2024
Characterization of pain and somatization and its relationship with psychopathology in early onset psychosis
van der Heijden H, Goldman M, Ray A, Golden E, Petty C, Deaso E, Hojlo M, Sethna N, Glahn D, Gonzalez-Heydrich J, Upadhyay J. Characterization of pain and somatization and its relationship with psychopathology in early onset psychosis. Journal Of Psychiatric Research 2024, 179: 77-82. PMID: 39260111, DOI: 10.1016/j.jpsychires.2024.09.006.Peer-Reviewed Original ResearchBrief Psychiatric Rating ScaleEarly onset psychosisDegree of psychopathologyBrief Psychiatric Rating Scale totalBrief Psychiatric Rating Scale subscalesDimensions of psychopathologyPsychiatric Rating ScaleChlorpromazine equivalent doseAntipsychotic medication usageControl of psychosisStructured Clinical InterviewAdult-onset psychosisPain catastrophizingSelf-report questionnairesCentral sensitizationPain severityFrequent cause of distressClinical interviewSomatic severityClinical presentation of painSomatic symptomsPsychopathologyPsychosisSubscale scoresRating ScaleCopy-number variants differ in frequency across genetic ancestry groups
Schultz L, Knighton A, Huguet G, Saci Z, Jean-Louis M, Mollon J, Knowles E, Glahn D, Jacquemont S, Almasy L. Copy-number variants differ in frequency across genetic ancestry groups. Human Genetics And Genomics Advances 2024, 5: 100340. PMID: 39138864, PMCID: PMC11401192, DOI: 10.1016/j.xhgg.2024.100340.Peer-Reviewed Original ResearchCopy number variantsAncestry groupsDeleterious copy number variantsRecurrent copy number variantsNon-European ancestry groupsUK BiobankGenetic ancestry groupsGenetic ancestryEuropean ancestry groupsReplication cohortFamily cohortProbe associationsAncestryCopyVariantsHealth outcomesCognitive phenotypesCommunity populationAutism spectrum disorderPhenotypeCohortLarge‐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 measuresFindingsAdmixture mapping of cognitive function in diverse Hispanic and Latino adults: Results from the Hispanic Community Health Study/Study of Latinos
Xia R, Jian X, Rodrigue A, Bressler J, Boerwinkle E, Cui B, Daviglus M, DeCarli C, Gallo L, Glahn D, Knowles E, Moon J, Mosley T, Satizabal C, Sofer T, Tarraf W, Testai F, Blangero J, Seshadri S, González H, Fornage M. Admixture mapping of cognitive function in diverse Hispanic and Latino adults: Results from the Hispanic Community Health Study/Study of Latinos. Alzheimer's & Dementia 2024, 20: 6070-6081. PMID: 38946675, DOI: 10.1002/alz.14082.Peer-Reviewed Original ResearchSingle nucleotide polymorphismsLatino adultsAssociated to cognitive functionAdmixture mappingHispanic Community Health Study/Study of LatinosCognitive functionHispanic Community Health Study/StudyPrioritized single nucleotide polymorphismsGenetic variantsGenome-wide association studiesNon-Hispanic whitesFine-mapping analysisLoci harboring genesSample of BlackHispanic/Latino adultsPhosphoinositide lipid metabolismNeurocognitive measuresIndependent replicationAdmixture signalsUnique haplotypesGenetic architectureChromosomal regionsHarboring genesAssociation studiesNucleotide polymorphismsA genetic association study of circulating coagulation factor VIII and von Willebrand factor levels
