Author name cluster

Xi Zhu

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8 papers

1 author row

EAAI Journal 2026 Journal Article

Machine-learning-based pattern recognition and key discharge mode diagnosis of multi-source discharge in a real switch cabinet: A perspective from optical signals

Hongtu Cheng
Yang Shen
Qi Hu
Jie Feng
Lei Cai
Xi Zhu
Zhi Fang

Partial discharge (PD) in gas-insulated switch cabinets exhibits spatial distribution, multiple sources, and diverse types, posing challenges for fault diagnosis. The existing detection and pattern recognition methods are unable to effectively address the issues of distributed detection and signal interference that arise when PD co-occurs at multiple locations within the equipment. Fluorescence optical fiber was employed to acquire optical signals corresponding to four typical types of PDs and their 15 spatially distributed multi-source combinations in a real switch cabinet. Discharge data for 15 types, with 1000 cycles per type, totaling 15000 cycles, were collected. The dataset was split into training and testing sets at an 8: 2 ratio, ensuring no overlap between them. To accurately classify the complex discharge modes, we developed a machine learning-based pattern recognition model. This model integrates extreme gradient boosting (XGB) and light gradient boosting machine (LGBM) via a soft voting ensemble, following Bayesian hyperparameter optimization. A dynamic weight adjustment mechanism was also incorporated to address class imbalance. Results demonstrate that the integrated model achieved an overall classification accuracy of 93%. The number of fault types with a diagnostic rate below 90% decreased from 4 to 2, with the diagnostic rate for all fault types exceeding 80%. Recall and F1 scores for most categories remain above 90%, indicating strong performance in identifying multiple types of PD under realistic operational conditions. This research provides a reference for real-time monitoring and intelligent early warning of multi-source PDs in power equipment.

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YNIMG Journal 2025 Journal Article

Deep diffusion MRI template (DDTemplate): A novel deep learning groupwise diffusion MRI registration method for brain template creation

Junyi Wang
Xi Zhu
Wei Zhang
Mubai Du
William M. Wells
Lauren J O’Donnell
Fan Zhang

Diffusion MRI (dMRI) is an advanced imaging technique that enables in-vivo tracking of white matter fiber tracts and estimates the underlying cellular microstructure of brain tissues. Groupwise registration of dMRI data from multiple individuals is an important task for brain template creation and investigation of inter-subject brain variability. However, groupwise registration is a challenging task due to the uniqueness of dMRI data that include multi-dimensional, orientation-dependent signals that describe not only the strength but also the orientation of water diffusion in brain tissues. Deep learning approaches have shown successful performance in standard subject-to-subject dMRI registration. However, no deep learning methods have yet been proposed for groupwise dMRI registration. . In this work, we propose Deep Diffusion MRI Template (DDTemplate), which is a novel deep-learning-based method building upon the popular VoxelMorph framework to take into account dMRI fiber tract information. DDTemplate enables joint usage of whole-brain tissue microstructure and tract-specific fiber orientation information to ensure alignment of white matter fiber tracts and whole brain anatomical structures. We propose a novel deep learning framework that simultaneously trains a groupwise dMRI registration network and generates a population brain template. During inference, the trained model can be applied to register unseen subjects to the learned template. We compare DDTemplate with several state-of-the-art registration methods and demonstrate superior performance on dMRI data from multiple cohorts (adolescents, young adults, and elderly adults) acquired from different scanners. Furthermore, as a testbed task, we perform a between-population analysis to investigate sex differences in the brain, using the popular Tract-Based Spatial Statistics (TBSS) method that relies on groupwise dMRI registration. We find that using DDTemplate can increase the sensitivity in population difference detection, showing the potential of our method's utility in real neuroscientific applications.

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YNIMG Journal 2025 Journal Article

Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis

Nick Steele
Ashley A. Huggins
Rajendra A. Morey
Ahmed Hussain
Courtney Russell
Benjamin Suarez-Jimenez
Elena Pozzi
Hadis Jameei

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

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YNIMG Journal 2023 Journal Article

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Xi Zhu
Yoojean Kim
Orren Ravid
Xiaofu He
Benjamin Suarez-Jimenez
Sigal Zilcha-Mano
Amit Lazarov
Seonjoo Lee

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

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YNIMG Journal 2022 Journal Article

A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Delin Sun
Gopalkumar Rakesh
Courtney C. Haswell
Mark Logue
C. Lexi Baird
Erin N. O'Leary
Andrew S. Cotton
Hong Xie

