Biology subjects

Kim, D.-I.

Publications and source records attributed to Kim, D.-I..

3 recordsLinked to original sources

bioRxiv2020

Unified neural pathways that gate affective pain and multisensory innate threat signals to the amygdala

Perception of aversive sensory stimuli such as pain and innate threat cues is essential for animal survival. The amygdala is critical for aversive sensory perception, and it has been suggested that multiple parallel pathways independently relay aversive cues from each sensory modality to the amygdala. However, a convergent pathway that relays multisensory aversive cues to the amygdala has not been identified. Here, we report that neurons expressing calcitonin gene-related peptide (CGRP) in the parvocellular subparafasicular thalamic nucleus (SPFp) are necessary and sufficient for affective-motivational pain perception by forming a spino-thalamo-amygdaloid pain pathway. In addition, we find that this thalamic CGRP pain pathway, together with well-known parabrachio-amygdaloid CGRP pain pathway, is critical for the perception of multisensory innate threat cues. The discovery of unified pathways that collectively gate aversive sensory stimuli from all sensory modalities may provide critical circuit-based insights for developing therapeutic interventions for affective pain- and innate fear-related disorders.

neuroscience↗

bioRxiv2020

Neural basis of opioid-induced respiratory depression and its rescue

Opioid-induced respiratory depression (OIRD) causes death following an opioid overdose, yet the neurobiological mechanisms of this process are not well understood. Here, we show that neurons within the lateral parabrachial nucleus that express the -opioid receptor (PBLOprm1 neurons) are involved in OIRD pathogenesis. PBLOprm1 neuronal activity is tightly correlated with respiratory rate, and this correlation is abolished following morphine injection. Chemogenetic inactivation of PBLOprm1 neurons mimics OIRD in mice, whereas their chemogenetic activation following morphine injection rescues respiratory rhythms to baseline levels. We identified several excitatory G-protein coupled receptors expressed by PBLOprm1 neurons and show that agonists for these receptors restore breathing rates in mice experiencing OIRD. Thus, PBLOprm1 neurons are critical for OIRD pathogenesis, providing a promising therapeutic target for treating OIRD in patients.

neuroscience↗

bioRxiv2020

Aggregation of Cohorts for Histopathological Diagnosis with Deep Morphological Analysis

There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks (DNNs) either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas (TCGA) dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.

pathology↗