de Vries P, Reventun P, Brown M, Heath A, Huffman J, Le N, Bebo A, Brody J, Temprano-Sagrera G, Raffield L, Ozel A, Thibord F, Jain D, Lewis J, Rodriguez B, Pankratz N, Taylor K, Polasek O, Chen M, Yanek L, Carrasquilla G, Marioni R, Kleber M, Trégouët D, Yao J, Li-Gao R, Joshi P, Trompet S, Martinez-Perez A, Ghanbari M, Howard T, Reiner A, Arvanitis M, Ryan K, Bartz T, Rudan I, Faraday N, Linneberg A, Ekunwe L, Davies G, Delgado G, Suchon P, Guo X, Rosendaal F, Klaric L, Noordam R, van Rooij F, Curran J, Wheeler M, Osburn W, O'Connell J, Boerwinkle E, Beswick A, Psaty B, Kolcic I, Souto J, Becker L, Hansen T, Doyle M, Harris S, Moissl A, Deleuze J, Rich S, van Hylckama Vlieg A, Campbell H, Stott D, Soria J, de Maat M, Almasy L, Brody L, Auer P, Mitchell B, Ben-Shlomo Y, Fornage M, Hayward C, Mathias R, Kilpeläinen T, Lange C, Cox S, März W, Morange P, Rotter J, Mook-Kanamori D, Wilson C, van der Harst P, Jukema J, Ikram M, Blangero J, Kooperberg C, Desch K, Johnson A, Sabater-Lleal M, Lowenstein C, Smith A, Morrison A, Abe N, Abecasis G, Aguet F, Albert C, Almasy L, Alonso A, Ament S, Anderson P, Anugu P, Applebaum-Bowden D, Ardlie K, Arking D, Arnett D, Ashley-Koch A, Aslibekyan S, Assimes T, Auer P, Avramopoulos D, Ayas N, Balasubramanian A, Barnard J, Barnes K, Barr R, Barron-Casella E, Barwick L, Beaty T, Beck G, Becker D, Becker L, Beer R, Beitelshees A, Benjamin E, Benos T, Bezerra M, Bielak L, Bis J, Blackwell T, Blangero J, Blue N, Boerwinkle E, Bowden D, Bowler R, Brody J, Broeckel U, Broome J, Brown D, Bunting K, Burchard E, Bustamante C, Buth E, Cade B, Cardwell J, Carey V, Carrier J, Carson A, Carty C, Casaburi R, Romero J, Casella J, Castaldi P, Chaffin M, Chang C, Chang Y, Chasman D, Chavan S, Chen B, Chen W, Chen Y, Cho M, Choi S, Chuang L, Chung M, Chung R, Clish C, Comhair S, Conomos M, Cornell E, Correa A, Crandall C, Crapo J, Cupples L, Curran J, Curtis J, Custer B, Damcott C, Darbar D, David S, Davis C, Daya M, de Andrade M, de las Fuentes L, de Vries P, DeBaun M, Deka R, DeMeo D, Devine S, Dinh H, Doddapaneni H, Duan Q, Dugan-Perez S, Duggirala R, Durda J, Dutcher S, Eaton C, Ekunwe L, Boueiz A, Ellinor P, Emery L, Erzurum S, Farber C, Farek J, Fingerlin T, Flickinger M, Fornage M, Franceschini N, Frazar C, Fu M, Fullerton S, Fulton L, Gabriel S, Gan W, Gao S, Gao Y, Gass M, Geiger H, Gelb B, Geraci M, Germer S, Gerszten R, Ghosh A, Gibbs R, Gignoux C, Gladwin M, Glahn D, Gogarten S, Gong D, Goring H, Graw S, Gray K, Grine D, Gross C, Gu C, Guan Y, Guo X, Gupta N, Haessler J, Hall M, Han Y, Hanly P, Harris D, Hawley N, He J, Heavner B, Heckbert S, Hernandez R, Herrington D, Hersh C, Hidalgo B, Hixson J, Hobbs B, Hokanson J, Hong E, Hoth K, Hsiung C, Hu J, Hung Y, Huston H, Hwu C, Irvin M, Jackson R, Jain D, Jaquish C, Johnsen J, Johnson C, Johnson A, Johnston R, Jones K, Kang H, Kaplan R, Kardia S, Kelly S, Kenny E, Kessler M, Khan A, Khan Z, Kim W, Kimoff J, Kinney G, Konkle B, Kooperberg C, Kramer H, Lange E, Lange L, Lange L, Laurie C, Laurie C, LeBoff M, Lee J, Lee S, Lee W, LeFaive J, Levine D, Levy D, Lewis J, Li X, Li Y, Lin H, Lin H, Lin X, Liu S, Liu Y, Liu Y, Loos R, Lubitz S, Lunetta K, Luo J, Magalang U, Mahaney M, Make B, Manichaikul A, Manning A, Manson J, Martin L, Marton M, Mathai S, Mathias R, May S, McArdle P, McDonald M, McFarland S, McGarvey S, McGoldrick D, McHugh C, McNeil B, Mei H, Meigs J, Menon V, Mestroni L, Metcalf G, Meyers