2025
Genetic Associations Among Inflammation, White Matter Architecture, and Extracellular Free Water
Rodrigue A, Knowles E, Mollon J, Mathias S, Peralta J, Leandro A, Fox P, Kochunov P, Olvera R, Almasy L, Curran J, Blangero J, Glahn D. Genetic Associations Among Inflammation, White Matter Architecture, and Extracellular Free Water. Human Brain Mapping 2025, 46: e70101. PMID: 39757975, PMCID: PMC11702472, DOI: 10.1002/hbm.70101.Peer-Reviewed Original ResearchConceptsGenetic correlationsDiffusion tensor imagingCirculating cytokine levelsPeripheral inflammatory responseWhite matter architectureFA measurementsCytokine levelsCytokine measurementsWhite matter integrityProinflammatory cytokinesSingle-compartment modelIL-8Inflammatory processInflammatory responseFree water volumeFree waterGenetic relationshipsWhite matterWhite matter microstructureExtracellular free waterGenetic association
2024
Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes
Huguet G, Renne T, Poulain C, Dubuc A, Kumar K, Kazem S, Engchuan W, Shanta O, Douard E, Proulx C, Jean-Louis M, Saci Z, Mollon J, Schultz L, Knowles E, Cox S, Porteous D, Davies G, Redmond P, Harris S, Schumann G, Dumas G, Labbe A, Pausova Z, Paus T, Scherer S, Sebat J, Almasy L, Glahn D, Jacquemont S. Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes. Cell Genomics 2024, 4: 100721. PMID: 39667348, PMCID: PMC11701252, DOI: 10.1016/j.xgen.2024.100721.Peer-Reviewed Original ResearchConceptsCopy-number variantsGenome-wide association studiesAssociation studiesCognitive abilitiesBiological processesEffect of gene dosageNeurodevelopmental disordersAssociated with cognitionHigher cognitive performanceGene dosageGene setsAssociated with higher cognitive performanceCognitive performanceGenesCell typesEffect sizeCognitionDeletionDuplicationDisordersNon-brain tissuesMedical comorbiditiesAbilityVariantsBrainLarge‐scale analysis of structural brain asymmetries during neurodevelopment: Associations with age and sex in 4265 children and adolescents
Kurth F, Schijven D, van den Heuvel O, Hoogman M, van Rooij D, Stein D, Buitelaar J, Bölte S, Auzias G, Kushki A, Venkatasubramanian G, Rubia K, Bollmann S, Isaksson J, Jaspers‐Fayer F, Marsh R, Batistuzzo M, Arnold P, Bressan R, Stewart S, Gruner P, Sorensen L, Pan P, Silk T, Gur R, Cubillo A, Haavik J, Tuura R, Hartman C, Calvo R, McGrath J, Calderoni S, Jackowski A, Chantiluke K, Satterthwaite T, Busatto G, Nigg J, Gur R, Retico A, Tosetti M, Gallagher L, Szeszko P, Neufeld J, Ortiz A, Ghisleni C, Lazaro L, Hoekstra P, Anagnostou E, Hoekstra L, Simpson B, Plessen J, Deruelle C, Soreni N, James A, Narayanaswamy J, Reddy J, Fitzgerald J, Bellgrove M, Salum G, Janssen J, Muratori F, Vila M, Giral M, Ameis S, Bosco P, Remnélius K, Huyser C, Pariente J, Jalbrzikowski M, Rosa P, O'Hearn K, Ehrlich S, Mollon J, Zugman A, Christakou A, Arango C, Fisher S, Kong X, Franke B, Medland S, Thomopoulos S, Jahanshad N, Glahn D, Thompson P, Francks C, Luders E. Large‐scale analysis of structural brain asymmetries during neurodevelopment: Associations with age and sex in 4265 children and adolescents. Human Brain Mapping 2024, 45: e26754. PMID: 39046031, PMCID: PMC11267452, DOI: 10.1002/hbm.26754.Peer-Reviewed Original ResearchConceptsBrain asymmetryBrain regionsSex differencesStructural brain asymmetryCerebral asymmetryFindings lack consistencyBrain featuresEffects of ageSamples to dateEffect sizeBrainDifferential effectsAdolescentsAge effectsChildhoodInvestigate associationsSignificant asymmetryAssociated with ageSexMaleFindingsNeurodevelopmentAssociationDifferencesHemisphereNeurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm
Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye L, Richard G, Fernandez-Cabello S, Parker N, Andreassen O, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin C, Tsai S, Rodrigue A, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León M, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul A, Uslu O, Burhanoglu B, Uyar Demir A, Rootes-Murdy K, Calhoun V, Sim K, Green M, Quidé Y, Chung Y, Kim W, Sponheim S, Demro C, Ramsay I, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park M, Kirschner M, Georgiadis F, Kaiser S, Van Rheenen T, Rossell S, Hughes M, Woods W, Carruthers S, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen D, Preda A, Thomopoulos S, Jahanshad N, Cui L, Yao D, Thompson P, Turner J, van Erp T, Cheng W, Feng J. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm. Nature Communications 2024, 15: 5996. PMID: 39013848, PMCID: PMC11252381, DOI: 10.1038/s41467-024-50267-3.Peer-Reviewed Original ResearchConceptsGray matter changesDisorder constructsEnlarged striatumPsychiatric conditionsMental disordersSubcortical regionsSchizophreniaBiological foundationsMatter changesBrain imagingStriatumDisordersBiological factorsIndividualsSubtypesHealthy subjectsCross-sectional brain imagingHippocampusTemporal trajectoriesInternational cohortSubgroup 2Subgroup 1SubgroupsBrain‐age prediction: Systematic evaluation of site effects, and sample age range and size
