Dewen Hu

JBHI Journal 2025 Journal Article

Cognitive Load Prediction From Multimodal Physiological Signals Using Multiview Learning

• Yingxin Liu
• Yang Yu
• Hong Tao
• Zeqi Ye
• Si Wang
• Hao Li
• Dewen Hu
• Zongtan Zhou

Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification, new research is still needed to adapt the current state-of-the-art multimodal fusion methods. Here, we proposed a feature selection framework based on multiview learning to address the challenges of information redundancy and reveal the common physiological mechanisms underlying cognitive load. Specifically, the multimodal signal features [electroencephalogram (EEG), electrodermal activity (EDA), electrocardiogram (ECG), electrooculogram (EOG), & eye movements] at three cognitive load levels were estimated during multiattribute task battery (MATB) tasks performed by 22 healthy participants and fed into a feature selection-multiview classification with cohesion and diversity (FS-MCCD) framework. The optimized feature set was extracted from the original feature set by integrating the weight of each view and the feature weights to formulate the ranking criteria. The cognitive load prediction model, evaluated using real-time classification results, achieved an average accuracy of 81. 08% and an average F1-score of 80. 94% for three-class classification among 22 participants. Furthermore, the weights of the physiological signal features revealed the physiological mechanisms related to cognitive load. Specifically, heightened cognitive load was linked to amplified $\delta$ and $\theta$ power in the frontal lobe, reduced $\alpha$ power in the parietal lobe, and an increase in pupil diameter. Thus, the proposed multimodal feature fusion framework emphasizes the effectiveness and efficiency of using these features to predict cognitive load.

IROS Conference 2025 Conference Paper

ETA: Learning Optical Flow with Efficient Temporal Attention

• Bo Wang 0144
• Zhenping Sun
• Yang Yu 0014
• Li Liu 0002
• Jian Li 0003
• Dewen Hu

Considering the potential of using multi-frame information to solve the occlusion problem, we introduce a novel idea of multi-frame information integration, which uses the attention mechanism to fuse the temporal information from the previous frame. The idea can effectively improve the estimation accuracy in occluded regions and optimize the inference speed under multi-frame settings. Meanwhile, we suggest the concept of attention confidence to provide an explicit value criterion for the model to utilize useful attention information more efficiently. Furthermore, we propose an Efficient Temporal Attention network (ETA), which achieves promising results on Sintel and KITTI benchmarks, especially with a 9. 4% error reduction compared to the baseline method GMA on Sintel (test) Clean.

JBHI Journal 2025 Journal Article

Few-Shot Class-Incremental Learning for Retinal Disease Recognition

• Jinghua Zhang
• Peng Zhao
• Yongkun Zhao
• Chen Li
• Dewen Hu

Few-Shot Class-Incremental Learning (FSCIL) techniques are essential for developing Deep Learning (DL) models that can continuously learn new classes with limited samples while retaining existing knowledge. This capability is particularly crucial for DL-based retinal disease diagnosis system, where acquiring large annotated datasets is challenging, and disease phenotypes evolve over time. This paper introduces Re-FSCIL, a novel framework for Few-Shot Class-Incremental Retinal Disease Recognition (FSCIRDR). Re-FSCIL integrates the RETFound model with a fine-grained module, employing a forward-compatible training strategy to improve adaptability, supervised contrastive learning to enhance feature discrimination, and feature fusion for robust representation quality. We convert existing datasets into the FSCIL format and reproduce numerous representative FSCIL methods to create two new benchmarks, RFMiD38 and JSIEC39, specifically for FSCIRDR. Our experimental results demonstrate that Re-FSCIL achieves State-of-the-art (SOTA) performance, significantly surpassing existing FSCIL methods on these benchmarks.

