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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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Han M. Predicted brain age (PBA), promising surrogate indicator for neurodevelopment in preterm baby: how to predict accurately? Eur Radiol 2024; 34:3598-3600. [PMID: 38078998 DOI: 10.1007/s00330-023-10481-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 11/05/2023] [Accepted: 11/25/2023] [Indexed: 06/12/2024]
Affiliation(s)
- Miran Han
- Department of Radiology, Ajou University Hospital, Ajou University School of Medicine, 164, World Cup-Ro, Yeongtong-Gu, Suwon, 16499, Republic of Korea.
- Department of Radiology, Graduate School of Kangwon National University, Chuncheon, Republic of Korea.
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Zhang Y, Banihashemi L, Versace A, Samolyk A, Taylor M, English G, Schmithorst VJ, Lee VK, Stiffler R, Aslam H, Panigrahy A, Hipwell AE, Phillips ML. Early Infant Prefrontal Cortical Microstructure Predicts Present and Future Emotionality. Biol Psychiatry 2024:S0006-3223(24)01220-4. [PMID: 38604525 DOI: 10.1016/j.biopsych.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 03/05/2024] [Accepted: 04/01/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND High levels of infant negative emotionality (NE) and low positive emotionality (PE) predict future emotional and behavioral problems. The prefrontal cortex (PFC) supports emotional regulation, with each PFC subregion specializing in specific emotional processes. Neurite orientation dispersion and density imaging estimates microstructural integrity and myelination via the neurite density index (NDI) and dispersion via the orientation dispersion index (ODI), with potential to more accurately evaluate microstructural alterations in the developing brain. Yet, no study has used these indices to examine associations between PFC microstructure and concurrent or developing infant emotionality. METHODS We modeled PFC subregional NDI and ODI at 3 months with caregiver-reported infant NE and PE at 3 months (n = 61) and at 9 months (n = 50), using multivariable and subsequent bivariate regression models. RESULTS The most robust statistically significant findings were positive associations among 3-month rostral anterior cingulate cortex (ACC) ODI and caudal ACC NDI and concurrent NE, a positive association between 3-month lateral orbitofrontal cortex ODI and prospective NE, and a negative association between 3-month dorsolateral PFC ODI and concurrent PE. Multivariate models also revealed that other PFC subregional microstructure measures, as well as infant and caregiver sociodemographic and clinical factors, predicted infant 3- and 9-month NE and PE. CONCLUSIONS Greater NDI and ODI, reflecting greater microstructural complexity, in PFC regions supporting salience perception (rostral ACC), decision making (lateral orbitofrontal cortex), action selection (caudal ACC), and attentional processes (dorsolateral PFC) might result in greater integration of these subregions with other neural networks and greater attention to salient negative external cues, thus higher NE and/or lower PE. These findings provide potential infant cortical markers of future psychopathology risk.
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Affiliation(s)
- Yicheng Zhang
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania.
| | - Layla Banihashemi
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Amelia Versace
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Alyssa Samolyk
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Megan Taylor
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Gabrielle English
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Vanessa J Schmithorst
- Department of Pediatric Radiology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Vincent K Lee
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Pediatric Radiology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Richelle Stiffler
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Haris Aslam
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Ashok Panigrahy
- Department of Pediatric Radiology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania
| | - Alison E Hipwell
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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Raturi AK, Narayanan SS, Jena SPK. Performance monitoring and error detection: The role of mid frontal theta and error-related negativity (ERN) among Indian adolescents from different socioeconomic background. APPLIED NEUROPSYCHOLOGY. CHILD 2024:1-13. [PMID: 38557246 DOI: 10.1080/21622965.2024.2333809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The aim of the study was to investigate the relationship between socioeconomic status (SES) and executive functioning, focusing specifically on performance monitoring, error detection, and their association with mid-frontal theta and error-related negativity (ERN). Employing the widely used flanker task, the research involved two phases with participants aged 10-16 years (15 individuals in the pilot phase and 35 in the second phase). Electroencephalogram (EEG) recordings from distinct brain regions were analyzed during various conditions. The study revealed a notable increase in both absolute and relative theta power at Fcz during the flanker task, with a stronger effect observed during incorrect trials. Furthermore, it underscored the influence of socioeconomic status (SES) on mid-frontal theta, highlighting interactions between SES, gender, and experimental conditions impacting both absolute and relative theta. Intriguingly, the research disclosed a positive correlation between parental occupation and error-related negativity (ERN), as well as between age and ERN. These findings underscore the significance of SES, gender, and age in shaping the neural mechanisms associated with performance monitoring and executive functions. The study contributes valuable insights into the intricate interplay between socio-demographic factors and cognitive processes, shedding light on their impact on goal-directed behaviors and brain activity.
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Affiliation(s)
| | | | - S P K Jena
- Department of Psychology, University of Delhi, New Delhi, India
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Demirci N, Holland MA. Scaling patterns of cortical folding and thickness in early human brain development in comparison with primates. Cereb Cortex 2024; 34:bhad462. [PMID: 38271274 DOI: 10.1093/cercor/bhad462] [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: 08/25/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 01/27/2024] Open
Abstract
Across mammalia, brain morphology follows specific scaling patterns. Bigger bodies have bigger brains, with surface area outpacing volume growth, resulting in increased foldedness. We have recently studied scaling rules of cortical thickness, both local and global, finding that the cortical thickness difference between thick gyri and thin sulci also increases with brain size and foldedness. Here, we investigate early brain development in humans, using subjects from the Developing Human Connectome Project, scanned shortly after pre-term or full-term birth, yielding magnetic resonance images of the brain from 29 to 43 postmenstrual weeks. While the global cortical thickness does not change significantly during this development period, its distribution does, with sulci thinning, while gyri thickening. By comparing our results with our recent work on humans and 11 non-human primate species, we also compare the trajectories of primate evolution with human development, noticing that the 2 trends are distinct for volume, surface area, cortical thickness, and gyrification index. Finally, we introduce the global shape index as a proxy for gyrification index; while correlating very strongly with gyrification index, it offers the advantage of being calculated only from local quantities without generating a convex hull or alpha surface.
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Affiliation(s)
- Nagehan Demirci
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Maria A Holland
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, United States
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
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Duan D, Wen D. MRI-based structural covariance network in early human brain development. Front Neurosci 2023; 17:1302069. [PMID: 38027513 PMCID: PMC10646325 DOI: 10.3389/fnins.2023.1302069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Affiliation(s)
- Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Dong Wen
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
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7
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Liao Z, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Papadopoulos Orfanos D, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Paus T. Hemispheric asymmetry in cortical thinning reflects intrinsic organization of the neurotransmitter systems and homotopic functional connectivity. Proc Natl Acad Sci U S A 2023; 120:e2306990120. [PMID: 37831741 PMCID: PMC10589642 DOI: 10.1073/pnas.2306990120] [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: 04/28/2023] [Accepted: 09/07/2023] [Indexed: 10/15/2023] Open
Abstract
Hemispheric lateralization and its origins have been of great interest in neuroscience for over a century. The left-right asymmetry in cortical thickness may stem from differential maturation of the cerebral cortex in the two hemispheres. Here, we investigated the spatial pattern of hemispheric differences in cortical thinning during adolescence, and its relationship with the density of neurotransmitter receptors and homotopic functional connectivity. Using longitudinal data from IMAGEN study (N = 532), we found that many cortical regions in the frontal and temporal lobes thinned more in the right hemisphere than in the left. Conversely, several regions in the occipital and parietal lobes thinned less in the right (vs. left) hemisphere. We then revealed that regions thinning more in the right (vs. left) hemispheres had higher density of neurotransmitter receptors and transporters in the right (vs. left) side. Moreover, the hemispheric differences in cortical thinning were predicted by homotopic functional connectivity. Specifically, regions with stronger homotopic functional connectivity showed a more symmetrical rate of cortical thinning between the left and right hemispheres, compared with regions with weaker homotopic functional connectivity. Based on these findings, we suggest that the typical patterns of hemispheric differences in cortical thinning may reflect the intrinsic organization of the neurotransmitter systems and related patterns of homotopic functional connectivity.
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Affiliation(s)
- Zhijie Liao
- Research Centre of Sainte-Justine University Hospital, Montreal, QCH3T 1C5, Canada
- Department of Psychiatry and Addictology, University of Montreal, Montreal, QCH3T 1J4, Canada
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim68159, Germany
| | - Arun L. W. Bokde
- Discipline of Psychiatry, School of Medicine, Trinity College Dublin, DublinD02 PN40, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, DublinD02 PN40, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology & Neuroscience, Social, Genetic & Developmental Psychiatry Centre, King’s College London, LondonSE5 8AF, United Kingdom
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim69117, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim68131, Germany
| | - Antoine Grigis
- NeuroSpin, Energies and Atomic Energy Commission, Université Paris-Saclay, Paris F-91191, France
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT05405
- Department of Psychology, University of Vermont, Burlington, VT05405
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, NottinghamNG7 2RD, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin10117, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin38116, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 “Developmental trajectories & psychiatry” Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Paris75006, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 “Developmental trajectories & psychiatry” Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Paris75006, France
- Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, AP-HP.Sorbonne Université, Paris75006, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 “Developmental trajectories & psychiatry” Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Paris75006, France
- Etablissement Public de Santé Barthélemy Durand, Paris91700, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim68159, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim69117, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel24118, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen37075, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim68159, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim68159, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden01087, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden01087, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin10117, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, DublinD02 PN40, Ireland
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology & Neuroscience, Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai200437, Peoples Republic of China
- Centre for Population Neuroscience and Precision Medicine, Charite Universitätsmedizin Berlin, Berlin10117, Germany
| | - Tomáš Paus
- Research Centre of Sainte-Justine University Hospital, Montreal, QCH3T 1C5, Canada
- Department of Psychiatry and Addictology, University of Montreal, Montreal, QCH3T 1J4, Canada
- Department of Neuroscience, University of Montreal, Montreal, QCH3T 1J4, Canada
| | - IMAGEN Consortium
- Research Centre of Sainte-Justine University Hospital, Montreal, QCH3T 1C5, Canada
- Department of Psychiatry and Addictology, University of Montreal, Montreal, QCH3T 1J4, Canada
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim68159, Germany
- Discipline of Psychiatry, School of Medicine, Trinity College Dublin, DublinD02 PN40, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, DublinD02 PN40, Ireland
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology & Neuroscience, Social, Genetic & Developmental Psychiatry Centre, King’s College London, LondonSE5 8AF, United Kingdom
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim69117, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim68131, Germany
- NeuroSpin, Energies and Atomic Energy Commission, Université Paris-Saclay, Paris F-91191, France
- Department of Psychiatry, University of Vermont, Burlington, VT05405
- Department of Psychology, University of Vermont, Burlington, VT05405
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, NottinghamNG7 2RD, United Kingdom
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin10117, Germany
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin38116, Germany
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 “Developmental trajectories & psychiatry” Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Paris75006, France
- Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, AP-HP.Sorbonne Université, Paris75006, France
- Etablissement Public de Santé Barthélemy Durand, Paris91700, France
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel24118, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen37075, Germany
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden01087, Germany
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, DublinD02 PN40, Ireland
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology & Neuroscience, Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai200437, Peoples Republic of China
- Centre for Population Neuroscience and Precision Medicine, Charite Universitätsmedizin Berlin, Berlin10117, Germany
- Department of Neuroscience, University of Montreal, Montreal, QCH3T 1J4, Canada
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8
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McDermott CL, Norton ES, Mackey AP. A systematic review of interventions to ameliorate the impact of adversity on brain development. Neurosci Biobehav Rev 2023; 153:105391. [PMID: 37708920 DOI: 10.1016/j.neubiorev.2023.105391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/13/2023] [Accepted: 09/10/2023] [Indexed: 09/16/2023]
Abstract
Adversity, including abuse, neglect, and poverty, impacts child brain development. However, the developing brain is highly plastic, and some of the impacts of childhood adversity may be mitigated by psychosocial interventions. The purpose of this review is to synthesize literature on neural outcomes of childhood interventions among individuals exposed to adversity. A systematic literature search identified 36 reports of 13 interventions. Overall, these studies provide evidence for experience-dependent plasticity in the developing brain. We synthesize studies in light of three themes. First, there was mixed evidence for a benefit of a younger age at intervention. Second, interventions tended to accelerate functional brain development, but the impact of interventions on the pace of structural brain development was less clear. Third, individual differences in intervention response were difficult to predict, in part due to small samples. However, there was significant variability in intervention type and timing, neuroimaging outcomes, and follow-up timing. Together, the studies reviewed here hold promise for the role of psychosocial interventions in ameliorating the neurodevelopmental consequences of childhood adversity.