D, Mignot E, Mikulla J, Min N, Minear M, Minster R, Mitchell B, Moll M, Momin Z, Montasser M, Montgomery C, Muzny D, Mychaleckyj J, Nadkarni G, Naik R, Naseri T, Natarajan P, Nekhai S, Nelson S, Neltner B, Nessner C, Nickerson D, Nkechinyere O, North K, O'Connell J, O'Connor T, Ochs-Balcom H, Okwuonu G, Pack A, Paik D, Palmer N, Pankow J, Papanicolaou G, Parker C, Peloso G, Peralta J, Perez M, Perry J, Peters U, Peyser P, Phillips L, Pleiness J, Pollin T, Post W, Becker J, Boorgula M, Preuss M, Psaty B, Qasba P, Qiao D, Qin Z, Rafaels N, Raffield L, Rajendran M, Ramachandran V, Rao D, Rasmussen-Torvik L, Ratan A, Redline S, Reed R, Reeves C, Regan E, Reiner A, Reupena M, Rice K, Rich S, Robillard R, Robine N, Roden D, Roselli C, Rotter J, Ruczinski I, Runnels A, Russell P, Ruuska S, Ryan K, Sabino E, Saleheen D, Salimi S, Salvi S, Salzberg S, Sandow K, Sankaran V, Santibanez J, Schwander K, Schwartz D, Sciurba F, Seidman C, Seidman J, Sériès F, Sheehan V, Sherman S, Shetty A, Shetty A, Sheu W, Shoemaker M, Silver B, Silverman E, Skomro R, Smith J, Smith J, Smith N, Smith T, Smith E, Smoller S, Snively B, Snyder M, Sofer T, Sotoodehnia N, Stilp A, Storm G, Streeten E, Su J, Sung Y, Sylvia J, Szpiro A, Taliun D, Tang H, Taub M, Taylor K, Taylor M, Taylor S, Telen M, Thornton T, Threlkeld M, Tinker L, Tirschwell D, Tishkoff S, Tiwari H, Tong C, Tracy R, Tsai M, Vaidya D, Van Den Berg D, VandeHaar P, Vrieze S, Walker T, Wallace R, Walts A, Wang F, Wang H, Wang J, Watson K, Watt J, Weeks D, Weinstock J, Weir B, Weiss S, Weng L, Wessel J, Willer C, Williams K, Williams L, Wilson J, Wilson J, Winterkorn L, Wong Q, Wu J, Xu H, Yanek L, Yang I, Yu K, Zekavat S, Zhang Y, Zhao S, Zhao W, Zhu X, Ziv E, Zody M, Zoellner S, Lindstrom S, Wang L, Smith N, Gordon W, van Hylckama Vlieg A, de Andrade M, Brody J, Pattee J, Haessler J, Brumpton B, Chasman D, Suchon P, Chen M, Turman C, Germain M, Wiggins K, MacDonald J, Braekkan S, Armasu S, Pankratz N, Jackson R, Nielsen J, Giulianini F, Puurunen M, Ibrahim M, Heckbert S, Bammler T, Frazer K, McCauley B, Taylor K, Pankow J, Reiner A, Gabrielsen M, Deleuze J, O'Donnell C, Kim J, McKnight B, Kraft P, Hansen J, Rosendaal F, Heit J, Psaty B, Tang W, Kooperberg C, Hveem K, Ridker P, Morange P, Johnson A, Kabrhel C, AlexandreTrégouët D, Smith N. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels. Blood 2024, 143: 1845-1855. PMID: 38320121, DOI: 10.1182/blood.2023021452.Peer-Reviewed Original ResearchMendelian randomizationGene-based aggregation testingImputation of genotypesGene-based analysisMulti-phenotype analysisAssociations of factor VIIIGenetic association studiesHuman umbilical vein endothelial cellsCausal genesTrans-OmicsAssociation studiesB3GNT2Genetic associationVon Willebrand factorProtein von Willebrand factorLociIdentified associationsPDIA3Umbilical vein endothelial cellsIncreased riskMeta-analysisCarrier protein von Willebrand factorVein endothelial cellsPrecision medicineEndothelial cells409. Cortico-Thalamic Structural Co-Variation Networks are Related to Familial Risk for Schizophrenia in the Context of Lower Nuclei Volume Estimates in Patients: An ENIGMA Study