Yu Y, Cui H, Haas S, New F, Sanford N, Yu K, Zhan D, Yang G, Gao J, Wei D, Qiu J, Banaj N, Boomsma D, Breier A, Brodaty H, Buckner R, Buitelaar J, Cannon D, Caseras X, Clark V, Conrod P, Crivello F, Crone E, Dannlowski U, Davey C, de Haan L, de Zubicaray G, Di Giorgio A, Fisch L, Fisher S, Franke B, Glahn D, Grotegerd D, Gruber O, Gur R, Gur R, Hahn T, Harrison B, Hatton S, Hickie I, Pol H, Jamieson A, Jernigan T, Jiang J, Kalnin A, Kang S, Kochan N, Kraus A, Lagopoulos J, Lazaro L, McDonald B, McDonald C, McMahon K, Mwangi B, Piras F, Rodriguez‐Cruces R, Royer J, Sachdev P, Satterthwaite T, Saykin A, Schumann G, Sevaggi P, Smoller J, Soares J, Spalletta G, Tamnes C, Trollor J, Ent D, Vecchio D, Walter H, Wang Y, Weber B, Wen W, Wierenga L, Williams S, Wu M, Zunta‐Soares G, Bernhardt B, Thompson P, Frangou S, Ge R, Group E. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size. Human Brain Mapping 2024, 45: e26768. PMID: 38949537, PMCID: PMC11215839, DOI: 10.1002/hbm.26768.Peer-Reviewed Original ResearchConceptsBrain-aging modelBrain-age predictionBrain-ageDiscovery sampleBrain morphometric measuresStructural neuroimaging dataSamples of healthy individualsSample age rangeNeuroimaging metricsNeuroimaging dataHealthy individualsLongitudinal consistencyBrain developmentIndependent samplesAge varianceAge rangeBrainSample sizeAge binsMorphometry dataIndividualsHuman lifespanEmpirical examinationMeaningful measuresFindingsUsing rare genetic mutations to revisit structural brain asymmetry
Kopal J, Kumar K, Shafighi K, Saltoun K, Modenato C, Moreau C, Huguet G, Jean-Louis M, Martin C, Saci Z, Younis N, Douard E, Jizi K, Beauchamp-Chatel A, Kushan L, Silva A, van den Bree M, Linden D, Owen M, Hall J, Lippé S, Draganski B, Sønderby I, Andreassen O, Glahn D, Thompson P, Bearden C, Zatorre R, Jacquemont S, Bzdok D. Using rare genetic mutations to revisit structural brain asymmetry. Nature Communications 2024, 15: 2639. PMID: 38531844, PMCID: PMC10966068, DOI: 10.1038/s41467-024-46784-w.Peer-Reviewed Original ResearchConceptsCopy number variationsGenome-wide association studiesBrain asymmetryPlanum temporale asymmetryHemispheric functional specializationStructural brain asymmetryCopy number variation carriersBrain-related phenotypesFacial cuesWord recognitionBrain lateralizationHuman cognitive capacitiesPerspective takingCognitive operationsRight hemispherePattern-learning approachBrain organizationLateralized functionsSusceptible to deletionGenetic influencesCognitive capacityAssociation studiesAsymmetry patternsGenomic deletionsGene sets
2017
Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: Results of the ENIGMA plasticity working group
Brouwer R, Panizzon M, Glahn D, Hibar D, Hua X, Jahanshad N, Abramovic L, de Zubicaray G, Franz C, Hansell N, Hickie I, Koenis M, Martin N, Mather K, McMahon K, Schnack H, Strike L, Swagerman S, Thalamuthu A, Wen W, Gilmore J, Gogtay N, Kahn R, Sachdev P, Wright M, Boomsma D, Kremen W, Thompson P, Pol H. Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: Results of the ENIGMA plasticity working group. Human Brain Mapping 2017, 38: 4444-4458. PMID: 28580697, PMCID: PMC5572837, DOI: 10.1002/hbm.23672.Peer-Reviewed Original ResearchConceptsSubcortical brain volumesBrain volumeGenetic influencesBrain changesBrain structuresDevelopmental trajectory of brain structureDeviant developmental trajectoriesMultivariate genetic modelsGeneral cognitive functionStructural brain changesBrain structural changesLongitudinal twin cohortCerebellar gray matterInfluence of genetic factorsSubcortical volumesCognitive functionBaseline volumeBrain plasticityHeritability estimatesDevelopmental changesMental healthBrain developmentTwin cohortEnvironmental influencesGray matter
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