My research focus spans over systems neuroscience, machine learning, and data science with a substantial experience in developing and applying novel computational frameworks to understand dynamical aspects of complex brain function in human and non-human models.

Honors & Awards

  • Alzheimer’s Association Research Fellowship, Alzheimer’s Association (2021-2024)
  • Best Doctoral Dissertation Award in Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), South Korea (2019)
  • National scholarship of Republic of Korea (Integrated M.S. & Ph.D. Course), South Korea (2011-2019)
  • National scholarship of Republic of Korea (B.Sc. Course), South Korea (2007-2011)

Professional Education

  • Doctor of Philosophy, Korea Advanced Institute Of Science and Technology (2019)
  • Ph.D., Korea Advanced Institute of Science and Technology (KAIST), Bio and Brain Engineering (2019)
  • B.S., Korea Advanced Institute of Science and Technology (KAIST), Electrical Engineering (2011)

Stanford Advisors

All Publications

  • Space wandering in the rodent default mode network. Proceedings of the National Academy of Sciences of the United States of America Nghiem, T. E., Lee, B., Chao, T. H., Branigan, N. K., Mistry, P. K., Shih, Y. I., Menon, V. 2024; 121 (15): e2315167121


    The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

    View details for DOI 10.1073/pnas.2315167121

    View details for PubMedID 38557177

  • Optogenetic stimulation of anterior insular cortex neurons in male rats reveals causal mechanisms underlying suppression of the default mode network by the salience network. Nature communications Menon, V., Cerri, D., Lee, B., Yuan, R., Lee, S. H., Shih, Y. I. 2023; 14 (1): 866


    The salience network (SN) and default mode network (DMN) play a crucial role in cognitive function. The SN, anchored in the anterior insular cortex (AI), has been hypothesized to modulate DMN activity during stimulus-driven cognition. However, the causal neural mechanisms underlying changes in DMN activity and its functional connectivity with the SN are poorly understood. Here we combine feedforward optogenetic stimulation with fMRI and computational modeling to dissect the causal role of AI neurons in dynamic functional interactions between SN and DMN nodes in the male rat brain. Optogenetic stimulation of Chronos-expressing AI neurons suppressed DMN activity, and decreased AI-DMN and intra-DMN functional connectivity. Our findings demonstrate that feedforward optogenetic stimulation of AI neurons induces dynamic suppression and decoupling of the DMN and elucidates previously unknown features of rodent brain network organization. Our study advances foundational knowledge of causal mechanisms underlying dynamic cross-network interactions and brain network switching.

    View details for DOI 10.1038/s41467-023-36616-8

    View details for PubMedID 36797303

    View details for PubMedCentralID 2899886

  • Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli. Science advances Chao, T. H., Lee, B., Hsu, L. M., Cerri, D. H., Zhang, W. T., Wang, T. W., Ryali, S., Menon, V., Shih, Y. I. 2023; 9 (7): eade5732


    The default mode network (DMN) is critical for self-referential mental processes, and its dysfunction is implicated in many neuropsychiatric disorders. However, the neurophysiological properties and task-based functional organization of the rodent DMN are poorly understood, limiting its translational utility. Here, we combine fiber photometry with functional magnetic resonance imaging (fMRI) and computational modeling to characterize dynamics of putative rat DMN nodes and their interactions with the anterior insular cortex (AI) of the salience network. Our analysis revealed neuronal activity changes in AI and DMN nodes preceding fMRI-derived DMN activations and cyclical transitions between brain network states. Furthermore, we demonstrate that salient oddball stimuli suppress the DMN and enhance AI neuronal activity and that the AI causally inhibits the retrosplenial cortex, a prominent DMN node. These findings elucidate the neurophysiological foundations of the rodent DMN, its spatiotemporal dynamical properties, and modulation by salient stimuli, paving the way for future translational studies.