Lella A, Antonucci L, Weinberger D, Glahn D, Sim K, Gruber O, Chung Y, Sugranyes G, Clote E, Marcelis M, Kircher T, Van Rheenen T, Sponheim S, Dannlowski U, Iasevoli F, Pearlson G, Green M, Spalletta G, Lee T, Turner J, van Erp T, Thompson P, Bertolino A, Pergola G. 409. Cortico-Thalamic Structural Co-Variation Networks are Related to Familial Risk for Schizophrenia in the Context of Lower Nuclei Volume Estimates in Patients: An ENIGMA Study. Biological Psychiatry 2024, 95: s267-s268. DOI: 10.1016/j.biopsych.2024.02.908.Peer-Reviewed Original Research417. Diagnostic Complexity, Symptomatology, and Functioning in Two Early-Onset Psychosis Family Studies: Epicenter and Epimex
Mollon J, Mathias S, Lanzagorta N, Rodrigue A, Knowles E, Deaso E, Cadavid L, Saavedra J, Phelps E, Polat N, Brownstein C, D’Angelo E, Deo A, Gonzalez-Heydrich J, Sarmiento E, Walsh C, Almasy L, Nicolini H, Glahn D. 417. Diagnostic Complexity, Symptomatology, and Functioning in Two Early-Onset Psychosis Family Studies: Epicenter and Epimex. Biological Psychiatry 2024, 95: s270-s271. DOI: 10.1016/j.biopsych.2024.02.916.Peer-Reviewed Original Research8. The Relationship Between Treatment, Symptom Severity, and Brain Connectivity in Bipolar Disorder: An International Study Across 16 Enigma-Bipolar Sites
Nabulsi L, Kang M, Jahanshad N, Haarman B, McDonald C, Stein D, Glahn D, Pomarol-Clotet E, Vieta E, Houenou J, Favre P, Polosan M, Brambilla P, Bellani M, Mitchell P, Dannlowski U, Wessa M, Phillips M, Kircher T, Thompson P, Andreassen O, Ching C, Cannon D, Group E. 8. The Relationship Between Treatment, Symptom Severity, and Brain Connectivity in Bipolar Disorder: An International Study Across 16 Enigma-Bipolar Sites. Biological Psychiatry 2024, 95: s76-s77. DOI: 10.1016/j.biopsych.2024.02.186.Peer-Reviewed Original Research295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population
Kumar K, Kazem S, Liao Z, Kopal J, Huguet G, Renne T, Jean-Louis M, Xie Z, Saci Z, Almasy L, Glahn D, Paus T, Dumas G, Bearden C, Thompson P, Bethlehem R, Warrier V, Jacquemont S. 295. Rare Variant Genetic Architecture of the Human Cortical MRI Phenotypes in General Population. Biological Psychiatry 2024, 95: s220-s221. DOI: 10.1016/j.biopsych.2024.02.794.Peer-Reviewed Original Research434. Improving Image Accuracy in Low-Field Magnetic Resonance Imaging to Make Accessible Biomarkers for Psychiatric Disorders
Cooper R, Hayes R, Corcoran M, Sheth K, Arnold T, Stein J, Glahn D, Jalbrzikowski M. 434. Improving Image Accuracy in Low-Field Magnetic Resonance Imaging to Make Accessible Biomarkers for Psychiatric Disorders. Biological Psychiatry 2024, 95: s277-s278. DOI: 10.1016/j.biopsych.2024.02.933.Peer-Reviewed Original ResearchRole of Neurocellular Endoplasmic Reticulum Stress Response in Alzheimer’s Disease and Related Dementias Risk
Aceves M, Granados J, Leandro A, Peralta J, Glahn D, Williams-Blangero S, Curran J, Blangero J, Kumar S. Role of Neurocellular Endoplasmic Reticulum Stress Response in Alzheimer’s Disease and Related Dementias Risk. Genes 2024, 15: 569. PMID: 38790197, PMCID: PMC11121587, DOI: 10.3390/genes15050569.Peer-Reviewed Original ResearchTranscriptional response to ER stressProtein kinase RNA-like ER kinaseActivating transcription factor 6Inositol-requiring enzyme-1ER stress responseUnfolded protein responseElevated endoplasmic reticulumAlzheimer's diseaseResponse to ER stressSarco/endoplasmic reticulum Ca2+-ATPaseNeural stem cellsRNA-like ER kinaseStress responseAnalysis of DE genesG1/S phase cell cycle arrestEndoplasmic reticulum stress responseInduced G1/S-phase cell cycle arrestPostmortem AD brainsGene network analysisTranscription factor 6E2F transcription factor 1Animal models of ADCell cycle arrestModel of ADBinding protein 1When virtual reality becomes psychoneuroendocrine reality: A stress(or) review
