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 representationTaskPrediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods
Antons S, Yip S, Lacadie C, Dadashkarimi J, Scheinost D, Brand M, Potenza M. Prediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods. Journal Of Behavioral Addictions 2024, 13: 695-701. PMID: 39356557, PMCID: PMC11457034, DOI: 10.1556/2006.2024.00050.Peer-Reviewed Original ResearchConceptsAddictive behaviorsDisorders due to addictive behaviorsConnectome-based predictive modelingPrediction of cravingInvestigate neural mechanismsSubstance use disordersNeural mechanismsCravingSubstance useMethodological considerationsDisordersMethodological featuresBehaviorConceptualizationCommentaryStudyFindingsSubstancesEdge-centric network control on the human brain structural network
Sun H, Rosenblatt M, Dadashkarimi J, Rodriguez R, Tejavibulya L, Scheinost D. Edge-centric network control on the human brain structural network. Imaging Neuroscience 2024, 2: 1-15. DOI: 10.1162/imag_a_00191.Peer-Reviewed Original ResearchHuman brain structural networksNetwork control theoryEdge controlWhole-brain networksHuman Connectome ProjectDiffusion MRI dataWhite matter connectivityConnectome ProjectBrain dynamicsExecutive functionBrain structural networksBrain network connectivityBrain connectivityFunctional connectomeState transitionsTransitionEnergy patternsTheory modelBrain energy consumptionDynamic processStructural networkStateNetwork control mechanismsCognitive statesNetwork pairs
2022
Transforming Connectomes to “Any” Parcellation via Graph Matching
Liang Q, Dadashkarimi J, Dai W, Karbasi A, Chang J, Zhou H, Scheinost D. Transforming Connectomes to “Any” Parcellation via Graph Matching. Lecture Notes In Computer Science 2022, 13754: 118-127. DOI: 10.1007/978-3-031-21083-9_12.Peer-Reviewed Original ResearchMachine 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 StatementsCombining Multiple Atlases to Estimate Data-Driven Mappings Between Functional Connectomes Using Optimal Transport
Dadashkarimi J, Karbasi A, Scheinost D. Combining Multiple Atlases to Estimate Data-Driven Mappings Between Functional Connectomes Using Optimal Transport. Lecture Notes In Computer Science 2022, 13431: 386-395. DOI: 10.1007/978-3-031-16431-6_37.Peer-Reviewed Original ResearchPredicting 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 StatementsPredicting Transdiagnostic Social Impairments in Childhood Using Connectome-Based Predictive Modeling
Dufford A, Kimble V, Tejavibulya L, Dadashkarimi J, Scheinost D. Predicting Transdiagnostic Social Impairments in Childhood Using Connectome-Based Predictive Modeling. Biological Psychiatry 2022, 91: s87. DOI: 10.1016/j.biopsych.2022.02.234.Peer-Reviewed Original Research
2021
Data-Driven Mapping Between Functional Connectomes Using Optimal Transport
Dadashkarimi J, Karbasi A, Scheinost D. Data-Driven Mapping Between Functional Connectomes Using Optimal Transport. Lecture Notes In Computer Science 2021, 12903: 293-302. DOI: 10.1007/978-3-030-87199-4_28.Peer-Reviewed Original ResearchFunctional connectivity during frustration: a preliminary study of predictive modeling of irritability in youth
Scheinost D, Dadashkarimi J, Finn ES, Wambach CG, MacGillivray C, Roule AL, Niendam TA, Pine DS, Brotman MA, Leibenluft E, Tseng WL. Functional connectivity during frustration: a preliminary study of predictive modeling of irritability in youth. Neuropsychopharmacology 2021, 46: 1300-1306. PMID: 33479511, PMCID: PMC8134471, DOI: 10.1038/s41386-020-00954-8.Peer-Reviewed Original ResearchConceptsFunctional connectivityAttention-deficit/hyperactivity disorderCognitive flexibility taskDisruptive mood dysregulation disorderAffective Reactivity IndexLevels of irritabilityPreliminary fMRI studyCognitive flexibilityFlexibility taskIndividual differencesTransdiagnostic sampleFrontal networkFMRI studyHyperactivity disorderTask difficultyNeural mechanismsParent reportAnxiety symptomsSalience networkAnxiety disordersFrustrative nonrewardDimensional measuresPreliminary evidenceReactivity indexChild psychiatry