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Affiliation(s)
- Cassidy L McDermott
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Elizabeth S Norton
- Department of Communication Sciences and Disorders, Department of Medical Social Sciences, and Institute for Innovations in Developmental Sciences, Northwestern University, Evanston, IL, United States
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
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9
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Xia J, Wang F, Wang Y, Wang L, Li G. Longitudinal mapping of the development of cortical thickness and surface area in rhesus macaques during the first three years. Proc Natl Acad Sci U S A 2023; 120:e2303313120. [PMID: 37523547 PMCID: PMC10410744 DOI: 10.1073/pnas.2303313120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 07/03/2023] [Indexed: 08/02/2023] Open
Abstract
Studying dynamic spatiotemporal patterns of early brain development in macaque monkeys is critical for understanding the cortical organization and evolution in humans, given the phylogenetic closeness between humans and macaques. However, due to huge challenges in the analysis of early brain Magnetic Resonance Imaging (MRI) data typically with extremely low contrast and dynamic imaging appearances, our knowledge of the early macaque cortical development remains scarce. To fill this critical gap, this paper characterizes the early developmental patterns of cortical thickness and surface area in rhesus macaques by leveraging advanced computing tools tailored for early developing brains based on a densely sampled longitudinal dataset with 140 rhesus macaque MRI scans seamlessly covering from birth to 36 mo of age. The average cortical thickness exhibits an inverted U-shaped trajectory with peak thickness at around 4.3 mo of age, which is remarkably in line with the age of peak thickness at 14 mo in humans, considering the around 3:1 age ratio of human to macaque. The total cortical surface area in macaques increases monotonically but with relatively lower expansions than in humans. The spatial distributions of thicker and thinner regions are quite consistent during development, with gyri having a thicker cortex than sulci. By 4 mo of age, over 81% of cortical vertices have reached their peaks in thickness, except for the insula and medial temporal cortices, while most cortical vertices keep expanding in surface area, except for the occipital cortex. These findings provide important insights into early brain development and evolution in primates.
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Affiliation(s)
- Jing Xia
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Fan Wang
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Ya Wang
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Li Wang
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Gang Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
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10
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Wang F, Zhang H, Wu Z, Hu D, Zhou Z, Girault JB, Wang L, Lin W, Li G. Fine-grained functional parcellation maps of the infant cerebral cortex. eLife 2023; 12:e75401. [PMID: 37526293 PMCID: PMC10393291 DOI: 10.7554/elife.75401] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 07/17/2023] [Indexed: 08/02/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.
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Affiliation(s)
- Fan Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'anChina
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Jessica B Girault
- Department of Psychiatry, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
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11
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Chen L, Wang Y, Wu Z, Shan Y, Li T, Hung SC, Xing L, Zhu H, Wang L, Lin W, Li G. Four-dimensional mapping of dynamic longitudinal brain subcortical development and early learning functions in infants. Nat Commun 2023; 14:3727. [PMID: 37349301 PMCID: PMC10287661 DOI: 10.1038/s41467-023-38974-9] [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: 05/10/2022] [Accepted: 05/23/2023] [Indexed: 06/24/2023] Open
Abstract
Brain subcortical structures are paramount in many cognitive functions and their aberrations during infancy are predisposed to various neurodevelopmental and neuropsychiatric disorders, making it highly essential to characterize the early subcortical normative growth patterns. This study investigates the volumetric development and surface area expansion of six subcortical structures and their associations with Mullen scales of early learning by leveraging 513 high-resolution longitudinal MRI scans within the first two postnatal years. Results show that (1) each subcortical structure (except for the amygdala with an approximately linear increase) undergoes rapid nonlinear volumetric growth after birth, which slows down at a structure-specific age with bilaterally similar developmental patterns; (2) Subcortical local area expansion reveals structure-specific and spatiotemporally heterogeneous patterns; (3) Positive associations between thalamus and both receptive and expressive languages and between caudate and putamen and fine motor are revealed. This study advances our understanding of the dynamic early subcortical developmental patterns.
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Affiliation(s)
- Liangjun Chen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Sheng-Che Hung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Rd, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA.
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12
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Roe JM, Vidal-Pineiro D, Amlien IK, Pan M, Sneve MH, Thiebaut de Schotten M, Friedrich P, Sha Z, Francks C, Eilertsen EM, Wang Y, Walhovd KB, Fjell AM, Westerhausen R. Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex. eLife 2023; 12:e84685. [PMID: 37335613 PMCID: PMC10368427 DOI: 10.7554/elife.84685] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 06/16/2023] [Indexed: 06/21/2023] Open
Abstract
Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the developmental timing of asymmetry and the extent to which it arises through genetic and later influences in childhood. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in seven datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry is low-moderately heritable (max h2SNP ~19%) and correlates phenotypically and genetically in specific regions, indicating coordinated development of asymmetries partly through genes. In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises early in life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.
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Affiliation(s)
- James M Roe
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Didac Vidal-Pineiro
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Inge K Amlien
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Mengyu Pan
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Markus H Sneve
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of BordeauxBordeauxFrance
- Brian Connectivity and Behaviour Laboratory, Sorbonne UniversityParisFrance
| | - Patrick Friedrich
- Institute of Neuroscience and Medicine, Research Centre JülichJülichGermany
| | - Zhiqiang Sha
- Language and Genetics Department, Max Planck Institute for PsycholinguisticsNijmegenNetherlands
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for PsycholinguisticsNijmegenNetherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegenNetherlands
- Department of Human Genetics, Radboud University Medical CenterNijmegenNetherlands
| | - Espen M Eilertsen
- PROMENTA Research Center, Department of Psychology, University of OsloOsloNorway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
- Department of Radiology and Nuclear Medicine, Oslo University HospitalOsloNorway
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
- Department of Radiology and Nuclear Medicine, Oslo University HospitalOsloNorway
| | - René Westerhausen
- Section for Cognitive and Clinical Neuroscience, Department of Psychology, University of OsloOsloNorway
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13
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Williams LZJ, Fitzgibbon SP, Bozek J, Winkler AM, Dimitrova R, Poppe T, Schuh A, Makropoulos A, Cupitt J, O'Muircheartaigh J, Duff EP, Cordero-Grande L, Price AN, Hajnal JV, Rueckert D, Smith SM, Edwards AD, Robinson EC. Structural and functional asymmetry of the neonatal cerebral cortex. Nat Hum Behav 2023; 7:942-955. [PMID: 36928781 DOI: 10.1038/s41562-023-01542-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/31/2023] [Indexed: 03/18/2023]
Abstract
Features of brain asymmetry have been implicated in a broad range of cognitive processes; however, their origins are still poorly understood. Here we investigated cortical asymmetries in 442 healthy term-born neonates using structural and functional magnetic resonance images from the Developing Human Connectome Project. Our results demonstrate that the neonatal cortex is markedly asymmetric in both structure and function. Cortical asymmetries observed in the term cohort were contextualized in two ways: by comparing them against cortical asymmetries observed in 103 preterm neonates scanned at term-equivalent age, and by comparing structural asymmetries against those observed in 1,110 healthy young adults from the Human Connectome Project. While associations with preterm birth and biological sex were minimal, significant differences exist between birth and adulthood.
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Affiliation(s)
- Logan Z J Williams
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK.
| | - Sean P Fitzgibbon
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Anderson M Winkler
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Tanya Poppe
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andreas Schuh
- Department of Computing, Imperial College London, London, UK
| | - Antonios Makropoulos
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John Cupitt
- Department of Computing, Imperial College London, London, UK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Eugene P Duff
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
- UK Dementia Research Institute, Department of Brain Sciences, Imperial College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, ISCIII, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
- Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Neonatal Intensive Care Unit, Evelina London Children's Hospital, London, UK
| | - Emma C Robinson
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK.
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14
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Yang J, Li XX, Liu F, Nie D, Lio P, Qi H, Shen D. Fast Multi-Contrast MRI Acquisition by Optimal Sampling of Information Complementary to Pre-Acquired MRI Contrast. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1363-1373. [PMID: 37015608 DOI: 10.1109/tmi.2022.3227262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Recent studies on multi-contrast MRI reconstruction have demonstrated the potential of further accelerating MRI acquisition by exploiting correlation between contrasts. Most of the state-of-the-art approaches have achieved improvement through the development of network architectures for fixed under-sampling patterns, without considering inter-contrast correlation in the under-sampling pattern design. On the other hand, sampling pattern learning methods have shown better reconstruction performance than those with fixed under-sampling patterns. However, most under-sampling pattern learning algorithms are designed for single contrast MRI without exploiting complementary information between contrasts. To this end, we propose a framework to optimize the under-sampling pattern of a target MRI contrast which complements the acquired fully-sampled reference contrast. Specifically, a novel image synthesis network is introduced to extract the redundant information contained in the reference contrast, which is exploited in the subsequent joint pattern optimization and reconstruction network. We have demonstrated superior performance of our learned under-sampling patterns on both public and in-house datasets, compared to the commonly used under-sampling patterns and state-of-the-art methods that jointly optimize the reconstruction network and the under-sampling patterns, up to 8-fold under-sampling factor.
