2024
Data leakage inflates prediction performance in connectome-based machine learning models
Rosenblatt M, Tejavibulya L, Jiang R, Noble S, Scheinost D. Data leakage inflates prediction performance in connectome-based machine learning models. Nature Communications 2024, 15: 1829. PMID: 38418819, PMCID: PMC10901797, DOI: 10.1038/s41467-024-46150-w.Peer-Reviewed Original Research
2023
The challenges and prospects of brain-based prediction of behaviour
Wu J, Li J, Eickhoff S, Scheinost D, Genon S. The challenges and prospects of brain-based prediction of behaviour. Nature Human Behaviour 2023, 7: 1255-1264. PMID: 37524932, DOI: 10.1038/s41562-023-01670-1.Peer-Reviewed Original ResearchConceptsInterindividual differencesIndividual brain patternsNeural correlatesBehavioral measuresBrain patternsSystems neuroscienceConceptual limitationsLarge open datasetData-driven fashionNeuroscienceBrain levelsComputational resourcesOpen datasetsPredictive modelling approachCorrelatesMindBehaviorMeasuresPredictive modellingModelling approachDifferencesPotential solutions
2022
Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer
Scheinost D, Pollatou A, Dufford A, Jiang R, Farruggia M, Rosenblatt M, Peterson H, Rodriguez R, Dadashkarimi J, Liang Q, Dai W, Foster M, Camp C, Tejavibulya L, Adkinson B, Sun H, Ye J, Cheng Q, Spann M, Rolison M, Noble S, Westwater M. Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer. Biological Psychiatry 2022, 93: 893-904. PMID: 36759257, PMCID: PMC10259670, DOI: 10.1016/j.biopsych.2022.10.014.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsA graph theory neuroimaging approach to distinguish the depression of bipolar disorder from major depressive disorder in adolescents and young adults
Goldman DA, Sankar A, Rich A, Kim JA, Pittman B, Constable RT, Scheinost D, Blumberg HP. A graph theory neuroimaging approach to distinguish the depression of bipolar disorder from major depressive disorder in adolescents and young adults. Journal Of Affective Disorders 2022, 319: 15-26. PMID: 36103935, PMCID: PMC9669784, DOI: 10.1016/j.jad.2022.09.016.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentBipolar DisorderBrainDepressive Disorder, MajorHumansMagnetic Resonance ImagingNeuroimagingYoung AdultConceptsAdolescents/young adultsMajor depressive disorderDepressive disorderYoung adultsICD increasesBipolar disorderInterhemispheric functional connectivityFunctional connectivity differencesSeed-based analysisFunctional connectivity patternsSeed-based connectivityFunctional magnetic resonanceFunctional connectivity measuresBasal gangliaFunctional dysconnectivityIllness progressionTreatment strategiesClinical measuresEarly diagnosisHC groupTargeted treatmentConnectivity differencesSuicide thoughtsFunctional connectivityDeleterious treatmentA Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity
Jiang R, Scheinost D, Zuo N, Wu J, Qi S, Liang Q, Zhi D, Luo N, Chung Y, Liu S, Xu Y, Sui J, Calhoun V. A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity. Advanced Science 2022, 9: 2201621. PMID: 35811304, PMCID: PMC9403648, DOI: 10.1002/advs.202201621.Peer-Reviewed Original ResearchConceptsCognitive declineNormal agingFunctional connectivitySimilar neural correlatesWhole-brain functional connectivityDorsal attention networkBrain network organizationNeural dedifferentiationFluid intelligenceCognitive agingCognitive abilitiesNeural correlatesAttention networkCognitive functionNetwork organizationHuman ageNeuroimaging signaturesCognitionUnique patternAgingConnectivityIntelligenceCorrelatesConstructsHealthy cohortPredicting the future of neuroimaging predictive models in mental health
