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Cheng Luo

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YNIMG Journal 2026 Journal Article

Cortical encoding of acoustic and linguistic rhythms reflects L2 narrative comprehension

  • Jiaying Zhang
  • Junying Liang
  • Yiguang Liu
  • Cheng Luo

Speech comprehension is a multistage process involving both acoustic encoding and linguistic processing. Accumulating evidence has demonstrated that low-frequency cortical activity can track perceived linguistic units (e.g., words) on top of basic acoustic features (e.g., speech envelope). However, it remains unclear how the neural tracking of acoustic and linguistic information relates to second language (L2) speech comprehension in narrative contexts. Here, we investigate neural tracking of narrative speech for L2 listeners using electroencephalography (EEG). Notably, we introduce amplitude modulation (AM) cues aligned with word rhythm onto the basic envelope of speech and employ a frequency-tagging paradigm to measure neural responses to word and AM rhythm separately. When narrative speech was presented to L2 listeners during a speech comprehension task, reliable neural tracking of word and AM rhythm was observed in low-frequency cortical activity. While the introduction of AM cues enhances both comprehension performance and word-tracking responses, listeners with high versus low comprehension performance exhibit differences in their word-tracking responses rather than AM-tracking responses. Furthermore, the power and phase associated with word-tracking responses jointly reflect individual comprehension performance of L2 listeners. Our results indicate that bottom-up acoustic cues and top-down linguistic knowledge predominantly modulate the low-frequency neural tracking of linguistic units, which contributes to speech comprehension in a nonnative language.

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YNICL Journal 2025 Journal Article

Disturbed hierarchy and mediation in reward-related circuits in depression

  • Ruikun Yang
  • Junxia Chen
  • Suping Yue
  • Yue Yu
  • Jiamin Fan
  • Yuling Luo
  • Hui He
  • Mingjun Duan

BACKGROUNDS/OBJECTIVE: Deep brain stimulation (DBS) has proved the viability of alleviating depression symptoms by stimulating deep reward-related nuclei. This study aims to investigate the abnormal connectivity profiles among superficial, intermediate, and deep brain regions within the reward circuit in major depressive disorder (MDD) and therefore provides references for identifying potential superficial cortical targets for non-invasive neuromodulation. METHODS: Resting-state functional magnetic resonance imaging data were collected from a cohort of depression patients (N = 52) and demographically matched healthy controls (N = 60). Utilizing existing DBS targets as seeds, we conducted step-wise functional connectivity (sFC) analyses to delineate hierarchical pathways linking to cerebral cortices. Subsequently, the mediation effects of cortical regions on the interaction within reward-related circuits were further explored by constructing mediation models. RESULTS: In both cohorts, sFC analysis revealed two reward-related pathways from the deepest DBS targets to intermediate regions including the thalamus, insula, and anterior cingulate cortex (ACC), then to the superficial cortical cortex including medial frontal cortex, posterior default mode network (pDMN), and right dorsolateral prefrontal cortex (DLPFC). Patients exhibited reduced sFC in bilateral thalamus and medial frontal cortex in short and long steps respectively compared to healthy controls. We also discovered the disappearance of the mediation effects of superficial cortical regions on the interaction between DBS targets and intermediate regions in reward-related pathways in patients with MDD. CONCLUSION: Our findings support abnormal hierarchical connectivity and mediation effects in reward-related brain regions at different depth levels in MDD, which might elucidate the underlying pathophysiological mechanisms and inspire novel targets for non-invasive interventions.

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YNIMG Journal 2025 Journal Article

Effects of antagonistic network-targeted tDCS on brain co-activation patterns depends on the networks’ electric field: a simultaneous tDCS-fMRI study

  • Hechun Li
  • Hongru Shi
  • Sisi Jiang
  • Changyue Hou
  • Haonan Pei
  • Hanxi Wu
  • María Luisa Bringas Vega
  • Gang Yao

