Biology subjects

Kwon, O.-S.

Publications and source records attributed to Kwon, O.-S..

7 recordsLinked to original sources

bioRxiv2022

Decoding Imagined Musical Pitch from Human Scalp Electroencephalograms

Brain-computer interfaces (BCIs) can restore the functions of communication and control in people with paralysis. In addition to the currently proven functions restored by BCIs, it would enrich life if one could regain a function of musical activity. However, it remains largely unknown whether it is feasible to decode imagined musical information directly from neural activity. Among various musical information, this study aimed to decode pitch information directly from scalp electroencephalography (EEG). Twenty healthy participants performed a task to imagine one of the seven musical pitches (C4 - B4) randomly. To find EEG features for pitch imagination, we took two approaches: exploring multi-band spectral power at individual channels (IC); and exploring power differences between bilaterally symmetric channels (DC). We classified these features into the seven pitch classes using various types of classifiers. The selected spectral power features revealed marked contrasts between left and right hemispheres, between low-, (<13 Hz) and high-frequency (> 13 Hz) bands, and between frontal and parietal areas. The best classification performance for seven pitches was obtained using the IC feature and SVM with the average accuracy of 35.68{+/-}7.47% (max. 50%) and the average information transfer rate (ITR) of 0.37{+/-}0.22 bits/sec. Yet, when we decoded a different number of classes (K = 2 [~] 6) by grouping adjacent pitches, ITR was similar across K as well as between IC and DC features, suggesting efficiency of DC features. This study would be the first to demonstrate the feasibility of decoding imagined musical pitch directly from human EEG.

bioengineering↗

bioRxiv2022

A key role of orientation in the coding of visual motion direction

Despite the fundamental importance of visual motion processing, our understanding of how the brain represents basic aspects of motion is incomplete. While it is generally believed that direction is the main representational feature of motion, motion processing is also influenced by non-directional orientation signals that are present in most motion stimuli. Here, we aimed to test whether this non-directional motion axis determines motion perception even when orientation is completely absent from the stimulus. Using stimuli with and without orientation signals, we found that serial dependence in a simple motion direction estimation task was predominantly determined by the orientation of the previous motion stimulus. Moreover, the observed attraction profiles closely matched the characteristic pattern of serial attraction found in orientation perception. Evidently, the sequential integration of motion signals depends solely on the orientation of motion, indicating a fundamental role of non-directional orientation in the coding of visual motion direction.

neuroscience↗

bioRxiv2022

Dissecting the effects of adaptive encoding and predictive inference on a single perceptual estimation

The human visual system exploits continuity in the visual environment, which induces two contrasting perceptual phenomena: repulsive aftereffects and attractive serial dependences. Recent studies have theorized that these two effects concurrently arise in perceptual processing, but empirical evidence that directly supports this hypothesis lacks. Here we show that the two effects concurrently modulate estimation behavior in a typical sequence of perceptual tasks. We first demonstrate that observers estimation error as a function of both the previous stimulus and response cannot be fully described by either attractive or repulsive bias but is instead well captured by a summation of repulsion from the previous stimulus and attraction toward the previous response. We then reveal that the repulsive bias is centered on the observers sensory encoding of the previous stimulus, which is again repelled away from the preceding trials, whereas the attractive bias is centered precisely on the previous response, which is the observers best prediction about the incoming stimuli. Our findings provide strong evidence that sensory coding is shaped by dynamic tuning of the system to the past stimuli, inducing repulsive aftereffects, and followed by inference incorporating the prediction from the past estimation, leading to attractive serial dependence.

neuroscience↗

bioRxiv2021

Effect of spatiotemporally changing environment on serial dependence in ensemble representations

The recent visual past has a strong impact on our current perception. Recent studies of serial dependence in perception show that low-level adaptation repels our current perception away from previous stimuli1-5 whereas post-perceptual decision attracts perceptual report toward the immediate past6-12. In their studies, these repulsive and attractive biases were observed with different task demands perturbing ongoing sequential process. Therefore, it is unclear whether the opposite biases arise naturally in navigating complex real-life environments. Here we only manipulated the environmental statistics to characterize how serially dependent perceptual decisions unfold in spatiotemporally changing visual environments. During sequential mean orientation adjustment task on the array of Gabor patches, we found that the repulsion effect dominated only when ensemble variance increased across consecutive trials whereas the attraction effect prevailed when ensemble variance decreased or remained the same. The observed attractive bias by high- to-low-variance stimuli and repulsive bias by low-to-high-variance stimuli were reinforced by the repeated exposure to the low and the high ensemble variance, respectively. Further, this variance-dependent differential pattern of serial dependence in ensemble representation remained the same regardless of whether observers had a prior knowledge of environmental statistics or not. We used a Bayesian observer model constrained by visual adaptation13,14 to provide a unifying account of both attractive and repulsive bias in perception. Our results establish that the temporal integration and segregation of visual information is flexibly adjusted through variance adaptation.

