Brendan Adkinson
MD-PhD StudentAbout
Research
Publications
Featured Publications
Broadening the Use of Machine Learning in Psychiatry
Adkinson B, Chekroud A. Broadening the Use of Machine Learning in Psychiatry. Biological Psychiatry 2023, 93: 4-5. PMID: 36456077, DOI: 10.1016/j.biopsych.2022.10.006.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 awardParvalbuminRescuing missing data in connectome-based predictive modeling
Liang Q, Jiang R, Adkinson B, Rosenblatt M, Mehta S, Foster M, Dong S, You C, Negahban S, Zhou H, Chang J, Scheinost D. Rescuing missing data in connectome-based predictive modeling. Imaging Neuroscience 2024, 2: 1-16. DOI: 10.1162/imag_a_00071.Peer-Reviewed Original Research
2023
Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available
Dadashkarimi J, Karbasi A, Liang Q, Rosenblatt M, Noble S, Foster M, Rodriguez R, Adkinson B, Ye J, Sun H, Camp C, Farruggia M, Tejavibulya L, Dai W, Jiang R, Pollatou A, Scheinost D. Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. Medical Image Analysis 2023, 88: 102864. PMID: 37352650, PMCID: PMC10526726, DOI: 10.1016/j.media.2023.102864.Peer-Reviewed Original ResearchConceptsDifferent atlasesRaw data accessWeb applicationData accessOpen source dataSource codePatient privacyOptimal transportRaw dataStorage concernsLarge-scale data collection effortsOriginal counterpartsExtensive setData collection effortsProcessing effortPredictive modelNeuroimaging dataDownstream analysisPrivacyAtlasesCollection effortsComputationalTime seriesDatasetConnectome
2022
Reward and loss incentives improve spatial working memory by shaping trial-by-trial posterior frontoparietal signals
Cho YT, Moujaes F, Schleifer CH, Starc M, Ji JL, Santamauro N, Adkinson B, Kolobaric A, Flynn M, Krystal JH, Murray JD, Repovs G, Anticevic A. Reward and loss incentives improve spatial working memory by shaping trial-by-trial posterior frontoparietal signals. NeuroImage 2022, 254: 119139. PMID: 35346841, PMCID: PMC9264479, DOI: 10.1016/j.neuroimage.2022.119139.Peer-Reviewed Original ResearchConceptsMemory precisionSpatial working memoryIntraparietal sulcusPrecentral sulcusWorking memoryMotivational signalsBOLD signalParietal cortexReward/lossVisual association regionsDorsolateral prefrontal cortexGoal-directed activityMemory paradigmMemory performanceMemory processesAnterior parietal cortexExecutive networkNeural changesSensory processesPrefrontal cortexLoss incentivesVentral striatumNon-human primate studiesTranslational neuroscienceMemory
2021
Mapping brain-behavior space relationships along the psychosis spectrum
Ji JL, Helmer M, Fonteneau C, Burt JB, Tamayo Z, Demšar J, Adkinson BD, Savić A, Preller KH, Moujaes F, Vollenweider FX, Martin WJ, Repovš G, Cho YT, Pittenger C, Murray JD, Anticevic A. Mapping brain-behavior space relationships along the psychosis spectrum. ELife 2021, 10: e66968. PMID: 34313219, PMCID: PMC8315806, DOI: 10.7554/elife.66968.Peer-Reviewed Original ResearchConceptsPsychosis spectrum disordersEffective patient-specific therapiesPSD patientsAllen Human Brain AtlasReceptor manipulationPatient-specific therapiesPsychiatric disordersBiomarker endpointsCognitive deficitsIndividualized predictionHuman Brain AtlasMolecular targetsActionable pathSymptom variationPsychosis spectrumPsychopathology symptomsBrain mapsCurrent sample sizeDisordersBrain atlasWhite matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis
Di Biase MA, Cetin-Karayumak S, Lyall AE, Zalesky A, Cho KIK, Zhang F, Kubicki M, Rathi Y, Lyons MG, Bouix S, Billah T, Anticevic A, Schleifer C, Adkinson BD, Ji JL, Tamayo Z, Addington J, Bearden CE, Cornblatt BA, Keshavan MS, Mathalon DH, McGlashan TH, Perkins DO, Cadenhead KS, Tsuang MT, Woods SW, Stone WS, Shenton ME, Cannon TD, Pasternak O. White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis. Molecular Psychiatry 2021, 26: 6833-6844. PMID: 34024906, PMCID: PMC8611104, DOI: 10.1038/s41380-021-01128-8.Peer-Reviewed Original ResearchConceptsClinical high riskWhite matter changesWhite matter microstructureHigh riskMatter changesCHR individualsAge-related white matter changesNorth American Prodrome Longitudinal StudyEmergence of psychosisWhite matter abnormalitiesYears of ageImpact of ageIllness onsetExtracellular free waterHealthy controlsLongitudinal cohortCHR subjectsMagnetic resonance imaging dataProspective analysisRegional fatLinear mixed effects modelsHigh fatPsychosisFractional anisotropyBaseline measures