magnetic Resonance Imaging study in 28-year-old offspring (N = 49) of mothers whose anxiety was monitored during pregnancy. Two offspring anxiety subgroups were defined: "High anxiety" (n = 13) group versus "low-to-medium anxiety" (n = 36) group, based on maternal self-reported state anxiety at 12-22 weeks of gestation. To predict resting-state functional connectivity of 32 by 32 ROIs, maternal state anxiety during pregnancy was included as a predictor in general linear models for both ROI-to-ROI and graph theoretical metrics. Sex, birth weight and postnatal anxiety were included as covariates. RESULTS Higher maternal anxiety was associated with weaker functional connectivity of medial prefrontal cortex with left inferior frontal gyrus (t = 3.45, pFDR < 0.05). Moreover, network-based statistics (NBS) confirmed our finding and revealed an additional association of weaker connectivity between left lateral prefontal cortex with left somatosensory motor gyrus in the offspring. While our results showed a general pattern of lower functional connectivity in adults prenatally exposed to maternal anxiety, we did not observe significant differences in global brain networks between groups. CONCLUSIONS Weaker (medial) prefrontal cortex functional connectivity in the high anxiety adult offspring group suggests a long-term negative impact of prenatal exposure to high maternal anxiety, extending into adulthood. To prevent mental health problems at population level, universal primary prevention strategies should aim at lowering maternal anxiety during pregnancy.
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Affiliation(s)
- Elise Turk
- Department of Cognitive Neuropsychology, Tilburg University, Warandelaan 2, 5037AB, Tilburg, The Netherlands.
| | - Marion I van den Heuvel
- Department of Cognitive Neuropsychology, Tilburg University, Warandelaan 2, 5037AB, Tilburg, The Netherlands
| | - Charlotte Sleurs
- Department of Cognitive Neuropsychology, Tilburg University, Warandelaan 2, 5037AB, Tilburg, The Netherlands
- Department of Oncology, Catholic University of Leuven, KU Leuven, Leuven, Belgium
| | | | - Anne Uyttebroeck
- Department of Oncology, Catholic University of Leuven, KU Leuven, Leuven, Belgium
| | | | - Maarten Mennes
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Bea R H Van den Bergh
- Health Psychology Research Group, Catholic University of Leuven, KU Leuven, Leuven, Belgium
- Department of Welfare, Public Health and Family, Flemish Government, Brussels, Belgium
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8
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Namburete AIL, Papież BW, Fernandes M, Wyburd MK, Hesse LS, Moser FA, Ismail LC, Gunier RB, Squier W, Ohuma EO, Carvalho M, Jaffer Y, Gravett M, Wu Q, Lambert A, Winsey A, Restrepo-Méndez MC, Bertino E, Purwar M, Barros FC, Stein A, Noble JA, Molnár Z, Jenkinson M, Bhutta ZA, Papageorghiou AT, Villar J, Kennedy SH. Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years. Nature 2023; 623:106-114. [PMID: 37880365 PMCID: PMC10620088 DOI: 10.1038/s41586-023-06630-3] [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: 10/13/2022] [Accepted: 09/08/2023] [Indexed: 10/27/2023]
Abstract
Maturation of the human fetal brain should follow precisely scheduled structural growth and folding of the cerebral cortex for optimal postnatal function1. We present a normative digital atlas of fetal brain maturation based on a prospective international cohort of healthy pregnant women2, selected using World Health Organization recommendations for growth standards3. Their fetuses were accurately dated in the first trimester, with satisfactory growth and neurodevelopment from early pregnancy to 2 years of age4,5. The atlas was produced using 1,059 optimal quality, three-dimensional ultrasound brain volumes from 899 of the fetuses and an automated analysis pipeline6-8. The atlas corresponds structurally to published magnetic resonance images9, but with finer anatomical details in deep grey matter. The between-study site variability represented less than 8.0% of the total variance of all brain measures, supporting pooling data from the eight study sites to produce patterns of normative maturation. We have thereby generated an average representation of each cerebral hemisphere between 14 and 31 weeks' gestation with quantification of intracranial volume variability and growth patterns. Emergent asymmetries were detectable from as early as 14 weeks, with peak asymmetries in regions associated with language development and functional lateralization between 20 and 26 weeks' gestation. These patterns were validated in 1,487 three-dimensional brain volumes from 1,295 different fetuses in the same cohort. We provide a unique spatiotemporal benchmark of fetal brain maturation from a large cohort with normative postnatal growth and neurodevelopment.
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Affiliation(s)
- Ana I L Namburete
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, UK.
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Bartłomiej W Papież
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Michelle Fernandes
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- MRC Lifecourse Epidemiology Centre, Human Development and Health Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Madeleine K Wyburd
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, UK
| | - Linde S Hesse
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Felipe A Moser
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, UK
| | - Leila Cheikh Ismail
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Robert B Gunier
- Center for Environmental Research and Children's Health, School of Public Health, University of California, Berkeley, CA, USA
| | - Waney Squier
- Department of Neuropathology, John Radcliffe Hospital, Oxford, UK
| | - Eric O Ohuma
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Maternal, Adolescent, Reproductive and Child Health Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Maria Carvalho
- Department of Obstetrics and Gynaecology, Faculty of Health Sciences, Aga Khan University Hospital, Nairobi, Kenya
| | - Yasmin Jaffer
- Department of Family and Community Health, Ministry of Health, Muscat, Sultanate of Oman
| | - Michael Gravett
- Departments of Obstetrics and Gynecology and of Global Health, University of Washington, Seattle, WA, USA
| | - Qingqing Wu
- School of Public Health, Peking University, Beijing, China
| | - Ann Lambert
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Adele Winsey
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | | | - Enrico Bertino
- Dipartimento di Scienze Pediatriche e dell' Adolescenza, SCDU Neonatologia, Universita di Torino, Turin, Italy
| | - Manorama Purwar
- Nagpur INTERGROWTH-21st Research Centre, Ketkar Hospital, Nagpur, India
| | - Fernando C Barros
- Programa de Pós-Graduação em Saúde e Comportamento, Universidade Católica de Pelotas, Pelotas, Brazil
| | - Alan Stein
- Department of Psychiatry, University of Oxford, Oxford, UK
- African Health Research Institute, KwaZulu-Natal, South Africa
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - J Alison Noble
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Zoltán Molnár
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Australian Institute for Machine Learning, Department of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Zulfiqar A Bhutta
- Center for Global Child Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Aris T Papageorghiou
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - José Villar
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Stephen H Kennedy
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
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9
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Ahmad E, Brumfield O, Masse O, Velasco-Annis C, Zhang J, Rollins CK, Connolly S, Barnewolt C, Shamshirsaz AA, Qaderi S, Javinani A, Warfield SK, Yang E, Gholipour A, Feldman HA, Estroff J, Grant PE, Vasung L. Atypical fetal brain development in fetuses with non-syndromic isolated musculoskeletal birth defects (niMSBDs). Cereb Cortex 2023; 33:10793-10801. [PMID: 37697904 PMCID: PMC10629896 DOI: 10.1093/cercor/bhad323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 09/13/2023] Open
Abstract
Non-syndromic, isolated musculoskeletal birth defects (niMSBDs) are among the leading causes of pediatric hospitalization. However, little is known about brain development in niMSBDs. Our study aimed to characterize prenatal brain development in fetuses with niMSBDs and identify altered brain regions compared to controls. We retrospectively analyzed in vivo structural T2-weighted MRIs of 99 fetuses (48 controls and 51 niMSBDs cases). For each group (19-31 and >31 gestational weeks (GW)), we conducted repeated-measures regression analysis with relative regional volume (% brain hemisphere) as a dependent variable (adjusted for age, side, and interactions). Between 19 and 31GW, fetuses with niMSBDs had a significantly (P < 0.001) smaller relative volume of the intermediate zone (-22.9 ± 3.2%) and cerebellum (-16.1 ± 3.5%,) and a larger relative volume of proliferative zones (38.3 ± 7.2%), the ganglionic eminence (34.8 ± 7.3%), and the ventricles (35.8 ± 8.0%). Between 32 and 37 GW, compared to the controls, niMSBDs showed significantly smaller volumes of central regions (-9.1 ± 2.1%) and larger volumes of the cortical plate. Our results suggest there is altered brain development in fetuses with niMSBDs compared to controls (13.1 ± 4.2%). Further basic and translational neuroscience research is needed to better visualize these differences and to characterize the altered development in fetuses with specific niMSBDs.