NeurIPS Conference 2025 Conference Paper

Fully Spiking Neural Networks for Unified Frame-Event Object Tracking

• Jingjun Yang
• Liangwei Fan
• Jinpu Zhang
• Xiangkai Lian
• Hui Shen
• Dewen Hu

The integration of image and event streams offers a promising approach for achieving robust visual object tracking in complex environments. However, current fusion methods achieve high performance at the cost of significant computational overhead and struggle to efficiently extract the sparse, asynchronous information from event streams, failing to leverage the energy-efficient advantages of event-driven spiking paradigms. To address this challenge, we propose the first fully Spiking Frame-Event Tracking framework called SpikeFET. This network achieves synergistic integration of convolutional local feature extraction and Transformer-based global modeling within the spiking paradigm, effectively fusing frame and event data. To overcome the degradation of translation invariance caused by convolutional padding, we introduce a Random Patchwork Module (RPM) that eliminates positional bias through randomized spatial reorganization and learnable type encoding while preserving residual structures. Furthermore, we propose a Spatial-Temporal Regularization (STR) strategy that overcomes similarity metric degradation from asymmetric features by enforcing spatio-temporal consistency among temporal template features in latent space. Extensive experiments across multiple benchmarks demonstrate that the proposed framework achieves superior tracking accuracy over existing methods while significantly reducing power consumption, attaining an optimal balance between performance and efficiency.

ICRA Conference 2025 Conference Paper

HGSLoc: 3DGS-Based Heuristic Camera Pose Refinement

• Zhongyan Niu
• Zhen Tan 0002
• Jinpu Zhang
• Xueliang Yang
• Dewen Hu

Visual localization refers to the process of determining camera poses and orientation within a known scene representation. This task is often complicated by factors such as changes in illumination and variations in viewing angles. In this paper, we propose HGSLoc, a novel lightweight plug-and-play pose optimization framework, which integrates 3D reconstruction with a heuristic refinement strategy to achieve higher pose estimation accuracy. Specifically, we introduce an explicit geometric map for 3D representation and high-fidelity rendering, allowing the generation of high-quality synthesized views to support accurate visual localization. Our method demonstrates higher localization accuracy compared to NeRFbased neural rendering localization approaches. We introduce a heuristic refinement strategy, its efficient optimization capability can quickly locate the target node, while we set the steplevel optimization step to enhance the pose accuracy in the scenarios with small errors. With carefully designed heuristic functions, it offers efficient optimization capabilities, enabling rapid error reduction in rough localization estimations. Our method mitigates the dependence on complex neural network models while demonstrating improved robustness against noise and higher localization accuracy in challenging environments, as compared to neural network joint optimization strategies. The optimization framework proposed in this paper introduces novel approaches to visual localization by integrating the advantages of 3D reconstruction and the heuristic refinement strategy, which demonstrates strong performance across multiple benchmark datasets, including 7Scenes and Deep Blending dataset. The implementation of our method has been released at https://github.com/anchang699/HGSLoc.

IROS Conference 2025 Conference Paper

Tracking Any Point with Frame-Event Fusion Network at High Frame Rate

• Jiaxiong Liu
• Bo Wang 0144
• Zhen Tan 0002
• Jinpu Zhang
• Hui Shen 0004
• Dewen Hu

Tracking any point based on image frames is constrained by frame rates, leading to instability in high-speed scenarios and limited generalization in real-world applications. To overcome these limitations, we propose an image-event fusion point tracker, FE-TAP, which combines the contextual information from image frames with the high temporal resolution of events, achieving high frame rate and robust point tracking under various challenging conditions. Specifically, we designed an Evolution Fusion module (EvoFusion) to model the image generation process guided by events. This module can effectively integrate valuable information from both modalities operating at different frequencies. To achieve smoother point trajectories, we employed a transformer-based refinement strategy that updates the point’s trajectories and features iteratively. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches, particularly improving expected feature age by 24% on EDS datasets. Finally, we qualitatively validated the robustness of our algorithm in real driving scenarios using our custom-designed image-event synchronization device.