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15
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Lu J, Zhang Z, Wu P, Liang X, Zhang H, Hong J, Clement C, Yen TC, Ding S, Wang M, Xiao Z, Rominger A, Shi K, Guan Y, Zuo C, Zhao Q. The heterogeneity of asymmetric tau distribution is associated with an early age at onset and poor prognosis in Alzheimer's disease. Neuroimage Clin 2023; 38:103416. [PMID: 37137254 PMCID: PMC10176076 DOI: 10.1016/j.nicl.2023.103416] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/13/2023] [Accepted: 04/22/2023] [Indexed: 05/05/2023]
Abstract
PURPOSE Left-right asymmetry, an important feature of brain development, has been implicated in neurodegenerative diseases, although it's less discussed in typical Alzheimer's disease (AD). We sought to investigate whether asymmetric tau deposition plays a potential role in AD heterogeneity. METHODS Two independent cohorts consisting of patients with mild cognitive impairment due to AD and AD dementia with tau PET imaging were enrolled [the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort with 18F-Flortaucipir, the Shanghai Memory Study (SMS) cohort with 18F-Florzolotau]. Based on the absolute global tau interhemispheric differences, each cohort was divided into two groups (asymmetric versus symmetric tau distribution). The two groups were cross-sectionally compared in terms of demographic, cognitive characteristics, and pathological burden. The cognitive decline trajectories were analyzed longitudinally. RESULTS Fourteen (23.3%) and 42 (48.3%) patients in the ADNI and SMS cohorts showed an asymmetric tau distribution, respectively. An asymmetric tau distribution was associated with an earlier age at disease onset (proportion of early-onset AD: ADNI/SMS/combined cohorts, p = 0.093/0.026/0.001) and more severe pathological burden (i.e., global tau burden: ADNI/SMS cohorts, p < 0.001/= 0.007). And patients with an asymmetric tau distribution were characterized by a steeper cognitive decline longitudinally (i.e., the annual decline of Mini-Mental Status Examination score: ADNI/SMS/combined cohorts, p = 0.053 / 0.035 / < 0.001). CONCLUSIONS Asymmetry in tau deposition, which may be associated with an earlier age at onset, more severe pathological burden, and a steeper cognitive decline, is potentially an important characteristic of AD heterogeneity.
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Affiliation(s)
- Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - Zhengwei Zhang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoniu Liang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jimin Hong
- Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - Christoph Clement
- Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | | | - Saineng Ding
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China; Department of Informatics, Technische Universität München, Munich, Germany
| | - Zhenxu Xiao
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland; Department of Informatics, Technische Universität München, Munich, Germany
| | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
| | - Qianhua Zhao
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
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16
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Wang L, Wu Z, Chen L, Sun Y, Lin W, Li G. iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nat Protoc 2023; 18:1488-1509. [PMID: 36869216 DOI: 10.1038/s41596-023-00806-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 11/03/2022] [Indexed: 03/05/2023]
Abstract
The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners.
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Affiliation(s)
- Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Sun
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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17
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Conejero Á, Rico-Picó J, Moyano S, Hoyo Á, Rueda MR. Predicting behavioral and brain markers of inhibitory control at preschool age from early measures of executive attention. Front Psychol 2023; 14:983361. [PMID: 36935994 PMCID: PMC10018214 DOI: 10.3389/fpsyg.2023.983361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 02/03/2023] [Indexed: 03/06/2023] Open
Abstract
Background Inhibitory control (IC) is the ability to prevent prepotent responses when inappropriate. Longitudinal research on IC development has mainly focused on early childhood and adolescence, while research on IC development in the first years of life is still scarce. To address this gap in the literature, we explored the association between executive attention (EA) and elementary forms of IC in infancy and toddlerhood, with individual differences in IC later at 5 years of age. Method We conducted a five-wave longitudinal study in which children's EA and IC (n = 96) were tested at the age of 9 and 16 months and 2, 3, and 5 years. Children performed various age-appropriate EA and IC tasks in each wave, measuring inhibition of attention, endogenous control of attention, inhibition of the response, and conflict inhibition. At 5 years of age, IC was measured with a Go/No-go task while recording event-related potentials. After correlation analyses, structural equation model analyses were performed to predict IC at 5 years of age from EA and early IC measures. Results The results revealed that EA at 9 months predicted IC measures at 2 years of age. Likewise, measures of IC at 2 years predicted performance on the Go/No-go task at behavioral and neural levels. No direct association was found between EA at 9 months and IC at 5 years of age. We further observed that some EA and IC measures were not associated across time. Conclusion As we expected, EA skills in infancy and toddlerhood were related to better performance of children on IC tasks, toghether with a more mature inhibition-related brain functioning. Altogether, the results indicate that IC in early childhood could be predicted from EA and IC at 9 months and 2 years of age and suggest that the early emergence of IC relies on the development of particular EA and basic IC skills. However, some discontinuities in the longitudinal development of IC are observed in the first 5 years of life. These findings provide further support for the hierarchical model of IC development.
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Affiliation(s)
- Ángela Conejero
- Department of Developmental and Educational Psychology, University of Granada, Granada, Spain
- Mind, Brain and Behaviour Research Centre (CIMCYC), University of Granada, Granada, Spain
- *Correspondence: Ángela Conejero,
| | - Josué Rico-Picó
- Mind, Brain and Behaviour Research Centre (CIMCYC), University of Granada, Granada, Spain
- Department of Experimental Psychology, University of Granada, Granada, Spain
| | - Sebastián Moyano
- Mind, Brain and Behaviour Research Centre (CIMCYC), University of Granada, Granada, Spain
- Department of Experimental Psychology, University of Granada, Granada, Spain
| | - Ángela Hoyo
- Mind, Brain and Behaviour Research Centre (CIMCYC), University of Granada, Granada, Spain
- Department of Experimental Psychology, University of Granada, Granada, Spain
| | - M. Rosario Rueda
- Mind, Brain and Behaviour Research Centre (CIMCYC), University of Granada, Granada, Spain
- Department of Experimental Psychology, University of Granada, Granada, Spain
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18
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Cai H, Li A, Yu G, Yang X, Liu M. Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images. Neuroinformatics 2023; 21:5-19. [PMID: 35962180 DOI: 10.1007/s12021-022-09596-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2022] [Indexed: 11/30/2022]
Abstract
It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of brain structure and function. The brain maturity is an important indicator for evaluating the normal development of children. In this paper, we propose a multimodal regression framework to combine the features from structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) data for age prediction of children. First, three types of features are extracted from sMRI and DTI data. Second, we propose to combine the sparse coding and Q-Learning for feature selection from each modality. Finally, the ensemble regression is performed by random forest based on proximity measures to fuse multimodal features for age prediction. The proposed method is evaluated on 212 participants, including 76 young children less than 2 years old and 136 children aged from 2-15 years old recruited from Shanghai Children's Hospital. The results show that integrating multimodal features has achieved the highest accuracies with the root mean squared error (RMSE) of 0.208 years and mean absolute error (MAE) of 0.150 years for age prediction of young children (0-2), and RMSE of 1.666 years and MAE of 1.087 years for older children (2-15). We have shown that the selected features by Q-Learning can consistently improve the prediction accuracy. The comparison of prediction results demonstrates that the proposed method performs better than other competing methods.
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Affiliation(s)
- Hongjie Cai
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Aojie Li
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Guangjun Yu
- Department of Child Health Care, Shanghai Children's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200062, China
| | - Xiujun Yang
- Department of Radiology, Shanghai Children's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200062, China.
| | - Manhua Liu
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China. .,MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
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19
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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] [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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20
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Tooley UA, Park AT, Leonard JA, Boroshok AL, McDermott CL, Tisdall MD, Bassett DS, Mackey AP. The Age of Reason: Functional Brain Network Development during Childhood. J Neurosci 2022; 42:8237-8251. [PMID: 36192151 PMCID: PMC9653278 DOI: 10.1523/jneurosci.0511-22.2022] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/25/2022] [Accepted: 09/03/2022] [Indexed: 01/27/2023] Open
Abstract
Human childhood is characterized by dramatic changes in the mind and brain. However, little is known about the large-scale intrinsic cortical network changes that occur during childhood because of methodological challenges in scanning young children. Here, we overcome this barrier by using sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 years ([Formula: see text]; 50 female, 42 male). At multiple spatial scales, age is positively associated with brain network segregation. At the system level, age was associated with segregation of systems involved in attention from those involved in abstract cognition, and with integration among attentional and perceptual systems. Associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, the two ends of a gradient from perceptual, externally oriented cortex to abstract, internally oriented cortex. These findings suggest that both ends of the sensory-association gradient may develop early, in contrast to the classical theories that cortical maturation proceeds from back to front, with sensory areas developing first and association areas developing last. More mature patterns of brain network architecture, controlling for age, were associated with better visuospatial reasoning abilities. Our results suggest that as cortical architecture becomes more specialized, children become more able to reason about the world and their place in it.SIGNIFICANCE STATEMENT Anthropologists have called the transition from early to middle childhood the "age of reason", when children across cultures become more independent. We employ cutting-edge neuroimaging acquisition and analysis approaches to investigate associations between age and functional brain architecture in childhood. Age was positively associated with segregation between cortical systems that process the external world and those that process abstract phenomena like the past, future, and minds of others. Surprisingly, we observed pronounced development at both ends of the sensory-association gradient, challenging the theory that sensory areas develop first and association areas develop last. Our results open new directions for research into how brains reorganize to support rapid gains in cognitive and socioemotional skills as children reach the age of reason.
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Affiliation(s)
- Ursula A Tooley
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Anne T Park
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Julia A Leonard
- Department of Psychology, Yale University, New Haven, Connecticut 06520
| | - Austin L Boroshok
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Cassidy L McDermott
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Matthew D Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Physics and Astronomy, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Santa Fe Institute, Santa Fe, New Mexico 87501
| | - Allyson P Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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21
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Zhao Y, Wang M, Hu K, Wang Q, Lou J, Fan L, Liu B. The development of cortical functional hierarchy is associated with the molecular organization of prenatal/postnatal periods. Cereb Cortex 2022; 33:4248-4261. [PMID: 36069939 DOI: 10.1093/cercor/bhac340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 07/14/2022] [Accepted: 08/02/2022] [Indexed: 11/14/2022] Open
Abstract
The human cerebral cortex conforms to specific functional hierarchies facilitating information processing and higher-order cognition. Prior studies in adults have unveiled a dominant functional hierarchy spanning from sensorimotor regions to transmodal regions, which is also present in younger cohorts. However, how the functional hierarchy develops and the underlying molecular mechanisms remain to be investigated. Here, we set out to investigate the developmental patterns of the functional hierarchy for preschool children (#scans = 141, age = 2.41-6.90 years) using a parsimonious general linear model and the underlying biological mechanisms by combining the neuroimaging developmental pattern with two separate transcriptomic datasets (i.e. Allen Human Brain Atlas and BrainSpan Atlas). Our results indicated that transmodal regions were further segregated from sensorimotor regions and that such changes were potentially driven by two gene clusters with distinct enrichment profiles, namely prenatal gene cluster and postnatal gene cluster. Additionally, we found similar developmental profiles manifested in subsequent developmental periods by conducting identical analyses on the Human Connectome Projects in Development (#scans = 638, age = 5.58-21.92 years) and Philadelphia Neurodevelopment Cohort datasets (#scans = 795, age = 8-21 years), driven by concordant two gene clusters. Together, these findings illuminate a comprehensive developmental principle of the functional hierarchy and the underpinning molecular factors, and thus may shed light on the potential pathobiology of neurodevelopmental disorders.
