Jones HM*, Yoo K, Chun MM, Rosenberg MD. Edge-based general linear models capture moment-to-moment fluctuations in attention. Journal of Neuroscience. 2024. [DOI: 10.1523/JNEUROSCI.1543-23.2024].

Jiang R*, Noble S, Sui J, Yoo K, Rosenblatt M, Horien C, Qi S, Liang Q, Sun H, Calhoun VD, Scheinost D. Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank. The Lancet Digital Health. 2023 June; 5(6): E350-E359. [DOI: 10.1016/S2589-7500(23)00043-2]. (2022 IF: 30.8; Medical Informatics Top 3.2%, rank 1/31)

Corriveau A*, Yoo K, Kwon YH, Chun MM, Rosenberg MD*. Functional connectome stability and optimality are markers of cognitive performance. Cerebral Cortex. 2023 Apr; 33(8): 5025–5041. [DOI: 10.1093/cercor/bhac396].

Kardan O*, Stier AJ, Cardenas-Iniguez C, Schertz KE, Pruin JC, Deng Y, Chamberlain T, Meredith WJ, Zhang X, Bowman JE, Lakhtakia T, Tindel L, Awery EW, Lin Q, Yoo K, Chun MM, Berman MG, Rosenberg MD*. Differences in the functional brain architecture of sustained attention and working memory in youth and adults. PLoS Biology. 2022 Dec; 20(12): e3001938. [DOI: 10.1371/journal.pbio.3001938]. (2022 IF: 9.8; Biology Top 4.3%, rank 4/92)

Yoo K*, Rosenberg MD, Kwon YH, Scheinost D, Constable RT, Chun MM. A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome. NeuroImage. 2022 Aug; 257: 119279. [DOI: 10.1016/j.neuroimage.2022.119279]. MATLAB Toolbox. (2022 IF: 5.7; Neuroimaging Top 7.1%, rank 1/14)

Yoo K*, Rosenberg MD, Kwon YH, Lin Q, Avery EW, Scheinost D, Constable RT, Chun MM*. A brain-based general measure of attention. Nature Human Behaviour.  2022 June; 6: 782-795. [DOI: 10.1038/s41562-022-01301-1]. MATLAB Toolbox. (2022 IF: 29.9; Neurosciences Top 0.7%, rank 2/272)

Wang X#, Yoo K#, Chen H*, Zou T, Wang H, Gao Q, Meng L*, Hu X*, Li R*. Antagonistic network signature of motor function in Parkinson’s disease revealed by connectome-based predictive modeling. npj Parkinson’s Disease. 2022 Apr; 8: 49. [DOI: 10.1038/s41531-022-00315-w]. (2022 IF: 8.7; Neurosciences Top 8.5%, rank 23/272)

Chung J, Lee P, Lee YB, Yoo K, Jeong Y*. Nonuniformity of whole-cerebral neural resource allocation; A neuromarker of the broad-task attention. eNeuro. 2022 Mar/Apr; 9(2): 0358-21. [DOI: 10.1523/ENEURO.0358-21.2022].

Kwon YH, Yoo K*, Nguyen H, Jeong Y*, Chun MM*. Predicting multilingual effects on executive function and individual connectomes in children: an ABCD Study. Proceedings of the National Academy of Sciences of the United States of America (PNAS). 2021 Dec; 118(49): e2110811118. [DOI: 10.1073/pnas.2110811118]. (2021 IF: 12.779; Multidisciplinary Sciences Top 12.162%, rank 9/74)

Lin Q*, Yoo K, Shen X, Constable RT, Chun MM. Functional connectivity during encoding predicts individual differences in long-term memory. Journal of Cognitive Neuroscience. 2021 Nov; 33(11): 2279-2296. [DOI: 10.1162/jocn_a_01759]. (work as a main mentor)

Stark GF*, Avery EW, Rosenberg MD, Greene AS, Gao S, Scheinost D, Constable RT, Chun MM, Yoo K. Using functional connectivity models to characterize relationships between working and episodic memory. Brain and Behavior. 2021 Aug; 11(8): e02105. [DOI: 10.1002/brb3.2105]. (work as a senior author)

Avery EW*, Yoo K, Rosenberg MD, Greene AS, Gao S, Na DL, Scheinost D, Constable RT, Chun MM. Distributed patterns of functional connectivity predict working memory performance in novel healthy and memory-impaired individuals. Journal of Cognitive Neuroscience. 2020 Feb; 32(2): 241-255. [DOI: 10.1162/jocn_a_01487]. (work as a main mentor)

