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Ying Han

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12 papers

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YNICL Journal 2026 Journal Article

Brain topology alteration in Alzheimer’s disease brain networks: A multi-center study

Longhao Ma
Pan Wang
Dawei Wang
Hongxiang Yao
Bo Zhou
Yonghua Zhao
Zhengluan Liao
Yan Chen

Alterations in brain network centrality are key features of Alzheimer's disease (AD) and may offer insights into the disruption of network organization underlying cognitive decline. We introduce a novel centrality metric, DomiRank, to characterize dominance-driven connectivity patterns in the human brain network, using a multi-center MRI dataset comprising 809 participants. Compared with conventional metrics, DomiRank centrality showed greater sensitivity in detecting AD-related network disruptions, particularly within the cingulate gyrus, precuneus, and subcortical hubs such as the basal ganglia-regions critical for cognition. Regional DomiRank alterations were significantly correlated with clinical cognitive scores, indicating their potential relevance to disease severity. Gene enrichment analysis revealed that areas with reduced DomiRank centrality were enriched for genes involved in synaptic signaling and neuronal communication, suggesting molecular mechanisms underlying network vulnerability. These findings highlight DomiRank centrality as a promising biomarker for characterizing network disorganization in AD, linking changes in brain connectivity with underlying molecular processes.

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JBHI Journal 2025 Journal Article

Disentangled Representation Learning for Capturing Individualized Brain Atrophy via Pseudo-Healthy Synthesis

Zhuangzhuang Li
Kun Zhao
Pindong Chen
Dawei Wang
Hongxiang Yao
Bo Zhou
Jie Lu
Pan Wang

Brain atrophy emerges as a distinctive hallmark in various neurodegenerative diseases, demonstrating a progressive trajectory across diverse disease stages and concurrently manifesting in tandem with a discernible decline in cognitive abilities. Understanding the individualized patterns of brain atrophy is critical for precision medicine and the prognosis of neurodegenerative diseases. However, it is difficult to obtain longitudinal data to compare changes before and after the onset of diseases. In this study, we present a deep disentangled generative model (DDGM) for capturing individualized atrophy patterns via disentangling patient images into “realistic” healthy counterfactual images and abnormal residual maps. The proposed DDGM consists of four modules: normal MRI synthesis, residual map synthesis, input reconstruction module, and mutual information neural estimator (MINE). The MINE and adversarial learning strategy together ensure independence between disease-related features and features shared by both disease and healthy controls. In addition, we proposed a comprehensive evaluation of the effectiveness of synthetic pseudo-healthy images, focusing on both their healthiness and subject identity. The results indicated that the proposed DDGM effectively preserves these characteristics in the synthesized pseudo-healthy images, outperforming existing methods. The proposed method demonstrates robust generalization capabilities across two independent datasets from different races and sites. Analysis of the disease residual/saliency maps revealed specific atrophy patterns associated with Alzheimer's disease (AD), particularly in the hippocampus and amygdala regions. These accurate individualized atrophy patterns enhance the performance of AD classification tasks, resulting in an improvement in classification accuracy to 92. 50 $\pm$ 2. 70%.

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JBHI Journal 2025 Journal Article

FROG: A Fine-Grained Spatiotemporal Graph Neural Network With Self-Supervised Guidance for Early Diagnosis of Alzheimer's Disease

Shuoyan Zhang
Qingmin Wang
Min Wei
Jiayi Zhong
Ying Zhang
Ziyan Song
Chenyang Li
Xiaochen Zhang

Functional magnetic resonance imaging (fMRI) has demonstrated significant potential in the early diagnosis and study of pathological mechanisms of Alzheimer's disease (AD). To fit subtle cross-spatiotemporal interactions and learn pathological features from fMRI, we propose a fine-grained spatiotemporal graph neural network with self-supervised learning (SSL) for diagnosis and biomarker extraction of early AD. First, considering the spatiotemporal interaction of the brain, we design two masks that leverage the spatial correlation and temporal repeatability of fMRI. Afterwards, temporal gated inception convolution and graph scalable inception convolution are proposed for the spatiotemporal autoencoder to enhance subtle cross-spatiotemporal variation and learn noise-suppressed signals. Furthermore, a spatiotemporal scalable cosine error with high selectivity for signal reconstruction is designed in SSL to guide the autoencoder to fit the fine-grained pathological features in an unsupervised manner. A total of 5, 687 samples from four cross-population cohorts are involved. The accuracy of our model was 5. 1% higher than the state-of-the-art models, which included four AD diagnostic models, four SSL strategies, and three multivariate time series models. The neuroimaging biomarkers were precisely localized to the abnormal brain regions, and correlated significantly with the cognitive scale and biomarkers (P $< $ 0. 001). Moreover, the AD progression was reflected through the mask reconstruction error of our SSL strategy. The results demonstrate that our model can effectively capture spatiotemporal and pathological features, and providing a novel and relevant framework for the early diagnosis of AD based on fMRI.