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Affiliation(s)
- Yuxin Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Meng Wang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ke Hu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Wang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Lou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
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22
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Huang Z, Gao W, Wu Z, Li G, Nie J. Functional brain activity is highly associated with cortical myelination in neonates. Cereb Cortex 2022; 33:3985-3995. [PMID: 36030387 DOI: 10.1093/cercor/bhac321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 11/12/2022] Open
Abstract
Functional organization of the human cerebral cortex is highly constrained by underlying brain structures, but how functional activity is associated with different brain structures during development is not clear, especially at the neonatal stage. Since long-range functional connectivity is far from mature in the dynamically developing neonatal brain, it is of great scientific significance to investigate the relationship between different structural and functional features at the local level. To this end, for the first time, correlation and regression analyses were performed to examine the relationship between cortical morphology, cortical myelination, age, and local brain functional activity, as well as functional connectivity strength using high-resolution structural and resting-state functional MRI data of 177 neonates (29-44 postmenopausal weeks, 98 male and 79 female) from both static and dynamic perspectives. We found that cortical myelination was most strongly associated with local brain functional activity across the cerebral cortex than other cortical structural features while controlling the age effect. These findings suggest the crucial role of cortical myelination in local brain functional development at birth, providing valuable insights into the fundamental biological basis of functional activity at this early developmental stage.
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Affiliation(s)
- Ziyi Huang
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Wenjian Gao
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University,Guangzhou 510631, China
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jingxin Nie
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
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23
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Sun HM, Li QY, Xiao RY, Zhang ZD, Yang XY, Yang J, Jin B, Wen JX, Wu YJ, Yang H, Wang F. A structural MRI study of global developmental delay in infants (<2 years old). Front Neurol 2022; 13:952405. [PMID: 36062014 PMCID: PMC9434372 DOI: 10.3389/fneur.2022.952405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/18/2022] [Indexed: 11/25/2022] Open
Abstract
Objective To use structural magnetic resonance imaging (3D-MRI) to evaluate the abnormal development of the cerebral cortex in infants with global developmental delay (GDD). Methods The GDD group includes 67 infants aged between 112 and 699 days with global developmental delay and who underwent T1-weighted MRI scans in Shanxi Children's Hospital from December 2019 to March 2022. The healthy control (HC) group includes 135 normal developing infants aged between 88 and 725 days in Shanxi Children's Hospital from September 2020 to August 2021. Whole-brain T1-weighted MRI scans were carried out with a 3.0-T magnetic resonance scanner, which was later processed using InfantSurfer to perform MR image processing and cortical surface reconstruction. Two morphological features of the cortical surface of the 68 brain regions were computed, i.e., the cortical thickness (CT) and cortical surface area (SA), and compared between the GDD and HC groups. Results With regard to the CT, the HC group showed a rapid decrease at first and then a slow increase after birth, and the CT of the GDD group decreased slowly and then became relatively stable. The GDD group showed bilaterally higher hemispherical average CT than those in the HC group. In detail, for the left hemisphere, except in the entorhinal and temporal poles in which the average CT values of the two brain regions were lower than those of the HC group, the CT of the 26 brain regions in the GDD group was higher than those of the HC group (p < 0.05). For the right hemisphere, the CT of the entorhinal in the GDD group was lower than that in the HC group. Otherwise, the CT of the remaining 28 brain regions was higher than those in the HC group (p < 0.05). With regard to the SA, both groups showed a rapid increase after birth till 23 months and remained quite stable afterward. The GDD group shows lower SA bilaterally than that in the HC group. In detail, SA in the GDD group was lower in most cortical regions of both hemispheres than in the HC group (p < 0.05), except for the right temporal pole and entorhinal. When testing for brain asymmetry, we found that the HC group showed obvious asymmetry of CT and SA, while only a few cortical regions in the GDD group showed asymmetry.
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Affiliation(s)
- Hui-miao Sun
- Department of Magnetic Resonance Imaging (MRI), Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan, China
- *Correspondence: Hui-miao Sun
| | - Qian-yun Li
- College of Medical Imaging, Shanxi Medical University, Taiyuan, China
| | - Ru-yi Xiao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an, China
| | - Ze-dong Zhang
- College of Medical Imaging, Shanxi Medical University, Taiyuan, China
| | - Xiao-yan Yang
- Department of Magnetic Resonance Imaging (MRI), Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan, China
| | - Jie Yang
- Department of Magnetic Resonance Imaging (MRI), Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan, China
| | - Bo Jin
- Department of Magnetic Resonance Imaging (MRI), Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan, China
| | - Jia-xiang Wen
- Department of Magnetic Resonance Imaging (MRI), Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan, China
| | - Yan-jun Wu
- Department of Magnetic Resonance Imaging (MRI), Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan, China
| | - Hong Yang
- Department of Magnetic Resonance Imaging (MRI), Children Hospital of Shanxi Province (Shanxi Maternal and Child Health Hospital), Taiyuan, China
| | - Fan Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an, China
- Fan Wang
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24
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Liu G, Huo E, Liu H, Jia G, Zhi Y, Dong Q, Niu H. Development and emergence of functional network asymmetry in 3- to 9-month-old infants. Cortex 2022; 154:390-404. [DOI: 10.1016/j.cortex.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 05/09/2022] [Accepted: 06/30/2022] [Indexed: 11/03/2022]
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25
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Demirci N, Holland MA. Cortical thickness systematically varies with curvature and depth in healthy human brains. Hum Brain Mapp 2022; 43:2064-2084. [PMID: 35098606 PMCID: PMC8933257 DOI: 10.1002/hbm.25776] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/30/2021] [Accepted: 01/05/2022] [Indexed: 12/30/2022] Open
Abstract
Cortical thickness varies throughout the cortex in a systematic way. However, it is challenging to investigate the patterns of cortical thickness due to the intricate geometry of the cortex. The cortex has a folded nature both in radial and tangential directions which forms not only gyri and sulci but also tangential folds and intersections. In this article, cortical curvature and depth are used to characterize the spatial distribution of the cortical thickness with much higher resolution than conventional regional atlases. To do this, a computational pipeline was developed that is capable of calculating a variety of quantitative measures such as surface area, cortical thickness, curvature (mean curvature, Gaussian curvature, shape index, intrinsic curvature index, and folding index), and sulcal depth. By analyzing 501 neurotypical adult human subjects from the ABIDE-I dataset, we show that cortex has a very organized structure and cortical thickness is strongly correlated with local shape. Our results indicate that cortical thickness consistently increases along the gyral-sulcal spectrum from concave to convex shape, encompassing the saddle shape along the way. Additionally, tangential folds influence cortical thickness in a similar way as gyral and sulcal folds; outer folds are consistently thicker than inner.
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Affiliation(s)
- Nagehan Demirci
- Bioengineering Graduate ProgramUniversity of Notre DameNotre DameIndianaUSA
| | - Maria A. Holland
- Bioengineering Graduate ProgramUniversity of Notre DameNotre DameIndianaUSA
- Department of Aerospace and Mechanical EngineeringUniversity of Notre DameNotre DameIndianaUSA
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26
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Frew S, Samara A, Shearer H, Eilbott J, Vanderwal T. Getting the nod: Pediatric head motion in a transdiagnostic sample during movie- and resting-state fMRI. PLoS One 2022; 17:e0265112. [PMID: 35421115 PMCID: PMC9009630 DOI: 10.1371/journal.pone.0265112] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 02/23/2022] [Indexed: 11/19/2022] Open
Abstract
Head motion continues to be a major problem in fMRI research, particularly in developmental studies where an inverse relationship exists between head motion and age. Despite multifaceted and costly efforts to mitigate motion and motion-related signal artifact, few studies have characterized in-scanner head motion itself. This study leverages a large transdiagnostic public dataset (N = 1388, age 5-21y, The Healthy Brain Network Biobank) to characterize pediatric head motion in space, frequency, and time. We focus on practical aspects of head motion that could impact future study design, including comparing motion across groups (low, medium, and high movers), across conditions (movie-watching and rest), and between males and females. Analyses showed that in all conditions, high movers exhibited a different pattern of motion than low and medium movers that was dominated by x-rotation, and z- and y-translation. High motion spikes (>0.3mm) from all participants also showed this pitch-z-y pattern. Problematic head motion is thus composed of a single type of biomechanical motion, which we infer to be a nodding movement, providing a focused target for motion reduction strategies. A second type of motion was evident via spectral analysis of raw displacement data. This was observed in low and medium movers and was consistent with respiration rates. We consider this to be a baseline of motion best targeted in data preprocessing. Further, we found that males moved more than, but not differently from, females. Significant cross-condition differences in head motion were found. Movies had lower mean motion, and especially in high movers, movie-watching reduced within-run linear increases in head motion (i.e., temporal drift). Finally, we used intersubject correlations of framewise displacement (FD-ISCs) to assess for stimulus-correlated motion trends. Subject motion was more correlated in movie than rest, and 8 out of top 10 FD-ISC windows had FD below the mean. Possible reasons and future implications of these findings are discussed.
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Affiliation(s)
- Simon Frew
- University of Waterloo, Waterloo, Ontario, Canada
| | - Ahmad Samara
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Hallee Shearer
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Jeffrey Eilbott
- BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
| | - Tamara Vanderwal
- University of British Columbia, Vancouver, British Columbia, Canada
- BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
- Yale Child Study Center, New Haven, Connecticut, United States of America
- * E-mail:
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27
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Wang Y, Hu D, Wu Z, Wang L, Huang W, Li G. Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years. Cereb Cortex 2022; 32:3786-3798. [PMID: 35034115 PMCID: PMC9433424 DOI: 10.1093/cercor/bhab448] [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: 07/09/2021] [Revised: 10/18/2021] [Accepted: 11/01/2021] [Indexed: 01/19/2023] Open
Abstract
Converging evidence supports that a collection of brain regions is functionally or anatomically abnormal in autistic subjects. Structural covariance networks (SCNs) representing patterns of coordinated regional maturation are widely used to study abnormalities associated with neurodisorders. However, the possible developmental changes of SCNs in autistic individuals during the first 2 postnatal years, which features dynamic development and can potentially serve as biomarkers, remain unexplored. To fill this gap, for the first time, SCNs of cortical thickness and surface area were constructed and investigated in infants at high familial risk for autism and typically developing infants in this study. Group differences of SCNs emerge at 12 months of age in surface area. By 24 months of age, the autism group shows significantly increased integration, decreased segregation, and decreased small-worldness, compared with controls. The SCNs of surface area are deteriorated and shifted toward randomness in autistic infants. The abnormal brain regions changed during development, and the group differences of the left lateral occipital cortex become more prominent with age. These results indicate that autism has more significant influences on coordinated development of surface area than that of cortical thickness and the occipital cortex maybe an important biomarker of autism during infancy.