Tejavibulya L, Rolison M, Gao S, Liang Q, Peterson H, Dadashkarimi J, Farruggia MC, Hahn CA, Noble S, Lichenstein SD, Pollatou A, Dufford AJ, Scheinost D. Predicting the future of neuroimaging predictive models in mental health. Molecular Psychiatry 2022, 27: 3129-3137. PMID: 35697759, PMCID: PMC9708554, DOI: 10.1038/s41380-022-01635-2.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMultimodal neuroimaging of metabotropic glutamate 5 receptors and functional connectivity in alcohol use disorder
Smart K, Worhunsky PD, Scheinost D, Angarita GA, Esterlis I, Carson RE, Krystal JH, O'Malley SS, Cosgrove KP, Hillmer AT. Multimodal neuroimaging of metabotropic glutamate 5 receptors and functional connectivity in alcohol use disorder. Alcohol Clinical And Experimental Research 2022, 46: 770-782. PMID: 35342968, PMCID: PMC9117461, DOI: 10.1111/acer.14816.Peer-Reviewed Original ResearchConceptsMetabotropic glutamate 5 receptorsDefault mode networkFunctional magnetic resonance imagingReceptor availabilityPositron emission tomographyAUD groupFunctional connectivityReceptor positron emission tomographyResting-state functional magnetic resonance imagingNetwork-level functional connectivityBrain connectivityWeeks of abstinenceGlobal functional connectivityAlcohol use disorderMagnetic resonance imagingFMRI outcomesHealthy controlsSupervised abstinencePET resultsUse disordersSynaptic plasticityResonance imagingBrain regionsEmission tomographyOrbitofrontal cortex(Un)common space in infant neuroimaging studies: A systematic review of infant templates
Dufford AJ, Hahn CA, Peterson H, Gini S, Mehta S, Alfano A, Scheinost D. (Un)common space in infant neuroimaging studies: A systematic review of infant templates. Human Brain Mapping 2022, 43: 3007-3016. PMID: 35261126, PMCID: PMC9120551, DOI: 10.1002/hbm.25816.Peer-Reviewed Original ResearchAn ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field
Pollatou A, Filippi CA, Aydin E, Vaughn K, Thompson D, Korom M, Dufford AJ, Howell B, Zöllei L, Di Martino A, Graham A, Group F, Scheinost D, Spann MN. An ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field. Developmental Cognitive Neuroscience 2022, 54: 101083. PMID: 35184026, PMCID: PMC8861425, DOI: 10.1016/j.dcn.2022.101083.Peer-Reviewed Original ResearchA protocol for working with open-source neuroimaging datasets
Horien C, Lee K, Westwater ML, Noble S, Tejavibulya L, Kayani T, Constable RT, Scheinost D. A protocol for working with open-source neuroimaging datasets. STAR Protocols 2022, 3: 101077. PMID: 35036958, PMCID: PMC8749295, DOI: 10.1016/j.xpro.2021.101077.Peer-Reviewed Original Research
2021
Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies
Korom M, Camacho MC, Filippi CA, Licandro R, Moore LA, Dufford A, Zöllei L, Graham AM, Spann M, Howell B, FIT’NG, Shultz S, Scheinost D. Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies. Developmental Cognitive Neuroscience 2021, 53: 101055. PMID: 34974250, PMCID: PMC8733260, DOI: 10.1016/j.dcn.2021.101055.Peer-Reviewed Original Research
2020
A hitchhiker’s guide to working with large, open-source neuroimaging datasets
Horien C, Noble S, Greene AS, Lee K, Barron DS, Gao S, O’Connor D, Salehi M, Dadashkarimi J, Shen X, Lake EMR, Constable RT, Scheinost D. A hitchhiker’s guide to working with large, open-source neuroimaging datasets. Nature Human Behaviour 2020, 5: 185-193. PMID: 33288916, PMCID: PMC7992920, DOI: 10.1038/s41562-020-01005-4.Peer-Reviewed Original Research
2019
Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research
Yip SW, Kiluk B, Scheinost D. Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2019, 5: 748-758. PMID: 31932230, PMCID: PMC8274215, DOI: 10.1016/j.bpsc.2019.11.001.Peer-Reviewed Original ResearchConceptsIndividual differencesBrain-behavior modelsFuture neuroimaging researchLikelihood of replicationEvidence-based treatmentsNeuroimaging researchEffect size estimatesSubstance useDirection of associationAnatomical locusBrain functionTreatment outcomesModeling findingsClinical samplesNovel subjectRelapse rateClinical outcomesPredictive modeling approachUnsuccessful treatmentLeading causeIndividualsClinical settingParticular riskSize estimatesOutcomesTen simple rules for predictive modeling of individual differences in neuroimaging