BACKGROUND: Brain networks should be ideal targets for non-invasive brain stimulation, as network dysfunction is a common feature of various neuropsychiatric disorders. Understanding the mechanisms of network-targeted stimulation is essential for advancing its clinical applications. MATERIAL AND METHOD: The current study utilized simultaneous network-targeted transcranial direct current stimulation(tDCS) and functional magnetic resonance imaging (fMRI) to investigate the effects of tDCS targeting antagonistic networks on brain dynamics. A total of 143 healthy participants were recruited and assigned to receive central executive network (CEN)-targeted tDCS (C-targeted group), default mode network (DMN)-targeted tDCS (D-targeted group), or sham tDCS (sham group). fMRI data with three sections (pre-stimulation, during-stimulation, post-stimulation) were collected across all subjects. Individual electric field (EF) strength was simulated using individual head model. Six recurring brain patterns (co-activation patterns, CAPs) were identified. The temporal indices of these CAPs (occurrence, fraction time, persistence time) and their transition probabilities were calculated. This study first examined the effects of C-targeted / D-targeted / sham tDCS on temporal indices and further explored the contribution of brain networks' EF strength on the altered temporal indices. RESULTS: C-targeted tDCS significantly increased the temporal indices of CAPs dominated by DMN and the transition probabilities from other CAPs to DMN-dominated CAPs during stimulation. Meanwhile, the decreased temporal indices of CAP dominated by CEN, and its transition probabilities to these CAPs were also found during C-targeted tDCS. In contrast, the d-targeted tDCS had only a slight effect on brain dynamics, while sham tDCS showed no significant impact. Further fusion analyses revealed that the EF strength in the salience network made a large contribution to the temporal indices of CAPs during stimulation, highlighting tight interactions within the triple networks. Moreover, integrating the EF strength of networks with large contributions and the pre-stimulation temporal indices effectively predicted the temporal indices of CAPs during stimulation. These findings suggest that C-targeted tDCS can modulate brain dynamics and emphasize the critical role of networks' EF during stimulation. CONCLUSION: This study demonstrates the effectiveness and feasibility of network-targeted tDCS in modulating brain dynamics, providing a new choice for treating neuropsychiatric disorders characterized by aberrant brain dynamics.

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NeurIPS Conference 2025 Conference Paper

OmniResponse: Online Multimodal Conversational Response Generation in Dyadic Interactions

  • Cheng Luo
  • Jianghui Wang
  • Bing Li
  • Siyang Song
  • Bernard Ghanem

In this paper, we introduce Online Multimodal Conversational Response Generation (OMCRG), a novel task designed to produce synchronized verbal and non-verbal listener feedback online, based on the speaker's multimodal inputs. OMCRG captures natural dyadic interactions and introduces new challenges in aligning generated audio with listeners' facial responses. To tackle these challenges, we incorporate text as an intermediate modality to connect audio and facial responses. We propose OmniResponse, a Multimodal Large Language Model (MLLM) that autoregressively generates accurate multimodal listener responses. OmniResponse leverages a pretrained LLM enhanced with two core components: Chrono-Text Markup, which precisely timestamps generated text tokens, and TempoVoice, a controllable online text-to-speech (TTS) module that outputs speech synchronized with facial responses. To advance OMCRG research, we offer ResponseNet, a dataset of 696 detailed dyadic interactions featuring synchronized split-screen videos, multichannel audio, transcripts, and annotated facial behaviors. Comprehensive evaluations on ResponseNet demonstrate that OmniResponse outperforms baseline models in terms of semantic speech content, audio-visual synchronization, and generation quality. Our dataset, code, and models are publicly available at https: //omniresponse. github. io/.

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NeurIPS Conference 2025 Conference Paper

R-KV: Redundancy-aware KV Cache Compression for Reasoning Models

  • Zefan Cai
  • Wen Xiao
  • Hanshi Sun
  • Cheng Luo
  • Yikai Zhang
  • Ke Wan
  • Yucheng Li
  • Yeyang Zhou

Reasoning models have demonstrated impressive performance in self-reflection and chain-of-thought reasoning. However, they often produce excessively long outputs, leading to prohibitively large key-value (KV) caches during inference. While chain-of-thought inference significantly improves performance on complex reasoning tasks, it can also lead to reasoning failures when deployed with existing KV cache compression approaches. To address this, we propose Redundancy-aware KV Cache Compression for Reasoning models (R-KV), a novel method specifically targeting redundant tokens in reasoning models. Our method preserves nearly 100% of the full KV cache performance using only 10% of the KV cache, substantially outperforming existing KV cache baselines, which reach only 60% of the performance. Remarkably, R-KV even achieves 105% of full KV cache performance with 38% of the KV cache. This KV-cache reduction also leads to a 50% memory saving and a 2x speedup over standard chain-of-thought reasoning inference. Experimental results show that R-KV consistently outperforms existing KV cache compression baselines across two mathematical reasoning datasets.