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Affiliation(s)
- Esha Ahmad
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Brumfield
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Masse
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Jennings Zhang
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Susan Connolly
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Carol Barnewolt
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Alireza A Shamshirsaz
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Shohra Qaderi
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Ali Javinani
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Simon K Warfield
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Judy Estroff
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Patricia E Grant
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Lana Vasung
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
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10
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Payette K, Li HB, de Dumast P, Licandro R, Ji H, Siddiquee MMR, Xu D, Myronenko A, Liu H, Pei Y, Wang L, Peng Y, Xie J, Zhang H, Dong G, Fu H, Wang G, Rieu Z, Kim D, Kim HG, Karimi D, Gholipour A, Torres HR, Oliveira B, Vilaça JL, Lin Y, Avisdris N, Ben-Zvi O, Bashat DB, Fidon L, Aertsen M, Vercauteren T, Sobotka D, Langs G, Alenyà M, Villanueva MI, Camara O, Fadida BS, Joskowicz L, Weibin L, Yi L, Xuesong L, Mazher M, Qayyum A, Puig D, Kebiri H, Zhang Z, Xu X, Wu D, Liao K, Wu Y, Chen J, Xu Y, Zhao L, Vasung L, Menze B, Cuadra MB, Jakab A. Fetal brain tissue annotation and segmentation challenge results. Med Image Anal 2023; 88:102833. [PMID: 37267773 DOI: 10.1016/j.media.2023.102833] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 03/16/2023] [Accepted: 04/20/2023] [Indexed: 06/04/2023]
Abstract
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.
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Affiliation(s)
- Kelly Payette
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.
| | - Hongwei Bran Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Roxane Licandro
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, United States; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Medical University of Vienna, Vienna, Austria
| | - Hui Ji
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | | | | | | | - Hao Liu
- Shanghai Jiaotong University, China
| | | | | | - Ying Peng
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Huiquan Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Guiming Dong
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Fu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - ZunHyan Rieu
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Hyun Gi Kim
- Department of Radiology, The Catholic University of Korea, Eunpyeong St. Mary's Hospital, Seoul 06247, South Korea
| | - Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Ali Gholipour
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Helena R Torres
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - Bruno Oliveira
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Yang Lin
- Department of Computer Science, Hong Kong University of Science and Technology, China
| | - Netanell Avisdris
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel; Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel
| | - Ori Ben-Zvi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel
| | - Dafna Ben Bashat
- Sagol School of Neuroscience, Tel Aviv University, Israel; Sackler Faculty of Medicine, Tel Aviv University, Israel
| | - Lucas Fidon
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Michael Aertsen
- Department of Radiology, University Hospitals Leuven, Leuven 3000, Belgium
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Mireia Alenyà
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Maria Inmaculada Villanueva
- Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Oscar Camara
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bella Specktor Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Liao Weibin
- School of Computer Science, Beijing Institute of Technology, China
| | - Lv Yi
- School of Computer Science, Beijing Institute of Technology, China
| | - Li Xuesong
- School of Computer Science, Beijing Institute of Technology, China
| | - Moona Mazher
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | | | - Domenec Puig
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Zelin Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | | | - Yixuan Wu
- Zhejiang University, Hangzhou, China
| | | | - Yunzhi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Lana Vasung
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, United States; Department of Pediatrics, Harvard Medical School, United States
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland; University Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
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11
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Taymourtash A, Schwartz E, Nenning KH, Sobotka D, Licandro R, Glatter S, Diogo MC, Golland P, Grant E, Prayer D, Kasprian G, Langs G. Fetal development of functional thalamocortical and cortico-cortical connectivity. Cereb Cortex 2023; 33:5613-5624. [PMID: 36520481 PMCID: PMC10152101 DOI: 10.1093/cercor/bhac446] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 12/23/2022] Open
Abstract
Measuring and understanding functional fetal brain development in utero is critical for the study of the developmental foundations of our cognitive abilities, possible early detection of disorders, and their prevention. Thalamocortical connections are an intricate component of shaping the cortical layout, but so far, only ex-vivo studies provide evidence of how axons enter the sub-plate and cortex during this highly dynamic phase. Evidence for normal in-utero development of the functional thalamocortical connectome in humans is missing. Here, we modeled fetal functional thalamocortical connectome development using in-utero functional magnetic resonance imaging in fetuses observed from 19th to 40th weeks of gestation (GW). We observed a peak increase of thalamocortical functional connectivity strength between 29th and 31st GW, right before axons establish synapses in the cortex. The cortico-cortical connectivity increases in a similar time window, and exhibits significant functional laterality in temporal-superior, -medial, and -inferior areas. Homologous regions exhibit overall similar mirrored connectivity profiles, but this similarity decreases during gestation giving way to a more diverse cortical interconnectedness. Our results complement the understanding of structural development of the human connectome and may serve as the basis for the investigation of disease and deviations from a normal developmental trajectory of connectivity development.
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Affiliation(s)
- Athena Taymourtash
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140, Old Orangeburg Road, Orangeburg, NY 10962, United States
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Roxane Licandro
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Laboratory for Computational Neuroimaging, A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Bldg. 149, 13th Street, Charlestown, MA 02129, United States
| | - Sarah Glatter
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Mariana Cardoso Diogo
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Radiology Department, Hospital CUF Tejo, Av. 24 de Julho 171A, 1350-352 Lisboa, Portugal
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, MA 02139, United States
| | - Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300, Longwood Avenue, Boston, MA 02115, United States
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, MA 02139, United States
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12
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Cook KM, De Asis-Cruz J, Lopez C, Quistorff J, Kapse K, Andersen N, Vezina G, Limperopoulos C. Robust sex differences in functional brain connectivity are present in utero. Cereb Cortex 2023; 33:2441-2454. [PMID: 35641152 PMCID: PMC10016060 DOI: 10.1093/cercor/bhac218] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/09/2022] [Accepted: 05/09/2022] [Indexed: 11/14/2022] Open
Abstract
Sex-based differences in brain structure and function are observable throughout development and are thought to contribute to differences in behavior, cognition, and the presentation of neurodevelopmental disorders. Using multiple support vector machine (SVM) models as a data-driven approach to assess sex differences, we sought to identify regions exhibiting sex-dependent differences in functional connectivity and determine whether they were robust and sufficiently reliable to classify sex even prior to birth. To accomplish this, we used a sample of 110 human fetal resting state fMRI scans from 95 fetuses, performed between 19 and 40 gestational weeks. Functional brain connectivity patterns classified fetal sex with 73% accuracy. Across SVM models, we identified features (functional connections) that reliably differentiated fetal sex. Highly consistent predictors included connections in the somatomotor and frontal areas alongside the hippocampus, cerebellum, and basal ganglia. Moreover, high consistency features also implicated a greater magnitude of cross-region connections in females, while male weighted features were predominately within anatomically bounded regions. Our findings indicate that these differences, which have been observed later in childhood, are present and reliably detectable even before birth. These results show that sex differences arise before birth in a manner that is consistent and reliable enough to be highly identifiable.
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Affiliation(s)
- Kevin M Cook
- Developing Brain Institute, Children’s National, 111 Michigan Ave NW, Washington DC 20010, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institute, Children’s National, 111 Michigan Ave NW, Washington DC 20010, USA
| | - Catherine Lopez
- Developing Brain Institute, Children’s National, 111 Michigan Ave NW, Washington DC 20010, USA
| | - Jessica Quistorff
- Developing Brain Institute, Children’s National, 111 Michigan Ave NW, Washington DC 20010, USA
| | - Kushal Kapse
- Developing Brain Institute, Children’s National, 111 Michigan Ave NW, Washington DC 20010, USA
| | - Nicole Andersen
- Developing Brain Institute, Children’s National, 111 Michigan Ave NW, Washington DC 20010, USA
| | - Gilbert Vezina
- Division of Diagnostic Imaging and Radiology, Children’s National, 111 Michigan Ave NW, Washington DC 20010, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Children’s National, 111 Michigan Ave NW, Washington DC 20010, USA
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13
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Ji L, Majbri A, Hendrix CL, Thomason ME. Fetal behavior during MRI changes with age and relates to network dynamics. Hum Brain Mapp 2023; 44:1683-1694. [PMID: 36564934 PMCID: PMC9921243 DOI: 10.1002/hbm.26167] [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: 05/23/2022] [Revised: 10/31/2022] [Accepted: 11/23/2022] [Indexed: 12/25/2022] Open
Abstract
Fetal motor behavior is an important clinical indicator of healthy development. However, our understanding of associations between fetal behavior and fetal brain development is limited. To fill this gap, this study introduced an approach to automatically and objectively classify long durations of fetal movement from a continuous four-dimensional functional magnetic resonance imaging (fMRI) data set, and paired behavior features with brain activity indicated by the fMRI time series. Twelve-minute fMRI scans were conducted in 120 normal fetuses. Postnatal motor function was evaluated at 7 and 36 months age. Fetal motor behavior was quantified by calculating the frame-wise displacement (FD) of fetal brains extracted by a deep-learning model along the whole time series. Analyzing only low motion data, we characterized the recurring coactivation patterns (CAPs) of the supplementary motor area (SMA). Results showed reduced motor activity with advancing gestational age (GA), likely due in part to loss of space (r = -.51, p < .001). Evaluation of individual variation in motor movement revealed a negative association between movement and the occurrence of coactivations within the left parietotemporal network, controlling for age and sex (p = .003). Further, we found that the occurrence of coactivations between the SMA to posterior brain regions, including visual cortex, was prospectively associated with postnatal motor function at 7 months (r = .43, p = .03). This is the first study to pair fetal movement and fMRI, highlighting potential for comparisons of fetal behavior and neural network development to enhance our understanding of fetal brain organization.
