Publications by authors named "Jicong Zhang"

21 Publications

  • Page 1 of 1

Automatic Artery/Vein Classification Using a Vessel-Constraint Network for Multicenter Fundus Images.

Front Cell Dev Biol 2021 11;9:659941. Epub 2021 Jun 11.

School of Biological Science and Medical Engineering, Beihang University, Beijing, China.

Retinal blood vessel morphological abnormalities are generally associated with cardiovascular, cerebrovascular, and systemic diseases, automatic artery/vein (A/V) classification is particularly important for medical image analysis and clinical decision making. However, the current method still has some limitations in A/V classification, especially the blood vessel edge and end error problems caused by the single scale and the blurred boundary of the A/V. To alleviate these problems, in this work, we propose a vessel-constraint network (VC-Net) that utilizes the information of vessel distribution and edge to enhance A/V classification, which is a high-precision A/V classification model based on data fusion. Particularly, the VC-Net introduces a vessel-constraint (VC) module that combines local and global vessel information to generate a weight map to constrain the A/V features, which suppresses the background-prone features and enhances the edge and end features of blood vessels. In addition, the VC-Net employs a multiscale feature (MSF) module to extract blood vessel information with different scales to improve the feature extraction capability and robustness of the model. And the VC-Net can get vessel segmentation results simultaneously. The proposed method is tested on publicly available fundus image datasets with different scales, namely, DRIVE, LES, and HRF, and validated on two newly created multicenter datasets: Tongren and Kailuan. We achieve a balance accuracy of 0.9554 and F1 scores of 0.7616 and 0.7971 for the arteries and veins, respectively, on the DRIVE dataset. The experimental results prove that the proposed model achieves competitive performance in A/V classification and vessel segmentation tasks compared with state-of-the-art methods. Finally, we test the Kailuan dataset with other trained fusion datasets, the results also show good robustness. To promote research in this area, the Tongren dataset and source code will be made publicly available. The dataset and code will be made available at https://github.com/huawang123/VC-Net.
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http://dx.doi.org/10.3389/fcell.2021.659941DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8226261PMC
June 2021

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram.

J Neural Eng 2021 Jul 21;18(4). Epub 2021 Jul 21.

School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, People's Republic of China.

Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time consumption and high subjectivity, a faster, more robust, and automated IED detector is strongly in demand.Based on deep learning, we proposed an end-to-end framework with multi-scale morphologic features in the time domain and correlation in sensor space to recognize IEDs from raw scalp EEG.Based on a balanced dataset of 30 patients with epilepsy, the results of the five-fold (leave-6-patients-out) cross-validation shows that our model achieved state-of-the-art detection performance (accuracy: 0.951, precision: 0.973, sensitivity: 0.938, specificity: 0.968, F1 score: 0.954, AUC: 0.973). Furthermore, our model maintained excellent IED detection rates in an independent test on three datasets.The proposed model could be used to assist neurologists in clinical EEG interpretation of patients with epilepsy. Additionally, this approach combines multi-level output and correlation among EEG sensors and provides new ideas for epileptic biomarker detection in scalp EEG.
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http://dx.doi.org/10.1088/1741-2552/ac0d60DOI Listing
July 2021

SA-Net: A scale-attention network for medical image segmentation.

PLoS One 2021 14;16(4):e0247388. Epub 2021 Apr 14.

School of Biological Science and Medical Engineering, Beihang University, Beijing, China.

Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical images. In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention capability. SA-Net can better learn the multi-scale features and achieve more accurate segmentation for different medical image. In addition, this work validates the proposed method across multiple datasets. The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst segmentation. To facilitate SA-Net utilization by the scientific community, the code implementation will be made publicly available.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247388PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046243PMC
April 2021

Multi-scale simulation of early kidney branching morphogenesis.

Phys Biol 2021 02 25;18(2):026005. Epub 2021 Feb 25.

CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, People's Republic of China.