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AAAI Conference 2024 Conference Paper

Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping

  • Qinliang Lin
  • Cheng Luo
  • Zenghao Niu
  • Xilin He
  • Weicheng Xie
  • Yuanbo Hou
  • Linlin Shen
  • Siyang Song

Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement approaches (e.g., input transformation and model augmentation) have been proposed. However, they show poor performances in attacking systems having different model genera from the surrogate model. In this paper, we propose a novel and generic attacking strategy, called Deformation-Constrained Warping Attack (DeCoWA), that can be effectively applied to cross model genus attack. Specifically, DeCoWA firstly augments input examples via an elastic deformation, namely Deformation-Constrained Warping (DeCoW), to obtain rich local details of the augmented input. To avoid severe distortion of global semantics led by random deformation, DeCoW further constrains the strength and direction of the warping transformation by a novel adaptive control strategy. Extensive experiments demonstrate that the transferable examples crafted by our DeCoWA on CNN surrogates can significantly hinder the performance of Transformers (and vice versa) on various tasks, including image classification, video action recognition, and audio recognition. Code is made available at https://github.com/LinQinLiang/DeCoWA.

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YNIMG Journal 2024 Journal Article

Cortical encoding of hierarchical linguistic information when syllabic rhythms are obscured by echoes

  • Cheng Luo
  • Nai Ding

In speech perception, low-frequency cortical activity tracks hierarchical linguistic units (e.g., syllables, phrases, and sentences) on top of acoustic features (e.g., speech envelope). Since the fluctuation of speech envelope typically corresponds to the syllabic boundaries, one common interpretation is that the acoustic envelope underlies the extraction of discrete syllables from continuous speech for subsequent linguistic processing. However, it remains unclear whether and how cortical activity encodes linguistic information when the speech envelope does not provide acoustic correlates of syllables. To address the issue, we introduced a frequency-tagging speech stream where the syllabic rhythm was obscured by echoic envelopes and investigated neural encoding of hierarchical linguistic information using electroencephalography (EEG). When listeners attended to the echoic speech, cortical activity showed reliable tracking of syllable, phrase, and sentence levels, among which the higher-level linguistic units elicited more robust neural responses. When attention was diverted from the echoic speech, reliable neural tracking of the syllable level was also observed in contrast to deteriorated neural tracking of the phrase and sentence levels. Further analyses revealed that the envelope aligned with the syllabic rhythm could be recovered from the echoic speech through a neural adaptation model, and the reconstructed envelope yielded higher predictive power for the neural tracking responses than either the original echoic envelope or anechoic envelope. Taken together, these results suggest that neural adaptation and attentional modulation jointly contribute to neural encoding of linguistic information in distorted speech where the syllabic rhythm is obscured by echoes.

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NeurIPS Conference 2024 Conference Paper

Mini-Sequence Transformers: Optimizing Intermediate Memory for Long Sequences Training

  • Cheng Luo
  • Jiawei Zhao
  • Zhuoming Chen
  • Beidi Chen
  • Anima Anandkumar

We introduce Mini-Sequence Transformer (MsT), a simple and effective methodology for highly efficient and accurate LLM training with extremely long sequences. MsT partitions input sequences and iteratively processes mini-sequences to reduce intermediate memory usage. Integrated with activation recomputation, it enables significant memory savings in both forward and backward passes. In experiments with the Llama3-8B model, with MsT, we measure no degradation in throughput or convergence even with 12x longer sequences than standard implementations. MsT is fully general, implementation-agnostic, and requires minimal code changes to integrate with existing LLM training frameworks. Integrated with the huggingface library, MsT successfully extends the maximum context length of Qwen, Mistral, and Gemma-2 by 12-24x.