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Affiliation(s)
- Lanxin Ji
- Department of Child & Adolescent PsychiatryNew York University School of MedicineNew YorkNew YorkUSA
| | - Amyn Majbri
- Department of Child & Adolescent PsychiatryNew York University School of MedicineNew YorkNew YorkUSA
| | - Cassandra L. Hendrix
- Department of Child & Adolescent PsychiatryNew York University School of MedicineNew YorkNew YorkUSA
| | - Moriah E. Thomason
- Department of Child & Adolescent PsychiatryNew York University School of MedicineNew YorkNew YorkUSA
- Department of Population HealthNew York University School of MedicineNew YorkNew YorkUSA
- Neuroscience InstituteNew York University School of MedicineNew YorkNew YorkUSA
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14
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Kim JH, De Asis-Cruz J, Cook KM, Limperopoulos C. Gestational age-related changes in the fetal functional connectome: in utero evidence for the global signal. Cereb Cortex 2023; 33:2302-2314. [PMID: 35641159 PMCID: PMC9977380 DOI: 10.1093/cercor/bhac209] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 05/06/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The human brain begins to develop in the third gestational week and rapidly grows and matures over the course of pregnancy. Compared to fetal structural neurodevelopment, less is known about emerging functional connectivity in utero. Here, we investigated gestational age (GA)-associated in vivo changes in functional brain connectivity during the second and third trimesters in a large dataset of 110 resting-state functional magnetic resonance imaging scans from a cohort of 95 healthy fetuses. Using representational similarity analysis, a multivariate analytical technique that reveals pair-wise similarity in high-order space, we showed that intersubject similarity of fetal functional connectome patterns was strongly related to between-subject GA differences (r = 0.28, P < 0.01) and that GA sensitivity of functional connectome was lateralized, especially at the frontal area. Our analysis also revealed a subnetwork of connections that were critical for predicting age (mean absolute error = 2.72 weeks); functional connectome patterns of individual fetuses reliably predicted their GA (r = 0.51, P < 0.001). Lastly, we identified the primary principal brain network that tracked fetal brain maturity. The main network showed a global synchronization pattern resembling global signal in the adult brain.
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Affiliation(s)
- Jung-Hoon Kim
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Kevin M Cook
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Catherine Limperopoulos
- Corresponding author: Developing Brain Institute, Children’s National, 111 Michigan Ave. N.W., Washington D.C. 20010.
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15
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Vasung L, Rollins CK, Zhang J, Velasco-Annis C, Yang E, Lin PY, Sutin J, Warfield SK, Soul J, Estroff J, Connolly S, Barnewolt C, Gholipour A, Feldman HA, Grant PE. Abnormal development of transient fetal zones in mild isolated fetal ventriculomegaly. Cereb Cortex 2023; 33:1130-1139. [PMID: 35349640 PMCID: PMC9930628 DOI: 10.1093/cercor/bhac125] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/03/2022] [Accepted: 03/04/2022] [Indexed: 11/12/2022] Open
Abstract
Mild isolated fetal ventriculomegaly (iFVM) is the most common abnormality of the fetal central nervous system. It is characterized by enlargement of one or both of the lateral ventricles (defined as ventricular width greater than 10 mm, but less than 12 mm). Despite its high prevalence, the pathophysiology of iFVM during fetal brain development and the neurobiological substrate beyond ventricular enlargement remain unexplored. In this work, we aimed to establish the relationships between the structural development of transient fetal brain zones/compartments and increased cerebrospinal fluid volume. For this purpose, we used in vivo structural T2-weighted magnetic resonance imaging of 89 fetuses (48 controls and 41 cases with iFVM). Our results indicate abnormal development of transient zones/compartments belonging to both hemispheres (i.e. on the side with and also on the contralateral side without a dilated ventricle) in fetuses with iFVM. Specifically, compared to controls, we observed enlargement of proliferative zones and overgrowth of the cortical plate in iFVM with associated reduction of volumes of central structures, subplate, and fetal white matter. These results indicate that enlarged lateral ventricles might be linked to the development of transient fetal zones and that global brain development should be taken into consideration when evaluating iFVM.
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Affiliation(s)
- Lana Vasung
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Jennings Zhang
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Pei-Yi Lin
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Jason Sutin
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Simon Keith Warfield
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Janet Soul
- Department of Neurology Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Judy Estroff
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Susan Connolly
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Carol Barnewolt
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Patricia Ellen Grant
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
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16
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Cha M, Eum YJ, Kim K, Kim L, Bak H, Sohn JH, Cheong C, Lee BH. Diffusion tensor imaging reveals sex differences in pain sensitivity of rats. Front Mol Neurosci 2023; 16:1073963. [PMID: 36937048 PMCID: PMC10017469 DOI: 10.3389/fnmol.2023.1073963] [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: 10/19/2022] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
Studies on differences in brain structure and function according to sex are reported to contribute to differences in behavior and cognition. However, few studies have investigated brain structures or used tractography to investigate gender differences in pain sensitivity. The identification of tracts involved in sex-based structural differences that show pain sensitivity has remained elusive to date. Here, we attempted to demonstrate the sex differences in pain sensitivity and to clarify its relationship with brain structural connectivity. In this study, pain behavior test and brain diffusion tensor imaging (DTI) were performed in male and female rats and tractography was performed on the whole brain using fiber tracking software. We selected eight brain regions related to pain and performed a tractography analysis of these regions. Fractional anisotropy (FA) measurements using automated tractography revealed sex differences in the anterior cingulate cortex (ACC)-, prefrontal cortex (PFC)-, and ventral posterior thalamus-related brain connections. In addition, the results of the correlation analysis of pain sensitivity and DTI tractography showed differences in mean, axial, and radial diffusivities, as well as FA. This study revealed the potential of DTI for exploring circuits involved in pain sensitivity. The behavioral and functional relevance's of measures derived from DTI tractography is demonstrated by their relationship with pain sensitivity.