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Affiliation(s)
- Ya Wang
- National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China,Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wenhua Huang
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
| | - Gang Li
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
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28
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Xia K, Schmitt JE, Jha SC, Girault JB, Cornea E, Li G, Shen D, Styner M, Gilmore JH. Genetic Influences on Longitudinal Trajectories of Cortical Thickness and Surface Area during the First 2 Years of Life. Cereb Cortex 2022; 32:367-379. [PMID: 34231837 PMCID: PMC8897991 DOI: 10.1093/cercor/bhab213] [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: 01/15/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 11/14/2022] Open
Abstract
Genetic influences on cortical thickness (CT) and surface area (SA) are known to vary across the life span. Little is known about the extent to which genetic factors influence CT and SA in infancy and toddlerhood. We performed the first longitudinal assessment of genetic influences on variation in CT and SA in 501 twins who were aged 0-2 years. We observed substantial additive genetic influences on both average CT (0.48 in neonates, 0.37 in 1-year-olds, and 0.44 in 2-year-olds) and total SA (0.59 in neonates, 0.74 in 1-year-olds, and 0.73 in 2-year-olds). In addition, we found strong heritability of the change in average CT (0.49) from neonates to 1-year-olds, but not from 1- to 2-year-olds. Moreover, we found strong genetic correlations for average CT (rG = 0.92) between 1- and 2-year-olds and strong genetic correlations for total SA across all timepoints (rG = 0.96 between neonates and 1-year-olds, rG = 1 between 1- and 2-year-olds). In addition, we found CT and SA are strongly genetic correlated at birth, but weaken over time. Overall, results suggest a dynamic genetic relationship between CT and SA during first 2 years of life and provide novel insights into how genetic influences shape the cortical structure during early brain development.
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Affiliation(s)
- Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - J Eric Schmitt
- Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shaili C Jha
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - Gang Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7320, USA
| | - Dinggang Shen
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7320, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
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29
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Feng Y, Collignon O, Maurer D, Yao K, Gao X. Brief Postnatal Visual Deprivation Triggers Long-Lasting Interactive Structural and Functional Reorganization of the Human Cortex. Front Med (Lausanne) 2021; 8:752021. [PMID: 34869446 PMCID: PMC8635780 DOI: 10.3389/fmed.2021.752021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Patients treated for bilateral congenital cataracts provide a unique model to test the role of early visual input in shaping the development of the human cortex. Previous studies showed that brief early visual deprivation triggers long-lasting changes in the human visual cortex. However, it remains unknown if such changes interact with the development of other parts of the cortex. With high-resolution structural and resting-state fMRI images, we found changes in cortical thickness within, but not limited to, the visual cortex in adult patients, who experienced transient visual deprivation early in life as a result of congenital cataracts. Importantly, the covariation of cortical thickness across regions was also altered in the patients. The areas with altered cortical thickness in patients also showed differences in functional connectivity between patients and normally sighted controls. Together, the current findings suggest an impact of early visual deprivation on the interactive development of the human cortex.
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Affiliation(s)
- Yixuan Feng
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.,Zhejiang Provincial Key Lab of Ophthalmology, Hangzhou, China
| | - Olivier Collignon
- Institute of Research in Psychology/Institute of Neuroscience, University of Louvain, Louvain-la-Neuve, Belgium.,Centro Interdipartimentale Mente/Cervello, Università di Trento, Trento, Italy
| | - Daphne Maurer
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada.,The Hospital for Sick Children, Toronto, ON, Canada
| | - Ke Yao
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.,Zhejiang Provincial Key Lab of Ophthalmology, Hangzhou, China
| | - Xiaoqing Gao
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
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30
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Sydnor VJ, Larsen B, Bassett DS, Alexander-Bloch A, Fair DA, Liston C, Mackey AP, Milham MP, Pines A, Roalf DR, Seidlitz J, Xu T, Raznahan A, Satterthwaite TD. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron 2021; 109:2820-2846. [PMID: 34270921 PMCID: PMC8448958 DOI: 10.1016/j.neuron.2021.06.016] [Citation(s) in RCA: 234] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/24/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The human brain undergoes a prolonged period of cortical development that spans multiple decades. During childhood and adolescence, cortical development progresses from lower-order, primary and unimodal cortices with sensory and motor functions to higher-order, transmodal association cortices subserving executive, socioemotional, and mentalizing functions. The spatiotemporal patterning of cortical maturation thus proceeds in a hierarchical manner, conforming to an evolutionarily rooted, sensorimotor-to-association axis of cortical organization. This developmental program has been characterized by data derived from multimodal human neuroimaging and is linked to the hierarchical unfolding of plasticity-related neurobiological events. Critically, this developmental program serves to enhance feature variation between lower-order and higher-order regions, thus endowing the brain's association cortices with unique functional properties. However, accumulating evidence suggests that protracted plasticity within late-maturing association cortices, which represents a defining feature of the human developmental program, also confers risk for diverse developmental psychopathologies.
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Affiliation(s)
- Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY 10962, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, NIMH Intramural Research Program, NIH, Bethesda, MD 20892, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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31
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Copeland A, Silver E, Korja R, Lehtola SJ, Merisaari H, Saukko E, Sinisalo S, Saunavaara J, Lähdesmäki T, Parkkola R, Nolvi S, Karlsson L, Karlsson H, Tuulari JJ. Infant and Child MRI: A Review of Scanning Procedures. Front Neurosci 2021; 15:666020. [PMID: 34321992 PMCID: PMC8311184 DOI: 10.3389/fnins.2021.666020] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/04/2021] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) is a safe method to examine human brain. However, a typical MR scan is very sensitive to motion, and it requires the subject to lie still during the acquisition, which is a major challenge for pediatric scans. Consequently, in a clinical setting, sedation or general anesthesia is often used. In the research setting including healthy subjects anesthetics are not recommended for ethical reasons and potential longer-term harm. Here we review the methods used to prepare a child for an MRI scan, but also on the techniques and tools used during the scanning to enable a successful scan. Additionally, we critically evaluate how studies have reported the scanning procedure and success of scanning. We searched articles based on special subject headings from PubMed and identified 86 studies using brain MRI in healthy subjects between 0 and 6 years of age. Scan preparations expectedly depended on subject's age; infants and young children were scanned asleep after feeding and swaddling and older children were scanned awake. Comparing the efficiency of different procedures was difficult because of the heterogeneous reporting of the used methods and the success rates. Based on this review, we recommend more detailed reporting of scanning procedure to help find out which are the factors affecting the success of scanning. In the long term, this could help the research field to get high quality data, but also the clinical field to reduce the use of anesthetics. Finally, we introduce the protocol used in scanning 2 to 5-week-old infants in the FinnBrain Birth Cohort Study, and tips for calming neonates during the scans.
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Affiliation(s)
- Anni Copeland
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Eero Silver
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Riikka Korja
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Satu J. Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Ekaterina Saukko
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Susanne Sinisalo
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Pediatric Neurology, Turku University Hospital, University of Turku, Turku, Finland
| | - Riitta Parkkola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Saara Nolvi
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychology and Speech-Language Pathology, Turku Institute for Advanced Studies, University of Turku, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Jetro J. Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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32
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Fu X, Richards JE. Investigating developmental changes in scalp-to-cortex correspondence using diffuse optical tomography sensitivity in infancy. NEUROPHOTONICS 2021; 8:035003. [PMID: 34322572 PMCID: PMC8305752 DOI: 10.1117/1.nph.8.3.035003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 07/09/2021] [Indexed: 05/25/2023]
Abstract
Significance: Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to measure changes in cerebral hemoglobin concentration. Anatomical interpretations of NIRS data require accurate descriptions of the cranio-cerebral relations and DOT sensitivity to the underlying cortical structures. Such information is limited for pediatric populations because they undergo rapid head and brain development. Aim: We aim to investigate age-related differences in scalp-to-cortex distance and mapping between scalp locations and cortical regions of interest (ROIs) among infants (2 weeks to 24 months with narrow age bins), children (4 and 12 years), and adults (20 to 24 years). Approach: We used spatial scalp projection and photon propagation simulation methods with age-matched realistic head models based on MRIs. Results: There were age-group differences in the scalp-to-cortex distances in infancy. The developmental increase was magnified in children and adults. There were systematic age-related differences in the probabilistic mappings between scalp locations and cortical ROIs. Conclusions: Our findings have important implications in the design of sensor placement and making anatomical interpretations in NIRS and fNIRS research. Age-appropriate, realistic head models should be used to provide anatomical guidance for standalone DOT data in infants.
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Affiliation(s)
- Xiaoxue Fu
- University of South Carolina, Department of Psychology, Columbia, South Carolina, United States
| | - John E. Richards
- University of South Carolina, Department of Psychology, Columbia, South Carolina, United States
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33
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Norbom LB, Ferschmann L, Parker N, Agartz I, Andreassen OA, Paus T, Westlye LT, Tamnes CK. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: Integrating macro- and microstructural MRI findings. Prog Neurobiol 2021; 204:102109. [PMID: 34147583 DOI: 10.1016/j.pneurobio.2021.102109] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/26/2021] [Accepted: 06/15/2021] [Indexed: 12/11/2022]
Abstract
Through dynamic transactional processes between genetic and environmental factors, childhood and adolescence involve reorganization and optimization of the cerebral cortex. The cortex and its development plays a crucial role for prototypical human cognitive abilities. At the same time, many common mental disorders appear during these critical phases of neurodevelopment. Magnetic resonance imaging (MRI) can indirectly capture several multifaceted changes of cortical macro- and microstructure, of high relevance to further our understanding of the neural foundation of cognition and mental health. Great progress has been made recently in mapping the typical development of cortical morphology. Moreover, newer less explored MRI signal intensity and specialized quantitative T2 measures have been applied to assess microstructural cortical development. We review recent findings of typical postnatal macro- and microstructural development of the cerebral cortex from early childhood to young adulthood. We cover studies of cortical volume, thickness, area, gyrification, T1-weighted (T1w) tissue contrasts such a grey/white matter contrast, T1w/T2w ratio, magnetization transfer and myelin water fraction. Finally, we integrate imaging studies with cortical gene expression findings to further our understanding of the underlying neurobiology of the developmental changes, bridging the gap between ex vivo histological- and in vivo MRI studies.
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Affiliation(s)
- Linn B Norbom
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
| | - Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway
| | - Nadine Parker
- Institute of Medical Science, University of Toronto, Ontario, Canada
| | - Ingrid Agartz
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway
| | - Ole A Andreassen
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Tomáš Paus
- ECOGENE-21, Chicoutimi, Quebec, Canada; Department of Psychology and Psychiatry, University of Toronto, Ontario, Canada; Department of Psychiatry and Centre hospitalier universitaire Sainte-Justine, University of Montreal, Canada
| | - Lars T Westlye
- K.G Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Christian K Tamnes
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
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34
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Girault JB, Cornea E, Goldman BD, Jha SC, Murphy VA, Li G, Wang L, Shen D, Knickmeyer RC, Styner M, Gilmore JH. Cortical Structure and Cognition in Infants and Toddlers. Cereb Cortex 2021; 30:786-800. [PMID: 31365070 DOI: 10.1093/cercor/bhz126] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 12/21/2022] Open
Abstract
Cortical structure has been consistently related to cognitive abilities in children and adults, yet we know little about how the cortex develops to support emergent cognition in infancy and toddlerhood when cortical thickness (CT) and surface area (SA) are maturing rapidly. In this report, we assessed how regional and global measures of CT and SA in a sample (N = 487) of healthy neonates, 1-year-olds, and 2-year-olds related to motor, language, visual reception, and general cognitive ability. We report novel findings that thicker cortices at ages 1 and 2 and larger SA at birth, age 1, and age 2 confer a cognitive advantage in infancy and toddlerhood. While several expected brain-cognition relationships were observed, overlapping cortical regions were also implicated across cognitive domains, suggesting that infancy marks a period of plasticity and refinement in cortical structure to support burgeoning motor, language, and cognitive abilities. CT may be a particularly important morphological indicator of ability, but its impact on cognition is relatively weak when compared with gestational age and maternal education. Findings suggest that prenatal and early postnatal cortical developments are important for cognition in infants and toddlers but should be considered in relation to other child and demographic factors.