IROS Conference 2024 Conference Paper

TD-NeRF: Novel Truncated Depth Prior for Joint Camera Pose and Neural Radiance Field Optimization

• Zhen Tan 0002
• Zongtan Zhou
• Yangbing Ge
• Zi Wang
• Xieyuanli Chen
• Dewen Hu

The reliance on accurate camera poses is a significant barrier to the widespread deployment of Neural Radiance Fields (NeRF) models for 3D reconstruction and SLAM tasks. The existing method introduces monocular depth priors to jointly optimize the camera poses and NeRF, which fails to fully exploit the depth priors and neglects the impact of their inherent noise. In this paper, we propose Truncated Depth NeRF (TD-NeRF), a novel approach that enables training NeRF from unknown camera poses - by jointly optimizing learnable parameters of the radiance field and camera poses. Our approach explicitly utilizes monocular depth priors through three key advancements: 1) we propose a novel depth-based ray sampling strategy based on the truncated normal distribution, which improves the convergence speed and accuracy of pose estimation; 2) to circumvent local minima and refine depth geometry, we introduce a coarse-to-fine training strategy that progressively improves the depth precision; 3) we propose a more robust inter-frame point constraint that enhances robustness against depth noise during training. The experimental results on three datasets demonstrate that TD-NeRF achieves superior performance in the joint optimization of camera pose and NeRF, surpassing prior works, and generates more accurate depth geometry. The implementation of our method has been released at https://github.com/nubot-nudt/TD-NeRF.

JMLR Journal 2023 Journal Article

Label Distribution Changing Learning with Sample Space Expanding

• Chao Xu
• Hong Tao
• Jing Zhang
• Dewen Hu
• Chenping Hou

With the evolution of data collection ways, label ambiguity has arisen from various applications. How to reduce its uncertainty and leverage its effectiveness is still a challenging task. As two types of representative label ambiguities, Label Distribution Learning (LDL), which annotates each instance with a label distribution, and Emerging New Class (ENC), which focuses on model reusing with new classes, have attached extensive attentions. Nevertheless, in many applications, such as emotion distribution recognition and facial age estimation, we may face a more complicated label ambiguity scenario, i.e., label distribution changing with sample space expanding owing to the new class. To solve this crucial but rarely studied problem, we propose a new framework named as Label Distribution Changing Learning (LDCL) in this paper, together with its theoretical guarantee with generalization error bound. Our approach expands the sample space by re-scaling previous distribution and then estimates the emerging label value via scaling constraint factor. For demonstration, we present two special cases within the framework, together with their optimizations and convergence analyses. Besides evaluating LDCL on most of the existing 13 data sets, we also apply it in the application of emotion distribution recognition. Experimental results demonstrate the effectiveness of our approach in both tackling label ambiguity problem and estimating facial emotion [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

YNIMG Journal 2020 Journal Article

Bayesian fusion and multimodal DCM for EEG and fMRI

• Huilin Wei
• Amirhossein Jafarian
• Peter Zeidman
• Vladimir Litvak
• Adeel Razi
• Dewen Hu
• Karl J. Friston

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors - derived from dynamic causal modelling (DCM) of EEG data - for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters - as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.

YNICL Journal 2019 Journal Article

Changes in default mode network connectivity in different glucose metabolism status and diabetes duration

• Huanghui Liu
• Jun Liu
• Limin Peng
• Zhichao Feng
• Lu Cao
• Huasheng Liu
• Hui Shen
• Dewen Hu

AIMS/HYPOTHESES: It is now generally accepted that diabetes increases the risk for cognitive impairment, but the precise mechanisms are poorly understood. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to investigate the neural basis of cognitive dysfunction in type 2 diabetes (T2D) patients. Alterations in brain functional connectivity may underlie diabetes-related cognitive dysfunction and brain damage. The aim of this study was to investigate the changes in default mode network (DMN) connectivity in different glucose metabolism status and diabetes duration. METHODS: We used a seed-based fMRI analysis to investigate positive and negative DMN connectivity in four groups (39 subjects with normal glucose metabolism [NGM], 23 subjects with impaired glucose metabolism [IGM; i.e., prediabetes], 59 T2D patients with a diabetes duration of <10 years, and 24 T2D patients with a diabetes duration of ≥10 years). RESULTS: Negative DMN connectivity increased and then regressed with deteriorating glucose metabolism status and extending diabetes duration. DMN connectivity showed a significant correlation with diabetes duration. CONCLUSION/INTERPRETATION: This study suggests that DMN connectivity may exhibit distinct patterns in different glucose metabolism status and diabetes duration, providing some potential neuroimaging evidence for early diagnosis and further understanding of the pathophysiological mechanisms of diabetic brain damage.