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Affiliation(s)
- Myeounghoon Cha
- Department of Physiology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young-Ji Eum
- Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju, Republic of Korea
| | - Kyeongmin Kim
- Department of Physiology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Leejeong Kim
- Department of Physiology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyeji Bak
- Department of Physiology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin-Hun Sohn
- Department of Physiology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chaejoon Cheong
- Bio-Chemical Analysis Team, Korea Basic Science Institute, Cheongju, Republic of Korea
- *Correspondence: Chaejoon Cheong,
| | - Bae Hwan Lee
- Department of Physiology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea
- Bae Hwan Lee,
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17
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Xu X, Sun C, Sun J, Shi W, Shen Y, Zhao R, Luo W, Li M, Wang G, Wu D. Spatiotemporal Atlas of the Fetal Brain Depicts Cortical Developmental Gradient. J Neurosci 2022; 42:9435-9449. [PMID: 36323525 PMCID: PMC9794379 DOI: 10.1523/jneurosci.1285-22.2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/17/2022] [Accepted: 10/25/2022] [Indexed: 11/12/2022] Open
Abstract
The fetal brains experience rapid and complex development in utero during the second and third trimesters. In utero MRI of the fetal brain in this period enables us to quantify normal fetal brain development in the spatiotemporal domain. In this study, we established a high-quality spatiotemporal atlas between 23 and 38 weeks gestational age (GA) from 90 healthy Chinese human fetuses of both sexes using a pairwise and groupwise registration pipeline. We quantified the fetal cortical morphology indices and characterized their spatiotemporal developmental pattern. The cortical thickness exhibited a biphasic pattern that first increased and then decreased; the curvature fitted well into the Gompertz growth model; sulcal depth increased linearly, while surface area expanded exponentially. The cortical thickness and curvature trajectories consistently pointed to a characteristic time point around GA of 31 weeks. The characteristic GA and growth rate obtained from individual cortical regions suggested a central-to-peripheral developmental gradient, with the earliest development in the parietal lobe, and we also observed a superior-to-inferior gradient within the temporal lobe. These findings may be linked to biophysical events, such as dendritic arborization and thalamocortical fibers ingrowth. The proposed atlas was also compared with an existing fetal atlas from a white/mixed population. Finally, we examined the structural asymmetry of the fetal brains and found extensive asymmetry that dynamically changed with development. The current study depicted a comprehensive profile of fetal cortical development, and the established atlas could be used as a normative reference for neurodevelopmental and diagnostic purposes, especially in the Chinese population.SIGNIFICANCE STATEMENT We generated a high-quality 4D spatiotemporal atlas of the normal fetal brain development from 23 to 38 gestational weeks in a Chinese population and characterized the spatiotemporal developmental pattern of cortical morphology. According to the cortical development trajectories, the fetal cerebral cortex development follows a central-to-peripheral developmental gradient that may be related to the underlying cellular events. The majority of cortical regions already exhibit significant asymmetry during the fetal period.
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Affiliation(s)
- Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, P. R. China
| | - Jiwei Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Wen Shi
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287
| | - Yao Shen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Ruoke Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Wanrong Luo
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, P. R. China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
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18
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De Asis-Cruz J, Limperopoulos C. Harnessing the Power of Advanced Fetal Neuroimaging to Understand In Utero Footprints for Later Neuropsychiatric Disorders. Biol Psychiatry 2022; 93:867-879. [PMID: 36804195 DOI: 10.1016/j.biopsych.2022.11.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/03/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
Adverse intrauterine events may profoundly impact fetal risk for future adult diseases. The mechanisms underlying this increased vulnerability are complex and remain poorly understood. Contemporary advances in fetal magnetic resonance imaging (MRI) have provided clinicians and scientists with unprecedented access to in vivo human fetal brain development to begin to identify emerging endophenotypes of neuropsychiatric disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder, and schizophrenia. In this review, we discuss salient findings of normal fetal neurodevelopment from studies using advanced, multimodal MRI that have provided unparalleled characterization of in utero prenatal brain morphology, metabolism, microstructure, and functional connectivity. We appraise the clinical utility of these normative data in identifying high-risk fetuses before birth. We highlight available studies that have investigated the predictive validity of advanced prenatal brain MRI findings and long-term neurodevelopmental outcomes. We then discuss how ex utero quantitative MRI findings can inform in utero investigations toward the pursuit of early biomarkers of risk. Lastly, we explore future opportunities to advance our understanding of the prenatal origins of neuropsychiatric disorders using precision fetal imaging.
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19
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Yun HJ, Lee HJ, Lee JY, Tarui T, Rollins CK, Ortinau CM, Feldman HA, Grant PE, Im K. Quantification of sulcal emergence timing and its variability in early fetal life: Hemispheric asymmetry and sex difference. Neuroimage 2022; 263:119629. [PMID: 36115591 PMCID: PMC10011016 DOI: 10.1016/j.neuroimage.2022.119629] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 08/07/2022] [Accepted: 09/12/2022] [Indexed: 12/25/2022] Open
Abstract
Human fetal brains show regionally different temporal patterns of sulcal emergence following a regular timeline, which may be associated with spatiotemporal patterns of gene expression among cortical regions. This study aims to quantify the timing of sulcal emergence and its temporal variability across typically developing fetuses by fitting a logistic curve to presence or absence of sulcus. We found that the sulcal emergence started from the central to the temporo-parieto-occipital lobes and frontal lobe, and the temporal variability of emergence in most of the sulci was similar between 1 and 2 weeks. Small variability (< 1 week) was found in the left central and postcentral sulci and larger variability (>2 weeks) was shown in the bilateral occipitotemporal and left superior temporal sulci. The temporal variability showed a positive correlation with the emergence timing that may be associated with differential contributions between genetic and environmental factors. Our statistical analysis revealed that the right superior temporal sulcus emerged earlier than the left. Female fetuses showed a trend of earlier sulcal emergence in the right superior temporal sulcus, lower temporal variability in the right intraparietal sulcus, and higher variability in the right precentral sulcus compared to male fetuses. Our quantitative and statistical approach quantified the temporal patterns of sulcal emergence in detail that can be a reference for assessing the normality of developing fetal gyrification.
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Affiliation(s)
- Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul 04763, Korea (the Republic of)
| | - Joo Young Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul 04763, Korea (the Republic of)
| | - Tomo Tarui
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Cynthia M Ortinau
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States; Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - P Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States; Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Kiho Im
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States; Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States.
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20
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Scheinost D, Chang J, Lacadie C, Brennan-Wydra E, Constable RT, Chawarska K, Ment LR. Functional connectivity for the language network in the developing brain: 30 weeks of gestation to 30 months of age. Cereb Cortex 2022; 32:3289-3301. [PMID: 34875024 PMCID: PMC9340393 DOI: 10.1093/cercor/bhab415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/20/2021] [Accepted: 10/12/2021] [Indexed: 11/14/2022] Open
Abstract
Although the neural scaffolding for language is putatively present before birth, the maturation of functional connections among the key nodes of the language network, Broca's and Wernicke's areas, is less known. We leveraged longitudinal and cross-sectional data from three sites collected through six studies to track the development of functional circuits between Broca's and Wernicke's areas from 30 weeks of gestation through 30 months of age in 127 unique participants. Using resting-state fMRI data, functional connectivity was calculated as the correlation between fMRI time courses from pairs of regions, defined as Broca's and Wernicke's in both hemispheres. The primary analysis evaluated 23 individuals longitudinally imaged from 30 weeks postmenstrual age (fetal) through the first postnatal month (neonatal). A secondary analysis in 127 individuals extended these curves into older infants and toddlers. These data demonstrated significant growth of interhemispheric connections including left Broca's and its homolog and left Wernicke's and its homolog from 30 weeks of gestation through the first postnatal month. In contrast, intrahemispheric connections did not show significant increases across this period. These data represent an important baseline for language systems in the developing brain against which to compare those neurobehavioral disorders with the potential fetal onset of disease.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA
| | - Joseph Chang
- Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA
| | - Cheryl Lacadie
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | | | - R Todd Constable
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06510, USA
| | - Katarzyna Chawarska
- Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Pediatrics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Laura R Ment
- Department of Pediatrics, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT 06510, USA
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21
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Wu Y, Lu YC, Kapse K, Jacobs M, Andescavage N, Donofrio MT, Lopez C, Quistorff JL, Vezina G, Krishnan A, du Plessis AJ, Limperopoulos C. In Utero MRI Identifies Impaired Second Trimester Subplate Growth in Fetuses with Congenital Heart Disease. Cereb Cortex 2022; 32:2858-2867. [PMID: 34882775 PMCID: PMC9247421 DOI: 10.1093/cercor/bhab386] [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: 04/15/2021] [Revised: 09/10/2021] [Accepted: 09/26/2021] [Indexed: 11/13/2022] Open
Abstract
The subplate is a transient brain structure which plays a key role in the maturation of the cerebral cortex. Altered brain growth and cortical development have been suggested in fetuses with complex congenital heart disease (CHD) in the third trimester. However, at an earlier gestation, the putative role of the subplate in altered brain development in CHD fetuses is poorly understood. This study aims to examine subplate growth (i.e., volume and thickness) and its relationship to cortical sulcal development in CHD fetuses compared with healthy fetuses by using 3D reconstructed fetal magnetic resonance imaging. We studied 260 fetuses, including 100 CHD fetuses (22.3-32 gestational weeks) and 160 healthy fetuses (19.6-31.9 gestational weeks). Compared with healthy fetuses, CHD fetuses had 1) decreased global and regional subplate volumes and 2) decreased subplate thickness in the right hemisphere overall, in frontal and temporal lobes, and insula. Compared with fetuses with two-ventricle CHD, those with single-ventricle CHD had reduced subplate volume and thickness in right occipital and temporal lobes. Finally, impaired subplate growth was associated with disturbances in cortical sulcal development in CHD fetuses. These findings suggested a potential mechanistic pathway and early biomarker for the third-trimester failure of brain development in fetuses with complex CHD. SIGNIFICANCE STATEMENT Our findings provide an early biomarker for brain maturational failure in fetuses with congenital heart disease, which may guide the development of future prenatal interventions aimed at reducing neurological compromise of prenatal origin in this high-risk population.