An important feature of the branch morphogenesis during kidney development is the termination of the tips on the outer surface of a kidney. This feature requires the avoidance of the intersection between the tips and existing ducts inside the kidney. Here, we started from a continuous model and implemented the coarse grained rules into a fast and discrete simulations. The ligand-receptor-based Turing mechanism suggests a repulsion that decreases exponentially with distance between interacting branches, preventing the intersection between neighboring branches. We considered this repulsive effect in numerical simulations and successfully reproduce the key features of the experimentally observed branch morphology for an E15.5 kidney. We examine the similarity of several geometrical parameters between the simulation results and experimental observations. The good agreement between the simulations and experiments suggests that the concentration decay caused by the absorption of glial cell line derived neurotrophic factor might be the key factor to affect the geometry in early kidney development.
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http://dx.doi.org/10.1088/1478-3975/abd844DOI Listing
February 2021

The Effect of Tactile Training on Sustained Attention in Young Adults.

Authors:
Yu Luo Jicong Zhang

Brain Sci 2020 Sep 30;10(10). Epub 2020 Sep 30.

School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.

Sustained attention is crucial for higher-order cognition and real-world activities. The idea that tactile training improves sustained attention is appealing and has clinical significance. The aim of this study was to explore whether tactile training could improve visual sustained attention. Using 128-channel electroencephalography (EEG), we found that participants with tactile training outperformed non-trainees in the accuracy and calculation efficiency measured by the Math task. Furthermore, trainees demonstrated significantly decreased omission error measured by the sustained attention to response task (SART). We also found that the improvements in behavioral performance were associated with parietal P300 amplitude enhancements. EEG source imaging analyses revealed stronger brain activation among the trainees in the prefrontal and sensorimotor regions at P300. These results suggest that the tactile training can improve sustained attention in young adults, and the improved sustained attention following training may be due to more effective attentional resources allocation. Our findings also indicate the use of a noninvasive tactile training paradigm to improve cognitive functions (e.g., sustained attention) in young adults, potentially leading to new training and rehabilitative protocols.
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http://dx.doi.org/10.3390/brainsci10100695DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7601015PMC
September 2020

Virus Disinfection from Environmental Water Sources Using Living Engineered Biofilm Materials.

Adv Sci (Weinh) 2020 Jul 22;7(14):1903558. Epub 2020 May 22.

Materials and Physical Biology Division School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China.

Waterborne viruses frequently cause disease outbreaks and existing strategies to remove such viral pathogens often involve harsh or energy-consuming water treatment processes. Here, a simple, efficient, and environmentally friendly approach is reported to achieve highly selective disinfection of specific viruses with living engineered biofilm materials. As a proof-of-concept, biofilm matrix protein CsgA was initially genetically fused with the influenza-virus-binding peptide (C5). The resultant engineered living biofilms could correspondingly capture virus particles directly from aqueous solutions, disinfecting samples to a level below the limit-of-detection for a qPCR-based detection assay. By exploiting the surface-adherence properties of biofilms, it is further shown that polypropylene filler materials colonized by the CsgA-C5 biofilms can be utilized to disinfect river water samples with influenza titers as high as 1 × 10 PFU L. Additionally, a suicide gene circuit is designed and applied in the engineered strain that strictly limits the growth of bacterial, therefore providing a viable route to reduce potential risks confronted with the use of genetically modified organisms. The study thus illustrates that engineered biofilms can be harvested for the disinfection of pathogens from environmental water samples in a controlled manner and highlights the unique biology-only properties of living substances for material applications.
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http://dx.doi.org/10.1002/advs.201903558DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375245PMC
July 2020

Differential longitudinal changes in structural complexity and volumetric measures in community-dwelling older individuals.

Neurobiol Aging 2020 07 5;91:26-35. Epub 2020 Mar 5.

Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia.