YNICL Journal 2019 Journal Article

fMRI functional connectivity as an indicator of interictal epileptic discharges

• Jianpo Su
• Hui Ming Khoo
• Nicolás von Ellenrieder
• Ling-Li Zeng
• Dewen Hu
• François Dubeau
• Jean Gotman

OBJECTIVE: To explore the relationship between functional connectivity and presence of interictal epileptic discharges (IEDs) in different brain regions in intracranial EEG (iEEG). METHODS: We studied 38 focal epilepsy patients who underwent simultaneous EEG/fMRI scanning and subsequent intracerebral stereo-EEG investigation. In EEG/fMRI analysis, IEDs with different spatial distributions were considered independent studies and IED-related maximal BOLD responses were evaluated. Studies with iEEG electrodes inside the maximal responses were selected and divided into three groups: Studies with 1. distinct maximal BOLD highly concordant with seizure-onset-zone (SOZ); 2. Moderate maximal BOLD concordant with SOZ; 3. maximal BOLD discordant with SOZ. Using maximal BOLD as seed, its functionally connected zone (FCZ) was determined. IED rates in iEEG channels inside and outside the FCZ were compared in the three groups. The effect of laterality and distance between channels and maximal BOLD, and correlation between functional connectivity values and IED rates were analyzed. RESULTS: ) and the inside-outside difference remained after regressing distance and laterality factors. In Group 1, connectivity values were significantly correlated with IED rates in channels inside the FCZ (p < 0.05). SIGNIFICANCE: Our results indicate a higher probability of finding intracranial IEDs in the FCZ of SOZ-concordant maximal BOLD responses than in other regions, regardless of distance and laterality. In studies with distinct maximal BOLD, connectivity values can partially predict IED rates in intracranial EEG. It is thus feasible to non-invasively delineate brain regions that are likely to have high IED rates.

YNICL Journal 2018 Journal Article

Classification of multi-site MR images in the presence of heterogeneity using multi-task learning

• Qiongmin Ma
• Tianhao Zhang
• Marcus V. Zanetti
• Hui Shen
• Theodore D. Satterthwaite
• Daniel H. Wolf
• Raquel E. Gur
• Yong Fan

With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity.

YNIMG Journal 2018 Journal Article

Impact of global signal regression on characterizing dynamic functional connectivity and brain states

• Huaze Xu
• Jianpo Su
• Jian Qin
• Ming Li
• Ling-Li Zeng
• Dewen Hu
• Hui Shen

Recently, resting-state functional magnetic resonance imaging (fMRI) studies have been extended to explore fluctuations in correlations over shorter timescales, referred to as dynamic functional connectivity (dFC). However, the impact of global signal regression (GSR) on dFC is not well established, despite the intensive investigations of the influence of GSR on static functional connectivity (sFC). This study aimed to examine the effect of GSR on the performance of the sliding-window correlation, a commonly used method for capturing functional connectivity (FC) dynamics based on resting-state fMRI and simultaneous electroencephalograph (EEG)-fMRI data. The results revealed that the impact of GSR on dFC was spatially heterogeneous, with some susceptible regions including the occipital cortex, sensorimotor area, precuneus, posterior insula and superior temporal gyrus, and that the impact was temporally modulated by the mean global signal (GS) magnitude across windows. Furthermore, GSR substantially changed the connectivity structures of the FC states responding to a high GS magnitude, as well as their temporal features, and even led to the emergence of new FC states. Conversely, those FC states marked by obvious anti-correlation structures associated with the default model network (DMN) were largely unaffected by GSR. Finally, we reported an association between the fluctuations in the windowed magnitude of GS and the time-varying EEG power within subjects, which implied changes in mental states underlying GS dynamics. Overall, this study suggested a potential neuropsychological basis, in addition to nuisance sources, for GS dynamics and highlighted the need for caution in applying GSR to sliding-window correlation analyses. At a minimum, the mental fluctuations of an individual subject, possibly related to ongoing vigilance, should be evaluated during the entire scan when the dynamics of FC is estimated.