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YNIMG Journal 2024 Journal Article

Structural and functional alterations in MRI-negative drug-resistant epilepsy and associated gene expression features

  • Ting Liu
  • Sheng Wang
  • Yingjie Tang
  • Sisi Jiang
  • Huixia Lin
  • Fei Li
  • Dezhong Yao
  • Xian Zhu

Neuroimaging techniques have been widely used in the study of epilepsy. However, structural and functional changes in the MRI-negative drug-resistant epilepsy (DRE) and the genetic mechanisms behind the structural alterations remain poorly understood. Using structural and functional MRI, we analyzed gray matter volume (GMV) and regional homogeneity (ReHo) in DRE, drug-sensitive epilepsy (DSE) and healthy controls. Gene expression data from Allen human brain atlas and GMV/ReHo were evaluated to obtain drug resistance-related and epilepsy-associated gene expression and compared with real transcriptional data in blood. We found structural and functional alterations in the cerebellum of DRE patients, which may be related to the mechanisms of drug resistance in DRE. Our study confirms that changes in brain morphology and regional activity in DRE patients may be associated with abnormal gene expression related to nervous system development. And SP1, as an important transcription factor, plays an important role in the mechanism of drug resistance.

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NeurIPS Conference 2024 Conference Paper

Towards Combating Frequency Simplicity-biased Learning for Domain Generalization

  • Xilin He
  • Jingyu Hu
  • Qinliang Lin
  • Cheng Luo
  • Weicheng Xie
  • Siyang Song
  • Muhammad Haris Khan
  • Linlin Shen

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Our code will be made publicly available.

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IROS Conference 2022 Conference Paper

Improved Task Space Locomotion Controller for a Quadruped Robot with Parallel Mechanisms

  • Shunpeng Yang
  • Wenchun Lin
  • Jaeho Noh
  • Cheng Luo
  • Bill Huang
  • Wei Zhang 0013
  • Hua Chen 0007

In this work, an advanced quadruped robot with abundant kinematic loops and passive joints is introduced. Due to the existence of many closed chains, the robot dynamic model is quite complex, and is derived using the Gauss's principle of least constraint. To explicitly consider the loop-closure constraints, we propose a task-space inverse dynamics based approach to obtain the robot locomotion controller. Besides, to meet the demand of high frequency (≥ 500Hz) in controller, an alternative method is provided. It uses the projected dynamics to find an analytical mapping from the desired contact force to the desired torque of actuators under full consideration of passive joints and loop-closure constraints. The effectiveness and efficiency of the proposed algorithms in this paper have been validated by simulation with a reliable physical engine MuJoCo.

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IJCAI Conference 2022 Conference Paper

Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition

  • Cheng Luo
  • Siyang Song
  • Weicheng Xie
  • Linlin Shen
  • Hatice Gunes

The activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display. This paper proposes an AU relationship modelling approach that deep learns a unique graph to explicitly describe the relationship between each pair of AUs of the target facial display. Our approach first encodes each AU's activation status and its association with other AUs into a node feature. Then, it learns a pair of multi-dimensional edge features to describe multiple task-specific relationship cues between each pair of AUs. During both node and edge feature learning, our approach also considers the influence of the unique facial display on AUs' relationship by taking the full face representation as an input. Experimental results on BP4D and DISFA datasets show that both node and edge feature learning modules provide large performance improvements for CNN and transformer-based backbones, with our best systems achieving the state-of-the-art AU recognition results. Our approach not only has a strong capability in modelling relationship cues for AU recognition but also can be easily incorporated into various backbones. Our PyTorch code is made available at https: //github. com/CVI-SZU/ME-GraphAU.

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YNICL Journal 2022 Journal Article

Linking cerebellar functional gradients to transdiagnostic behavioral dimensions of psychopathology

  • Debo Dong
  • Xavier Guell
  • Sarah Genon
  • Yulin Wang
  • Ji Chen
  • Simon B. Eickhoff
  • Dezhong Yao
  • Cheng Luo

