Biology subjects

Im, Y.

Publications and source records attributed to Im, Y..

3 recordsLinked to original sources

bioRxiv2025

A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry

3D surface-based computational mapping is more sensitive to localized brain alterations in neurological, developmental and psychiatric conditions than traditional gross volumetric analysis, providing fine-scale 3D maps of a wide range of surface-based features. Here we introduce a scalable toolkit for large-scale computational surface analysis, with efficient algorithms for multisite data integration, statistical harmonization, accelerated multivariate statistics, and visualization. We showcase the utility of the toolkit by mapping subcortical shape variations and factors that affect them across 21 international samples from the ENIGMA Bipolar Disorder Working Group (N=3,373).

neuroscience↗

bioRxiv2024

A Surface-based deep learning approach for cortical shape analysis

Advances in deep learning hold promise for predicting clinical factors from human brain images. In this study, we applied a spherical harmonics-based convolutional neural network approach (SPHARM-Net) to MRI-derived brain shape metrics to predict age, sex, and Alzheimers disease (AD) diagnosis. MRI-derived brain features included vertex-wise cortical curvature, convexity, thickness, and surface area. SPHARM-Net performs convolutions using the spherical harmonic transforms, eliminating the need to explicitly define neighborhood size, and achieving rotational equivariance. Sex classification and age regression were carried out in a large sample of healthy adults (UK Biobank; N=32,979), and AD classification performance was tested in a large, publicly available sample (ADNI; N=1,213). SPHARM-Net showed strong performance for sex classification (accuracy=0.91; balanced accuracy= 0.91; AUC=0.97), and age regression (average absolute error=2.97 years; R-squared=0.77; Pearsons coefficient=0.9). AD classification also performed well (accuracy=0.86; balanced accuracy=0.83; AUC=0.9). Our experiments demonstrate promising preliminary performance using the SPHARM-Net for two widely studied benchmarking tasks and for AD classification. Future work will include comparisons of shape-based methods and extending these analysis to more challenging tasks such as mood disorder classification.

neuroscience↗

bioRxiv2023

A cerebellar disinhibitory circuit supports synaptic plasticity

How does the cerebellum learn how to control motion? The cerebellar motor learning critically depends on the long-term depression of the synapses between granule cells and Purkinje cells, which encode motor commands and inhibitory modifications to motor outputs, respectively, for simultaneous granule cell inputs and climbing fibre inputs, the latter of which encode the error signals1-3. However, recent studies have revealed that inhibitory inputs to Purkinje cells may disrupt long-term depression4-8, and it is not clear how long-term depression can occur without disruption. In search of a clue, we investigated the synaptic connectivity among the neurons reconstructed from serial electron microscopy images of the cerebellar molecular layer9,10. We discovered synapses between climbing fibres and a subset of inhibitory interneurons, which synapse onto the remaining interneurons, which in turn synapse onto Purkinje cells. Such connectivity redefines the interneuron types, which have been defined morphologically or molecularly11-13. Together with climbing fibres to Purkinje cell connections, those cell types form a feedforward disinhibitory circuit14. We argued that this circuit secures long-term depression by suppressing inhibition whenever climbing fibre input is provided and long-term depression needs to occur15, and we validated the hypothesis through a computational model. This finding implies a general principle of circuit mechanism in which disinhibition supports synaptic plasticity16,17.

neuroscience↗