YNIMG Journal 2016 Journal Article

Changes in functional connectivity dynamics associated with vigilance network in taxi drivers

• Hui Shen
• Zhenfeng Li
• Jian Qin
• Qiang Liu
• Lubin Wang
• Ling-Li Zeng
• Hong Li
• Dewen Hu

An increasing number of neuroimaging studies have suggested that the fluctuations of low-frequency resting-state functional connectivity (FC) are not noise but are instead linked to the shift between distinct cognitive states. However, there is very limited knowledge about whether and how the fluctuations of FC at rest are influenced by long-term training and experience. Here, we investigated how the dynamics of resting-state FC are linked to driving behavior by comparing 20 licensed taxi drivers with 20 healthy non-drivers using a sliding window approach. We found that the driving experience could be effectively decoded with 90% (p <0. 001) accuracy by the amplitude of low-frequency fluctuations in some specific connections, based on a multivariate pattern analysis technique. Interestingly, the majority of these connections fell within a set of distributed regions named “the vigilance network”. Moreover, the decreased amplitude of the FC fluctuations within the vigilance network in the drivers was negatively correlated with the number of years that they had driven a taxi. Furthermore, temporally quasi-stable functional connectivity segmentation revealed significant differences between the drivers and non-drivers in the dwell time of specific vigilance-related transient brain states, although the brain's repertoire of functional states was preserved. Overall, these results suggested a significant link between the changes in the time-dependent aspects of resting-state FC within the vigilance network and long-term driving experiences. The results not only improve our understanding of how the brain supports driving behavior but also shed new light on the relationship between the dynamics of functional brain networks and individual behaviors.

YNICL Journal 2015 Journal Article

Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy

• Peng Fang
• Jie An
• Ling-Li Zeng
• Hui Shen
• Fanglin Chen
• Wensheng Wang
• Shijun Qiu
• Dewen Hu

Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE), but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right) mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

YNIMG Journal 2014 Journal Article

Hemodynamic and electrophysiological spontaneous low-frequency oscillations in the cortex: Directional influences revealed by Granger causality

• Liangming Huang
• Yadong Liu
• Ming Li
• Dewen Hu

We used a combined electrophysiological/hemodynamic system to examine low-frequency oscillations (LFOs) in spontaneous neuronal activities (spike trains and local field potentials) and hemodynamic signals (cerebral blood flow) recorded from the anesthetized rat somatosensory and visual cortices. The laser Doppler flowmetry (LDF) probe was tilted slightly to approach the area in which a microelectrode array (MEA) was implanted for simultaneous recordings. Spike trains (STs) were converted into continuous-time rate functions (CRFs) using the ST instantaneous firing rates. LFOs were detected for all three of the components using the multi-taper method (MTM). The frequencies of these LFOs ranged from 0. 052 to 0. 167Hz (mean±SD, 0. 10±0. 026Hz) for cerebral blood flow (CBF), from 0. 027 to 0. 26Hz (mean±SD, 0. 12±0. 041Hz) for the CRFs of the STs and from 0. 04 to 0. 19Hz (mean±SD, 0. 11±0. 035Hz) for local field potentials (LFPs). We evaluated the Granger causal relationships of spontaneous LFOs among CBF, LFPs and CRFs using Granger causality (GC) analysis. Significant Granger causal relationships were observed from LFPs to CBF, from STs to CBF and from LFPs to STs at approximately 0. 1Hz. The present results indicate that spontaneous LFOs exist not only in hemodynamic components but also in neuronal activities of the rat cortex. To the best of our knowledge, the present study is the first to identify Granger causal influences among CBF, LFPs and STs and show that spontaneous LFOs carry important Granger causal influences from neural activities to hemodynamic signals.

YNIMG Journal 2012 Journal Article

Combined structural and resting-state functional MRI analysis of sexual dimorphism in the young adult human brain: An MVPA approach

