Brendan Adkinson
MD-PhD StudentAbout
Research
Publications
2026
Optimizing functional connectivity scanning conditions for predicting autistic traits
Horien C, Mandino F, Greene A, Shen X, Powell K, Vernetti A, O’Connor D, Adkinson B, Tejavibulya L, McPartland J, Volkmar F, Chun M, Chawarska K, Lake E, Rosenberg M, Satterthwaite T, Scheinost D, Finn E, Constable R. Optimizing functional connectivity scanning conditions for predicting autistic traits. Nature Mental Health 2026, 4: 792-805. PMID: 42137910, PMCID: PMC13167459, DOI: 10.1038/s44220-026-00623-7.Peer-Reviewed Original ResearchSustained attention taskAutistic traitsAttention taskConnectome-based predictive modelingAutism Brain Imaging Data ExchangeBrain-phenotype relationshipsScanning conditionsMagnetic resonance imaging-based studiesMeasures of attentionSocial attention taskOptimal brain stateMeasure of social responsibilityHealthy Brain NetworkSample of youthResting-state conditionNeurobiological correlatesAttentional challengesNeurotypical adultsNeurotypical participantsBrain networksAutistic featuresBrain statesAutismClinically relevant phenotypesImaging-based studiesFeature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers
Adkinson B, Rosenblatt M, Sun H, Dadashkarimi J, Tejavibulya L, Horien C, Westwater M, Rodriguez R, Noble S, Scheinost D. Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers. Nature Human Behaviour 2026, 1-15. PMID: 41986741, DOI: 10.1038/s41562-026-02447-y.Peer-Reviewed Original ResearchNeurobiological interpretationNeurobiological basisBrain-behavior associationsBrain connectivity dataHuman neuroimagingPsychiatric phenotypesBehavioral phenotypesFunctional MRIFunctional connectivityNeuroimaging dataMental healthLarge-scale neuroimaging datasetsConnectivity dataNeuroimaging datasetsNeurobiologyMRI studiesNeuroimagingExternal validationCognitionConnectomeGeneralizabilityParticipants
2025
BrainEffeX: A Web App for Exploring fMRI Effect Sizes
Shearer H, Rosenblatt M, Ye J, Jiang R, Tejavibulya L, Foster M, Liang Q, Dadashkarimi J, Westwater M, Cahill C, Cheng I, Fischbach A, Humphries A, Baskaran A, Rolison M, Peterson H, Adkinson B, Mehta S, Camp C, Nichols T, Curtiss J, Scheinost D, Noble S. BrainEffeX: A Web App for Exploring fMRI Effect Sizes. Aperture Neuro 2025, 5: 10.52294/001c.146251. PMID: 41675933, PMCID: PMC12889895, DOI: 10.52294/001c.146251.Peer-Reviewed Original ResearchTrends in self-citation rates in high-impact neurology, neuroscience, and psychiatry journals
Rosenblatt M, Mehta S, Peterson H, Dadashkarimi J, Rodriguez R, Foster M, Adkinson B, Liang Q, Kimble V, Ye J, McCusker M, Farruggia M, Rolison M, Westwater M, Jiang R, Noble S, Scheinost D. Trends in self-citation rates in high-impact neurology, neuroscience, and psychiatry journals. ELife 2025, 12 DOI: 10.7554/elife.88540.4.Peer-Reviewed Original ResearchMeta-Learning for Generalizable Connectome Modeling Across Heterogeneous Atlas Spaces
Liang Q, Adkinson B, Scheinost D. Meta-Learning for Generalizable Connectome Modeling Across Heterogeneous Atlas Spaces. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10981255.Peer-Reviewed Original ResearchEditorial: Reward processing in motivational and affective disorders, volume II
Ryan F, Kumar P, Skandali N, Adkinson B. Editorial: Reward processing in motivational and affective disorders, volume II. Frontiers In Psychology 2025, 16: 1549667. PMID: 40313892, PMCID: PMC12043889, DOI: 10.3389/fpsyg.2025.1549667.Peer-Reviewed Original Research
2024
Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations
Adkinson B, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations. Developmental Cognitive Neuroscience 2024, 70: 101464. PMID: 39447452, PMCID: PMC11538622, DOI: 10.1016/j.dcn.2024.101464.Peer-Reviewed Original ResearchBrain-phenotype associationsConnectome-based predictive modelingBrain-behavior associationsPrediction of languagePhiladelphia Neurodevelopmental CohortHealthy Brain NetworkClinical symptom burdenFMRI taskHuman Connectome ProjectExecutive functionBehavioral measuresDevelopmental populationsNeurodevelopmental CohortBrain networksDevelopmental sampleConnectome ProjectResearch settingsGeneralizabilitySymptom burdenExternal validationFMRIClinical settingAssociationEthnic minority representationTaskOvercoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling
Liang Q, Adkinson B, Jiang R, Scheinost D. Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling. Lecture Notes In Computer Science 2024, 15010: 579-588. DOI: 10.1007/978-3-031-72117-5_54.Peer-Reviewed Original ResearchPower and reproducibility in the external validation of brain-phenotype predictions
Rosenblatt M, Tejavibulya L, Sun H, Camp C, Khaitova M, Adkinson B, Jiang R, Westwater M, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nature Human Behaviour 2024, 8: 2018-2033. PMID: 39085406, DOI: 10.1038/s41562-024-01931-7.Peer-Reviewed Original ResearchHuman Connectome ProjectAdolescent Brain Cognitive Development StudyConnectome ProjectCognitive Development StudyPhiladelphia Neurodevelopmental CohortHealthy Brain NetworkStructural connectivity dataMatrix ReasoningWorking memoryAnxiety/depression symptomsAttention problemsNeurodevelopmental CohortBrain networksBrain-phenotype associationsEffect sizeConnectivity dataExternal validationRelated processesValidation studySample sizeBrain ProjectDevelopment studiesTraining sample sizeGeneralizability of modelsExternal samplesKetamine induces multiple individually distinct whole-brain functional connectivity signatures
Moujaes F, Ji J, Rahmati M, Burt J, Schleifer C, Adkinson B, Savic A, Santamauro N, Tamayo Z, Diehl C, Kolobaric A, Flynn M, Rieser N, Fonteneau C, Camarro T, Xu J, Cho Y, Repovs G, Fineberg S, Morgan P, Seifritz E, Vollenweider F, Krystal J, Murray J, Preller K, Anticevic A. Ketamine induces multiple individually distinct whole-brain functional connectivity signatures. ELife 2024, 13: e84173. PMID: 38629811, PMCID: PMC11023699, DOI: 10.7554/elife.84173.Peer-Reviewed Original ResearchConceptsResponse to ketamineAcute ketamineBehavioral effectsQuantified resting-state functional connectivityEffects of acute ketamineSymptom variationResting-state functional connectivityTreatment-resistant depressionFunctional connectivity signaturesGlobal brain connectivitySingle-subject levelInter-individual variabilityPlacebo-controlled studyFunctional connectivityConnectivity signaturesBrain connectivityHealthy participantsSingle-blind placebo-controlled studyNeural variationsTreatment conditionsKetamineGene expression targetsPharmacological biomarkersPilot awardParvalbumin