Biology subjects

Lee, K. H.

Publications and source records attributed to Lee, K. H..

40 records · Page 3Linked to original sources

bioRxiv2019

Multivariate pattern classification on BOLD activation pattern induced by deep brain stimulation in motor, associative, and limbic brain networks

Functional magnetic resonance imaging (fMRI) concurrently conducted with the deep brain stimulation (DBS) has shown that diffuse BOLD activation occurred not only near stimulation locus, but in multiple brain networks, supporting that network-wide modulation would underlie its therapeutic effect. While the extent and pattern of activation varies depending on specific anatomical locus stimulated by DBS, some stimulation targets could induce similar activation pattern in cerebral cortex, albeit different therapeutic and adverse effects were yielded.\n\nIn order to characterize the unique network-level activation effects of three DBS targets (subthalamic nucleus, the globus pallidus internus, and the nucleus accumbens), we trained the pattern classifier with DBS-fMRI data from three stimulation groups (21 healthy swine), wherein five six seconds of electrical stimulation was conducted while gradient-echo echo planar imaging was on going. Then whole brain regions were systematically grouped into different size of network-of-interest and the classification accuracy for individual target region was quantitatively assessed. We demonstrated that the pattern classifier could successfully differentiate BOLD activation pattern of cortical and subcortical brain regions originated from each individual stimulation target. Moreover, the success rate of classification indicated that some brain regions evoked indistinguishable BOLD pattern, suggesting the presence of commonly activated regions, which was influenced by stimulating different DBS targets.\n\nOur results provide an understanding of the biomarker of BOLD pattern that is associated with clinical effectiveness as well as an adverse effect associated to the stimulation. Further, we provide the proof-of-concept for multivariate pattern analysis that is capable of disentangling the complicated BOLD activation pattern, which cannot be readily achieved by a conventional univariate analysis.

bioinformatics↗

bioRxiv2019

Resting-state functional connectivity modulates the BOLD activation induced by nucleus accumbens stimulation in the swine brain

While it is known that the clinical efficacy of deep brain stimulation (DBS) alleviates motor-related symptoms, cognitive and behavioral effects of DBS and its action mechanism on brain circuits are not clearly understood. By combining functional magnetic resonance imaging (fMRI) and DBS, we investigated the pattern of blood-oxygenation-level-dependent (BOLD) signal changes induced by stimulating the nucleus accumbens and how inter-regional resting-state functional connectivity is related with the stimulation DBS effect in a healthy swine model. We found that the pattern of stimulation-induced BOLD activation was diffused across multiple functional networks including the prefrontal, limbic, and thalamic regions, altering inter-regional functional connectivity after stimulation. Furthermore, our results showed that the strength of the DBS effect is closely related to the strength of inter-regional resting-state functional connectivity including stimulation locus and remote brain regions. Our results reveal the impact of nucleus accumbens stimulation on major functional networks, highlighting functional connectivity may mediate the modulation effect of DBS via large-scale brain networks.

neuroscience↗

bioRxiv2018

Deep Learning provides exceptional accuracy to ECoG-based Functional Language Mapping for epilepsy surgery

The success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECOG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm made an exceptional 23% improvement with respect to the conventional ECoG-FM analysis ([~]60%). We obtained the state-of-the-art accuracy of 83.05% in identifying language regions, which has never been achieved before. Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.

neuroscience↗

bioRxiv2018

The respiratory microbiome and susceptibility to influenza virus infection

Influenza is a major cause of morbidity and mortality worldwide. However, vaccine effectiveness has been low to moderate in recent years and vaccine coverage remains low, especially in low- and middle-income countries. Supplementary methods of prevention should be explored to reduce the high burden of influenza.\n\nA potential target is the respiratory tract microbiome, complex microbial communities which envelop the respiratory epithelium and play an important role in shaping host immunity. Using a household transmission study, we examined whether the nose/throat microbiota was associated with influenza susceptibility among participants exposed to influenza virus in the household. Further, we characterized changes in the nose/throat microbiota to explore whether community stability was influenced by influenza virus infection.\n\nUsing a generalized linear mixed effects model, we found a bacterial community type associated with decreased susceptibility to influenza. The community type was rare and transitory among young children but a prevalent and stable community type among adults. Using boosting and linear mixed effects models, we found associations between the nose/throat microbiota and influenza also existed at the taxa level, specifically with the relative abundance of Alloprevotella, Prevotella, and Bacteroides oligotypes.\n\nWe found high rates of change in the bacterial community among both secondary cases and household contacts who were not infected during follow up. Preliminary results suggest short-term changes in the bacterial community structure may differ between the two groups, but further work is needed to validate our observations.\n\nLastly, age was strongly associated with susceptibility to influenza and the nose/throat bacterial community structure. Although additional studies are needed to determine causality, our results suggest the nose/throat microbiome may be a potential target for reducing the burden of influenza.\n\nAuthor summaryMicrobiome research has transformed our understanding of microbes and human health. Resident bacteria can protect the host from pathogens by shaping immunological responses. These new insights suggest the microbiome could be a target for preventing influenza virus infection, a major cause of illness and death worldwide. In this study, we explored the relationship between the nose/throat microbiota and influenza virus in Nicaraguan households.\n\nHousehold members were enrolled immediately after one member was diagnosed with influenza virus infection. This study design allowed us to identify associations between the microbiota and influenza susceptibility. We also explored whether influenza virus infection altered the bacterial community structure and found short-term changes were common among both secondary cases and household members who remained influenza negative during follow up. Lastly, we found age played major roles in both influenza susceptibility and in short-terms changes in the microbiota. Although much work is needed to determine causal relationships, our findings suggest strategies that appropriately modify the microbiome might be useful in preventing influenza virus infections.

epidemiology↗