Publications by authors named "Yingnan Nie"

7 Publications

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Real-time removal of stimulation artifacts in closed-loop deep brain stimulation.

J Neural Eng 2021 Nov 24. Epub 2021 Nov 24.

Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433, CHINA.

Objective: Closed-loop deep brain stimulation (DBS) with neural feedback has shown great potential in improving the therapeutic effect and reducing side effects. However, the amplitude of stimulation artifacts is much larger than the local field potentials, which remains a bottleneck in developing a closed-loop stimulation strategy with varied parameters.

Approach: We proposed an irregular sampling method for the real-time removal of stimulation artifacts. The artifact peaks were detected by applying a threshold to the raw recordings, and the samples within the contaminated period of the stimulation pulses were excluded and replaced with the interpolation of the samples prior to and after the stimulation artifact duration. This method was evaluated with both simulation signals and in vivo closed-loop DBS applications in Parkinsonian animal models.

Main Results: The irregular sampling method was able to remove the stimulation artifacts effectively with the simulation signals. The relative errors between the power spectral density of the recovered and true signals within a wide frequency band (2-150 Hz) were 2.14%, 3.93%, 7.22%, 7.97% and 6.25% for stimulation at 20 Hz, 60 Hz, 130 Hz, 180 Hz, and stimulation with variable low and high frequencies, respectively. This stimulation artifact removal method was verified in real-time closed-loop DBS application in vivo, and the artifacts were effectively removed during stimulation with frequency continuously changing from 130 Hz to 1 Hz and stimulation adaptive to beta oscillations.

Significance: The proposed method provides an approach for real-time removal in closed-loop DBS applications, which is effective in stimulation with low frequency, high frequency, and variable frequency. This method can facilitate the development of more advanced closed-loop DBS strategies.
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http://dx.doi.org/10.1088/1741-2552/ac3cc5DOI Listing
November 2021

Subthalamic dynamic neural states correlate with motor symptoms in Parkinson's Disease.

Clin Neurophysiol 2021 11 25;132(11):2789-2797. Epub 2021 Aug 25.

Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Ministry of Education, Fudan University, Shanghai, China; Shanghai Engineering Research Center of AI & Robotics, Fudan University, Shanghai, China; Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai, China. Electronic address:

Objective: This study aims to discriminate the dynamic synchronization states from the subthalamic local field potentials and investigate their correlations with the motor symptoms in Parkinson's Disease (PD).

Methods: The resting-state local field potentials of 10 patients with PD were recorded from the subthalamic nucleus. The dynamic neural states of multiple oscillations were discriminated and analyzed. The Spearman correlation was used to investigate the correlations between occurrence rate or duration of dynamic neural states and the severity of motor symptoms.

Results: The proportion of long low-beta and theta synchronized state was significantly correlated with the general motor symptom and tremor, respectively. The duration of combined low/high-beta state was significantly correlated with rigidity, and the duration of combined alpha/high-beta state was significantly correlated with bradykinesia.

Conclusions: This study provides evidence that motor symptoms are associated with the neural states coded with multiple oscillations in PD.

Significance: This study may advance the understanding of the neurophysiological mechanisms of the motor symptoms and provide potential biomarkers for closed-loop deep brain stimulation in PD.
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http://dx.doi.org/10.1016/j.clinph.2021.07.022DOI Listing
November 2021

EasyMEG: An easy-to-use toolbox for MEG analysis.

Comput Methods Programs Biomed 2020 Apr 12;186:105199. Epub 2019 Nov 12.

Beijing Tian Tan Hospital, Capital Medical University, Beijing, China.

Background And Objective: Magnetoencephalography (MEG) is an advanced magnetic source imaging technology that measures the magnetic fields produced by neural activities. It has been extensively used in scientific research and clinical diagnosis due to its high temporal and spatial resolution. Considering the special nature of MEG data, it needs to perform a series of processes and analysis to obtain valuable information. Therefore, the identification of data processing is a key point of MEG studies. At present, the software for MEG analysis such as FieldTrip has no Graphic User Interface (GUI) and users must write their own script to perform concrete analysis. It brings the difficulties to researchers like the doctors without experience in programming or newcomers to MEG. Thus, an open-sourced software-EasyMEG was developed. It has friendly interface with highly functions-integration.

Methods: The functions of EasyMEG are developed based on MATLAB language to ensure the consistency of the user interface under different operating systems. EasyMEG is a highly integrated software that contains a set of functions for preprocessing, time-lock analysis, time-frequency analysis, source analysis, and plotting. EasyMEG provides a friendly GUI and allows users to complete analyses through a simple and clean interface.