Lee YB, Yoo K, Rho JH, Moon WJ, Jeong Y*. Brain-state extraction algorithm based on the state transition (BEST): A dynamic functional brain network analysis in fMRI study. Brain Topography. 2019 Sep; 32: 897-913. [DOI: 10.1007/s10548-019-00719-7]. (work as a main mentor)

Kumar S*, Yoo K, Rosenberg MD, Scheinost D, Constable RT, Zhang S, Li CSR, Chun MM. An Information Network Flow Approach for Measuring Functional Connectivity and Predicting Behavior. Brain and Behavior. 2019 Aug; 9(8):  e01346. [DOI:  10.1022/brb3.1346]. (work as a main mentor)

Yoo K*, Rosenberg MD, Noble S, Scheinost D, Constable RT, Chun MM. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. NeuroImage. 2019 Aug; 197: 212-223. [DOI: 10.1016/j.neuroimage.2019.04.060].

Fong AHC*, Yoo K, Rosenberg MD, Zhang S, Li CSR, Scheinost D, Constable RT, Chun MM. Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. NeuroImage. 2019 Mar; 188: 14-25. [DOI: 10.1016/j.neuroimage.2018.11.057]. (work as a main mentor)

Lin Q*, Rosenberg MD, Yoo K, Hsu W-T, O'Connell TP, Chun MM. Resting-state Functional Connectivity Predicts Cognitive Impairment related to Alzheimer’s Disease. Frontiers in Aging Neuroscience. 2018 Apr; 10: 94. [DOI: 10.3389/fnagi.2018.00094].

Yoo K*, Rosenberg MD, Hsu W-T, Zhang S, Li CSR, Scheinost D, Constable RT, Chun MM. Connectome-based predictive modeling of attention: comparing different functional connectivity features and prediction methods across datasets. NeuroImage. 2018 Feb; 167: 11-22. [DOI: 10.1016/j.neuroimage.2017.11.010].

Kang M, Lee YB, Gohel B, Yoo K, Lee P, Chung J, Jeong Y*. Momentary level of slow default mode network activity is associated with distinct propagation and connectivity patterns in the anesthetized mouse cortex. Journal of Neurophyshiology. 2018 Feb; 119(2): 441-458. [DOI: 10.1152/jn.00163.2017].

Ghahremani M, Yoo J, Chung SJ, Yoo K, Ye JC*, Jeong Y*. Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson’s Disease. Journal of Movement Disorders. 2018 Jan; 11: 13-23. [DOI: 10.14802/jmd.17061].

Chung J, Yoo K, Lee P, Kim CM, Roh JH, Park JE, Kim SJ, Seo SW, Shin JH, Seong JK*, Jeong Y*. Normalization of cortical thickness measurements across different T1 magnetic resonance imaging protocols by novel w-score standardization. NeuroImage. 2017 Oct; 159(1): 224-235. [DOI:10.1016/j.neuroimage.2017.07.053]. (work as a main mentor)

Sohn WS, Lee TY, Yoo K, Kim M, Yun JY, Hur JW, Yoon YB, Seo SW, Na DL, Jeong Y*, Kwon JS*. Node identification using inter-regional correlation analysis for mapping detailed connections in resting state networks. Frontiers in Neuroscience. 2017 May; 11: 238. [DOI: 10.3389/fnins.2017.00238].

Yoo K, Lee P, Chung MK, Sohn WS, Chung SJ, Na DL, Ju D, Jeong Y*. Degree-based statistic and center persistency for brain connectivity analysis. Human Brain Mapping. 2017 Jan; 38(1): 165-181. [DOI: 10.1002/hbm.23352]. (Cover illustration)

Lee E#, Yoo K#, Lee YB, Chung J, Lim JE, Yoon B*, Jeong Y*, ADNI. Default mode network functional connectivity in early and late mild cognitive impairment: results from the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer Disease & Associated Disorders. 2016 Oct-Dec; 30(4): 289-296. [DOI: 10.1097/WAD.0000000000000143]

Chung J, Yoo K, Kim E, Na DL, Jeong Y*. Glucose metabolic brain networks in early-onset versus late-onset Alzheimer’s disease. Frontiers in Aging Neuroscience. 2016 Jun; 8: 159. [DOI: 10.3389/fnagi.2016.00159]. (work as a main mentor)

Sohn WS, Yoo K, Lee YB, Seo SW, Na DL, Jeong Y*. Influence of ROI selection on resting functional connectivity: an individualized approach for resting fMRI analysis. Frontiers in Neuroscience. 2015 Aug; 9:280. [DOI: 10.3389/fnins.2015.00280].

