Jie Tian

A longitudinal study of hand motor recovery after sub-acute stroke: a study combined FMRI with diffusion tensor imaging.

Previous studies have shown that motor recovery of stroke can be assessed by the cortical activity and the structural integrity of the corticospinal tract (CST), but little is known about the relation between the cortical activity and the structural integrity during motor recovery. In the present study, we investigated the changes in brain activities evoked by twenty days’ functional electrical stimulation (FES) training in twelve sub-acute stroke patients with unilateral upper-limb disability. We compared cortex activity evoked by wrist movement of eleven stroke patients to that of eleven age-matched healthy subjects to figure out how cortex activity changed after stroke. We also measured the structural integrity represented by the fractional anisotropy (FA) asymmetry of the posterior limb of the internal capsule (PLIC) to find the relationship between the brain activity and the structure integrity. In our study, we found that patients with sub-acute stroke have shown greater activity in the contralesional primary motor cortex (M1) during the affected hand’s movement compared with healthy group, while the activity in ipsilesional M1 was decreased after the therapy compared to that before therapy, and the contralesional non-primary motor cortex showed greater activity after therapy. At the baseline we found that the positive correlation between the FA asymmetry of PLIC and the contralesional non-primary motor cortex activity showed that the greater damaged CST, the greater contralesional non-primary motor cortex recruited. While the negative correlation between them after the FES training indicates that after recovery the non-primary motor cortex plays different role in different stroke phases. Our study demonstrates that functional organization of a residual distributed motor system is related to the degree of disruption to the CST, and the non-primary motor areas plays an important role in motor recovery.

Multi-scale local binary pattern with filters for spoof fingerprint detection

Fingerprint recognition systems are being increasingly deployed in both government and civilian applications. But the emergence of fake fingerprints brings on a new challenge. Among the numerous fingerprint vitality detection methods, local binary pattern (LBP) is considered as one of the best operators. But the original LBP operator has the limitation of its small spatial support area. So we proposed a novel spoof fingerprint detection method based on multi-scale local binary pattern (MSLBP). Generally speaking, the MSLBP can be realized in two different ways. We implemented both types of MSLBP. Each MSLBP was combined with a set of filters. In this way, each sample in the LBP circle could be made to collect intensity information from a large area rather than a single pixel. The experimental results in the database of the Liveness Detection Competition 2011 (LivDet2011) have shown that both types of MSLBP are effective and superior in spoof fingerprint detection.

Investigation of the effective connectivity of resting state networks in Alzheimer's disease: a functional MRI study combining independent components analysis and multivariate Granger causality analysis.

Recent neuroimaging studies have shown that the cognitive and memory decline in patients with Alzheimers disease (AD) is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses in small‐world attributes. However, the causal interactions among the spatially isolated, but functionally related, resting state networks (RSNs) are still largely unexplored. In this study, we first identified eight RSNs by independent components analysis from resting state functional MRI data of 18 patients with AD and 18 age‐matched healthy subjects. We then performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that patients with AD exhibited decreased causal interactions among the RSNs in both intensity and quantity relative to normal controls. Results from mGCA indicated that the causal interactions involving the default mode network and auditory network were weaker in patients with AD, whereas stronger causal connectivity emerged in relation to the memory network and executive control network. Our findings suggest that the default mode network plays a less important role in patients with AD. Increased causal connectivity of the memory network and self‐referential network may elucidate the dysfunctional and compensatory processes in the brain networks of patients with AD. These preliminary findings may provide a new pathway towards the determination of the neurophysiological mechanisms of AD. Copyright

Staging of cervical cancer based on tumor heterogeneity characterized by texture features on F-18-FDG PET images

