2024
Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention
Jones H, Yoo K, Chun M, Rosenberg M. Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention. Journal Of Neuroscience 2024, 44: e1543232024. PMID: 38316565, PMCID: PMC10993033, DOI: 10.1523/jneurosci.1543-23.2024.Peer-Reviewed Original ResearchMoment-to-moment fluctuationsFMRI functional connectivityFMRI analysisProcessing of task-relevant informationSustained attention taskTask-based fMRITask-relevant informationDynamic FC approachParametric fMRI analysisField of network neuroscienceTraditional fMRI analysisNeural underpinningsAttention taskSustained attentionIndividual differencesCognitive processesTask eventsFunctional connectivityNetwork neuroscienceRegions-of-interestGeneralized linear modelAttentional stateFMRI datasetsYoung adultsIndividual's ability
2022
A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth
Horien C, Greene A, Shen X, Fortes D, Brennan-Wydra E, Banarjee C, Foster R, Donthireddy V, Butler M, Powell K, Vernetti A, Mandino F, O'Connor D, Lake E, McPartland J, Volkmar F, Chun M, Chawarska K, Rosenberg M, Scheinost D, Constable R. A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth. Cerebral Cortex 2022, 33: 6320-6334. PMID: 36573438, PMCID: PMC10183743, DOI: 10.1093/cercor/bhac506.Peer-Reviewed Original ResearchConceptsAttention taskAttentional stateConnectome-based predictive modelingNeurodiverse conditionsSustained attention taskAttention network modelSample of youthNeurotypical participantsSustained attentionBrain correlatesNeurobiological correlatesAttention networkIndividual participantsSeparate samplesYouthParticipantsHead motionTaskCorrelatesAttentionAutismConfoundsNetwork modelGeneralizesHealthcare settings