2020
Functional connectivity predicts changes in attention observed across minutes, days, and months
Rosenberg MD, Scheinost D, Greene AS, Avery EW, Kwon YH, Finn ES, Ramani R, Qiu M, Constable RT, Chun MM. Functional connectivity predicts changes in attention observed across minutes, days, and months. Proceedings Of The National Academy Of Sciences Of The United States Of America 2020, 117: 3797-3807. PMID: 32019892, PMCID: PMC7035597, DOI: 10.1073/pnas.1912226117.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelsAttentional stateSustained attentionIndividual differencesSustained attention functionFunctional connectivity signaturesFunctional brain connectivityFunctional connectivity patternsAttention functionConnectivity signaturesFunctional connectivityBrain connectivityConnectivity patternsAttentionSingle personSame patternIndividualsConnectivityIndependent studiesRecent workState changesPersonsPeopleDifferencesAbility
2019
Connectome-Based Prediction of Cocaine Abstinence
Yip SW, Scheinost D, Potenza MN, Carroll KM. Connectome-Based Prediction of Cocaine Abstinence. American Journal Of Psychiatry 2019, 176: 156-164. PMID: 30606049, PMCID: PMC6481181, DOI: 10.1176/appi.ajp.2018.17101147.Peer-Reviewed Original ResearchMeSH KeywordsAdultBehavior TherapyBrainCholinesterase InhibitorsCocaine-Related DisordersCognitionConnectomeExecutive FunctionFemaleFunctional NeuroimagingGalantamineHumansIndividualityMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeural PathwaysOpiate Substitution TreatmentOpioid-Related DisordersPrognosisRewardTreatment OutcomeConceptsConnectome-based predictive modelingCocaine use disorderUse disordersBrain-based predictorsLarge-scale neural networksFunctional MRI dataCocaine abstinenceExecutive controlReward responsivenessIndividual differencesBaseline cocaine usePosttreatment assessmentConnectivity strengthHeterogeneous sampleAbstinenceIndependent samplesNovel interventionsCanonical networksSpecific behaviorsCocaine useSignificant correspondenceDisordersTreatment outcomesNetwork strengthMRI data
2017
Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets
Yoo K, Rosenberg MD, Hsu WT, Zhang S, Li CR, Scheinost D, Constable RT, Chun MM. Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets. NeuroImage 2017, 167: 11-22. PMID: 29122720, PMCID: PMC5845789, DOI: 10.1016/j.neuroimage.2017.11.010.Peer-Reviewed Original ResearchConnectome-based Models Predict Separable Components of Attention in Novel Individuals
Rosenberg MD, Hsu WT, Scheinost D, Constable R, Chun MM. Connectome-based Models Predict Separable Components of Attention in Novel Individuals. Journal Of Cognitive Neuroscience 2017, 30: 160-173. PMID: 29040013, DOI: 10.1162/jocn_a_01197.Peer-Reviewed Original ResearchConceptsConnectome-based predictive modelingAttention Network TaskExecutive controlIntrinsic functional organizationRT variabilityANT performanceInfluential modelFunctional connectivityBrain's intrinsic functional organizationComponents of attentionExecutive control scoresResting-state functional connectivityResting-state dataFunctional brain networksFunctional organizationTask-based dataAttentional abilitiesUpcoming stimulusExplicit taskSustained attentionFMRI scanningAttention factorNovel individualsAdditional independent componentNetwork tasks