Scheinost D, Noble S, Horien C, Greene AS, Lake EM, Salehi M, Gao S, Shen X, O’Connor D, Barron DS, Yip SW, Rosenberg MD, Constable RT. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 2019, 193: 35-45. PMID: 30831310, PMCID: PMC6521850, DOI: 10.1016/j.neuroimage.2019.02.057.Peer-Reviewed Original ResearchMeSH KeywordsBrainConnectomeHumansMachine LearningMagnetic Resonance ImagingModels, NeurologicalNeuroimagingConceptsBrain-behavior associations
2018
Can neuroimaging help combat the opioid epidemic? A systematic review of clinical and pharmacological challenge fMRI studies with recommendations for future research
Moningka H, Lichenstein S, Worhunsky PD, DeVito EE, Scheinost D, Yip SW. Can neuroimaging help combat the opioid epidemic? A systematic review of clinical and pharmacological challenge fMRI studies with recommendations for future research. Neuropsychopharmacology 2018, 44: 259-273. PMID: 30283002, PMCID: PMC6300537, DOI: 10.1038/s41386-018-0232-4.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsBrainHumansMagnetic Resonance ImagingNeuroimagingOpioid-Related DisordersResearch DesignConceptsOpioid use disorderOpioid epidemicTreatment responseTask-based fMRI paradigmsUrgent public health problemMedication-assisted treatmentPrescription opioid usersCurrent opioid epidemicPublic health problemEvidence-based treatmentsPaucity of literatureHeroin cuesOpioid medicationsRelapse rateOpioid systemOpioid usersFuture neuroimaging studiesSignificant individual variabilityHealthcare costsWithdrawal effectsHealthy individualsHealth problemsSystematic reviewExtended abstinenceNeuroimaging studies
2017
Considering factors affecting the connectome-based identification process: Comment on Waller et al.
Horien C, Noble S, Finn ES, Shen X, Scheinost D, Constable RT. Considering factors affecting the connectome-based identification process: Comment on Waller et al. NeuroImage 2017, 169: 172-175. PMID: 29253655, PMCID: PMC5856612, DOI: 10.1016/j.neuroimage.2017.12.045.Peer-Reviewed Original ResearchMultimodal Investigation of Network Level Effects Using Intrinsic Functional Connectivity, Anatomical Covariance, and Structure-to-Function Correlations in Unmedicated Major Depressive Disorder
Scheinost D, Holmes SE, DellaGioia N, Schleifer C, Matuskey D, Abdallah CG, Hampson M, Krystal JH, Anticevic A, Esterlis I. Multimodal Investigation of Network Level Effects Using Intrinsic Functional Connectivity, Anatomical Covariance, and Structure-to-Function Correlations in Unmedicated Major Depressive Disorder. Neuropsychopharmacology 2017, 43: 1119-1127. PMID: 28944772, PMCID: PMC5854800, DOI: 10.1038/npp.2017.229.Peer-Reviewed Original ResearchConceptsMajor depressive disorderAnterior cingulate cortexIntrinsic functional connectivityMedial prefrontal cortexFunctional connectivityLarge-scale brain networksDepressive disorderMDD groupAnatomical covarianceBrain networksUnmedicated major depressive disorderWhole-brain intrinsic functional connectivitySystem-level disorderIntrinsic connectivity distributionRegional brain structureMultiple brain networksAltered connectivityCommon findingHealthy comparison participantsDepressive symptomsAltered volumeUnmedicated individualsLocal circuitryCingulate cortexDepressive symptomatology
2015
The (in)stability of functional brain network measures across thresholds
Garrison KA, Scheinost D, Finn ES, Shen X, Constable RT. The (in)stability of functional brain network measures across thresholds. NeuroImage 2015, 118: 651-661. PMID: 26021218, PMCID: PMC4554838, DOI: 10.1016/j.neuroimage.2015.05.046.Peer-Reviewed Original Research