Biology subjects

Kim, T. H.

Publications and source records attributed to Kim, T. H..

3 recordsLinked to original sources

bioRxiv2018

Right hemisphere superiority for executive control of attention

Over forty years have passed since the first evidence showing the unbalanced attentional allocation of humans across the two visual fields, and since then, a wealth of behavioral, neurophysiological, and clinical data increasingly showed a right hemisphere dominance for orienting of attention. However, inconsistent evidence exists regarding the right-hemisphere dominance for executive control of attention, possibly due to a lack of consideration of its dynamics with the alerting and orienting functions. In this study, we used a version of the Attentional Network Test with lateralized presentation of the stimuli to the left visual field (processed by the right hemisphere, RH) and right visual field (processed by the left hemisphere, LH) to examine visual field differences in executive control of attention under conditions of alerting or orienting. Analyses of behavioral performance (reaction time and error rate) showed a more efficient executive control (reduced conflict effect) in the RH compared to the LH for the reaction time, under conditions of increased alerting and of informative spatial orienting. These results demonstrate the right-hemisphere superiority for executive control, and that this effect depends on the activation of the alerting and orienting functions.

neuroscience↗

bioRxiv2017

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

Perceptions, thoughts and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor components analysis (TCA) can meet this challenge by extracting three interconnected low dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.

neuroscience↗

bioRxiv2017

Comparative transcriptome profiling of the human and mouse dorsal root ganglia: An RNA-seq-based resource for pain and sensory neuroscience research

Molecular neurobiological insight into human nervous tissues is needed to generate next generation therapeutics for neurological disorders like chronic pain. We obtained human Dorsal Root Ganglia (DRG) samples from organ donors and performed RNA-sequencing (RNA-seq) to study the human DRG (hDRG) transcriptional landscape, systematically comparing it with publicly available data from a variety of human and orthologous mouse tissues, including mouse DRG (mDRG). We characterized the hDRG transcriptional profile in terms of tissue-restricted gene co-expression patterns and putative transcriptional regulators, and formulated an information-theoretic framework to quantify DRG enrichment. Our analyses reveal an hDRG-enriched protein-coding gene set (~140), some of which have not been described in the context of DRG or pain signaling. A majority of these show conserved enrichment in mDRG, and were mined for known drug - gene product interactions. Comparison of hDRG and tibial nerve transcriptomes suggest pervasive mRNA transport of sensory neuronal genes to axons in adult hDRG, with potential implications for mechanistic insight into chronic pain in patients. Relevant gene families and pathways were also analyzed, including transcription factors (TFs), g-protein coupled receptors (GCPRs) and ion channels. We present our work as an online, searchable repository (http://www.utdallas.edu/bbs/painneurosciencelab/DRGtranscriptome), creating a valuable resource for the community. Our analyses provide insight into DRG biology for guiding development of novel therapeutics, and a blueprint for cross-species transcriptomic analyses.\n\nSummaryWe generated RNA sequencing data from human DRG samples and comprehensively compared this transcriptome to other human tissues and a matching panel of mouse tissues. Our analysis uncovered functionally enriched genes in the human and mouse DRG with important implications for understanding sensory biology and pain drug discovery.

neuroscience↗