• Lubin Wang
• Hui Shen
• Feng Tang
• Yufeng Zang
• Dewen Hu

There has been growing interest recently in the use of multivariate pattern analysis (MVPA) to decode information from high-dimensional neuroimaging data. The present study employed a support vector machine-based MVPA approach to identify the complex patterns of sex differences in brain structure and resting-state function. We also aimed to assess the role of anatomy on functional sex differences during rest. One hundred and forty healthy young Chinese adults (70 men and 70 women) underwent structural and resting-state functional MRI scans. Gray matter density and regional homogeneity (ReHo) were used to map brain structure and resting-state function, respectively. After combining these two feature vectors into one union-vector, a pattern classifier was designed using principal component analysis and linear support vector machine to identify brain areas that had distinct characteristics between the groups. We found that: (1) male and female brains were different with a mean classification accuracy of 89%; (2) sex differences in gray matter density were widely distributed in the brain, notably in the occipital lobe and the cerebellum; (3) men primarily showed higher ReHo in their right hemispheres and women tended to show greater ReHo in their left hemispheres; (4) about 50% of brain areas with functional sex differences exhibited significant positive correlations between gray matter density and ReHo. Our results suggest that sex is an important factor that account for interindividual variability in the healthy brain.

YNIMG Journal 2011 Journal Article

Generalised filtering and stochastic DCM for fMRI

• Baojuan Li
• Jean Daunizeau
• Klaas E. Stephan
• Will Penny
• Dewen Hu
• Karl Friston

This paper is about the fitting or inversion of dynamic causal models (DCMs) of fMRI time series. It tries to establish the validity of stochastic DCMs that accommodate random fluctuations in hidden neuronal and physiological states. We compare and contrast deterministic and stochastic DCMs, which do and do not ignore random fluctuations or noise on hidden states. We then compare stochastic DCMs, which do and do not ignore conditional dependence between hidden states and model parameters (generalised filtering and dynamic expectation maximisation, respectively). We first characterise state-noise by comparing the log evidence of models with different a priori assumptions about its amplitude, form and smoothness. Face validity of the inversion scheme is then established using data simulated with and without state-noise to ensure that DCM can identify the parameters and model that generated the data. Finally, we address construct validity using real data from an fMRI study of internet addiction. Our analyses suggest the following. (i) The inversion of stochastic causal models is feasible, given typical fMRI data. (ii) State-noise has nontrivial amplitude and smoothness. (iii) Stochastic DCM has face validity, in the sense that Bayesian model comparison can distinguish between data that have been generated with high and low levels of physiological noise and model inversion provides veridical estimates of effective connectivity. (iv) Relaxing conditional independence assumptions can have greater construct validity, in terms of revealing group differences not disclosed by variational schemes. Finally, we note that the ability to model endogenous or random fluctuations on hidden neuronal (and physiological) states provides a new and possibly more plausible perspective on how regionally specific signals in fMRI are generated.

YNIMG Journal 2010 Journal Article

Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI

• Hui Shen
• Lubin Wang
• Yadong Liu
• Dewen Hu

Recently, a functional disconnectivity hypothesis of schizophrenia has been proposed for the physiological explanation of behavioral syndromes of this complex mental disorder. In this paper, we aim at further examining whether syndromes of schizophrenia could be decoded by some special spatiotemporal patterns of resting-state functional connectivity. We designed a data-driven classifier based on machine learning to extract highly discriminative functional connectivity features and to discriminate schizophrenic patients from healthy controls. The proposed classifier consisted of two separate steps. First, we used feature selection based on a correlation coefficient method to extract highly discriminative regions and construct the optimal feature set for classification. Then, an unsupervised-learning classifier combining low-dimensional embedding and self-organized clustering of fMRI was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a leave-one-out cross-validation strategy. The experimental results demonstrated not only high classification accuracy (93. 75% for schizophrenic patients, 75. 0% for healthy controls), but also good generalization and stability with respect to the number of extracted features. In addition, some functional connectivities between certain brain regions of the cerebellum and frontal cortex were found to exhibit the highest discriminative power, which might provide further evidence for the cognitive dysmetria hypothesis of schizophrenia. This primary study demonstrated that machine learning could extract exciting new information from the resting-state activity of a brain with schizophrenia, which might have potential ability to improve current diagnosis and treatment evaluation of schizophrenia.