Fractal geometry provides a method of analyzing natural and especially biological morphologies. To investigate the relationship between the complexity measure, which is indexed as fractal dimensionality (FD), and the traditional Euclidean metrics, such as the volume and thickness, of the brain in older age, we analyzed 483 MRI scans of 161 community-dwelling, nondemented individuals aged 70-90 years at the baseline and their 2-year and 6-year follow-ups. We quantified changes in neuroimaging metrics in cortical lobes and subcortical structures and investigated the effects of age, sex, hemisphere, and education on FD. We also analyzed the mediating effects of these metrics for further investigation. FD showed significant age-related decline in all structures, and its trajectory was best modeled quadratically in the bilateral frontal, parietal, and occipital lobes, as well as in the bilateral caudate, putamen, hippocampus, amygdala, and accumbens. FD showed specific mediating effects on aging and cognitive decline in some regions. Our findings suggest that FD is reliable yet shows a different pattern of decline compared with volumetric measures.
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http://dx.doi.org/10.1016/j.neurobiolaging.2020.02.023DOI Listing
July 2020

DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography.

Comput Med Imaging Graph 2020 03 23;80:101690. Epub 2019 Dec 23.

Department of Ultrasound, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. Electronic address:

Fetal echocardiography (FE) is a widely used medical examination for early diagnosis of congenital heart disease (CHD). The apical four-chamber view (A4C) is an important view among early FE images. Accurate segmentation of crucial anatomical structures in the A4C view is a useful and important step for early diagnosis and timely treatment of CHDs. However, it is a challenging task due to several unfavorable factors: (a) artifacts and speckle noise produced by ultrasound imaging. (b) category confusion caused by the similarity of anatomical structures and variations of scanning angles. (c) missing boundaries. In this paper, we propose an end-to-end DW-Net for accurate segmentation of seven important anatomical structures in the A4C view. The network comprises two components: 1) a Dilated Convolutional Chain (DCC) for "gridding issue" reduction, multi-scale contextual information aggregation and accurate localization of cardiac chambers. 2) a W-Net for gaining more precise boundaries and yielding refined segmentation results. Extensive experiments of the proposed method on a dataset of 895 A4C views have demonstrated that DW-Net can achieve good segmentation results, including the Dice Similarity Coefficient (DSC) of 0.827, the Pixel Accuracy (PA) of 0.933, the AUC of 0.990 and it substantially outperformed some well-known segmentation methods. Our work was highly valued by experienced clinicians. The accurate and automatic segmentation of the A4C view using the proposed DW-Net can benefit further extractions of useful clinical indicators in early FE and improve the prenatal diagnostic accuracy and efficiency of CHDs.
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http://dx.doi.org/10.1016/j.compmedimag.2019.101690DOI Listing
March 2020

EEG-Based Brain Functional Connectivity in First-Episode Schizophrenia Patients, Ultra-High-Risk Individuals, and Healthy Controls During P50 Suppression.

Front Hum Neurosci 2019 14;13:379. Epub 2019 Nov 14.

The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.

Dysfunctional processing of auditory sensory gating has generally been found in schizophrenic patients and ultra-high-risk (UHR) individuals. The aim of the study was to investigate the differences of functional interaction between brain regions and performance during the P50 sensory gating in UHR group compared with those in first-episode schizophrenia patients (FESZ) and healthy controls (HC) groups. The study included 128-channel scalp Electroencephalogram (EEG) recordings during the P50 auditory paradigm for 35 unmedicated FESZ, 30 drug-free UHR, and 40 HC. Cortical sources of scalp electrical activity were recomputed using exact low-resolution electromagnetic tomography (eLORETA), and functional brain networks were built at the source level and compared between the groups (FESZ, UHR, HC). A classifier using decision tree was designed for differentiating the three groups, which uses demographic characteristics, MATRICS Consensus Cognitive Battery parameters, behavioral features in P50 paradigm, and the measures of functional brain networks based on graph theory during P50 sensory gating. The results showed that very few brain connectivities were significantly different between FESZ and UHR groups during P50 sensory gating, and that a large number of brain connectivities were significantly different between FESZ and HC groups and between UHR and HC groups. Furthermore, the FESZ group had a stronger connection in the right superior frontal gyrus and right insula than the HC group. And the UHR group had an enhanced connection in the paracentral lobule and the middle temporal gyrus compared with the HC group. Moreover, comparison of classification analysis results showed that brain network metrics during P50 sensory gating can improve the accuracy of the classification for FESZ, UHR and HC groups. Our findings provide insight into the mechanisms of P50 suppression in schizophrenia and could potentially improve the performance of early identification and diagnosis of schizophrenia for the earliest intervention.
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http://dx.doi.org/10.3389/fnhum.2019.00379DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6870009PMC
November 2019