neuroscience↗

bioRxiv2020

Subjective optimality in finite sequential decision-making

Many decisions in life are sequential and constrained by a time window. Although mathematically derived optimal solutions exist, it has been reported that humans often deviate from making optimal choices. Here, we used a secretary problem, a classic example of finite sequential decision-making, and investigated the mechanisms underlying individuals suboptimal choices. Across three independent experiments, we found that a dynamic programming model comprising subjective value function explains individuals deviations from optimality and predicts the choice behaviors under fewer opportunities. We further identified that pupil dilation reflected the levels of decision difficulty and subsequent choices to accept or reject the stimulus at each opportunity. The value sensitivity, a model-based estimate that characterizes each individuals subjective valuation, correlated with the extent to which individuals physiological responses tracked stimuli information. Our results provide model-based and physiological evidence for subjective valuation in finite sequential decision-making, rediscovering human suboptimality in subjectively optimal decision-making processes.

neuroscience↗

bioRxiv2020

Systematic identification of a nuclear receptor-enriched predictive signature for erastin-induced ferroptosis

Erastin, which has been initially identified as a synthetic lethal compound against cancer expressing an RAS oncogene, inhibits cystine/glutamate antiporters and causes ferroptic cell death in various cell types, including therapy-resistant mesenchymal cancer cells. However, despite recent emerging evidence for the mechanisms underlying ferroptosis, molecular biomarkers associated with erastin-dependent ferroptosis have not yet been identified. In the present study, we employed isogenic lung cancer cell models with therapy-resistant mesenchymal properties to show that a redox imbalance leads to glutathione depletion and ferroptotic cell death. Subsequent gene expression analysis of pan-cancer cell lines revealed that the activity of transcription factors, including nuclear factor erythroid 2-related factor 2 (NRF2) and aryl hydrocarbon receptor (AhR), serve as important markers of erastin resistance. Based on the integrated expression of genes in the nuclear receptor meta-pathway (NRM), we constructed an NRM model and validated its robustness using an independent pharmacogenomics dataset. The NRM model was further evaluated by employing it in the sensitivity testing of nine cancer cell lines for which erastin sensitivities had not yet been undetermined. Our pharmacogenomics approach has the potential to pave the way for the efficient classification of patients for therapeutic intervention using erastin or erastin analogs.

systems biology↗

bioRxiv2018

In silico Drug Repositioning of bortezomib to reverse metastatic effect of GALNT14 in lung cancer

Although many molecular targets for cancer therapy have been discovered, they often show poor druggability, which is a major obstacle to develop targeted drugs. As an alternative route to drug discovery, we adopted an in silico drug repositioning (in silico DR) approach based on large-scale gene expression signatures, with the goal of identifying inhibitors of lung cancer metastasis. Our analysis of clinicogenomic data identified GALNT14, an enzyme involved in O-linked N-acetyl galactosamine glycosylation, as a putative driver of lung cancer metastasis leading to poor survival. To overcome the poor druggability of GALNT14, we leveraged Connectivity Map approach, an in silico screening for drugs that are likely to revert the metastatic expression patterns. It leads to identification of bortezomib (BTZ) as a potent metastatic inhibitor, bypassing direct inhibition of poorly druggable target, GALNT14. The anti-metastatic effect of BTZ was verified in vitro and in vivo. Notably, both BTZ treatment and GALNT14 knockdown attenuated TGF{beta}-mediated gene expression and suppressed TGF{beta}-dependent metastatic genes, suggesting that BTZ acts by modulating TGF{beta} signalingTaken together, these results demonstrate that our in silico DR approach is a viable strategy to identify a candidate drug for undruggable targets, and to uncover its underlying mechanisms.

cancer biology↗