High co-morbidity and substantial overlap across psychiatric disorders encourage a transition in psychiatry research from categorical to dimensional approaches that integrate neuroscience and psychopathology. Converging evidence suggests that the cerebellum is involved in a wide range of cognitive functions and mental disorders. An important question thus centers on the extent to which cerebellar function can be linked to transdiagnostic dimensions of psychopathology. To address this question, we used a multivariate data-driven statistical technique (partial least squares) to identify latent dimensions linking human cerebellar connectome as assessed by functional MRI to a large set of clinical, cognitive, and trait measures across 198 participants, including healthy controls (n = 92) as well as patients diagnosed with attention-deficit/hyperactivity disorder (n = 35), bipolar disorder (n = 36), and schizophrenia (n = 35). Macroscale spatial gradients of connectivity at voxel level were used to characterize cerebellar connectome properties, which provide a low-dimensional representation of cerebellar connectivity, i.e., a sensorimotor-supramodal hierarchical organization. This multivariate analysis revealed significant correlated patterns of cerebellar connectivity gradients and behavioral measures that could be represented into four latent dimensions: general psychopathology, impulsivity and mood, internalizing symptoms and executive dysfunction. Each dimension was associated with a unique spatial pattern of cerebellar connectivity gradients across all participants. Multiple control analyses and 10-fold cross-validation confirmed the robustness and generalizability of the yielded four dimensions. These findings highlight the relevance of cerebellar connectivity as a necessity for the study and classification of transdiagnostic dimensions of psychopathology and call on researcher to pay more attention to the role of cerebellum in the dimensions of psychopathology, not just within the cerebral cortex.

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YNIMG Journal 2022 Journal Article

Working memory asymmetrically modulates auditory and linguistic processing of speech

  • Yiguang Liu
  • Cheng Luo
  • Jing Zheng
  • Junying Liang
  • Nai Ding

Working memory load can modulate speech perception. However, since speech perception and working memory are both complex functions, it remains elusive how each component of the working memory system interacts with each speech processing stage. To investigate this issue, we concurrently measure how the working memory load modulates neural activity tracking three levels of linguistic units, i.e., syllables, phrases, and sentences, using a multiscale frequency-tagging approach. Participants engage in a sentence comprehension task and the working memory load is manipulated by asking them to memorize either auditory verbal sequences or visual patterns. It is found that verbal and visual working memory load modulate speech processing in similar manners: Higher working memory load attenuates neural activity tracking of phrases and sentences but enhances neural activity tracking of syllables. Since verbal and visual WM load similarly influence the neural responses to speech, such influences may derive from the domain-general component of WM system. More importantly, working memory load asymmetrically modulates lower-level auditory encoding and higher-level linguistic processing of speech, possibly reflecting reallocation of attention induced by mnemonic load.

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YNICL Journal 2021 Journal Article

Structural and functional reorganization of contralateral hippocampus after temporal lobe epilepsy surgery

  • Wei Li
  • Yuchao Jiang
  • Yingjie Qin
  • Baiwan Zhou
  • Du Lei
  • Heng Zhang
  • Ding Lei
  • Dezhong Yao

OBJECTIVE: To explore the structural and functional reorganization of contralateral hippocampus in patients with unilateral mesial temporal lobe epilepsy (mTLE) who achieved seizure-freedom after anterior temporal lobectomy (ATL). METHODS: We obtained high-resolution structural MRI and resting-state functional MRI data in 28 unilateral mTLE patients and 29 healthy controls. Patients were scanned before and three and 24 months after surgery while controls were scanned only once. Hippocampal gray matter volume (GMV) and functional connectivity (FC) were assessed. RESULTS: No obvious GMV changes were observed in contralateral hippocampus before and after successful surgery. Before surgery, ipsilateral hippocampus showed increased FC with ipsilateral insula (INS) and temporoparietal junction (TPJ), but decreased FC with widespread bilateral regions, as well as contralateral hippocampus. After successful ATL, contralateral hippocampus showed: (1) decreased FC with ipsilateral INS at three months follow-up, without further changes; (2) decreased FC with ipsilateral TPJ, postcentral gyrus and rolandic operculum at three months, with an obvious increase at 24 months follow-up; (3) increased FC with bilateral medial prefrontal cortex (MPFC) and superior frontal gyrus (SFG) at three months follow-up, without further changes. CONCLUSIONS: Successful ATL may not lead to an obvious structural reorganization in contralateral hippocampus. Surgical manipulation may lead to a transient FC reduction of contralateral hippocampus. Increased FC between contralateral hippocampus and bilateral MPFC and SFG may be related to postoperative functional remodeling.