Yoo SW, Guevara P, Jeong Y, Yoo K, Shin JS, Mangin J-F, Seong JK. An example-based multi-atlas approach to automatic labeling of white matter tracts. PLoS One. 2015 July; 10(7): e01333337. [DOI: 10.1371/journal.pone.0133337].

Kim H#, Yoo K#, NA DL, Seo SW, Jeong J*, Jeong Y*. Non-monotonic reorganization of brain networks with Alzheimer’s disease progression. Frontiers in Aging Neuroscience. 2015 June; 7:111. [DOI: 10.3389/fnagi.2015.00111].

Kim TE, Lee DH, Kim YJ, Mok JO, Kim CH, Park JH, Lee TK, Yoo K, Jeong Y, Lee Y, Park SA*. The relationship between cognitive performance and insulin resistance in non-diabetic patients with mild cognitive impairment. International journal of geriatric psychiatry. 2015 June; 30(6): 551-557. [DOI: 10.1002/gps.4181].

Yoo K, Chung SJ, Kim HS, Choung O, Lee YB, Kim MJ, You S, Jeong Y*. Neural substrates of motor and non-motor symptoms in Parkinson’s disease: a resting fMRI study. PLoS One. 2015 Apr; 10(4): e0125455. [DOI: 10.1371/journal.pone.0125455].

Lee E, Lee JE, Yoo K, Hong JY, Oh J, Sunwoo MK, Kim JS, Jeong Y, Lee PH, Sohn YH, Kang SY*. Neural correlates of progressive reduction of bradykinesia in de novo Parkinson’s disease. Parkinsonism & related disorders. 2014 Dec; 20(12): 1376-1381. [DOI: 10.1016/j.parkreldis.2014.09.027].

Sohn WS, Yoo K, Na DL, Jeong Y*. Progressive changes in hippocampal resting-state connectivity across cognitive impairment: a cross-sectional study from normal to Alzheimer disease. Alzheimer Disease and Associated Disorders. 2014 Jul-Sep; 28(3): 239-246. [DOI: 10.1097/WAD.0000000000000027].

Yoo K, Sohn WS, Jeong Y*. Tool-use practice induces changes in intrinsic functional connectivity of parietal areas. Frontiers in Human Neuroscience. 2013 Feb; 7(49). [DOI: 10.3389/fnhum.2013.00049].

Sohn WS, Yoo K, Jeong Y*. Independent component analysis of localized resting-state functional magnetic resonance imaging reveals specific motor subnetworks. Brain connectivity. 2012 Sep; 2(4): 218-224. [DOI: 10.1089/brain.2012.0079].

Tak S, Yoon SJ, Jang J, Yoo K, Jeong Y*, Ye JC*. Quantitative analysis of hemodynamic and metabolic changes in subcortical vascular dementia using simultaneous near-infrared spectroscopy and fMRI measurements. NeuroImage. 2011 Mar; 55(1): 179-184. [DOI: 10.1016/j.neuroimage.2010.11.046].

[Letter to the Editor] Sohn WS*, Yoo K, Jeong Y. On the Novelty of Localized Independent Component Analysis (Response to Igelström, et al., Neural Processes in the Human Temporoparietal Cortex Separated by Localized Independent Component Analysis). The Journal of Neuroscience. 2015 Aug. (link)

Sohn WS, Yoo K, Kim J, Jeong Y*. Resting state brain networks and their implications in neurodegenerative disease. Proc. SPIE 8548, Nanosystems in Engineering and Medicine. 2012 Oct; 85483Z. [DOI: 10.1117/12.2000254]

Chamberlain T*, Corriveau A, Song H, Kwon YH, Yoo K, Chun MM, Rosenberg MD*. (submitted). High performers demonstrate greater neural synchrony than low performers across behavioral domains. bioRxiv. 2023. https://doi.org/10.1101/2023.06.22.546173.

Kwon YH, Salvo JJ, Anderson N, Holubecki AM, Lakshman M, Yoo K, Kay K, Gratton C, Braga RM. (submitted). Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity. bioRxiv. 2023. https://doi.org/10.1101/2023.08.16.553585.

Yoo K*, Kwon YH, Rosenberg MD, Chun MM*. (Submitted). Higher attentional demands change functional connectivity patterns and amplify individual differences.

Yoo K*, Chun MM, et al. (in preparation). Multivariate functional connectivity in brain fingerprinting and predictive modeling of attention.