Biofilm Nanofiber-Coated Separators for Dendrite-Free Lithium Metal Anode and Ultrahigh-Rate Lithium Batteries.

ACS Appl Mater Interfaces 2019 Sep 23;11(35):32373-32380. Epub 2019 Aug 23.

Shanghai Institute of Ceramics , Chinese Academy of Sciences , Shanghai 200050 , China.

Rechargeable batteries that combine high energy density with high power density are highly demanded. However, the wide utilization of lithium metal anode is limited by the uncontrollable dendrite growth, and the conventional lithium-ion batteries (LIBs) commonly suffer from low rate capability. Here, we for the first time develop a biofilm-coated separator for high-energy and high-power batteries. It reveals that the coating of protein nanofibers can improve electrolyte wettability and lithium transference number and enhance adhesion between separators and electrodes. Thus, lithium dendrite growth is impeded because of the uniform distribution of the Li-ion flux. The modified separator also enables the stable cycling of high-voltage Li|LiMnNiO (LNMO) cells at an extremely high rate of 20 C, delivering a high specific capacity of 83.1 mA h g, which exceeds the conventional counterpart. In addition, the modified separator in the LiTiO|LNMO full cell also exhibits a larger capacity of 68.2 mA h g at 10 C than the uncoated separator of 37.4 mA h g. Such remarkable performances of the modified separators arise from the conformal, adhesive, and endurable coating of biofilm nanofibers. Our work opens up a new opportunity for protein-based biomaterials in practical application of high-energy and high-power batteries.
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http://dx.doi.org/10.1021/acsami.9b08656DOI Listing
September 2019

Discriminating schizophrenia disease progression using a P50 sensory gating task with dense-array EEG, clinical assessments, and cognitive tests.

Expert Rev Neurother 2019 05;19(5):459-470

e Beijing Anding Hospital , Capital Medical University , Beijing , 100088 , China.

Background: Schizophrenia affects approximately 10% of the world's population. Early detection of schizophrenia may significantly delay its progression. Although sensory gating deficits are reported in schizophrenia, it remains challenging how sensory gating deficits can be used with other metrics for risk detection and early diagnosis.

Research Design And Methods: Using EEG, the authors examined effects of sensory gating on the performance of 136 participants in a P50 sensory gating task, including patients with first-episode schizophrenia (FESZ), ultra-high risk (UHR) individuals, high-risk (HR) individuals, and age- and sex-matched healthy controls (HCs). The authors also explored the differences among all groups using clinical assessments and cognitive tests.

Results: Compared with HCs, HR, UHR and FESZ groups showed significant P50 suppression impairment. Furthermore, EEG source localization analyses identified successively stronger activation in prefrontal and anterior temporal regions in the HR, UHR and FESZ groups than in the HC group. Moreover, brain connectivity (HC < HR < UHR < FESZ) in the gamma band of P50 components was increasingly enhanced in accordance with the level of psychosis risks.

Conclusions: These findings suggest that EEG source imaging techniques, brain network dynamics, and behavioral tests, can help clearly distinguish different stages of schizophrenia, and may assist in the early diagnosis of schizophrenia.
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http://dx.doi.org/10.1080/14737175.2019.1601558DOI Listing
May 2019

Enhanced executive attention efficiency after adaptive force control training: Behavioural and physiological results.

Behav Brain Res 2019 12 18;376:111859. Epub 2019 Mar 18.

Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China; School of Mechanical Engineering and Automation, Beihang University, Beijing, China; State Key Lab of Virtual Reality Technology and Systems, Beihang University, Beijing, China. Electronic address:

Attention plays an important role in perception and cognition, and developing an effective method to train and improve attention is an essential and challenging task. In this study, fingertip-based adaptive force control tasks (AFCT) were explored for attention training, and the visual-channel task called an attention network test (ANT) was used to measure the level of attention before and after AFCT. The purposes of this study were to investigate whether AFCT can enhance the attention level on the ANT task and to elucidate the underlying electrophysiological mechanisms. The results showed that the efficiency of the executive control network during ANT was significantly improved by the AFCT training, indicating that the AFCT training may enhance the executive attention level during visual-channel tasks. To measure the behavioural performance during the AFCT training, we used tolerance, variance and duration of the forces to design a comprehensive score of behavioural performance (CSBP), and the electrophysiological mechanisms were also explored using electroencephalography (EEG) recordings. The AFCT and ANT results showed consistency in medial frontal theta activity and in connectivity strength at frontal-parietal regions in the alpha band. These results indicated that the observed attention improvement across tasks executed using different sensory channels may be due to the training of overlapping components of the relevant attention networks. Thus, this study provides further insight into the design of training tasks that stimulate multi-sensory channels, which can be used to improve attention and treat various attention deficit disorders.
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http://dx.doi.org/10.1016/j.bbr.2019.03.028DOI Listing
December 2019

Programmable and printable Bacillus subtilis biofilms as engineered living materials.

Nat Chem Biol 2019 01 3;15(1):34-41. Epub 2018 Dec 3.

Materials and Physical Biology Division School of Physical Science and Technology, ShanghaiTech University, Shanghai, China.

Bacterial biofilms can be programmed to produce living materials with self-healing and evolvable functionalities. However, the wider use of artificial biofilms has been hindered by limitations on processability and functional protein secretion capacity. We describe a highly flexible and tunable living functional materials platform based on the TasA amyloid machinery of the bacterium Bacillus subtilis. We demonstrate that genetically programmable TasA fusion proteins harboring diverse functional proteins or domains can be secreted and can assemble into diverse extracellular nano-architectures with tunable physicochemical properties. Our engineered biofilms have the viscoelastic behaviors of hydrogels and can be precisely fabricated into microstructures having a diversity of three-dimensional (3D) shapes using 3D printing and microencapsulation techniques. Notably, these long-lasting and environmentally responsive fabricated living materials remain alive, self-regenerative, and functional. This new tunable platform offers previously unattainable properties for a variety of living functional materials having potential applications in biomaterials, biotechnology, and biomedicine.
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http://dx.doi.org/10.1038/s41589-018-0169-2DOI Listing
January 2019

Partial Least Square Aided Beamforming Algorithm in Magnetoencephalography Source Imaging.

Front Neurosci 2018 5;12:616. Epub 2018 Sep 5.

Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.

Beamforming techniques have played a prominent role in source imaging in neuroimaging and in locating epileptogenic zones. However, existing vector-beamformers are sensitive to noise on localization of epileptogenic zones. In this study, partial least square (PLS) was used to aid the minimum variance beamforming approach for source imaging with magnetoencephalography (MEG) arrays, and verified its effectiveness in simulated data and epilepsy data. First, PLS was employed to extract the components of the MEG arrays by maximizing the covariance between a linear combination of the predictors and the class variable. Noise was then removed by reconstructing the MEG arrays based on those components. The minimum variance beamforming method was used to estimate a source model. Simulations with a realistic head model and varying noise levels indicated that the proposed approach can provide higher spatial accuracy than other well-known beamforming methods. For real MEG recordings in 10 patients with temporal lobe epilepsy, the ratios of the number of spikes localized in the surgical excised region to the total number of spikes using the proposed method were higher than that of the dipole fitting method. These localization results using the proposed method are more consistent with the clinical evaluation. The proposed method may provide a new imaging marker for localization of epileptogenic zones.
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http://dx.doi.org/10.3389/fnins.2018.00616DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134212PMC
September 2018