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1, 340 cases with posttraumatic stress disorder (PTSD) (6. 2–81. 8 years old) and 2, 057 trauma-exposed controls without PTSD (6. 3–85. 2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63. 704, p < 0. 001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12. 082, p = 0. 007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9. 114, p = 0. 028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0. 001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0. 001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0. 001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0. 001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0. 001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

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YNIMG Journal 2022 Journal Article

Transfer learning for cognitive reserve quantification

Xi Zhu
Yi Liu
Christian G. Habeck
Yaakov Stern
Seonjoo Lee
for-the-Alzheimer's-Disease-Neuroimaging-Initiative

Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the Structural Magnetic Resonance Imaging (sMRI) data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (age: 20-80) from RANN, 620 healthy adults (age: 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 adults (age: 55-92) from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. CR was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (RANN) to the target domains (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.

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IJCAI Conference 2020 Conference Paper

Overcoming Language Priors with Self-supervised Learning for Visual Question Answering

Xi Zhu
Zhendong Mao
Chunxiao Liu
Peng Zhang
Bin Wang
Yongdong Zhang

Most Visual Question Answering (VQA) models suffer from the language prior problem, which is caused by inherent data biases. Specifically, VQA models tend to answer questions (e. g. , what color is the banana? ) based on the high-frequency answers (e. g. , yellow) ignoring image contents. Existing approaches tackle this problem by creating delicate models or introducing additional visual annotations to reduce question dependency and strengthen image dependency. However, they are still subject to the language prior problem since the data biases have not been fundamentally addressed. In this paper, we introduce a self-supervised learning framework to solve this problem. Concretely, we first automatically generate labeled data to balance the biased data, and then propose a self-supervised auxiliary task to utilize the balanced data to assist the VQA model to overcome language priors. Our method can compensate for the data biases by generating balanced data without introducing external annotations. Experimental results show that our method achieves state-of-the-art performance, improving the overall accuracy from 49. 50% to 57. 59% on the most commonly used benchmark VQA-CP v2. In other words, we can increase the performance of annotation-based methods by 16% without using external annotations. Our code is available on GitHub.

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YNICL Journal 2016 Journal Article

Neural changes in extinction recall following prolonged exposure treatment for PTSD: A longitudinal fMRI study

Liat Helpman
Marie-France Marin
Santiago Papini
Xi Zhu
Gregory M. Sullivan
Franklin Schneier
Mariana Neria
Erel Shvil

BACKGROUND: Neurobiological models of posttraumatic stress disorder (PTSD) implicate fear processing impairments in the maintenance of the disorder. Specific deficits in extinction recall, the retention of learned extinction, have been demonstrated. While deficient extinction recall, and the associated activation pattern of prefrontal and hippocampal regions, distinguishes individuals with PTSD from controls, research has not yet examined changes following treatment. We examined the behavioral and neural correlates of extinction recall before and after cognitive behavioral treatment of PTSD. METHODS: Fifty-eight participants (30 with PTSD, 28 trauma-exposed matched controls) underwent a 2-day behavioral fear conditioning, extinction, and recall paradigm during functional magnetic resonance imaging (fMRI). The same procedures were repeated 10 weeks later, after PTSD patients had completed prolonged exposure treatment. We analyzed fMRI data from 32 subjects (16 PTSD; 16 controls) and skin conductance response (SCR) data from 33 subjects (16 PTSD; 17 controls). Neural activity during extinction recall, SCR, and PTSD symptoms were compared across groups and over time. RESULTS: PTSD patients exhibited pre- to post-treatment reduction in rostral anterior cingulate cortex (rACC) activation during extinction recall, and increase in functional coherence between the rACC and the ventromedial prefrontal cortex (vmPFC) and subgenual anterior cingulate cortex (sgACC). Reduced PTSD symptom severity from pre- to post-treatment was significantly associated with reduced subgenual ACC and parahippocampal activation during this task. SCR during the extinction recall phase did not significantly change with treatment in the PTSD group, but change in SCR was associated with reduction in PTSD symptom severity. CONCLUSIONS: Prolonged exposure treatment appears to alter neural activation in PTSD patients during recall of fear extinction, and change in extinction recall (measured by SCR) is associated with symptom reduction. We discuss results in the context of neural systems involved in response to affective stimuli.

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