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Affiliation(s)
- Jessica B Girault
- Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Barbara D Goldman
- Department of Psychology & Neuroscience and FPG Child Development Institute, University of North Carolina, Chapel Hill, NC, USA
| | - Shaili C Jha
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Veronica A Murphy
- Neuroscience Curriculum, University of North Carolina, Chapel Hill, NC, USA
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.,Department of Pediatrics and Human Development, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.,Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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35
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Sheffield JM, Huang AS, Rogers BP, Blackford JU, Heckers S, Woodward ND. Insula sub-regions across the psychosis spectrum: morphology and clinical correlates. Transl Psychiatry 2021; 11:346. [PMID: 34088895 PMCID: PMC8178380 DOI: 10.1038/s41398-021-01461-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/04/2021] [Accepted: 05/14/2021] [Indexed: 02/05/2023] Open
Abstract
The insula is a heterogeneous cortical region, comprised of three cytoarchitecturally distinct sub-regions (agranular, dysgranular, and granular), which traverse the anterior-posterior axis and are differentially involved in affective, cognitive, and somatosensory processing. Smaller insula volume is consistently reported in psychosis-spectrum disorders and is hypothesized to result, in part, from abnormal neurodevelopment. To better understand the regional and diagnostic specificity of insula abnormalities in psychosis, their developmental etiology, and clinical correlates, we characterized insula volume and morphology in a large group of adults with a psychotic disorder (schizophrenia spectrum, psychotic bipolar disorder) and a community-ascertained cohort of psychosis-spectrum youth (age 8-21). Insula volume and morphology (cortical thickness, gyrification, sulcal depth) were quantified from T1-weighted structural brain images using the Computational Anatomy Toolbox (CAT12). Healthy adults (n = 196), people with a psychotic disorder (n = 303), and 1368 individuals from the Philadelphia Neurodevelopmental Cohort (PNC) (381 typically developing (TD), 381 psychosis-spectrum (PS) youth, 606 youth with other psychopathology (OP)), were investigated. Insula volume was significantly reduced in adults with psychotic disorders and psychosis-spectrum youth, following an anterior-posterior gradient across granular sub-regions. Morphological abnormalities were limited to lower gyrification in psychotic disorders, which was specific to schizophrenia and associated with cognitive ability. Insula volume and thickness were associated with cognition, and positive and negative symptoms of psychosis. We conclude that smaller insula volume follows an anterior-posterior gradient in psychosis and confers a broad risk for psychosis-spectrum disorders. Reduced gyrification is specific to schizophrenia and may reflect altered prenatal development that contributes to cognitive impairment.
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Affiliation(s)
- Julia M Sheffield
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Anna S Huang
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Baxter P Rogers
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | | | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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36
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Woodburn M, Bricken CL, Wu Z, Li G, Wang L, Lin W, Sheridan MA, Cohen JR. The maturation and cognitive relevance of structural brain network organization from early infancy to childhood. Neuroimage 2021; 238:118232. [PMID: 34091033 PMCID: PMC8372198 DOI: 10.1016/j.neuroimage.2021.118232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 04/30/2021] [Accepted: 06/01/2021] [Indexed: 01/14/2023] Open
Abstract
The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0–6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8–10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8–10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes.
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Affiliation(s)
- Mackenzie Woodburn
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States.
| | - Cheyenne L Bricken
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States
| | - Zhengwang Wu
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Margaret A Sheridan
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United States
| | - Jessica R Cohen
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United States
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37
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Abstract
Childhood socio-economic status (SES), a measure of the availability of material and social resources, is one of the strongest predictors of lifelong well-being. Here we review evidence that experiences associated with childhood SES affect not only the outcome but also the pace of brain development. We argue that higher childhood SES is associated with protracted structural brain development and a prolonged trajectory of functional network segregation, ultimately leading to more efficient cortical networks in adulthood. We hypothesize that greater exposure to chronic stress accelerates brain maturation, whereas greater access to novel positive experiences decelerates maturation. We discuss the impact of variation in the pace of brain development on plasticity and learning. We provide a generative theoretical framework to catalyse future basic science and translational research on environmental influences on brain development.
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Affiliation(s)
- Ursula A Tooley
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, USA
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Allyson P Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
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38
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Lucignani M, Longo D, Fontana E, Rossi-Espagnet MC, Lucignani G, Savelli S, Bascetta S, Sgrò S, Morini F, Giliberti P, Napolitano A. Morphometric Analysis of Brain in Newborn with Congenital Diaphragmatic Hernia. Brain Sci 2021; 11:brainsci11040455. [PMID: 33918479 PMCID: PMC8065764 DOI: 10.3390/brainsci11040455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 03/26/2021] [Accepted: 03/28/2021] [Indexed: 11/16/2022] Open
Abstract
Congenital diaphragmatic hernia (CDH) is a severe pediatric disorder with herniation of abdominal viscera into the thoracic cavity. Since neurodevelopmental impairment constitutes a common outcome, we performed morphometric magnetic resonance imaging (MRI) analysis on CDH infants to investigate cortical parameters such as cortical thickness (CT) and local gyrification index (LGI). By assessing CT and LGI distributions and their correlations with variables which might have an impact on oxygen delivery (total lung volume, TLV), we aimed to detect how altered perfusion affects cortical development in CDH. A group of CDH patients received both prenatal (i.e., fetal stage) and postnatal MRI. From postnatal high-resolution T2-weighted images, mean CT and LGI distributions of 16 CDH were computed and statistically compared to those of 13 controls. Moreover, TLV measures obtained from fetal MRI were further correlated to LGI. Compared to controls, CDH infants exhibited areas of hypogiria within bilateral fronto-temporo-parietal labels, while no differences were found for CT. LGI significantly correlated with TLV within bilateral temporal lobes and left frontal lobe, involving language- and auditory-related brain areas. Although the causes of neurodevelopmental impairment in CDH are still unclear, our results may suggest their link with altered cortical maturation and possible impaired oxygen perfusion.
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Affiliation(s)
- Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Daniela Longo
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (D.L.); (E.F.); (M.C.R.-E.); (G.L.)
| | - Elena Fontana
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (D.L.); (E.F.); (M.C.R.-E.); (G.L.)
| | - Maria Camilla Rossi-Espagnet
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (D.L.); (E.F.); (M.C.R.-E.); (G.L.)
- NESMOS Department, Sant’Andrea Hospital, Sapienza University, 00189 Rome, Italy
| | - Giulia Lucignani
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (D.L.); (E.F.); (M.C.R.-E.); (G.L.)
| | - Sara Savelli
- Imaging Department, Bambino Gesù Children’s Hospital and Research Institute, 00165 Rome, Italy; (S.S.); (S.B.)
| | - Stefano Bascetta
- Imaging Department, Bambino Gesù Children’s Hospital and Research Institute, 00165 Rome, Italy; (S.S.); (S.B.)
| | - Stefania Sgrò
- Department of Anesthesia and Critical Care, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Francesco Morini
- Department of Medical and Surgical Neonatology, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (F.M.); (P.G.)
| | - Paola Giliberti
- Department of Medical and Surgical Neonatology, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (F.M.); (P.G.)
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
- Correspondence: ; Tel.: +39-333-3214614
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39
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Zhao F, Wu Z, Wang L, Lin W, Gilmore JH, Xia S, Shen D, Li G. Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1217-1228. [PMID: 33417540 PMCID: PMC8016713 DOI: 10.1109/tmi.2021.3050072] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent spherical topology in a manifold space, e.g., the convoluted brain cortical surfaces represented by triangular meshes. There is no consistent neighborhood definition and thus no straightforward convolution/pooling operations for such cortical surface data. In this paper, leveraging the regular and hierarchical geometric structure of the resampled spherical cortical surfaces, we create the 1-ring filter on spherical cortical triangular meshes and accordingly develop convolution/pooling operations for constructing Spherical U-Net for cortical surface data. However, the regular nature of the 1-ring filter makes it inherently limited to model fixed geometric transformations. To further enhance the transformation modeling capability of Spherical U-Net, we introduce the deformable convolution and deformable pooling to cortical surface data and accordingly propose the Spherical Deformable U-Net (SDU-Net). Specifically, spherical offsets are learned to freely deform the 1-ring filter on the sphere to adaptively localize cortical structures with different sizes and shapes. We then apply the SDU-Net to two challenging and scientifically important tasks in neuroimaging: cortical surface parcellation and cortical attribute map prediction. Both applications validate the competitive performance of our approach in accuracy and computational efficiency in comparison with state-of-the-art methods.
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40
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Liu M, Lepage C, Kim SY, Jeon S, Kim SH, Simon JP, Tanaka N, Yuan S, Islam T, Peng B, Arutyunyan K, Surento W, Kim J, Jahanshad N, Styner MA, Toga AW, Barkovich AJ, Xu D, Evans AC, Kim H. Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets. Front Neurosci 2021; 15:650082. [PMID: 33815050 PMCID: PMC8010150 DOI: 10.3389/fnins.2021.650082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.
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Affiliation(s)
- Mengting Liu
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Claude Lepage
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sharon Y Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Seun Jeon
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Julia Pia Simon
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Nina Tanaka
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Shiyu Yuan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Tasfiya Islam
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Bailin Peng
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Knarik Arutyunyan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Wesley Surento
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Justin Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Neda Jahanshad
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arthur W Toga
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Anthony James Barkovich
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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41
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Roe JM, Vidal-Piñeiro D, Sørensen Ø, Brandmaier AM, Düzel S, Gonzalez HA, Kievit RA, Knights E, Kühn S, Lindenberger U, Mowinckel AM, Nyberg L, Park DC, Pudas S, Rundle MM, Walhovd KB, Fjell AM, Westerhausen R. Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer's disease. Nat Commun 2021; 12:721. [PMID: 33526780 PMCID: PMC7851164 DOI: 10.1038/s41467-021-21057-y] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 01/06/2021] [Indexed: 01/30/2023] Open
Abstract
Aging and Alzheimer's disease (AD) are associated with progressive brain disorganization. Although structural asymmetry is an organizing feature of the cerebral cortex it is unknown whether continuous age- and AD-related cortical degradation alters cortical asymmetry. Here, in multiple longitudinal adult lifespan cohorts we show that higher-order cortical regions exhibiting pronounced asymmetry at age ~20 also show progressive asymmetry-loss across the adult lifespan. Hence, accelerated thinning of the (previously) thicker homotopic hemisphere is a feature of aging. This organizational principle showed high consistency across cohorts in the Lifebrain consortium, and both the topological patterns and temporal dynamics of asymmetry-loss were markedly similar across replicating samples. Asymmetry-change was further accelerated in AD. Results suggest a system-wide dedifferentiation of the adaptive asymmetric organization of heteromodal cortex in aging and AD.