2020
Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders
Barron DS, Gao S, Dadashkarimi J, Greene AS, Spann MN, Noble S, Lake EMR, Krystal JH, Constable RT, Scheinost D. Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders. Cerebral Cortex 2020, 31: 2523-2533. PMID: 33345271, PMCID: PMC8023861, DOI: 10.1093/cercor/bhaa371.Peer-Reviewed Original ResearchConceptsMacroscale brain networksIndividual differencesBrain networksMemory deficitsFunctional connectivityAttention deficit hyper-activity disorderTask-based functional MRI dataLong-term memoryWhole-brain functional connectivityDiagnostic groupsWhole-brain patternsDefault mode networkFunctional MRI dataHuman Connectome ProjectPsychiatric disordersMemory constructsMemory performanceTransdiagnostic sampleBrain correlatesMode networkFunctional connectomeConnectome ProjectLimbic networkHealthy participantsMemoryA 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
A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences
Dadashkarimi J, Gao S, Yeagle E, Noble S, Scheinost D. A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences. Lecture Notes In Computer Science 2019, 11848: 64-73. DOI: 10.1007/978-3-030-32391-2_7.Peer-Reviewed Original Research
2017
Dimension Projection Among Languages Based on Pseudo-Relevant Documents for Query Translation
Dadashkarimi J, Shahshahani M, Tebbifakhr A, Faili H, Shakery A. Dimension Projection Among Languages Based on Pseudo-Relevant Documents for Query Translation. Lecture Notes In Computer Science 2017, 10193: 493-499. DOI: 10.1007/978-3-319-56608-5_39.Peer-Reviewed Original ResearchAn expectation-maximization algorithm for query translation based on pseudo-relevant documents
Dadashkarimi J, Shakery A, Faili H, Zamani H. An expectation-maximization algorithm for query translation based on pseudo-relevant documents. Information Processing & Management 2017, 53: 371-387. DOI: 10.1016/j.ipm.2016.11.007.Peer-Reviewed Original Research
2016
Pseudo-Relevance Feedback Based on Matrix Factorization
Zamani H, Dadashkarimi J, Shakery A, Croft W. Pseudo-Relevance Feedback Based on Matrix Factorization. 2016, 1483-1492. DOI: 10.1145/2983323.2983844.Peer-Reviewed Original ResearchPseudo-relevance feedbackQuery modelMatrix factorization techniqueCollaborative recommender systemsFactorization techniqueMatrix factorizationVector space modelWeights of termsInformation retrievalRecommendation techniquesRecommender systemsDocument retrievalRecommendation problemRetrieval performanceTREC collectionsCompetitive baselinesExtensive experimentsRetrieval modelLanguage modelingQueriesArt techniquesFeedback BasedGeneral frameworkRetrievalSpace modelSS4MCT: A Statistical Stemmer for Morphologically Complex Texts
Dadashkarimi J, Nasr Esfahani H, Faili H, Shakery A. SS4MCT: A Statistical Stemmer for Morphologically Complex Texts. Lecture Notes In Computer Science 2016, 9822: 201-207. DOI: 10.1007/978-3-319-44564-9_16.Peer-Reviewed Original ResearchBuilding a multi-domain comparable corpus using a learning to rank method†
RAHIMI R, SHAKERY A, DADASHKARIMI J, ARIANNEZHAD M, DEHGHANI M, ESFAHANI H. Building a multi-domain comparable corpus using a learning to rank method†. Natural Language Engineering 2016, 22: 627-653. DOI: 10.1017/s1351324916000164.Peer-Reviewed Original Research
2015
Revisiting Optimal Rank Aggregation
Tabrizi S, Dadashkarimi J, Dehghani M, Esfahani H, Shakery A. Revisiting Optimal Rank Aggregation. 2015, 353-356. DOI: 10.1145/2808194.2809490.Peer-Reviewed Original Research