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YNICL Journal 2019 Journal Article

BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy

  • Yun Qin
  • Sisi Jiang
  • Qiqi Zhang
  • Li Dong
  • Xiaoyan Jia
  • Hui He
  • Yutong Yao
  • Huanghao Yang

Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.

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YNICL Journal 2019 Journal Article

Common increased hippocampal volume but specific changes in functional connectivity in schizophrenia patients in remission and non-remission following electroconvulsive therapy: A preliminary study

  • Yuchao Jiang
  • Lihua Xu
  • Xiangkui Li
  • Yingying Tang
  • Pingfu Wang
  • Chunbo Li
  • Dezhong Yao
  • Jijun Wang

Electroconvulsive therapy (ECT) is considered a treatment option in patients with drug-resistant schizophrenia (SZ). However, approximately one-third of patients do not benefit from ECT in the clinic. Thus, it is critical to investigate differences between ECT responders and non-responders. Accumulated evidence has indicated that one region of ECT action is the hippocampus, which also plays an important role in SZ pathophysiology. To date, no studies have investigated differences in ECT effects in the hippocampus between treatment responders and non-responders. This study recruited twenty-one SZ patients treated for four weeks with ECT (MSZ, n = 21) and twenty-one SZ patients who received pharmaceutical therapy (DSZ, n = 21). The MSZ group was further categorized into responders (MSR, n = 10) or non-responders (MNR, n = 11) based on treatment outcomes by the criterion of a 50% reduction in the Positive and Negative Syndrome Scale total scores. Using structural and resting-state functional MRI, we measured the hippocampal volume and functional connectivity (FC) in all SZ patients (before and after treatment) and 23 healthy controls. In contrast to pharmaceutical therapy, ECT induced bilateral hippocampal volume increases in the MSZ. Both the MSR and MNR exhibited hippocampal expansion after ECT, whereas a lower baseline volume in one of hippocampal subfield (hippocampus-amygdala transition area) was found in the MNR. After ECT, increased FC between the hippocampus and brain networks associated with cognitive function was only observed in the MSR. The mechanism of action of ECT in schizophrenia is complex. A combination of baseline impairment level, ECT-introduced morphological changes and post-ECT FC increases in the hippocampus may jointly contribute to the post-ECT symptom improvements in patients with SZ.

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YNICL Journal 2019 Journal Article

Different patterns of white matter changes after successful surgery of mesial temporal lobe epilepsy

  • Wei Li
  • Dongmei An
  • Xin Tong
  • Wenyu Liu
  • Fenglai Xiao
  • Jiechuan Ren
  • Running Niu
  • Yingying Tang

OBJECTIVES: To explore the dynamic changes of white matters following anterior temporal lobectomy (ATL) in mesial temporal lobe epilepsy (MTLE) patients who achieved seizure-free at two-year follow-up. METHODS: Diffusion tensor imaging (DTI) was obtained in ten MTLE patients at five serial time points: before surgery, three months, six months, 12 months and 24 months after surgery, as well as in 11 age- and sex-matched healthy controls at one time point. Regions with significant postoperative fractional anisotropy (FA) changes and their dynamic changes were confirmed by comparing all preoperative and postoperative data using Tract-Based Spatial Statistics (TBSS). RESULTS: After successful ATL, significant FA changes were found in widespread ipsilateral and contralateral white matter regions (P <.05, FWE correction). Ipsilateral external capsule, cingulum, superior corona radiate, body of corpus callosum, inferior longitudinal fasciculus, optic radiation and contralateral inferior cerebellar peduncle, inferior longitudinal fasciculus showed significant FA decrease at three months after surgery, without further changes. Ipsilateral superior cerebellar peduncle and contralateral corpus callosum, anterior corona radiate, external capsule, optic radiation showed significant FA decrease at three months follow up but increase later. Ipsilateral cerebral peduncle and contralateral middle cerebellar peduncle showed significant FA decrease at three months follow up, with further decrease after that. While ipsilateral posterior limb of internal capsule, retrolenticular part of internal capsule and contralateral posterior corona radiate showed significant FA increase after surgery. CONCLUSIONS: FA changes after successful ATL presented as four distinct patterns, reflecting different structural adaptions following epilepsy surgery. Some FA increases indicated the reversibility of preoperative diffusion abnormalities and the possibility of structural reorganization, especially in the contralateral hemisphere.