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Affiliation(s)
- James M. Roe
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Didac Vidal-Piñeiro
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Øystein Sørensen
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Andreas M. Brandmaier
- grid.419526.d0000 0000 9859 7917Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Sandra Düzel
- grid.419526.d0000 0000 9859 7917Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | | | - Rogier A. Kievit
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Ethan Knights
- grid.5335.00000000121885934MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Simone Kühn
- grid.419526.d0000 0000 9859 7917Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany ,grid.13648.380000 0001 2180 3484Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ulman Lindenberger
- grid.419526.d0000 0000 9859 7917Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany ,grid.4372.20000 0001 2105 1091Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Athanasia M. Mowinckel
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars Nyberg
- grid.12650.300000 0001 1034 3451Umeå Center for Functional Brain Imaging and Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Denise C. Park
- Center for Vital Longevity, University of Texas, Dallas, TX USA
| | - Sara Pudas
- grid.12650.300000 0001 1034 3451Umeå Center for Functional Brain Imaging and Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | | | - Kristine B. Walhovd
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Anders M. Fjell
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - René Westerhausen
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
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42
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Hu D, Zhang H, Wu Z, Wang F, Wang L, Smith JK, Lin W, Li G, Shen D. Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4137-4149. [PMID: 32746154 PMCID: PMC7773223 DOI: 10.1109/tmi.2020.3013825] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity measured by fMRI during infancy is largely immature and noisy compared to the morphological features from sMRI, thus making the sMRI and fMRI fusion for infant brain analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy than that merely using sMRI data. To address this issue, we develop a novel model termed as disentangled-multimodal adversarial autoencoder (DMM-AAE) for infant age prediction based on multimodal brain MRI. Specifically, we disentangle the latent variables of autoencoder into common and specific codes to represent the shared and complementary information among modalities, respectively. Then, cross-reconstruction requirement and common-specific distance ratio loss are designed as regularizations to ensure the effectiveness and thoroughness of the disentanglement. By arranging relatively independent autoencoders to separate the modalities and employing disentanglement under cross-reconstruction requirement to integrate them, our DMM-AAE method effectively restrains the possible interference cross modalities, while realizing effective information fusion. Taking advantage of the latent variable disentanglement, a new strategy is further proposed and embedded into DMM-AAE to address the issue of incompleteness of the multimodal neuroimages, which can also be used as an independent algorithm for missing modality imputation. By taking six types of cortical morphometric features from sMRI and brain functional connectivity from fMRI as predictors, the superiority of the proposed DMM-AAE is validated on infant age (35 to 848 days after birth) prediction using incomplete multimodal neuroimages. The mean absolute error of the prediction based on DMM-AAE reaches 37.6 days, outperforming state-of-the-art methods. Generally, our proposed DMM-AAE can serve as a promising model for prediction with multimodal data.
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Holland MA, Budday S, Li G, Shen D, Goriely A, Kuhl E. Folding drives cortical thickness variations. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2020; 229:2757-2778. [PMID: 37275766 PMCID: PMC10237175 DOI: 10.1140/epjst/e2020-000001-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 07/27/2020] [Indexed: 06/07/2023]
Abstract
The cortical thickness is a characteristic biomarker for a wide variety of neurological disorders. While the structural organization of the cerebral cortex is tightly regulated and evolutionarily preserved, its thickness varies widely between 1.5 and 4.5 mm across the healthy adult human brain. It remains unclear whether these thickness variations are a cause or consequence of cortical development. Recent studies suggest that cortical thickness variations are primarily a result of genetic effects. Previous studies showed that a simple homogeneous bilayered system with a growing layer on an elastic substrate undergoes a unique symmetry breaking into a spatially heterogeneous system with discrete gyri and sulci. Here, we expand on that work to explore the evolution of cortical thickness variations over time to support our finding that cortical pattern formation and thickness variations can be explained - at least in part - by the physical forces that emerge during cortical folding. Strikingly, as growth progresses, the developing gyri universally thicken and the sulci thin, even in the complete absence of regional information. Using magnetic resonance images, we demonstrate that these naturally emerging thickness variations agree with the cortical folding pattern in n = 9 healthy adult human brains, in n = 564 healthy human brains ages 7-64, and in n = 73 infant brains scanned at birth, and at ages one and two. Additionally, we show that cortical organoids develop similar patterns throughout their growth. Our results suggest that genetic, geometric, and physical events during brain development are closely interrelated. Understanding regional and temporal variations in cortical thickness can provide insight into the evolution and causative factors of neurological disorders, inform the diagnosis of neurological conditions, and assess the efficacy of treatment options.
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Affiliation(s)
- Maria A. Holland
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Silvia Budday
- Department of Mechanical Engineering, Friedrich-Alexander University, 91058 Erlangen, Germany
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
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van der Plas E, Spencer Noakes TL, Butcher DT, Weksberg R, Galin-Corini L, Wanstall EA, Te P, Hopf L, Guger S, Spiegler BJ, Hitzler J, Schachar RJ, Ito S, Nieman BJ. Quantitative MRI outcomes in child and adolescent leukemia survivors: Evidence for global alterations in gray and white matter. NEUROIMAGE-CLINICAL 2020; 28:102428. [PMID: 32980598 PMCID: PMC7522853 DOI: 10.1016/j.nicl.2020.102428] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/22/2020] [Accepted: 09/08/2020] [Indexed: 12/19/2022]
Abstract
Studies of cognition in ALL survivors consistently show evidence of mild neurocognitive difficulties. Structural MRI showed widespread differences between survivors and controls. Gray and white matter volumes were respectively 5 and 6% smaller in survivors. Survivors also exhibited altered white matter diffusion overall.
Introduction Cure rates for pediatric acute lymphoblastic leukemia (ALL) have reached an all-time high (>90%); however, neurocognitive difficulties continue to affect quality of life in at least a subset of survivors. There are relatively few quantitative neuroimaging studies in child and adolescent ALL survivors treated with chemotherapy only. Use of different outcome measures or limited sample sizes restrict our ability to make inferences about patterns of brain development following chemotherapy treatment. In this study, we used magnetic resonance imaging (MRI) to evaluate brain outcomes in ALL survivors, comparing against a group of typically developing, cancer free peers. Materials and methods Participants included 71 ALL survivors, on average 8 years after diagnosis and 8–18 years of age, and 83 typically developing controls. Anatomical MRI was performed to evaluate brain structure; diffusion and magnetization transfer MRI were used to examine brain tissue microstructure. Results Successful MRI scans were acquired in 67 survivors (94%) and 82 controls (99%). Structurally, ALL survivors exhibited widespread reductions in brain volume, with 6% less white matter and 5% less gray matter than controls (p = 0.003 and 0.0006 respectively). Much of the brain appeared affected – 71 of 90 evaluated structures showed smaller volume – with the most notable exception being the occipital lobe, where no significant differences were observed. Average full-scale IQ in the survivor and control groups were 95 (CI 92–99) and 110 (CI 107–113), respectively. Using data from the NIH Pediatric MRI Data Repository, we evaluated the extent to which elevated IQ in the control group might affect the structural differences observed. We estimated that two thirds of the observed brain differences were attributable to ALL and its treatment. In addition to the structural changes, survivors showed, on average, globally lower white matter fractional anisotropy (-3%) and higher radial diffusivity (+5%) (p < 10−6), but no differences in magnetization transfer ratio. Conclusions Neuroanatomical alterations in late childhood and adolescent ALL survivors treated with chemotherapy-only protocols are widespread, with white matter being somewhat more affected than gray matter. These MRI results indicate brain development is altered in ALL survivors and highlight the need to examine how these alterations emerge.
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Affiliation(s)
- Ellen van der Plas
- Department of Psychiatry, University of Iowa Hospital & Clinics, Iowa City, IA, USA
| | - T Leigh Spencer Noakes
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada; Translational Medicine, Hospital for Sick Children, Toronto, ON, Canada
| | - Darci T Butcher
- Genetics & Genome Biology, Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Rosanna Weksberg
- Genetics & Genome Biology, Hospital for Sick Children Research Institute, Toronto, ON, Canada; Clinical and Metabolic Genetics, Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | | | - Elizabeth A Wanstall
- Department of Psychology, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychology, York University, Toronto, ON, Canada
| | - Patrick Te
- Translational Medicine, Hospital for Sick Children, Toronto, ON, Canada; Clinical Pharmacology and Toxicology, Hospital for Sick Children, Toronto, ON, Canada
| | - Laura Hopf
- Department of Psychology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Sharon Guger
- Department of Psychology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Brenda J Spiegler
- Department of Psychology, The Hospital for Sick Children, Toronto, ON, Canada; Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Johann Hitzler
- Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Developmental and Stem Cell Biology, Hospital for Sick Children, Toronto, ON, Canada; Division of Hematology/Oncology, Hospital for Sick Children, Toronto, ON, Canada
| | - Russell J Schachar
- Department of Psychiatry, Hospital for Sick Children, Toronto, ON, Canada; Psychiatry Research, Hospital for Sick Children, Toronto, ON, Canada
| | - Shinya Ito
- Translational Medicine, Hospital for Sick Children, Toronto, ON, Canada; Clinical Pharmacology and Toxicology, Hospital for Sick Children, Toronto, ON, Canada; Pharmacology and Toxicology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Brian J Nieman
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada; Translational Medicine, Hospital for Sick Children, Toronto, ON, Canada; Ontario Institute for Cancer Research, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
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45
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Mihailov A, Philippe C, Gloaguen A, Grigis A, Laidi C, Piguet C, Houenou J, Frouin V. Cortical signatures in behaviorally clustered autistic traits subgroups: a population-based study. Transl Psychiatry 2020; 10:207. [PMID: 32594096 PMCID: PMC7320967 DOI: 10.1038/s41398-020-00894-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 06/05/2020] [Accepted: 06/09/2020] [Indexed: 12/22/2022] Open
Abstract
Extensive heterogeneity in autism spectrum disorder (ASD) has hindered the characterization of consistent biomarkers, which has led to widespread negative results. Isolating homogenized subtypes could provide insight into underlying biological mechanisms and an overall better understanding of ASD. A total of 1093 participants from the population-based "Healthy Brain Network" cohort (Child Mind Institute in the New York City area, USA) were selected based on score availability in behaviors relevant to ASD, aged 6-18 and IQ >= 70. All participants underwent an unsupervised clustering analysis on behavioral dimensions to reveal subgroups with ASD traits, identified by the presence of social deficits. Analysis revealed three socially impaired ASD traits subgroups: (1) high in emotionally dysfunctional traits, (2) high in ADHD-like traits, and (3) high in anxiety and depressive symptoms. 527 subjects had good quality structural MRI T1 data. Site effects on cortical features were adjusted using the ComBat method. Neuroimaging analyses compared cortical thickness, gyrification, and surface area, and were controlled for age, gender, and IQ, and corrected for multiple comparisons. Structural neuroimaging analyses contrasting one combined heterogeneous ASD traits group against controls did not yield any significant differences. Unique cortical signatures, however, were observed within each of the three individual ASD traits subgroups versus controls. These observations provide evidence of ASD traits subtypes, and confirm the necessity of applying dimensional approaches to extract meaningful differences, thus reducing heterogeneity and paving the way to better understanding ASD traits.
