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Junhao Huang

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4 papers
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4

JBHI Journal 2025 Journal Article

Inducing Long-Term Plastic Changes and Visual Attention Enhancement Via One-Week Cerebellar Crus II Intermittent Theta Burst Stimulation (iTBS): An EEG Study

  • Meiliang Liu
  • Chao Yu
  • Minjie Tian
  • Jingping Shi
  • Yunfang Xu
  • Zijin Li
  • Zhengye Si
  • Xiaoxiao Yang

Intermittent theta burst stimulation (iTBS) is a non-invasive technique frequently employed to induce neural plastic changes and enhance visual attention. Currently, most studies utilized a single iTBS session on healthy subjects to induce short-term neural plastic changes within tens of minutes post-stimulation and investigate its single-session effect on attention performance. Few studies have conducted multiple iTBS sessions on the cerebellum to explore long-term effects on the cerebral cortex and daily effects on visual attention performance. In this study, 18 healthy subjects were involved in a randomized, sham-controlled experiment over one week. All the subjects received daily session of bilateral cerebellar Crus II iTBS or sham stimulation and completed a visual search task. Resting-state electroencephalogram (EEG) was collected 48 hours pre- and post-experiment to assess plastic changes induced by iTBS. The results indicated that the iTBS group exhibited higher accuracy and lower time costs than the sham group after three sessions of iTBS. In addition, iTBS-induced plastic changes persisted up to 48 hours post-experiment, including left-shifted individual alpha frequency, increased intrinsic excitability (the likelihood that a neuron will generate an output in response to a given input), and enhanced PLV functional connectivity (phase synchronization between different brain region). Furthermore, we found that cerebellar iTBS induced a remote effect on the frontal region. Our study revealed the capacity of cerebellar Crus II iTBS to induce plastic changes and enhance attention performance, providing a potential avenue for using iTBS to promote rehabilitation.

JMLR Journal 2022 Journal Article

abess: A Fast Best-Subset Selection Library in Python and R

  • Jin Zhu
  • Xueqin Wang
  • Liyuan Hu
  • Junhao Huang
  • Kangkang Jiang
  • Yanhang Zhang
  • Shiyun Lin
  • Junxian Zhu

We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, abess certifiably gets the optimal solution within polynomial time with high probability under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 20x faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best subset of groups selection and $\ell_2$ regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for convenient integration with scikit-learn, and it can be installed from the Python Package Index (PyPI). In addition, a user-friendly R library is available at the Comprehensive R Archive Network (CRAN). The source code is available at: https://github.com/abess-team/abess. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )