Kangjoo Lee, PhD
she/her/hers
Associate Research Scientist in Psychiatry
Research & Publications
Biography
Locations
Research Summary
Multimodal Precision Neuroimaging in Psychiatry
- Understanding the relationship between baseline neural activity and brain function
- Understanding the relationship between brain metabolism and connectivity
- Understanding state-dependent or pathological brain dynamics
- Psychiatric illnesses - Schizophrenia, Depression, PTSD (Yale collaborations)
- Neurological disorders - Epilepsy (Montreal collaborations: McGill University, Concordia University and Perform Centre)
- functional MRI, Structural MRI, EEG, MEG, NIRS, Calcium Imaging, Pupillometry
- Open science, machine learning, numerical analysis, statistical modeling
Coauthors
Selected Publications
- A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns.Berkovitch L, Lee K, Ji JL, Helmer M, Rahmati M, Demšar J, Kraljič A, Matkovič A, Tamayo Z, Murray JD, Repovš G, Krystal JH, Martin WJ, Fonteneau C, Anticevic A. A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns. MedRxiv 2023 PMID: 38168378, DOI: 10.1101/2023.12.15.23300019.
- Human brain state dynamics reflect individual neuro-phenotypes.Lee K, Ji JL, Fonteneau C, Berkovitch L, Rahmati M, Pan L, Repovš G, Krystal JH, Murray JD, Anticevic A. Human brain state dynamics reflect individual neuro-phenotypes. BioRxiv 2023 PMID: 37790400, DOI: 10.1101/2023.09.18.557763.
- Arousal impacts distributed hubs modulating the integration of brain functional connectivityLee K, Horien C, O’Connor D, Garand-Sheridan B, Tokoglu F, Scheinost D, Lake EMR, Constable RT. Arousal impacts distributed hubs modulating the integration of brain functional connectivity. NeuroImage 2022, 258: 119364. PMID: 35690257, PMCID: PMC9341222, DOI: 10.1016/j.neuroimage.2022.119364.
- A protocol for working with open-source neuroimaging datasetsHorien 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.
- An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivationCross NE, Pomares FB, Nguyen A, Perrault AA, Jegou A, Uji M, Lee K, Razavipour F, Bin Ka’b Ali O, Aydin U, Benali H, Grova C, Dang-Vu TT. An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation. PLOS Biology 2021, 19: e3001232. PMID: 34735431, PMCID: PMC8568176, DOI: 10.1371/journal.pbio.3001232.
- Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocolHorien C, Fontenelle S, Joseph K, Powell N, Nutor C, Fortes D, Butler M, Powell K, Macris D, Lee K, Greene AS, McPartland JC, Volkmar FR, Scheinost D, Chawarska K, Constable RT. Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol. Scientific Reports 2020, 10: 21855. PMID: 33318557, PMCID: PMC7736342, DOI: 10.1038/s41598-020-78885-z.
- A hitchhiker’s guide to working with large, open-source neuroimaging datasetsHorien 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.
- Effects of sleep deprivation on functional connectivity during a psychomotor vigilance taskNguyen A, Cross N, Pomares F, Jegou A, Perrault A, Lee K, Smith D, Aydin U, Grova C, Dang-Vu T. Effects of sleep deprivation on functional connectivity during a psychomotor vigilance task. Sleep Medicine 2019, 64: s83. DOI: 10.1016/j.sleep.2019.11.227.
- Cognitive performance and brain activation recovery after a nap following total sleep deprivationPomares F, Cross N, Jegou A, Nguyen A, Perrault A, Lee K, Smith D, Aydin U, Grova C, Dang-Vu T. Cognitive performance and brain activation recovery after a nap following total sleep deprivation. Sleep Medicine 2019, 64: s305-s306. DOI: 10.1016/j.sleep.2019.11.856.
- Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysisLee K, Khoo HM, Fourcade C, Gotman J, Grova C. Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis. Magnetic Resonance Imaging 2019, 58: 97-107. PMID: 30695721, DOI: 10.1016/j.mri.2019.01.019.
- Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsyLee K, Khoo HM, Lina JM, Dubeau F, Gotman J, Grova C. Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy. NeuroImage Clinical 2018, 20: 71-84. PMID: 30094158, PMCID: PMC6070692, DOI: 10.1016/j.nicl.2018.06.029.
- SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivityLee K, Lina JM, Gotman J, Grova C. SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity. NeuroImage 2016, 134: 434-449. PMID: 27046111, DOI: 10.1016/j.neuroimage.2016.03.049.
- Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysisLee YB, Lee J, Tak S, Lee K, Na DL, Seo SW, Jeong Y, Ye JC, Initiative T. Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis. NeuroImage 2015, 125: 1032-1045. PMID: 26524138, DOI: 10.1016/j.neuroimage.2015.10.081.
- Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessmentDansereau CL, Bellec P, Lee K, Pittau F, Gotman J, Grova C. Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment. Frontiers In Neuroscience 2014, 8: 419. PMID: 25565949, PMCID: PMC4274904, DOI: 10.3389/fnins.2014.00419.
- Sparse dictionary learning for resting-state fMRI analysisLee K, Han P, Ye J. Sparse dictionary learning for resting-state fMRI analysis. Proceedings Of SPIE--the International Society For Optical Engineering 2011, 8138: 81381x-81381x-7. DOI: 10.1117/12.894241.
- A DATA-DRIVEN SPATIALLY ADAPTIVE SPARSE GENERALIZED LINEAR MODEL FOR FUNCTIONAL MRI ANALYSISLee K, Tak S, Ye J. A DATA-DRIVEN SPATIALLY ADAPTIVE SPARSE GENERALIZED LINEAR MODEL FOR FUNCTIONAL MRI ANALYSIS. 2011, 1027-1030. DOI: 10.1109/isbi.2011.5872576.
- A Data-Driven Sparse GLM for fMRI Analysis using Sparse Dictionary Learning with MDL CriterionLee K, Tak S, Ye JC. A Data-Driven Sparse GLM for fMRI Analysis using Sparse Dictionary Learning with MDL Criterion. IEEE Transactions On Medical Imaging 2010, 30: 1076-1089. PMID: 21138799, DOI: 10.1109/tmi.2010.2097275.
- Quantification of CMRO2 without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurementsTak S, Jang J, Lee K, Ye JC. Quantification of CMRO2 without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements. Physics In Medicine And Biology 2010, 55: 3249-3269. PMID: 20479515, DOI: 10.1088/0031-9155/55/11/017.
- STATISTICAL PARAMETRIC MAPPING OF FMRI DATA USING SPARSE DICTIONARY LEARNINGLee K, Ye J. STATISTICAL PARAMETRIC MAPPING OF FMRI DATA USING SPARSE DICTIONARY LEARNING. 2010, 660-663. DOI: 10.1109/isbi.2010.5490090.
- Characterization and optimization of a quasi-monolithic detector module with depth-encoding for small animal PETYong Hyun Chung, Seung-Jae Lee, Cheol-Ha Baek, Kang-Joo Lee, and Yong Choi, “Characterization and optimization of a quasi-monolithic detector module with depth-encoding for small animal PET”, J. Korean Phys. Soc., vol. 54, no. 1, pp. 244–249, January 2009