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YNIMG Journal 2025 Journal Article

Multimodal integration of plasma biomarkers, MRI, and genetic risk to predict cerebral amyloid burden in Alzheimer’s disease

Yichen Wang
Hao-Jie Chen
Yuxin Cheng
YaoXin Xie
Yuyan Cheng
Shiyun Zhao
Yidong Jiang
Tianyu Bai

Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ positron emission tomography (Aβ-PET) remains the gold standard for assessing cerebral Aβ burden, its clinical utility is hindered by cost, radiation exposure, and limited availability. Plasma biomarkers have emerged as promising, non‑invasive indicators of Aβ pathology, yet they do not incorporate individual genetic risk or neuroanatomical context. To address this gap, we developed a multimodal machine‑learning framework that integrates plasma biomarkers, MRI‑derived brain structural features (regional volumes, cortical thickness, cortical area and structural connectivity), and genetic risk profiles to predict cerebral Aβ burden. This approach was evaluated in 150 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 101 participants from a domestic Chinese Sino Longitudinal Study of Cognitive Decline (SILCODE). Incorporating multimodal features substantially improved predictive performance: the baseline model using plasma and clinical variables alone achieved an R2 of 0. 56, whereas integrating neuroimaging and genetic information increased accuracy (R2 = 0. 63 with apolipoprotein E genotypes and R2 = 0. 64 with polygenic risk scores). Furthermore, a multiclass classifier trained on the same multimodal features achieved robust discrimination of cognitive status, with area‑under‑the‑curve values of 0. 87 for normal controls, 0. 76 for mild cognitive impairment, and 0. 95 for AD dementia. These findings highlight the value of combining plasma, imaging, and genetic data to non-invasively estimate cerebral Aβ burden, offering a potential alternative to PET imaging for early AD risk assessment.

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YNIMG Journal 2025 Journal Article

Transcranial photobiomodulation improves functional brain networks and working memory in healthy older adults: An fNIRS study

Qin Yang
Xiujuan Qu
Can Sheng
Xing Zhao
Guanqun Chen
Xiaoni Wang
Yuxia Li
Wenying Du

BACKGROUND: Transcranial photobiomodulation (tPBM), as a novel non-invasive neurostimulation technique, has shown the compelling potential for improving cognitive function in aging population. However, the potential mechanism remains unclear. Neuroimaging studies have found that tPBM-induced physiological changes exist in both targeted and non-targeted brain areas, suggesting the necessity of understanding the modulation mechanism from the perspective of the whole brain level. OBJECTIVE: This randomized, single-blind, sham-controlled crossover study aimed to investigate the hypothesis that tPBM improved working memory in healthy older adults through the mechanism of optimizing the properties of the resting-state functional brain networks. METHODS: A total of 55 right-handed healthy older adults were randomly assigned to sham tPBM session group or active tPBM session group. After a washout interval, they were assigned to the opposite intervention session. Each session included the following: active or sham tPBM application with a 1064-nm laser to the left forehead; before and after, resting-state functional near-infrared spectroscopy (fNIRS) measurements; and the digital n-back task. Differences in accuracy and reaction time of the n-back task, and changes in functional connectivity and graph metrics of the brain networks were investigated and compared between the active and sham tPBM sessions. In addition, correlations between tPBM-induced changes in functional brain networks, and the n-back task were examined. RESULTS: The results showed that compared with the sham tPBM session, the accuracy and reaction time during 3-back task significantly improved in the active tPBM session. In addition, the global efficiency, local efficiency, nodal efficiency, and functional connectivity significantly increased in the active tPBM session, particularly in the frontoparietal areas. Importantly, the altered 3-back accuracy was positively correlated with the changes of functional connectivity and nodal efficiency mainly in left prefrontal cortex in those who had increased 3-back accuracy in the active tPBM session. CONCLUSION: This study suggests that tPBM may serve as an effective tool to improve working memory in older adults through the modulation of resting-state functional brain network properties. Investigations in large-scale samples are needed to further validate the findings of this study.