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Affiliation(s)
- Yao Wu
- Developing Brain Institute, Children’s National Hospital, Washington, DC 20010, USA
| | - Yuan-Chiao Lu
- Developing Brain Institute, Children’s National Hospital, Washington, DC 20010, USA
| | - Kushal Kapse
- Developing Brain Institute, Children’s National Hospital, Washington, DC 20010, USA
| | - Marni Jacobs
- School of Health Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Nickie Andescavage
- Division of Neonatology, Children’s National Hospital, Washington, DC 20010, USA
| | - Mary T Donofrio
- Division of Cardiology, Children’s National Hospital, Washington, DC 20010, USA
| | - Catherine Lopez
- Developing Brain Institute, Children’s National Hospital, Washington, DC 20010, USA
| | | | - Gilbert Vezina
- Department of Diagnostic Imaging and Radiology, Children’s National Hospital, Washington, DC 20010, USA
| | - Anita Krishnan
- Division of Cardiology, Children’s National Hospital, Washington, DC 20010, USA
| | - Adré J du Plessis
- Prenatal Pediatrics Institute, Children’s National Hospital, Washington, DC 20010, USA
| | - Catherine Limperopoulos
- Address correspondence to Catherine Limperopoulos, Developing Brain Institute, Children's National Hospital, Washington, DC 20010, USA.
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22
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Jang YH, Kim J, Kim S, Lee K, Na JY, Ahn JH, Kim H, Kim BN, Lee HJ. Abnormal thalamocortical connectivity of preterm infants with elevated thyroid stimulating hormone identified with diffusion tensor imaging. Sci Rep 2022; 12:9257. [PMID: 35661740 PMCID: PMC9166724 DOI: 10.1038/s41598-022-12864-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 05/16/2022] [Indexed: 11/08/2022] Open
Abstract
While thyroid disturbances during perinatal and postnatal periods in preterm infants with congenital hypothyroidism reportedly disrupt neuronal development, no study has considered the effect of thyroid disturbances in premature infants with subclinical hypothyroidism with elevations of thyroid stimulating hormone. We aimed to identify altered fiber integrity from the thalamus to cortices in preterm infants with subclinical hypothyroidism. All preterm infants born were categorized according to thyroid stimulating hormone levels through serial thyroid function tests (36 preterm controls and 29 preterm infants with subclinical hypothyroidism). Diffusion tensor images were acquired to determine differences in thalamocortical fiber lengths between the groups, and cerebral asymmetries were investigated to observe neurodevelopmental changes. Thalamocortical fiber lengths in the subclinical hypothyroidism group were significantly reduced in the bilateral superior temporal gyrus, heschl's gyrus, lingual gyrus, and calcarine cortex (all p < 0.05). According to the asymmetric value in the orbitofrontal regions, there is a left dominance in the subclinical hypothyroidism group contrary to the controls (p = 0.012), and that of the cuneus areas showed significant decreases in the subclinical hypothyroidism group (p = 0.035). These findings could reflect altered neurodevelopment, which could help treatment plans using biomarkers for subclinical hypothyroidism.
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Affiliation(s)
- Yong Hun Jang
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Jinsup Kim
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Sangwoo Kim
- Department of Radiological Science, Daewon University College, Jecheon, Republic of Korea
| | - Kyungmi Lee
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Jae Yoon Na
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Ja-Hye Ahn
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
- Clinical Research Institute of Developmental Medicine, Seoul Hanyang University Hospital, Seoul, Republic of Korea
| | - Hyuna Kim
- Department of Child Psychotherapy, Hanyang University Graduate School of Medicine, Seoul, Republic of Korea
| | - Bung-Nyun Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Institute of Human Behavioral Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea.
- Clinical Research Institute of Developmental Medicine, Seoul Hanyang University Hospital, Seoul, Republic of Korea.
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23
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Gagoski B, Xu J, Wighton P, Tisdall MD, Frost R, Lo WC, Golland P, van der Kouwe A, Adalsteinsson E, Grant PE. Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T. Magn Reson Med 2022; 87:1914-1922. [PMID: 34888942 PMCID: PMC8810713 DOI: 10.1002/mrm.29106] [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] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/19/2021] [Accepted: 11/12/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE Fetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction. METHODS A convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired. RESULTS The closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899. CONCLUSION The proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.
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Affiliation(s)
- Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Junshen Xu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Paul Wighton
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert Frost
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Wei-Ching Lo
- Siemens Medical Solutions USA, Inc, Charlestown, Massachusetts, USA
| | - Polina Golland
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Andre van der Kouwe
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - P. Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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24
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Limb Preference in Animals: New Insights into the Evolution of Manual Laterality in Hominids. Symmetry (Basel) 2022. [DOI: 10.3390/sym14010096] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Until the 1990s, the notion of brain lateralization—the division of labor between the two hemispheres—and its more visible behavioral manifestation, handedness, remained fiercely defined as a human specific trait. Since then, many studies have evidenced lateralized functions in a wide range of species, including both vertebrates and invertebrates. In this review, we highlight the great contribution of comparative research to the understanding of human handedness’ evolutionary and developmental pathways, by distinguishing animal forelimb asymmetries for functionally different actions—i.e., potentially depending on different hemispheric specializations. Firstly, lateralization for the manipulation of inanimate objects has been associated with genetic and ontogenetic factors, with specific brain regions’ activity, and with morphological limb specializations. These could have emerged under selective pressures notably related to the animal locomotion and social styles. Secondly, lateralization for actions directed to living targets (to self or conspecifics) seems to be in relationship with the brain lateralization for emotion processing. Thirdly, findings on primates’ hand preferences for communicative gestures accounts for a link between gestural laterality and a left-hemispheric specialization for intentional communication and language. Throughout this review, we highlight the value of functional neuroimaging and developmental approaches to shed light on the mechanisms underlying human handedness.
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25
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Machado-Rivas F, Afacan O, Khan S, Marami B, Velasco-Annis C, Lidov H, Warfield SK, Gholipour A, Jaimes C. Spatiotemporal changes in diffusivity and anisotropy in fetal brain tractography. Hum Brain Mapp 2021; 42:5771-5784. [PMID: 34487404 PMCID: PMC8559496 DOI: 10.1002/hbm.25653] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/22/2021] [Accepted: 08/25/2021] [Indexed: 02/03/2023] Open
Abstract
Population averaged diffusion atlases can be utilized to characterize complex microstructural changes with less bias than data from individual subjects. In this study, a fetal diffusion tensor imaging (DTI) atlas was used to investigate tract-based changes in anisotropy and diffusivity in vivo from 23 to 38 weeks of gestational age (GA). Healthy pregnant volunteers with typically developing fetuses were imaged at 3 T. Acquisition included structural images processed with a super-resolution algorithm and DTI images processed with a motion-tracked slice-to-volume registration algorithm. The DTI from individual subjects were used to generate 16 templates, each specific to a week of GA; this was accomplished by means of a tensor-to-tensor diffeomorphic deformable registration method integrated with kernel regression in age. Deterministic tractography was performed to outline the forceps major, forceps minor, bilateral corticospinal tracts (CST), bilateral inferior fronto-occipital fasciculus (IFOF), bilateral inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculus (UF). The mean fractional anisotropy (FA) and mean diffusivity (MD) was recorded for all tracts. For a subset of tracts (forceps major, CST, and IFOF) we manually divided the tractograms into anatomy conforming segments to evaluate within-tract changes. We found tract-specific, nonlinear, age related changes in FA and MD. Early in gestation, these trends appear to be dominated by cytoarchitectonic changes in the transient white matter fetal zones while later in gestation, trends conforming to the progression of myelination were observed. We also observed significant (local) heterogeneity in within-tract developmental trajectories for the CST, IFOF, and forceps major.