    View details for DOI 10.1126/sciadv.ade5732

    View details for PubMedID 36791185

  • Dopaminergic medication normalizes aberrant cognitive control circuit signalling in Parkinson's disease. Brain : a journal of neurology Cai, W., Young, C. B., Yuan, R., Lee, B., Ryman, S., Kim, J., Yang, L., Henderson, V. W., Poston, K. L., Menon, V. 2022


    Dopaminergic medication is widely used to alleviate motor symptoms of Parkinson's disease (PD), but these medications also impact cognition with significant variability across patients. It is hypothesized that dopaminergic medication impacts cognition and working memory in PD by modulating frontoparietal-basal ganglia cognitive control circuits, but little is known about the underlying causal signalling mechanisms and their relation to individual differences in response to dopaminergic medication. Here we use a novel state-space computational model with ultra-fast (490 msec resolution) fMRI to investigate dynamic causal signalling in frontoparietal-basal ganglia circuits associated with working memory in 44 PD patients ON and OFF dopaminergic medication, as well as matched 36 healthy controls. Our analysis revealed aberrant causal signaling in frontoparietal-basal ganglia circuits in PD patients OFF medication. Importantly, aberrant signaling was normalized by dopaminergic medication and a novel quantitative distance measure predicted individual differences in cognitive change associated with medication in PD patients. These findings were specific to causal signaling measures, as no such effects were detected with conventional non-causal connectivity measures. Our analysis also identified a specific frontoparietal causal signaling pathway from right middle frontal gyrus to right posterior parietal cortex that is impaired in PD. Unlike in healthy controls, the strength of causal interactions in this pathway did not increase with working memory load and the strength of load-dependent causal weights was not related to individual differences in working memory task performance in PD patients OFF medication. However, dopaminergic medication in PD patients reinstated the relation with working memory performance. Our findings provide new insights into aberrant causal brain circuit dynamics during working memory and identify mechanisms by which dopaminergic medication normalizes cognitive control circuits.

    View details for DOI 10.1093/brain/awac007

    View details for PubMedID 35357463

  • Latent brain state dynamics and cognitive flexibility in older adults. Progress in neurobiology Lee, B., Cai, W., Young, C. B., Yuan, R., Ryman, S., Kim, J., Santini, V., Henderson, V. W., Poston, K. L., Menon, V. 2021: 102180


    Cognitive impairment in older adults is a rapidly growing public health concern as the elderly population dramatically grows worldwide. While it is generally assumed that cognitive deficits in older adults are associated with reduced brain flexibility, quantitative evidence has been lacking. Here, we investigate brain flexibility in healthy older adults (ages 60-85) using a novel Bayesian switching dynamical system algorithm and ultrafast temporal resolution (490msec) whole-brain fMRI data during performance of a Sternberg working memory task. We identify latent brain states and characterize their dynamic temporal properties, including state transitions, associated with encoding, maintenance, and retrieval. Crucially, we demonstrate that brain inflexibility is associated with slower and more fragmented transitions between latent brain states, and that brain inflexibility mediates the relation between age and cognitive inflexibility. Our study provides a novel neurocomputational framework for investigating latent dynamic circuit processes underlying brain flexibility and cognition in the context of aging.

    View details for DOI 10.1016/j.pneurobio.2021.102180

    View details for PubMedID 34627994

  • Combined Positive and Negative Feedback Allows Modulation of Neuronal Oscillation Frequency during Sensory Processing CELL REPORTS Lee, B., Shin, D., Gross, S. P., Cho, K. 2018; 25 (6): 1548-+


    A key step in sensory information processing involves modulation and integration of neuronal oscillations in disparate frequency bands, a poorly understood process. Here, we investigate how top-down input causes frequency changes in slow oscillations during sensory processing and, in turn, how the slow oscillations are combined with fast oscillations (which encode sensory input). Using experimental connectivity patterns and strengths of interneurons, we develop a system-level model of a neuronal circuit controlling these oscillatory behaviors, allowing us to understand the mechanisms responsible for the observed oscillatory behaviors. Our analysis discovers a circuit capable of producing the observed oscillatory behaviors and finds that a detailed balance in the strength of synaptic connections is the critical determinant to produce such oscillatory behaviors. We not only uncover how disparate frequency bands are modulated and combined but also give insights into the causes of abnormal neuronal activities present in brain disorders.

    View details for DOI 10.1016/j.celrep.2018.10.029

    View details for Web of Science ID 000449476500014

    View details for PubMedID 30404009

  • The Hidden Control Architecture of Complex Brain Networks ISCIENCE Lee, B., Kang, U., Chang, H., Cho, K. 2019; 13: 154-+


    The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the brain networks have a distributed and overlapping control architecture governed by a small number of control nodes, which may be responsible for the robust and efficient brain functions. Moreover, our artificial network evolution analysis showed that the distributed and overlapping control architecture of the brain network emerges when it evolves toward increasing both robustness and efficiency.

    View details for DOI 10.1016/j.isci.2019.02.017

    View details for Web of Science ID 000462829500013

    View details for PubMedID 30844695

    View details for PubMedCentralID PMC6402303