The aim of the study is to assess the staging value of the tumor heterogeneity characterized by texture features and other commonly used semi-quantitative indices extracted from (18)F-FDG PET images of cervical cancer (CC) patients. Forty-two patients suffering CC at different stages were enrolled in this study. Firstly, we proposed a new tumor segmentation method by combining the intensity and gradient field information in a level set framework. Secondly, fifty-four 3D texture features were studied besides of SUVs (SUVmax, SUVmean, SUVpeak) and metabolic tumor volume (MTV). Through correlation analysis, receiver-operating-characteristic (ROC) curves analysis, some independent indices showed statistically significant differences between the early stage (ES, stages I and II) and the advanced stage (AS, stages III and IV). Then the tumors represented by those independent indices could be automatically classified into ES and AS, and the most discriminative feature could be chosen. Finally, the robustness of the optimal index with respect to sampling schemes and the quality of the PET images were validated. Using the proposed segmentation method, the dice similarity coefficient and Hausdorff distance were 91.78u2009u2009u2009±u2009u2009u20091.66% and 7.94u2009u2009u2009±u2009u2009u20091.99u2009mm, respectively. According to the correlation analysis, all the fifty-eight indices could be divided into 20 groups. Six independent indices were selected for their highest areas under the ROC curves (AUROC), and showed significant differences between ES and AS (Pu2009u2009<u2009u20090.05). Through automatic classification with the support vector machine (SVM) Classifier, run percentage (RP) was the most discriminative index with the higher accuracy (88.10%) and larger AUROC (0.88). The Pearson correlation of RP under different sampling schemes is 0.9991u2009u2009u2009±u2009u2009u20090.0011. RP is a highly stable feature and well correlated with tumor stage in CC, which suggests it could differentiate ES and AS with high accuracy.

Altered Hub Configurations within Default Mode Network following Acupuncture at ST36: A Multimodal Investigation Combining fMRI and MEG

Acupuncture, an externally somatosensory stimulation in the Traditional Chinese Medicine, has been proposed about its modulations on the brains default mode network (DMN). However, it is still unknown on how the internal brain resting networks are modulated and what inferences can be made about the physiological processes underlying these changes. Combining high spatial resolution of functional magnetic resonance imaging (fMRI) with high temporal resolution of magnetoencephalography (MEG), in the current multimodal study, we sought to explore spatiotemporally whether or not band-specific DMN hub configurations would be induced by verum acupuncture, compared with sham control. Spatial independent component analysis was applied to fMRI data, followed by the discrete regional sources seeded into MEG data. Partial correlation analysis was further adopted to estimate the intrinsic functional connectivity and network hub configurations. One of the most striking findings is that the posterior cingulate cortex is not only validated as a robust DMN hub, but served as a hub only within the delta and gamma bands following the verum acupuncture, compared with its consistently being a DMN hub in sham control group. Our preliminary results may provide a new perspective to lend support for the specificity of neural mechanism underlying acupuncture.

Multi-scale block local ternary patterns for fingerprints vitality detection

Fingerprint recognition systems are widely deployed in both government and civilian applications. But the emergence of fake fingerprints poses a new threat to privacy security. Among the numerous fingerprint vitality detection methods, the local binary pattern (LBP) is considered as one of the state-of-the-art operators. However, the local binary pattern tends to be sensitive to noise, as there are no types of filters involved in the LBP operator. Worse still, the LBP operator can not reflect these difference whether the pixel value is bigger than the threshold or equal to the threshold. So we proposed a novel fingerprint vitality detection method based on multi-scale block local ternary patterns (MBLTP). Instead of a single pixel, its computation is done based on the average value of blocks. The ternary pattern is adopted to reflect the differences between the pixels and the threshold. The experimental results in the databases of Competition on Fingerprint Liveness Detection 2011 (LivDet 2011) show its superiority.

Security-Enhanced Fuzzy Fingerprint Vault Based on Minutiae's Local Ridge Information

Fuzzy vault is a practical and promising fingerprint template protection technology. However, this scheme has some security issues, in which cross-matching between different vaults may be the most serious one. In this paper, we develop an improvement version of fuzzy vault integrating minutiaes local ridge orientation information. The improved fuzzy fingerprint vault, a two factor authentication scheme to some extent, can effectively prevent cross-matching between different fingerprint vaults. Experimental results show that, if and only if the fingerprint and the password of users are simultaneity obtained by the attacker, the fuzzy vault can be cracked. Results under three scenarios indicate that, although the authentication performance of Scena.1 decreases a little in term of GAR, the security of Scena.2 and Scena.3, hence the security of the whole scheme, is enhanced greatly.