[Biomarker extraction of sustained attention based on brain functional network].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2018 04;35(2):176-181

School of Biological Science and Medical Engineering, Beihang University, Beijing 100191,

Although attention plays an important role in cognitive and perception, there is no simple way to measure one's attention abilities. We identified that the strength of brain functional network in sustained attention task can be used as the physiological indicator to predict behavioral performance. Behavioral and electroencephalogram (EEG) data from 14 subjects during three force control tasks were collected in this paper. The reciprocal of the product of force tolerance and variance were used to calculate the score of behavioral performance. EEG data were used to construct brain network connectivity by wavelet coherence method and then correlation analysis between each edge in connectivity matrices and behavioral score was performed. The linear regression model combined those with significantly correlated network connections into physiological indicator to predict participant's performance on three force control tasks, all of which had correlation coefficients greater than 0.7. These results indicate that brain functional network strength can provide a widely applicable biomarker for sustained attention tasks.
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http://dx.doi.org/10.7507/1001-5515.201611045DOI Listing
April 2018

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers.

Front Aging Neurosci 2017 26;9:309. Epub 2017 Sep 26.

Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73-85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment.
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http://dx.doi.org/10.3389/fnagi.2017.00309DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5649145PMC
September 2017

Variation in longitudinal trajectories of cortical sulci in normal elderly.

Neuroimage 2018 02 4;166:1-9. Epub 2017 Nov 4.

Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia.

Sulcal morphology has been reported to change with age-related neurological diseases, but the trajectories of sulcal change in normal ageing in the elderly is still unclear. We conducted a study of sulcal morphological changes over seven years in 132 normal elderly participants aged 70-90 years at baseline, and who remained cognitively normal for the next seven years. We examined the fold opening and sulcal depth of sixteen (eight on each hemisphere) prominent sulci based on T1-weighted MRI using automated methods with visual quality control. The trajectory of each individual sulcus with respect to age was examined separately by linear mixed models. Fold opening was best modelled by cubic fits in five sulci, by quadratic models in six sulci and by linear models in five sulci, indicating an accelerated widening of a number of sulci in older age. Sulcal depth showed significant linear decline in three sulci and quadratic trend in one sulcus. Turning points of non-linear trajectories towards accelerated widening of the fold were found to be around the age between 75 and 80, indicating an accelerated atrophy of brain cortex starting in the age of late 70s. Our findings of cortical sulcal changes in normal ageing could provide a reference for studies of neurocognitive disorders, including neurodegenerative diseases, in the elderly.
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http://dx.doi.org/10.1016/j.neuroimage.2017.10.010DOI Listing
February 2018

Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming.

Sensors (Basel) 2017 Aug 11;17(8). Epub 2017 Aug 11.

School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.

In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals. Next, a sensor covariance matrix was estimated under the new reconstructed space. Then, a well-known vector beamforming approach, which was a linearly constraint minimum variance (LCMV) approach, was applied to compute the solution for the inverse problem. It can be shown that the proposed source localization approach can give better localization accuracy than two other commonly-used beamforming methods (LCMV, MUSIC) in simulated MEG measurements generated with deep sources. Further, we applied the proposed approach to real MEG data recorded from ten patients with medically-refractory mesial temporal lobe epilepsy (mTLE) for finding epileptogenic zone(s), and there was a good agreement between those findings by the proposed approach and the clinical comprehensive results.
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http://dx.doi.org/10.3390/s17081860DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579488PMC
August 2017

Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices.

PLoS One 2017 27;12(1):e0170875. Epub 2017 Jan 27.

Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.

