Biology subjects

Jang, M. S.

Publications and source records attributed to Jang, M. S..

4 recordsLinked to original sources

bioRxiv2024

Aberrant neuronal hyperactivation causes an age- and diet-dependent decline in associative learning behavior

The impairment of memory, cognition, and behavior during aging is generally thought to arise from diminished neuronal activities. The nematode Caenorhabditis elegans exhibits age-dependent declines in an associative learning behavior called thermotaxis. Genetic ablation of individual neurons revealed that an absence of either AWC sensory or AIA inter-neurons preserved the thermotaxis ability of aged animals. Calcium imaging showed age-dependent spontaneous hyperactivities in both neurons. The age-dependent neuronal hyperactivity and behavioral decline were ameliorated by changing diets. We further demonstrate that the enhanced activities of AWC and AIA were differentially dependent on the forms of neurotransmission mediated by neurotransmitters and neuropeptides. Together, our data provides evidence that aberrantly enhanced, not diminished, neuronal responses can impair behavior during aging. One-Sentence SummaryEnhanced neuronal activity during aging impairs C. elegans learning behavior.

neuroscience↗

bioRxiv2022

Deducing ensemble dynamics and information flow from the whole-brain imaging data

Recent development of large-scale activity imaging of neuronal ensembles provides opportunities for understanding how activity patterns are generated in the brain and how information is transmitted between neurons or neuronal ensembles. However, methodologies for extracting the component properties that generate overall dynamics are still limited. In this study, the results of time-lapse 3D imaging (4D imaging) of head neurons of the nematode C. elegans were analyzed by hitherto unemployed methodologies. By combining time-delay embedding with independent component analysis, the whole-brain activities were decomposed to a small number of component dynamics. Results from multiple samples, where different subsets of neurons were observed, were further combined by matrix factorization, revealing common dynamics from neuronal activities that are apparently divergent across sampled animals. By this analysis, we could identify components that show common relationships across different samples and those that show relationships distinct between individual samples. We also constructed a network model building on time-lagged prediction models of synaptic communications. This was achieved by dimension reduction of 4D imaging data using the general framework gKDR (gradient kernel dimension reduction). The model is able to decompose basal dynamics of the network. We further extended the model by incorporating probabilistic distribution, resulting in models that we call gKDR-GMM and gKDR-GP. The models capture the overall relationships of neural activities and reproduce the stochastic but coordinated dynamics in the neural network simulation. By virtual manipulation of individual neurons and synaptic contacts in this model, information flow could be estimated from whole-brain imaging results.

neuroscience↗

bioRxiv2019

An annotation dataset facilitates automatic annotation of whole-brain activity imaging of C. elegans

Annotation of cell identity is an essential process in neuroscience that allows for comparing neural activities across different animals. In C. elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal is limited in practice and comprehensive methods for cell annotation are required. Here we propose an efficient annotation method that can be integrated with the whole-brain imaging technique. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. The large positional variations illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters to tackle this problem. We also developed an automatic annotation method with human interaction functionality that facilitates annotation for whole-brain imaging.

neuroscience↗

bioRxiv2017

An ensemble learning approach to auto-annotation for whole-brain C. elegans imaging

Shifting from individual neuron analysis to whole-brain neural network analysis opens up new research opportunities for Caenorhabditis elegans (C. elegans). An automated data processing pipeline, including neuron detection, segmentation, tracking and annotation, will significantly improve the efficiency of analyzing whole-brain C. elegans imaging. The resulting large data sets may motivate new scientific discovery by exploiting many promising analysis tools for big data. In this study, we focus on the development of an automated annotation procedure. With only around 180 neurons in the central nervous system of a C. elegans, the annotation of each individual neuron still remains a major challenge because of the high density in space, similarity in neuron shape, unpredictable distortion of the worms head during motion, intrinsic variations during worm development, etc. We use an ensemble learning approach to achieve around 25% error for a test based on real experimental data. Also, we demonstrate the importance of exploring extra source of information for annotation other than the neuron positions.

bioinformatics↗