Exploring the Patterns of Acupuncture on Mild Cognitive Impairment Patients Using Regional Homogeneity

Purpose To investigate the different responses to acupuncture in MCI patients and age-matched healthy subjects reflected by the Regional Homogeneity (ReHo) indices. Methods The experiment was performed at the acupoint KI3 in 12 MCI patients and 12 healthy controls, respectively. A novel non-repeated event-related (NRER) fMRI design paradigm was applied to separately detect neural activities related to different stages of acupuncture (pre-acupuncture resting state, needling manipulation and post-acupuncture resting state). ReHo values were calculated for MCI patients and healthy controls in pre- and post-acupuncture resting state. Then, a two-way ANCOVA with repeated measures with post-hoc two sample t-tests was performed to explore the different responses to acupuncture in the two groups. Results The ANCOVA revealed a significant main effect of group, but no significant main effect of acupuncture and interactions between group and acupuncture. During the pre-acupuncture resting state, ReHo values increased in the precentral gyrus (PCG), superior frontal gyrus (SFG), and insula (INS) and decreased mainly in middle temporal gyrus (MTG), parahippocampal (PHIP) and cingulate cortex in MCI patients compared with healthy controls. Furthermore, we found that the regions including precuneus (PCUN), and cingulate cortex showed increased ReHo values for MCI patients following acupuncture. For healthy controls, the medial frontal gyrus, PCG, anterior cingulate cortex (ACC) and INS showed enhanced ReHo values following acupuncture. During the post-acupuncture resting state, MCI patients showed increased ReHo values mainly in the MTG, superior parietal lobule (SPL), middle frontal gyrus (MFG), supramarginal (SMG), and PCG, and decreased ReHo values mainly in the frontal regions, PHIP, and posterior cingulated cortex (PCC) compared to healthy controls. Conclusion Though we found some ReHo changes between MCI patients and healthy controls, the two-way ANCOVA results showed no significant effects after multiple corrections. Further study is needed to reveal the real acupuncture effects on MCI patients.

Fingerprint Matching Based on Neighboring Information and Penalized Logistic Regression

This paper proposes a novel minutiae-based fingerprint matching algorithm. A fingerprint is represented by minutiae set and sampling points on all ridges. Therefore, the foreground of a fingerprint image can be accurately estimated by the sampling points. The similarity between two minutiae is measured by two parts: neighboring minutiae which are different in minutiae pattern and neighboring sampling points which are different in orientation and frequency. After alignment and minutiae pairing, Nine features are extracted to represent the matching status and penalized logistic regression (PLR) is adopted to calculate the matching score. The proposed algorithm is evaluated on fingerprint databases of FVC2002 and compared with the participants in FVC 2002. Experimental results show that the proposed algorithm achieves good performance and ranks 5th according to average equal error rate.

A novel matching algorithm for distorted fingerprints based on penalized quadratic model

At present, one of the most challenging problems in fingerprint recognition is the matching of distorted fingerprints. In this paper, we propose penalized quadratic model to deal with the non-linear distortion. Firstly, minutiae as well as sampling points on all the ridges are employed to represent fingerprint. Secondly, similarity between minutiae is estimated by their neighboring sampling points. Thirdly, greedy matching algorithm is adopted to establish the initial minutiae correspondences which are used to select landmarks to calculate the quadratic model parameters. At last, input fingerprint is warped and matching process is conducted again to obtain similarity score between warped fingerprint and template fingerprint. In order to diminish the impact of the erroneous landmarks, we introduce a penalty term into the quadratic model to keep it smoothing. Experimental results on FVC2004 DB1 approve that quadratic model is effective to describe the inner-image transformation of a quadratic skin surface, and the proposed strategy can improve the performance of fingerprint matching algorithm.