AIIM Journal 2007 Journal Article

Advanced fuzzy cellular neural network: Application to CT liver images

• Shitong Wang
• Duan Fu
• Min Xu
• Dewen Hu

Objective To achieve better boundary integrities and recall accuracies for segmented liver images, use of the advanced fuzzy cellular neural network (AFCNN), as a variant of the fuzzy cellular neural network (FCNN), is proposed to effectively segment CT liver images. Materials and methods In order to better utilize relevant contour and gray information from liver images, we have improved the FCNN [Wang S, Wang M. A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inform Technol Biomed, in press], which proved to be very effective for the segmentation of microscopic white blood cell images, to create the novel neural network, AFCNN. Its convergent property and global stability are proved. Based on the FCNN-based NDA algorithm [Wang S, Wang M. A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection. IEEE Trans Inform Technol Biomed, in press], we developed the AFCNN-based NDA algorithm, which we used to segment 5 CT liver images. For comparison, we also segmented the same 5 CT liver images using the FCNN-based NDA algorithm. Results and conclusion: AFCNN has distinct advantages over FCNN in both boundary integrity and recall accuracy. In particular, the performance index Binary_rate is generally much higher for AFCNN than for FCNN when applied to CT liver images.

YNIMG Journal 2005 Journal Article

Unified SPM–ICA for fMRI analysis

• Dewen Hu
• Lirong Yan
• Yadong Liu
• Zongtan Zhou
• Karl J. Friston
• Changlian Tan
• Daxing Wu

A widely used tool for functional magnetic resonance imaging (fMRI) data analysis, statistical parametric mapping (SPM), is based on the general linear model (GLM). SPM therefore requires a priori knowledge or specific assumptions about the time courses contributing to signal changes. In contradistinction, independent component analysis (ICA) is a data-driven method based on the assumption that the causes of responses are statistically independent. Here we describe a unified method, which combines ICA, temporal ICA (tICA), and SPM for analyzing fMRI data. tICA was applied to fMRI datasets to disclose independent components, whose number was determined by the Bayesian information criterion (BIC). The resulting components were used to construct the design matrix of a GLM. Parameters were estimated and regionally-specific statistical inferences were made about activations in the usual way. The sensitivity and specificity were evaluated using Monte Carlo simulations. The receiver operating characteristic (ROC) curves indicated that the unified SPM–ICA method had a better performance. Moreover, SPM–ICA was applied to fMRI datasets from twelve normal subjects performing left and right hand movements. The areas identified corresponded to motor (premotor, sensorimotor areas and SMA) areas and were consistently task related. Part of the frontal lobe, parietal cortex, and cingulate gyrus also showed transiently task-related responses. The unified method requires less supervision than the conventional SPM and enables classical inference about the expression of independent components. Our results also suggest that the method has a higher sensitivity than SPM analyses.

YNIMG Journal 2001 Journal Article

Optimal stimulus sequence design for event-related fMRI

• Dewen Hu
• Xuchu Weng

YNIMG Journal 1998 Journal Article

An Evaluation of Linear Model Analysis Techniques for Processing Images of Microcirculation Activity

• John Mayhew
• Dewen Hu
• Ying Zheng
• Steven Askew
• Yuqian Hou
• Jason Berwick
• Peter J. Coffey
• Nicky Brown

Sequences of images of the cortical surface can be processed to reveal information about the cortical microcirculation, regional cerebral blood flow (rCBF), and changes induced by neuronal activity. This study examined the use of different analysis methodologies on intrinsic optical images taken from rat sensory motor cortex and testes. Generalized linear model (GLM) analysis was used and compared with standard signal processing methods including principal component analysis. The GLM method has been used by Fristonet al. (1994, Hum. Brain Map. ,1: 214–220) in the analysis of functional magnetic resonance imagery to identify regions of focal activity. We investigated the use of this method to analyze video image data of the modulation of rCBF from rat cortex. The results revealed spatiotemporal variations in rCBF in response to stimulation within local regions of cortex. The advantage of the GLM method is that it augments ordinary signal processing methods with an estimate of statistical reliability. The use of different wavelengths of illumination reveals spatial structures with different temporal relationships. In image time series data collected under green and red illumination a phase difference was found in the low frequency ≈0. 1 Hz vasomotion oscillation. This phase difference occurred in data from both cortex and testes. A possible explanation of these differences is that the spectral absorption characteristics of the tissue reflect changes in the volume proportions of the different hemoglobin derivatives in interacting with the modulation of the volume of blood. It is suggested that the combination of these effects produces the phase differences we detect.