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Affiliation(s)
- Fedel Machado-Rivas
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Shadab Khan
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Bahram Marami
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Hart Lidov
- Department of Pathology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
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26
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Terashima M, Ishikawa A, Männer J, Yamada S, Takakuwa T. Early development of the cortical layers in the human brain. J Anat 2021; 239:1039-1049. [PMID: 34142368 PMCID: PMC8546516 DOI: 10.1111/joa.13488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/28/2021] [Accepted: 06/01/2021] [Indexed: 11/28/2022] Open
Abstract
The cortical plate (CP) first appears at seven postconceptional weeks (pcw), when it splits the preexisting preplate into two layers, the marginal zone and the presubplate (pSP). Although three-dimensional (3D) analysis using fetal magnetic resonance imaging and two-dimensional tissue observations have been reported, there have been no studies analyzing the early development of the layer structure corresponding to the pSP stage in 3D. Here, we reconstructed 3-D models of the brain with a focus on the cortical layers in pSP stage. To achieve this, we digitized serial tissue sections of embryos between CS20 and CS23 from the Kyoto Collection (n = 7, approximately 7-8.5 pcw), and specimens at early fetal phase from the Blechschmidt Collection (n = 2, approximately 9.5-12 pcw, crown rump length [CRL] 39 and 64 mm). We observed tissue sections and 3D images and performed quantitative analysis of the thickness, surface area, and volume. Because the boundary between pSP and the intermediate zone (IZ) could not be distinguished in hematoxylin and eosin-stained sections, the two layers were analyzed together as a single layer in this study. The histology of the layers was observed from CS21 and became distinct at CS22. Subsequently, we observed the 3-D models; pSP-IZ was present in a midlateral region of the cerebral wall at CS21, and an expansion centered around this region was observed after CS22. We observed it over the entire cerebral hemisphere at early fetal phase (CRL 39 mm). The thickness of pSP-IZ was visible in 3D and was greater in the midlateral region. At the end of the pSP stage (CRL 64 mm), the thick region expanded to lateral, superior, and posterior regions around the primordium of the insula. While, the region near the basal ganglia was not included in the thickest 10% of the pSP-IZ area. Middle cerebral artery was found in the midlateral region of the cerebral wall, near the area where pSP-IZ was observed. Feature of layer structure growth was revealed by quantitative assessment as thickness, surface area, and volume. The maximum thickness value of pSP-IZ and CP increased significantly according to CRL, whereas the median value increased slightly. The layer structure appeared to grow and spread thin, rather than thickening during early development, which is characteristic during pSP stages. The surface area of the cerebral total tissue, CP, and pSP-IZ increased in proportion to the square of CRL. The surface area of CP and pSP-IZ approached that of the total tissue at the end of the pSP stage. Volume of each layer increased in proportion to the cube of CRL. pSP-IZ and CP constituted over 50% of the total tissue in volume at the end of the pSP stages. We could visualize the growth of pSP-IZ in 3D and quantify it during pSP stage. Our approach allowed us to observe the process of rapid expansion of pSP-IZ from the midlateral regions of the cerebral wall, which subsequently becomes the insula.
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Affiliation(s)
- Mei Terashima
- Human Health Science, Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Aoi Ishikawa
- Human Health Science, Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Jörg Männer
- Institute of Anatomy and EmbryologyUMGGeorg‐August‐University of GöttingenGöttingenGermany
| | - Shigehito Yamada
- Human Health Science, Graduate School of MedicineKyoto UniversityKyotoJapan
- Congenital Anomaly Research CenterGraduate School of MedicineKyoto UniversityKyotoJapan
| | - Tetsuya Takakuwa
- Human Health Science, Graduate School of MedicineKyoto UniversityKyotoJapan
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27
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Morton SU, Maleyeff L, Wypij D, Yun HJ, Rollins CK, Watson CG, Newburger JW, Bellinger DC, Roberts AE, Rivkin MJ, Grant PE, Im K. Abnormal Right-Hemispheric Sulcal Patterns Correlate with Executive Function in Adolescents with Tetralogy of Fallot. Cereb Cortex 2021; 31:4670-4680. [PMID: 34009260 PMCID: PMC8408447 DOI: 10.1093/cercor/bhab114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 11/15/2022] Open
Abstract
Neurodevelopmental disabilities are the most common noncardiac conditions in patients with congenital heart disease (CHD). Executive function skills have been frequently observed to be decreased among children and adults with CHD compared with peers, but a neuroanatomical basis for the association is yet to be identified. In this study, we quantified sulcal pattern features from brain magnetic resonance imaging data obtained during adolescence among 41 participants with tetralogy of Fallot (ToF) and 49 control participants using a graph-based pattern analysis technique. Among patients with ToF, right-hemispheric sulcal pattern similarity to the control group was decreased (0.7514 vs. 0.7553, P = 0.01) and positively correlated with neuropsychological testing values including executive function (r = 0.48, P < 0.001). Together these findings suggest that sulcal pattern analysis may be a useful marker of neurodevelopmental risk in patients with CHD. Further studies may elucidate the mechanisms leading to different alterations in sulcal patterning.
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Affiliation(s)
- Sarah U Morton
- Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Lara Maleyeff
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - David Wypij
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Hyuk Jin Yun
- Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Caitlin K Rollins
- Department of Neurology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Jane W Newburger
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - David C Bellinger
- Department of Neurology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Amy E Roberts
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
- Department of Cardiology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Michael J Rivkin
- Department of Neurology, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA 02115, USA
- Division of Radiology, Boston Children’s Hospital, Boston, MA 02115, USA
- Stroke and Cerebrovascular Center, Boston Children’s Hospital, Boston, MA 02115, USA
| | - P Ellen Grant
- Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Kiho Im
- Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA 02115, USA
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28
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Abstract
The alignment of visceral and brain asymmetry observed in some vertebrate species raises the question of whether this association also exists in humans. While the visceral and brain systems may have developed asymmetry for different reasons, basic visceral left–right differentiation mechanisms could have been duplicated to establish brain asymmetry. We describe the main phenotypical anomalies and the general mechanism of left–right differentiation of vertebrate visceral and brain laterality. Next, we systematically review the available human studies that explored the prevalence of atypical behavioral and brain asymmetry in visceral situs anomalies, which almost exclusively involved participants with the mirrored visceral organization (situs inversus). The data show no direct link between human visceral and brain functional laterality as most participants with situs inversus show the typical population bias for handedness and brain functional asymmetry, although an increased prevalence of functional crowding may be present. At the same time, several independent studies present evidence for a possible relation between situs inversus and the gross morphological asymmetry of the brain torque with potential differences between subtypes of situs inversus with ciliary and non-ciliary etiologies.
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29
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Vasung L, Zhao C, Barkovich M, Rollins CK, Zhang J, Lepage C, Corcoran T, Velasco-Annis C, Yun HJ, Im K, Warfield SK, Evans AC, Huang H, Gholipour A, Grant PE. Association between Quantitative MR Markers of Cortical Evolving Organization and Gene Expression during Human Prenatal Brain Development. Cereb Cortex 2021; 31:3610-3621. [PMID: 33836056 PMCID: PMC8258434 DOI: 10.1093/cercor/bhab035] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 11/13/2022] Open
Abstract
The relationship between structural changes of the cerebral cortex revealed by Magnetic Resonance Imaging (MRI) and gene expression in the human fetal brain has not been explored. In this study, we aimed to test the hypothesis that relative regional thickness (a measure of cortical evolving organization) of fetal cortical compartments (cortical plate [CP] and subplate [SP]) is associated with expression levels of genes with known cortical phenotype. Mean regional SP/CP thickness ratios across age measured on in utero MRI of 25 healthy fetuses (20-33 gestational weeks [GWs]) were correlated with publicly available regional gene expression levels (23-24 GW fetuses). Larger SP/CP thickness ratios (more pronounced cortical evolving organization) was found in perisylvian regions. Furthermore, we found a significant association between SP/CP thickness ratio and expression levels of the FLNA gene (mutated in periventricular heterotopia, congenital heart disease, and vascular malformations). Further work is needed to identify early MRI biomarkers of gene expression that lead to abnormal cortical development.
