Kangjoo Lee, Ph.D.

Computational and clinical neuroscientist

Kangjoo Lee

Kangjoo Lee, Ph.D.

Computational and clinical neuroscientist

Research Interests

The identification of neural circuit abnormalities associated with psychiatric symptoms is key for developing neural markers for early risk detection and ultimately for developing rationally-guided therapeutics. Traditional categorical approaches for psychiatric diagnoses, such as the DSM-5 criteria, often fail to align with individual clinical profiles and lack consideration of the neural mechanisms or circuits underlying specific symptoms, complicating treatment planning. I use precision functional and anatomical neuroimaging measurements in humans (functional magnetic resonance imaging [fMRI], Electroencephalography [EEG], and Positron Emission Tomography [PET]) to identify neural circuit abnormalities underlying symptoms of mental illness. My research program integrates longitudinal assessments and time-series analyses to map neural signal measures to symptoms that manifest over various timescales. Using machine learning (ML) techniques, such as dimensionality reduction, multivariate linear models and artificial intelligence (AI)-driven models, I conduct multimodal neuroimaging studies combined with longitudinal phenotyping, computational modelling and neuroinformatics to establish reproducible brain-behavior associations in psychiatric illnesses. I aim to develop methods to identify neural circuit abnormalities underlying psychiatric and cognitive symptoms, develop multimodal neural markers of psychopathology, and develop symptom-specific pharmacological treatment strategies.

1. Innovations in Functional Neuroimaging Analysis
2. Development of Neural Markers of Epilepsy, Sleep and Cognition
3. Metabolic and Physiological Signatures of Human Brain Networks
4. Reproducible Mental Health Research with Open Science and Artificial Intelligence
5. Developing Neural Markers of Psychiatric Symptoms and Personalized Treatments

Innovations in Functional Neuroimaging Analysis

Understanding how brain functions are mapped within the brain is a key question in neuroscience. I develop analysis methods and software tools to study the organizations and functions of the human brain.

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Development of Neural Markers of Epilepsy, Sleep and Cognition

I use multimodal neuroimaging (e.g. EEG/fMRI) to identify the neurobiological mechnisms and develop neural markers of cognitive impairments and symptoms of brain disorders such as epilepsy and sleep deprivation.

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Metabolic and Physiological Signatures of Human Brain Networks

I integrate multimodal neuroimaging with physiological recordings to understand physiological brain state variations and brain metabolism associated with the brain's functional organizations.

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Reproducible Mental Health Research with Open Science and Artificial Intelligence

I am committed to improve the reproducibility of brain research by leveraging open and inclusive scientific approaches and integrating these principles into advanced statistical/machine learning frameworks for neuroimaging.

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Developing Neural Markers of Psychiatric Symptoms and Personalized Treatments

I develop methods to identify neural circuit abnormalities underlying psychiatric and cognitive symptoms, develop multimodal neural markers of psychopathology, and develop symptom-specific pharmacological treatment strategies, focusing on psychosis spectrum disorders and depression.

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Skills and Expertise

Expertise I: Neuroimaging Data Acquisition

  • Functional MRI (fMRI; task / rest / sleep / hypercapnia / hyperoxia / arterial spin labeling)
  • Simultaneous Pupillometry and fMRI
  • Simultaneous Near-infrared Spectroscopy (NIRS) and fMRI
  • Simultaneous Electroencephalography (EEG) and fMRI
  • Simultaneous Calcium imaging and fMRI (animal)
  • High Resolution Research Positron Emission Tomography (HRRT-PET)-MRI (7 Tesla) fusion
  • Expertise II: Neuroimaging Data Analysis

  • BOLD fMRI: preprocessing (BioImage Suite; Neuroimaging Analysis Toolkit - NIAK; MINC Tool kit; Statistical Parametric Mapping -SPM; FSL; QuNex), analysis (SPM, Sparse SPM: own development, SPARK: own development, Connectome-based Predictive Modeling - CPM, Nilearn Machine learning for Neuroimaging in Python)
  • Pupillometry: preprocessing and analysis
  • Calibrated fMRI (ASL) preprocessing and analysis
  • NIRS: preprocessing and analysis (NIRS-SPM)
  • Calcium imaging: preprocessing and analysis
  • Monte-Carlo Simulation for PET: Geant4 Application for Tomographic Emission
  • Expertise III: Software Skills

  • Programming: Python, R, MATLAB, Octave, High Performance Computing (HPC), Shell
  • Operating Systems: Linux, Unix, Mac OS X, Windows
  • Text/Graphic Software: MS Office, Latex, GIMP, Adobe Illustrator/Photoshop, Keynote