Identifying Alzheimer's disease (AD) at its early stage is of major interest in AD research. Previous studies have suggested that abnormalities in regional sulcal width and global sulcal index (g-SI) are characteristics of patients with early-stage AD. In this study, we investigated sulcal width and three other common neuroimaging morphological measures (cortical thickness, cortical volume, and subcortical volume) to identify early-stage AD. These measures were evaluated in 150 participants, including 75 normal controls (NC) and 75 patients with early-stage AD. The global sulcal index (g-SI) and the width of five individual sulci (the superior frontal, intra-parietal, superior temporal, central, and Sylvian fissure) were extracted from 3D T1-weighted images. The discriminative performances of the other three traditional neuroimaging morphological measures were also examined. Information Gain (IG) was used to select a subset of features to provide significant information for separating NC and early-stage AD subjects. Based on the four modalities of the individual measures, i.e., sulcal measures, cortical thickness, cortical volume, subcortical volume, and combinations of these individual measures, three types of classifiers (Naïve Bayes, Logistic Regression and Support Vector Machine) were applied to compare the classification performances. We observed that sulcal measures were either superior than or equal to the other measures used for classification. Specifically, the g-SI and the width of the Sylvian fissure were two of the most sensitive sulcal measures and could be useful neuroanatomical markers for detecting early-stage AD. There were no significant differences between the three classifiers that we tested when using the same neuroanatomical features.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0170875PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5271367PMC
August 2017

A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model.

Annu Int Conf IEEE Eng Med Biol Soc 2009 ;2009:2184-7

Industrial and Systems Engineering Department at University of Florida, Gainesville, FL 32611, USA.

Animal Models are used extensively in basic epilepsy research. In many studies, there is a need to accurately score and quantify all epileptic spike and wave discharges (SWDs) as captured by electroencephalographic (EEG) recordings. Manual scoring of long term EEG recordings is a time-consuming and tedious task that requires inordinate amount of time of laboratory personnel and an experienced electroencephalographer. In this paper, we adapt a SWD detection algorithm, originally proposed by the authors for absence (petit mal) seizure detection in humans, to detect SWDs appearing in EEG recordings of Fischer 334 rats. The algorithm is robust with respect to the threshold parameters. Results are compared to manual scoring and the effect of different threshold parameters is discussed.
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http://dx.doi.org/10.1109/IEMBS.2009.5334941DOI Listing
March 2010

Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis: a pilot study.

Epilepsia 2010 Feb 3;51(2):243-50. Epub 2009 Sep 3.

Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA.

Purpose: Distinguishing nonconvulsive status epilepticus (NCSE) from some nonepileptic encephalopathies is a challenging problem. In many situations, NCSE and nonepileptic encephalopathies are indistinguishable by clinical symptoms and can produce very similar electroencephalography (EEG) patterns. Misdiagnosis or delay to diagnosis of NCSE may increase the rate of morbidity and mortality.

Methods: We developed a fast-differentiating algorithm using quantitative EEG analysis to distinguish NCSE patients from patients with toxic/metabolic encephalopathy (TME). EEG recordings were collected from 11 patients, including 6 with NCSE and 5 with TME. Three nonlinear dynamic measures were used in the proposed algorithm: the maximum short-term Lyapunov exponent (STLmax), phase of attractor (phase/angular frequency), and approximate entropy (ApEn). A further refined metric derived from STLmax and phase of attractor (the mean distance to EEG epoch samples from their centroid in the feature space) was also utilized as a criterion. Paired t tests were carried out to further clarify the separation between the EEG patterns of NCSE and TME.

Results: Computational results showed that the performance of the proposed algorithm was sufficient to distinguish NCSE from TME. The results were consistent in all subjects in our study.

Conclusions: The study presents evidence that the maximum short-term Lyapunov exponents (STLmax) and phase of attractors (phase/angular frequency) can be useful in assisting clinical diagnosis of NCSE. Findings presented in this article provide a promising indication that the proposed algorithm may correctly distinguish NCSE from TME. Although the exact mechanism of this association remains unknown, the authors suggest that epileptic activity is highly associated with and can be modeled by dynamic systems.
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http://dx.doi.org/10.1111/j.1528-1167.2009.02286.xDOI Listing
February 2010
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