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Affiliation(s)
- Lana Vasung
- The Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Intelligent Medical Imaging Research Group, Boston Children's Hospital, Boston, MA 02115, USA
| | - Chenying Zhao
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Barkovich
- Department of Radiology, UCSF Benioff Children's Hospital, San Francisco, CA 94158, USA.,Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94115, USA
| | - Caitlin K Rollins
- Intelligent Medical Imaging Research Group, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Neurology, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Jennings Zhang
- The Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Boston, MA 02115, USA
| | - Claude Lepage
- ACELab, McGill Centre for Integrative Neuroscience, McGill University, Montreal, QC H3A 2B4, Canada
| | - Teddy Corcoran
- Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Clemente Velasco-Annis
- Intelligent Medical Imaging Research Group, Boston Children's Hospital, Boston, MA 02115, USA.,Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Radiology, Boston Children's Hospital; and Harvard Medical School, Boston, MA 02115, USA
| | - Hyuk Jin Yun
- The Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Kiho Im
- The Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Simon Keith Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Radiology, Boston Children's Hospital; and Harvard Medical School, Boston, MA 02115, USA
| | - Alan Charles Evans
- ACELab, McGill Centre for Integrative Neuroscience, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ali Gholipour
- Intelligent Medical Imaging Research Group, Boston Children's Hospital, Boston, MA 02115, USA.,Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Radiology, Boston Children's Hospital; and Harvard Medical School, Boston, MA 02115, USA
| | - Patricia Ellen Grant
- The Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Department of Radiology, Boston Children's Hospital; and Harvard Medical School, Boston, MA 02115, USA
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30
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Dou H, Karimi D, Rollins CK, Ortinau CM, Vasung L, Velasco-Annis C, Ouaalam A, Yang X, Ni D, Gholipour A. A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1123-1133. [PMID: 33351755 PMCID: PMC8016740 DOI: 10.1109/tmi.2020.3046579] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. We evaluated our method quantitatively based on several performance measures and expert evaluations. Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique. We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned in the gestational age range of 16 to 39 weeks (28.6± 5.3). With a computation time of less than 1 minute per fetal brain, our method can facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.
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31
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Kienast P, Schwartz E, Diogo MC, Gruber GM, Brugger PC, Kiss H, Ulm B, Bartha-Doering L, Seidl R, Weber M, Langs G, Prayer D, Kasprian G. The Prenatal Origins of Human Brain Asymmetry: Lessons Learned from a Cohort of Fetuses with Body Lateralization Defects. Cereb Cortex 2021; 31:3713-3722. [PMID: 33772541 DOI: 10.1093/cercor/bhab042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 01/13/2021] [Accepted: 02/01/2021] [Indexed: 11/14/2022] Open
Abstract
Knowledge about structural brain asymmetries of human fetuses with body lateralization defects-congenital diseases in which visceral organs are partially or completely incorrectly positioned-can improve our understanding of the developmental origins of hemispheric brain asymmetry. This study investigated structural brain asymmetry in 21 fetuses, which were diagnosed with different types of lateralization defects; 5 fetuses with ciliopathies and 26 age-matched healthy control cases, between 22 and 34 gestational weeks of age. For this purpose, a database of 4007 fetal magnetic resonance imagings (MRIs) was accessed and searched for the corresponding diagnoses. Specific temporal lobe brain asymmetry indices were quantified using in vivo, super-resolution-processed MR brain imaging data. Results revealed that the perisylvian fetal structural brain lateralization patterns and asymmetry indices did not differ between cases with lateralization defects, ciliopathies, and normal controls. Molecular mechanisms involved in the definition of the right/left body axis-including cilium-dependent lateralization processes-appear to occur independently from those involved in the early establishment of structural human brain asymmetries. Atypically inverted early structural brain asymmetries are similarly rare in individuals with lateralization defects and may have a complex, multifactorial, and neurodevelopmental background with currently unknown postnatal functional consequences.
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Affiliation(s)
- Patric Kienast
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria
| | - Ernst Schwartz
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria
| | - Mariana C Diogo
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria
| | - Gerlinde M Gruber
- Department of Anatomy and Biomechanics, Karl Landsteiner University of Health Sciences, Krems, Lower Austria 3500, Austria
| | - Peter C Brugger
- Center for Anatomy and Cell Biology, Medical University of Vienna, Vienna 1090, Austria
| | - Herbert Kiss
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna 1090, Austria
| | - Barbara Ulm
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna 1090, Austria
| | - Lisa Bartha-Doering
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna 1090, Austria
| | - Rainer Seidl
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna 1090, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna 1090, Austria
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32
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Bisiacchi P, Cainelli E. Structural and functional brain asymmetries in the early phases of life: a scoping review. Brain Struct Funct 2021; 227:479-496. [PMID: 33738578 PMCID: PMC8843922 DOI: 10.1007/s00429-021-02256-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 03/07/2021] [Indexed: 12/13/2022]
Abstract
Asymmetry characterizes the brain in both structure and function. Anatomical asymmetries explain only a fraction of functional variability in lateralization, with structural and functional asymmetries developing at different periods of life and in different ways. In this work, we perform a scoping review of the cerebral asymmetries in the first brain development phases. We included all English-written studies providing direct evidence of hemispheric asymmetries in full-term neonates, foetuses, and premature infants, both at term post-conception and before. The final analysis included 57 studies. The reviewed literature shows large variability in the used techniques and methodological procedures. Most structural studies investigated the temporal lobe, showing a temporal planum more pronounced on the left than on the right (although not all data agree), a morphological asymmetry already present from the 29th week of gestation. Other brain structures have been poorly investigated, and the results are even more discordant. Unlike data on structural asymmetries, functional data agree with each other, identifying a leftward dominance for speech stimuli and an overall dominance of the right hemisphere in all other functional conditions. This generalized dominance of the right hemisphere for all conditions (except linguistic stimuli) is in line with theories stating that the right hemisphere develops earlier and that its development is less subject to external influences because it sustains functions necessary to survive.
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Affiliation(s)
- Patrizia Bisiacchi
- Department of General Psychology, University of Padova, Via Venezia, 8, 35121, Padova, Italy. .,Padova Neuroscience Centre, PNC, Padova, Italy.
| | - Elisa Cainelli
- Department of General Psychology, University of Padova, Via Venezia, 8, 35121, Padova, Italy
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33
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De Asis-Cruz J, Andersen N, Kapse K, Khrisnamurthy D, Quistorff J, Lopez C, Vezina G, Limperopoulos C. Global Network Organization of the Fetal Functional Connectome. Cereb Cortex 2021; 31:3034-3046. [PMID: 33558873 DOI: 10.1093/cercor/bhaa410] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 12/11/2020] [Accepted: 12/11/2020] [Indexed: 12/21/2022] Open
Abstract
Recent advances in brain imaging have enabled non-invasive in vivo assessment of the fetal brain. Characterizing brain development in healthy fetuses provides baseline measures for identifying deviations in brain function in high-risk clinical groups. We examined 110 resting state MRI data sets from fetuses at 19 to 40 weeks' gestation. Using graph-theoretic techniques, we characterized global organizational features of the fetal functional connectome and their prenatal trajectories. Topological features related to network integration (i.e., global efficiency) and segregation (i.e., clustering) were assessed. Fetal networks exhibited small-world topology, showing high clustering and short average path length relative to reference networks. Likewise, fetal networks' quantitative small world indices met criteria for small-worldness (σ > 1, ω = [-0.5 0.5]). Along with this, fetal networks demonstrated global and local efficiency, economy, and modularity. A right-tailed degree distribution, suggesting the presence of central areas that are more highly connected to other regions, was also observed. Metrics, however, were not static during gestation; measures associated with segregation-local efficiency and modularity-decreased with advancing gestational age. Altogether, these suggest that the neural circuitry underpinning the brain's ability to segregate and integrate information exists as early as the late 2nd trimester of pregnancy and reorganizes during the prenatal period. Significance statement. Mounting evidence for the fetal origins of some neurodevelopmental disorders underscores the importance of identifying features of healthy fetal brain functional development. Alterations in prenatal brain connectomics may serve as early markers for identifying fetal-onset neurodevelopmental disorders, which in turn provide improved surveillance of at-risk fetuses and support the initiation of early interventions.