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YNICL Journal 2019 Journal Article

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder

  • Wei Han
  • Christian Sorg
  • Changgang Zheng
  • Qinli Yang
  • Xiaosong Zhang
  • Arvid Ternblom
  • Cobbinah Bernard Mawuli
  • Lianli Gao

Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophrenia. However, similar changes have also been found in patients with major depressive disorder, prompting the question of specific triple network signatures for the two disorders. In this study, we proposed Supervised Convex Nonnegative Matrix Factorization (SCNMF) to extract distributed multi-modal brain patterns. These patterns distinguish schizophrenia and major depressive disorder in a latent low-dimensional space of the triple brain network. Specifically, 21 patients of schizophrenia and 25 patients of major depressive disorder were assessed by T1-weighted, diffusion-weighted, and resting-state functional MRIs. Individual structural and functional connectivity networks, based on pre-defined regions of the triple network were constructed, respectively. Afterwards, SCNMF was employed to extract the discriminative patterns. Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine. Experimental results show the specific brain patterns for schizophrenia and major depressive disorder that are multi-modal, complex, and distributed in the triple network. Parts of the prefrontal cortex including superior frontal gyri showed variation between patients with schizophrenia and major depression due to structural properties. In terms of functional properties, the middle cingulate cortex, inferior parietal lobule, and cingulate cortex were the most discriminative regions.

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IJCAI Conference 2018 Conference Paper

Your Tweets Reveal What You Like: Introducing Cross-media Content Information into Multi-domain Recommendation

  • Weizhi Ma
  • Min Zhang
  • Chenyang Wang
  • Cheng Luo
  • Yiqun Liu
  • Shaoping Ma

Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users' preferences in the target domain. Thought to be unrelated, users' off-topic content information (such as user tweets) is usually omitted. However, the off-topic content information also helps to indicate the similarity of users on their tastes, interests, and opinions, which matches the underlying assumption of Collaborative Filtering (CF) algorithms. In this paper, we propose a framework to capture the features from user's off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms. The framework is easy to understand and flexible in different embedding approaches and MF based algorithms. To the best of our knowledge, there is no previous study in which user's off-topic content in other platforms is taken into consideration. By capturing the cross-platform content including both on-topic and off-topic information, multiple algorithms with several embedding learning approaches have achieved significant improvements in rating prediction on three datasets. Especially in cold start scenarios, we observe greater enhancement. The results confirm our suggestion that off-topic cross-media information also contributes to the recommendation.

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YNICL Journal 2014 Journal Article

Patient-specific connectivity pattern of epileptic network in frontal lobe epilepsy

  • Cheng Luo
  • Dongmei An
  • Dezhong Yao
  • Jean Gotman

There is evidence that focal epilepsy may involve the dysfunction of a brain network in addition to the focal region. To delineate the characteristics of this epileptic network, we collected EEG/fMRI data from 23 patients with frontal lobe epilepsy. For each patient, EEG/fMRI analysis was first performed to determine the BOLD response to epileptic spikes. The maximum activation cluster in the frontal lobe was then chosen as the seed to identify the epileptic network in fMRI data. Functional connectivity analysis seeded at the same region was also performed in 63 healthy control subjects. Nine features were used to evaluate the differences of epileptic network patterns in three connection levels between patients and controls. Compared with control subjects, patients showed overall more functional connections between the epileptogenic region and the rest of the brain and higher laterality. However, the significantly increased connections were located in the neighborhood of the seed, but the connections between the seed and remote regions actually decreased. Comparing fMRI runs with interictal epileptic discharges (IEDs) and without IEDs, the patient-specific connectivity pattern was not changed significantly. These findings regarding patient-specific connectivity patterns of epileptic networks in FLE reflect local high connectivity and connections with distant regions differing from those of healthy controls. Moreover, the difference between the two groups in most features was observed in the strictest of the three connection levels. The abnormally high connectivity might reflect a predominant attribute of the epileptic network, which may facilitate propagation of epileptic activity among regions in the network.

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