Finseth T, Smith B, Van Steenis A, Glahn D, Johnson M, Ruttle P, Shirtcliff B, Shirtcliff E. When virtual reality becomes psychoneuroendocrine reality: A stress(or) review. Psychoneuroendocrinology 2024, 166: 107061. PMID: 38701607, DOI: 10.1016/j.psyneuen.2024.107061.Peer-Reviewed Original ResearchAuthor Correction: Using 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. Author Correction: Using rare genetic mutations to revisit structural brain asymmetry. Nature Communications 2024, 15: 3098. PMID: 38600109, PMCID: PMC11006936, DOI: 10.1038/s41467-024-47545-5.Peer-Reviewed Original ResearchRelating depressive and manic symptomatology to 1H-MRS spectra
Choquette A, Dager A, Marjańska M, Zatony M, Pearlson G, Glahn D, Knowles E. Relating depressive and manic symptomatology to 1H-MRS spectra. Journal Of Affective Disorders Reports 2024, 16: 100774. DOI: 10.1016/j.jadr.2024.100774.Peer-Reviewed Original ResearchAnterior cingulate cortexN-acetylaspartateRight anterior cingulate cortexAssociated with maniaDepressive symptom scalesSymptom Rating ScaleProfiles of associationDisorder symptomatologyManic symptomsReduced N-acetylaspartateCingulate cortexAffective disordersNeurochemical levelsSymptom ScaleManiaCholine-containing compoundsDichotomous diagnosisMagnetic resonance spectroscopyRating ScalePotential in vivo biomarkersHippocampusIllness etiologyMagnetic resonance spectroscopy dataTotal cholineFollow-upUsing 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 setsBridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people
Cooper R, Hayes R, Corcoran M, Sheth K, Arnold T, Stein J, Glahn D, Jalbrzikowski M. Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people. Frontiers In Neurology 2024, 15: 1339223. PMID: 38585353, PMCID: PMC10995930, DOI: 10.3389/fneur.2024.1339223.Peer-Reviewed Original ResearchSuper-resolution approachLow fieldsConvolutional neural networkSuper-resolution methodsImage qualityLow image qualityImage correspondencesNeural networkProcessing imagesLow-field imagesHigh-field systemsStandard imagesLow-field MR systemImproved correspondenceLow-field systemSurface areaImagesSingle pairWhite matter volumeMR systemSubcortical volumesMR technologyGlobal mean cortical thicknessCortical thicknessCortical volume
2023
Riemannian frameworks for the harmonization of resting-state functional MRI scans
Honnorat N, Seshadri S, Killiany R, Blangero J, Glahn D, Fox P, Habes M. Riemannian frameworks for the harmonization of resting-state functional MRI scans. Medical Image Analysis 2023, 91: 103043. PMID: 38029722, PMCID: PMC11157681, DOI: 10.1016/j.media.2023.103043.Peer-Reviewed Original ResearchRiemannian geometric frameworkHigh-dimensional data setsMathematical propertiesFunctional time seriesTime seriesGeometric frameworkLow-dimensional data setsStatistical methodsCovariate effectsData setsGreater statistical powerSynthetic dataStatistical powerImpact of covariatesLarge setSetABIDE consortiumNew framework