Shengfeng Liu
Chinese Academy of Sciences
Deep learningMachine learningNeuroscienceWorking memoryPopulationNeuroimagingSchizophreniaFractional anisotropy
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Publications 5
#1Weizheng Yan (CAS: Chinese Academy of Sciences)H-Index: 2
#2Vince D. CalhounH-Index: 93
Last. Jing Sui (CAS: Chinese Academy of Sciences)H-Index: 31
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Abstract Background Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. Methods Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fM...
1 CitationsSource
#1Shengfeng LiuH-Index: 4
#2Haiying Wang (Harbin University of Science and Technology)H-Index: 1
Last. Jing SuiH-Index: 31
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Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fr...
5 CitationsSource
#1Na Luo (CAS: Chinese Academy of Sciences)H-Index: 2
#2Jing SuiH-Index: 31
Last. Tianzi JiangH-Index: 67
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Abstract Background In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset. Methods Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-su...
3 CitationsSource
#1Shile Qi (CAS: Chinese Academy of Sciences)H-Index: 5
#2Xiao Yang (Sichuan University)H-Index: 7
Last. Xiaohong Ma (Sichuan University)H-Index: 23
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There is compelling evidence that epigenetic factors contribute to the manifestation of depression, in which microRNA132 (miR-132) is suggested to play a pivotal role in the pathogenesis and neuronal mechanisms underlying the symptoms of depression. Additionally, several depression-associated genes [MECP2, ARHGAP32 (p250GAP), CREB, and period genes] were experimentally validated as miR-132 targets. However, most studies regarding miR-132 in major depressive disorder are based on post-mortem, ani...
8 CitationsSource
Sep 1, 2017 in MLSP (International Workshop on Machine Learning for Signal Processing)
#1Weizheng Yan (CAS: Chinese Academy of Sciences)H-Index: 2
#2Sergey M. Plis (CAS: Chinese Academy of Sciences)H-Index: 2
Last. Jing Sui (CAS: Chinese Academy of Sciences)H-Index: 31
view all 7 authors...
Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called “deep neural network (DNN)+ layer-wise relevance propagation (LRP)”, to distinguish schizophrenia patients (SZ) from healthy controls (HCs) using functional network connectivity (FNC). 1100 Chinese subjects of 7...
4 CitationsSource