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Affiliation(s)
- Josepheen De Asis-Cruz
- Developing Brain Institute, Children's National, 111 Michigan Ave NW, Washington DC 20010
| | - Nicole Andersen
- Developing Brain Institute, Children's National, 111 Michigan Ave NW, Washington DC 20010
| | - Kushal Kapse
- Developing Brain Institute, Children's National, 111 Michigan Ave NW, Washington DC 20010
| | | | - Jessica Quistorff
- Developing Brain Institute, Children's National, 111 Michigan Ave NW, Washington DC 20010
| | - Catherine Lopez
- Developing Brain Institute, Children's National, 111 Michigan Ave NW, Washington DC 20010
| | - Gilbert Vezina
- Division of Diagnostic Imaging and Radiology, 111 Michigan Ave NW, Washington DC 20010
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34
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Ouyang M, Peng Q, Jeon T, Heyne R, Chalak L, Huang H. Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds. eLife 2020; 9:58116. [PMID: 33350380 PMCID: PMC7755384 DOI: 10.7554/elife.58116] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 12/06/2020] [Indexed: 12/11/2022] Open
Abstract
Cerebral cortical architecture at birth encodes regionally differential dendritic arborization and synaptic formation. It underlies behavioral emergence of 2-year-olds. Brain changes in 0-2 years are most dynamic across the lifespan. Effective prediction of future behavior with brain microstructure at birth will reveal structural basis of behavioral emergence in typical development and identify biomarkers for early detection and tailored intervention in atypical development. Here we aimed to evaluate the neonate whole-brain cortical microstructure quantified by diffusion MRI for predicting future behavior. We found that individual cognitive and language functions assessed at the age of 2 years were robustly predicted by neonate cortical microstructure using support vector regression. Remarkably, cortical regions contributing heavily to the prediction models exhibited distinctive functional selectivity for cognition and language. These findings highlight regional cortical microstructure at birth as a potential sensitive biomarker in predicting future neurodevelopmental outcomes and identifying individual risks of brain disorders.
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Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Qinmu Peng
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Tina Jeon
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Roy Heyne
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
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35
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Ball G, Seidlitz J, O’Muircheartaigh J, Dimitrova R, Fenchel D, Makropoulos A, Christiaens D, Schuh A, Passerat-Palmbach J, Hutter J, Cordero-Grande L, Hughes E, Price A, Hajnal JV, Rueckert D, Robinson EC, Edwards AD. Cortical morphology at birth reflects spatiotemporal patterns of gene expression in the fetal human brain. PLoS Biol 2020; 18:e3000976. [PMID: 33226978 PMCID: PMC7721147 DOI: 10.1371/journal.pbio.3000976] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 12/07/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023] Open
Abstract
Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. We compared cortical morphology captured by high-resolution, multimodal magnetic resonance imaging (MRI) in n = 292 healthy newborn infants (mean age at birth = 39.9 weeks) with regional patterns of gene expression in the fetal cortex across gestation (n = 156 samples from 16 brains, aged 12 to 37 postconceptional weeks [pcw]). We tested the hypothesis that noninvasive measures of cortical structure at birth mirror areal differences in cortical gene expression across gestation, and in a cohort of n = 64 preterm infants (mean age at birth = 32.0 weeks), we tested whether cortical alterations observed after preterm birth were associated with altered gene expression in specific developmental cell populations. Neonatal cortical structure was aligned to differential patterns of cell-specific gene expression in the fetal cortex. Principal component analysis (PCA) of 6 measures of cortical morphology and microstructure showed that cortical regions were ordered along a principal axis, with primary cortex clearly separated from heteromodal cortex. This axis was correlated with estimated tissue maturity, indexed by differential expression of genes expressed by progenitor cells and neurons, and engaged in stem cell differentiation, neuron migration, and forebrain development. Preterm birth was associated with altered regional MRI metrics and patterns of differential gene expression in glial cell populations. The spatial patterning of gene expression in the developing cortex was thus mirrored by regional variation in cortical morphology and microstructure at term, and this was disrupted by preterm birth. This work provides a framework to link molecular mechanisms to noninvasive measures of cortical development in early life and highlights novel pathways to injury in neonatal populations at increased risk of neurodevelopmental disorder. Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. A large neuroimaging study of newborn infants reveals how their cortical structure at birth is associated with patterns of gene expression in the fetal cortex and how this relationship is affected by preterm birth.
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Affiliation(s)
- Gareth Ball
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
- * E-mail:
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, United States of America
- Department of Psychiatry, University of Cambridge, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daphna Fenchel
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom
| | | | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Jo V. Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
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Jaimes C, Machado‐Rivas F, Afacan O, Khan S, Marami B, Ortinau CM, Rollins CK, Velasco‐Annis C, Warfield SK, Gholipour A. In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester. Hum Brain Mapp 2020. [DOI: 10.1002/hbm.25006 32374063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Camilo Jaimes
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Fedel Machado‐Rivas
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Onur Afacan
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Shadab Khan
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Bahram Marami
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Cynthia M. Ortinau
- Department of PediatricsWashington University in St. Louis School of Medicine St. Louis Missouri
| | - Caitlin K. Rollins
- Harvard Medical School Boston Massachusetts
- Department of NeurologyBoston Children's Hospital Boston Massachusetts
| | | | - Simon K. Warfield
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Ali Gholipour
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
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37
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Vasung L, Rollins CK, Velasco-Annis C, Yun HJ, Zhang J, Warfield SK, Feldman HA, Gholipour A, Grant PE. Spatiotemporal Differences in the Regional Cortical Plate and Subplate Volume Growth during Fetal Development. Cereb Cortex 2020; 30:4438-4453. [PMID: 32147720 PMCID: PMC7325717 DOI: 10.1093/cercor/bhaa033] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 12/16/2022] Open
Abstract
The regional specification of the cerebral cortex can be described by protomap and protocortex hypotheses. The protomap hypothesis suggests that the regional destiny of cortical neurons and the relative size of the cortical area are genetically determined early during embryonic development. The protocortex hypothesis suggests that the regional growth rate is predominantly shaped by external influences. In order to determine regional volumes of cortical compartments (cortical plate (CP) or subplate (SP)) and estimate their growth rates, we acquired T2-weighted in utero MRIs of 40 healthy fetuses and grouped them into early (<25.5 GW), mid- (25.5-31.6 GW), and late (>31.6 GW) prenatal periods. MRIs were segmented into CP and SP and further parcellated into 22 gyral regions. No significant difference was found between periods in regional volume fractions of the CP or SP. However, during the early and mid-prenatal periods, we found significant differences in relative growth rates (% increase per GW) between regions of cortical compartments. Thus, the relative size of these regions are most likely conserved and determined early during development whereas more subtle growth differences between regions are fine-tuned later, during periods of peak thalamocortical growth. This is in agreement with both the protomap and protocortex hypothesis.
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Affiliation(s)
- Lana Vasung
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin K Rollins
- Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Hyuk Jin Yun
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jennings Zhang
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
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38
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Jaimes C, Machado-Rivas F, Afacan O, Khan S, Marami B, Ortinau CM, Rollins CK, Velasco-Annis C, Warfield SK, Gholipour A. In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester. Hum Brain Mapp 2020; 41:3177-3185. [PMID: 32374063 PMCID: PMC7375105 DOI: 10.1002/hbm.25006] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/26/2020] [Accepted: 04/03/2020] [Indexed: 12/13/2022] Open
Abstract
The third trimester of pregnancy is a period of rapid development of fiber bundles in the fetal white matter. Using a recently developed motion‐tracked slice‐to‐volume registration (MT‐SVR) method, we aimed to quantify tract‐specific developmental changes in apparent diffusion coefficient (ADC), fractional anisotropy (FA), and volume in third trimester healthy fetuses. To this end, we reconstructed diffusion tensor images from motion corrected fetal diffusion magnetic resonance imaging data. With an approved protocol, fetal MRI exams were performed on healthy pregnant women at 3 Tesla and included multiple (2–8) diffusion scans of the fetal head (1–2 b = 0 s/mm2 images and 12 diffusion‐sensitized images at b = 500 s/mm2). Diffusion data from 32 fetuses (13 females) with median gestational age (GA) of 33 weeks 4 days were processed with MT‐SVR and deterministic tractography seeded by regions of interest corresponding to 12 major fiber tracts. Multivariable regression analysis was used to evaluate the association of GA with volume, FA, and ADC for each tract. For all tracts, the volume and FA increased, and the ADC decreased with GA. Associations reached statistical significance for: FA and ADC of the forceps major; volume and ADC for the forceps minor; FA, ADC, and volume for the cingulum; ADC, FA, and volume for the uncinate fasciculi; ADC of the inferior fronto‐occipital fasciculi, ADC of the inferior longitudinal fasciculi; and FA and ADC for the corticospinal tracts. These quantitative results demonstrate the complex pattern and rates of tract‐specific, GA‐related microstructural changes of the developing white matter in human fetal brain.
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Affiliation(s)
- Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Fedel Machado-Rivas
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Shadab Khan
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Bahram Marami
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Cynthia M Ortinau
- Department of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Caitlin K Rollins
- Harvard Medical School, Boston, Massachusetts.,Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
| | | | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
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