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YNIMG Journal 2024 Journal Article

Greater up-modulation of intra-individual brain signal variability makes a high-load cognitive task more arduous for older adults

Hong Li
Ying Han
Haijing Niu

The extent to which brain responses are less distinctive across varying cognitive loads in older adults is referred to as neural dedifferentiation. Moment-to-moment brain signal variability, an emerging indicator, reveals not only the adaptability of an individual's brain as an inter-individual trait, but also the allocation of neural resources within an individual due to ever-changing task demands, thus shedding novel insight into the process of neural dedifferentiation. However, how the modulation of intra-individual brain signal variability reflects behavioral differences related to cognitively demanding tasks remains unclear. In this study, we employed functional near-infrared spectroscopy (fNIRS) imaging to capture the variability of brain signals, which was quantified by the standard deviation, during both the resting state and an n-back task (n = 1, 2, 3) in 57 healthy older adults. Using multivariate Partial Least Squares (PLS) analysis, we found that fNIRS signal variability increased from the resting state to the task and increased with working memory load in older adults. We further confirmed that greater fNIRS signal variability generally supported faster and more stable response time in the 2- and 3-back conditions. However, the intra-individual level analysis showed that the greater the up-modulation in fNIRS signal variability with cognitive loads, the more its accuracy decreases and mean response time increases, suggesting that a greater intra-individual brain signal variability up-modulation may reflect decreased efficiency in neural information processing. Taken together, our findings offer new insights into the nature of brain signal variability, suggesting that inter- and intra-individual brain signal variability may index distinct theoretical constructs.

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EAAI Journal 2023 Journal Article

Deep learning-based real-time 3D human pose estimation

Xiaoyan Zhang
Zhengchun Zhou
Ying Han
Hua Meng
Meng Yang
Sutharshan Rajasegarar

Human body pose estimation represented by joint rotations is essential for driving the virtual characters. The present paper developed a novel end-to-end point-to-pose mesh fitting network (P2P-MeshNet) to directly estimate the body joint rotations. P2P-MeshNet provided a strong collaboration between the deep learning network, an inverse kinematics network for body pose estimation (IKNet-body), and the self-correcting network, an iterative error feedback network (IEF). The introduced P2P-MeshNet was then applied to the free mocap (FreeMocap) dataset covering OpenPose 3D joint locations reconstructed from multi-view OpenPose 2D joint locations. The generated joint rotations were tested using the mean per joint position error (MPJPE), as well as the percentage of correct keypoints (PCK) along with the area under the PCK curve (AUC) with a threshold range of 0–60 mm after Procrustes aligned. Based on the compared metrics, P2P-MeshNet with 11. 31 mm and 99. 7% in estimate error and success rate as well as an AUC of 80. 9 demonstrated a more consistent tool for future human body pose estimation. The runtime performance of 100 frames per second implied its potential application prospects.

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JBHI Journal 2023 Journal Article

Repeated Photobiomodulation Induced Reduction of Bilateral Cortical Hemodynamic Activation During a Working Memory Task in Healthy Older Adults

Zhishan Hu
Xiujuan Qu
Lexuan Li
Xiaohan Zhou
Qin Yang
Qi Dong
Hesheng Liu
Xiaobo Li

Transcranial photobiomodulation (tPBM) is an emerging non-invasive light-based neuromodulation technique that shows promising potential for improving working memory (WM) performance in older adults. However, the neurophysiological mechanisms associated with tPBM that underlie the improvement of WM and the persistence of such improvement have not been investigated. Sixty-one healthy older adults were recruited to receive a baseline sham stimulation, followed by one-week active tPBM (12 min daily, 1064-nm laser, 250 mW/cm 2 ) and three-week follow-ups. N-back WM task was conducted on post-stimulation of the baseline, the first (Day 1) and seventh (Day 7) days of the active treatment, and at the follow-ups. During the task, functional near-infrared spectroscopy (fNIRS) imaging was employed to record the cortical hemodynamic changes. Brain activations during the active and follow-up sessions were compared with the baseline to determine how tPBM had changed cortical hemodynamic activity and how long these changes persisted. We found that tPBM stimulation on Day 1 induced significantly decreased activation in the right hemisphere during the 3-back. The decreased activation expanded from only the right hemisphere on Day 1 to both hemispheres on Day 7. The decreased activation persisted for one week in the right supramarginal gyrus and the left angular gyrus and two weeks in the left somatosensory association cortex. These activation changes were accompanied by significantly improved task accuracy during the N-back. These findings provide important evidence for understanding neural mechanisms underlying cognitive enhancement after tPBM.

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YNICL Journal 2022 Journal Article

Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer’s disease: A cross-validation study with Chinese and ADNI cohorts

Tao-Ran Li
Qiu-Yue Dong
Xue-Yan Jiang
Gui-Xia Kang
Xin Li
Yun-Yan Xie
Jie-Hui Jiang
Ying Han

OBJECTIVE: Disease-related metabolic brain patterns have been verified for a variety of neurodegenerative diseases including Alzheimer's disease (AD). This study aimed to explore and validate the pattern derived from cognitively normal controls (NCs) in the Alzheimer's continuum. METHODS: F]florbetapir-PET imaging. Participants were binary-grouped based on β-amyloid (Aβ) status, and the positivity was defined as Aβ+. Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate the "at-risk AD-related metabolic pattern (ARADRP)" for NCs. The pattern expression score was obtained and compared between the groups, and receiver operating characteristic curves were drawn. Notably, we conducted cross-validation to verify the robustness and correlation analyses to explore the relationships between the score and AD-related pathological biomarkers. RESULTS: F]florbetapir-PET (p > 0.23). CONCLUSIONS: ARADRP exists for NCs, and the acquired pattern expression score shows a certain ability to discriminate Aβ+ NCs from Aβ- NCs. The SSM/PCA method is expected to be helpful in the ultra-early diagnosis of AD in clinical practice.