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Affiliation(s)
- Angeline Mihailov
- Neurospin, Institut Joliot, CEA, Université Paris-Saclay, Gif-sur-Yvette, 91191, France.
| | - Cathy Philippe
- grid.460789.40000 0004 4910 6535Neurospin, Institut Joliot, CEA, Université Paris-Saclay, Gif-sur-Yvette, 91191 France
| | - Arnaud Gloaguen
- grid.460789.40000 0004 4910 6535Neurospin, Institut Joliot, CEA, Université Paris-Saclay, Gif-sur-Yvette, 91191 France ,CNRS-Centrale Supélec, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette, France
| | - Antoine Grigis
- grid.460789.40000 0004 4910 6535Neurospin, Institut Joliot, CEA, Université Paris-Saclay, Gif-sur-Yvette, 91191 France
| | - Charles Laidi
- grid.460789.40000 0004 4910 6535Neurospin, Institut Joliot, CEA, Université Paris-Saclay, Gif-sur-Yvette, 91191 France ,APHP, Mondor Univ. Hospitals, DMU IMPACT, INSERM, U955, Translational Neuropsychiatry Team, University of Paris-Est Créteil, 94000 Créteil, France
| | - Camille Piguet
- grid.460789.40000 0004 4910 6535Neurospin, Institut Joliot, CEA, Université Paris-Saclay, Gif-sur-Yvette, 91191 France ,grid.8591.50000 0001 2322 4988Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Josselin Houenou
- grid.460789.40000 0004 4910 6535Neurospin, Institut Joliot, CEA, Université Paris-Saclay, Gif-sur-Yvette, 91191 France ,APHP, Mondor Univ. Hospitals, DMU IMPACT, INSERM, U955, Translational Neuropsychiatry Team, University of Paris-Est Créteil, 94000 Créteil, France
| | - Vincent Frouin
- grid.460789.40000 0004 4910 6535Neurospin, Institut Joliot, CEA, Université Paris-Saclay, Gif-sur-Yvette, 91191 France
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46
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Jha SC, Xia K, Ahn M, Girault JB, Li G, Wang L, Shen D, Zou F, Zhu H, Styner M, Gilmore JH, Knickmeyer RC. Environmental Influences on Infant Cortical Thickness and Surface Area. Cereb Cortex 2020; 29:1139-1149. [PMID: 29420697 DOI: 10.1093/cercor/bhy020] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Indexed: 01/07/2023] Open
Abstract
Cortical thickness (CT) and surface area (SA) vary widely between individuals and are associated with intellectual ability and risk for various psychiatric and neurodevelopmental conditions. Factors influencing this variability remain poorly understood, but the radial unit hypothesis, as well as the more recent supragranular cortex expansion hypothesis, suggests that prenatal and perinatal influences may be particularly important. In this report, we examine the impact of 17 major demographic and obstetric history variables on interindividual variation in CT and SA in a unique sample of 805 neonates who received MRI scans of the brain around 2 weeks of age. Birth weight, postnatal age at MRI, gestational age at birth, and sex emerged as important predictors of SA. Postnatal age at MRI, paternal education, and maternal ethnicity emerged as important predictors of CT. These findings suggest that individual variation in infant CT and SA is explained by different sets of environmental factors with neonatal SA more strongly influenced by sex and obstetric history and CT more strongly influenced by socioeconomic and ethnic disparities. Findings raise the possibility that interventions aimed at reducing disparities and improving obstetric outcomes may alter prenatal/perinatal cortical development.
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Affiliation(s)
- Shaili C Jha
- Curriculum in Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Mihye Ahn
- Department of Mathematics and Statistics, University of Nevada, Reno, NV, USA
| | - Jessica B Girault
- Curriculum in Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.,Department of Biostatistics, University of Texas, MD Andersen Cancer Center, Houston, TX, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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Adibpour P, Lebenberg J, Kabdebon C, Dehaene-Lambertz G, Dubois J. Anatomo-functional correlates of auditory development in infancy. Dev Cogn Neurosci 2020; 42:100752. [PMID: 32072930 PMCID: PMC6992933 DOI: 10.1016/j.dcn.2019.100752] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 10/23/2019] [Accepted: 12/20/2019] [Indexed: 10/29/2022] Open
Abstract
Infant brain development incorporates several intermingled mechanisms leading to intense and asynchronous maturation across cerebral networks and functional modalities. Combining electroencephalography (EEG) and diffusion magnetic resonance imaging (MRI), previous studies in the visual modality showed that the functional maturation of the event-related potentials (ERP) during the first postnatal semester relates to structural changes in the corresponding white matter pathways. Here investigated similar issues in the auditory modality. We measured ERPs to syllables in 1- to 6-month-old infants and related them to the maturational properties of underlying neural substrates measured with diffusion tensor imaging (DTI). We first observed a decrease in the latency of the auditory P2, and in the diffusivities in the auditory tracts and perisylvian regions with age. Secondly, we highlighted some of the early functional and structural substrates of lateralization. Contralateral responses to monoaural syllables were stronger and faster than ipsilateral responses, particularly in the left hemisphere. Besides, the acoustic radiations, arcuate fasciculus, middle temporal and angular gyri showed DTI asymmetries with a more complex and advanced microstructure in the left hemisphere, whereas the reverse was observed for the inferior frontal and superior temporal gyri. Finally, after accounting for the age-related variance, we correlated the inter-individual variability in P2 responses and in the microstructural properties of callosal fibers and inferior frontal regions. This study combining dedicated EEG and MRI approaches in infants highlights the complex relation between the functional responses to auditory stimuli and the maturational properties of the corresponding neural network.
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Affiliation(s)
- Parvaneh Adibpour
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, Gif/Yvette, France.
| | - Jessica Lebenberg
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, Gif/Yvette, France; UNATI, CEA DRF Institut Joliot, Gif/Yvette, France
| | - Claire Kabdebon
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, Gif/Yvette, France
| | | | - Jessica Dubois
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, Gif/Yvette, France; Université de Paris, NeuroDiderot, Inserm, F-75019 Paris, France
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48
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Lucaccioni L, Trevisani V, Boncompagni A, Marrozzini L, Berardi A, Iughetti L. Minipuberty: Looking Back to Understand Moving Forward. Front Pediatr 2020; 8:612235. [PMID: 33537266 PMCID: PMC7848193 DOI: 10.3389/fped.2020.612235] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/14/2020] [Indexed: 12/20/2022] Open
Abstract
Hypothalamic-pituitary-gonadal (HPG) axis activation occurs three times in life: the first is during fetal life, and has a crucial role in sex determination, the second time is during the first postnatal months of life, and the third is with the onset of puberty. These windows of activation recall the three windows of the "Developmental Origin of Health and Disease" (DOHaD) paradigm and may play a substantial role in several aspects of human development, such as growth, behavior, and neurodevelopment. From the second trimester of pregnancy there is a peak in gonadotropin levels, followed by a decrease toward term and complete suppression at birth. This is due to the negative feedback of placental estrogens. Studies have shown that in this prenatal HPG axis activation, gonadotropin levels display a sex-related pattern which plays a crucial role in sex differentiation of internal and external genitalia. Soon after birth, there is a new increase in LH, FSH, and sex hormone concentrations, both in males and females, due to HPG re-activation. This postnatal activation is known as "minipuberty." The HPG axis activity in infancy demonstrates a pulsatile pattern with hormone levels similar to those of true puberty. We review the studies on the changes of these hormones in infancy and their influence on several aspects of future development, from linear growth to fertility and neurobehavior.
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Affiliation(s)
- Laura Lucaccioni
- Pediatric Unit, Department of Medical and Surgical Sciences of the Mothers, Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Viola Trevisani
- Department of Medical and Surgical Sciences of the Mothers, Children and Adults, Post Graduate School of Pediatrics, University of Modena and Reggio Emilia, Modena, Italy
| | - Alessandra Boncompagni
- Neonatal Intensive Care Unit, Department of Medical and Surgical Sciences of the Mothers, Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Lucia Marrozzini
- Department of Medical and Surgical Sciences of the Mothers, Children and Adults, Post Graduate School of Pediatrics, University of Modena and Reggio Emilia, Modena, Italy
| | - Alberto Berardi
- Neonatal Intensive Care Unit, Department of Medical and Surgical Sciences of the Mothers, Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Lorenzo Iughetti
- Pediatric Unit, Department of Medical and Surgical Sciences of the Mothers, Children and Adults, University of Modena and Reggio Emilia, Modena, Italy.,Department of Medical and Surgical Sciences of the Mothers, Children and Adults, Post Graduate School of Pediatrics, University of Modena and Reggio Emilia, Modena, Italy
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49
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Salminen LE, Wilcox RR, Zhu AH, Riedel BC, Ching CRK, Rashid F, Thomopoulos SI, Saremi A, Harrison MB, Ragothaman A, Knight V, Boyle CP, Medland SE, Thompson PM, Jahanshad N. Altered Cortical Brain Structure and Increased Risk for Disease Seen Decades After Perinatal Exposure to Maternal Smoking: A Study of 9000 Adults in the UK Biobank. Cereb Cortex 2019; 29:5217-5233. [PMID: 31271414 PMCID: PMC6918926 DOI: 10.1093/cercor/bhz060] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Revised: 02/20/2019] [Accepted: 02/28/2019] [Indexed: 01/08/2023] Open
Abstract
Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44-80) who were exposed (n = 2510) or unexposed (n = 6079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volumes. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE-) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n = 109, unexposed, n = 315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.
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Affiliation(s)
- Lauren E Salminen
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Rand R Wilcox
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Alyssa H Zhu
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Brandalyn C Riedel
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Christopher R K Ching
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Faisal Rashid
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Arvin Saremi
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Marc B Harrison
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Anjanibhargavi Ragothaman
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Victoria Knight
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Christina P Boyle
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Paul M Thompson
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - Neda Jahanshad
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
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50
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Ahmad S, Wu Z, Li G, Wang L, Lin W, Yap PT, Shen D. Surface-constrained volumetric registration for the early developing brain. Med Image Anal 2019; 58:101540. [PMID: 31398617 PMCID: PMC6815721 DOI: 10.1016/j.media.2019.101540] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 07/26/2019] [Accepted: 07/29/2019] [Indexed: 12/24/2022]
Abstract
The T1-weighted and T2-weighted MRI contrasts of the infant brain evolve drastically during the first year of life. This poses significant challenges to inter- and intra-subject registration, which is key to subsequent statistical analyses. Existing registration methods that do not consider temporal contrast changes are ineffective for infant brain MRI data. To address this problem, we present in this paper a method for deformable registration of infant brain MRI. The key advantage of our method is threefold: (i) To deal with appearance changes, registration is performed based on segmented tissue maps instead of image intensity. Segmentation is performed by using an infant-centric algorithm previously developed by our group. (ii) Registration is carried out with respect to both cortical surfaces and volumetric tissue maps, thus allowing precise alignment of both cortical and subcortical structures. (iii) A dynamic elasticity model is utilized to allow large non-linear deformation. Experimental results in comparison with well-established registration methods indicate that our method yields superior accuracy in both cortical and subcortical alignment.
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Affiliation(s)
- Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States.
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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