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YNIMG Journal 2021 Journal Article

Co-activation patterns across multiple tasks reveal robust anti-correlated functional networks

Meiling Li
Louisa Dahmani
Danhong Wang
Jianxun Ren
Sophia Stocklein
Yuanxiang Lin
Guoming Luan
Zhiqiang Zhang

Whether antagonistic brain states constitute a fundamental principle of human brain organization has been debated over the past decade. Some argue that intrinsically anti-correlated brain networks in resting-state functional connectivity are an artifact of preprocessing. Others argue that anti-correlations are biologically meaningful predictors of how the brain will respond to different stimuli. Here, we investigated the co-activation patterns across the whole brain in various tasks and test whether brain regions demonstrate anti-correlated activity similar to those observed at rest. We examined brain activity in 47 task contrasts from the Human Connectome Project (N = 680) and found robust antagonistic interactions between networks. Regions of the default network exhibited the highest degree of cortex-wide negative connectivity. The negative co-activation patterns across tasks showed good correspondence to that derived from resting-state data processed with global signal regression (GSR). Interestingly, GSR-processed resting-state data was a significantly better predictor of task-induced modulation than data processed without GSR. Finally, in a cohort of 25 patients with depression, we found that task-based anti-correlations between the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex were associated with clinical efficacy of transcranial magnetic stimulation therapy targeting the DLPFC. Overall, our findings indicate that anti-correlations are a biologically meaningful phenomenon and may reflect an important principle of functional brain organization.

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EAAI Journal 2020 Journal Article

Path planning of multiple UAVs with online changing tasks by an ORPFOA algorithm

Kun Li
Fawei Ge
Ying Han
Yi’an Wang
Wensu Xu

The unmanned aerial vehicle (UAV) is a new type oilfield inspection tool which is characterized by high flexibility, low cost and high efficiency. In the UAV based oilfield inspection technology, the path planning is an indispensable element which finds an optimal flight path for UAV to finish the inspection jobs successfully. In comparison with the other researches, our study focuses on two challenging issues: path planning of multiple UAVs by traversing a certain amount of task points in the three-dimensional environment within the required completion time, and optimizing solving for the best flight path with online changing tasks. In the research, a novel task assignment method including the initial task assignment and the task assignment with changing tasks is proposed to determine the initial task sequences of each UAV and rapidly replan task sequences after tasks change. An improved fruit fly optimization algorithm (named ORPFOA) is proposed to solve the path planning problem in both initial task sequences and new task sequences after tasks change, in which the optimal reference point and a distance cost matrix are used to reach both faster solving and higher optimizing precision for the optimal flight path. In ORPFOA, two cost functions are defined to evaluate the optimizing results in the initial phase and the new phase after task changes, respectively. A simulation model of the three-dimensional oilfield environment is established to verify the effectiveness of the proposed method in comparison with other six algorithms.

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YNIMG Journal 2011 Journal Article

Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study

Ying Han
Jinhui Wang
Zhilian Zhao
Baoquan Min
Jie Lu
Kuncheng Li
Yong He
Jianping Jia

Here we utilized resting-state functional magnetic resonance imaging (R-fMRI) to measure the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) in 24 patients with amnestic mild cognitive impairment (aMCI) and 24 age- and sex-matched healthy controls. Two different frequency bands (slow-5: 0. 01–0. 027 Hz; slow-4: 0. 027–0. 073 Hz) were analyzed. We showed that there were widespread differences in ALFF/fALFF between the two bands in many brain regions, predominantly including the medial prefrontal cortex (MPFC), posterior cingulate cortex/precuneus (PCC/PCu), basal ganglia, and hippocampus/parahippocampal gyrus (PHG). Compared to controls, the aMCI patients had decreased ALFF/fALFF values in the PCC/PCu, MPFC, hippocampus/PHG, basal ganglia, and prefrontal regions, and increased ALFF/fALFF values mainly in several occipital and temporal regions. Specifically, we observed that the ALFF/fALFF abnormalities in the PCC/PCu, PHG, and several occipital regions were greater in the slow-5 band than in the slow-4 band. Finally, our results of functional analysis were not significantly influenced by the gray matter loss in the MCI patients, suggesting that the results reflect functional differences between groups. Together, our data suggest that aMCI patients have widespread abnormalities in intrinsic brain activity, and the abnormalities depend on the studied frequency bands of R-fMRI data.

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