Results: This toolbox has been released as an open-source software on GitHub under the GNU General Public License: https://tonywu2018.github.io/EasyMEG/.

Conclusions: We hope to improve this toolbox by the power of community and wish to make EasyMEG a simple and powerful toolbox for further MEG studies.
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http://dx.doi.org/10.1016/j.cmpb.2019.105199DOI Listing
April 2020

Functional dynamics of thalamic local field potentials correlate with modulation of neuropathic pain.

Eur J Neurosci 2020 01 23;51(2):628-640. Epub 2019 Sep 23.

Neural and Intelligence Engineering Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.

Understanding the functional dynamics of neural oscillations in the sensory thalamus is essential for elucidating the perception and modulation of neuropathic pain. Local field potentials were recorded from the sensory thalamus of twelve neuropathic pain patients. Single and combinational neural states were defined by the activity state of a single or paired oscillations. Relationships between the duration or occurrence rate of neural state and pre-operative pain level or pain relief induced by deep brain stimulation were evaluated. Results showed that the occurrence rate of the single neural state of low-beta oscillation was significantly correlated with pain relief. The duration and occurrence rate of combinational neural states of the paired low-beta with delta, theta, alpha, high-beta or low-gamma oscillations were more significantly correlated with pain relief than the single neural states. Moreover, these significant combinational neural states formed a local oscillatory network with low-beta oscillation as a key node. The results also showed correlations between measures of combinational neural states and subjective pain level as well. The duration of combinational neural states of paired alpha with delta or theta oscillations and the occurrence rate of neural states of the paired delta with low-beta or low-gamma oscillations were significantly correlated with pre-operative pain level. In conclusion, this study revealed that the integration of oscillations and the functional dynamics of neural states were differentially involved in modulation and perception of neuropathic pain. The functional dynamics could be biomarkers for developing neural state-dependent deep brain stimulation for neuropathic pain.
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http://dx.doi.org/10.1111/ejn.14569DOI Listing
January 2020

Alterations of Graphic Properties and Related Cognitive Functioning Changes in Mild Alzheimer's Disease Revealed by Individual Morphological Brain Network.

Front Neurosci 2018 10;12:927. Epub 2018 Dec 10.

Department of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.

Alzheimer's disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition. With the assistance of multimodal brain networks and graph-based analysis approaches, AD-related network disruptions support the hypothesis that AD can be identified as a dysconnectivity syndrome. However, as the recent emerging of individual-based morphological network research of AD, the utilization of multiple morphometric features may provide a broader horizon for locating the lesions. Therefore, the present study applied the newly proposed individual morphological brain network with five commonly used morphometric features (cortical thickness, regional volume, surface area, mean curvature, and fold index) to explore the topological aberrations and their relationship with cognitive functioning alterations in the early stage of AD. A total of 40 right-handed participants were selected from Open Access Series of Imaging Studies Database with 20 AD patients (age ranged from 70 to 79, CDR = 0.5) and 20 age/gender-matched healthy controls. The significantly affected connections ( < 0.05 with FDR correction) were observed across multiple regions, both enhanced and attenuated correlations, primarily related to the left entorhinal cortex (ENT). In addition, profoundly changed Mini Mental State Examination (MMSE) score and global efficiency ( < 0.05) were noted in the AD patients, as well as the pronounced inter-group distinctions of betweenness centrality, global and local efficiency ( < 0.05) in the higher MMSE score zone (28-30), which indicating the potential role of graphic properties in determination of early-stage AD patients. Moreover, the reservations (regions in the occipital and frontal lobes) and alterations (regions in the right temporal lobe and cingulate cortex) of hubs were also detected in the AD patients. Overall, the findings further confirm the selective AD-related disruptions in morphological brain networks and also suggest the feasibility of applying the morphological graphic properties in the discrimination of early-stage AD patients.
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http://dx.doi.org/10.3389/fnins.2018.00927DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295573PMC
December 2018

Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

Brain Topogr 2018 07 16;31(4):577-590. Epub 2018 Apr 16.

College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.

Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.
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http://dx.doi.org/10.1007/s10548-018-0642-yDOI Listing
July 2018

Construction of Individual Morphological Brain Networks with Multiple Morphometric Features.

Front Neuroanat 2017 25;11:34. Epub 2017 Apr 25.

College of Life Science and Bioengineering, Beijing University of TechnologyBeijing, China.

In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28) each scanned twice, and reproducibility was evaluated through test-retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20-40%), and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78) indicates the robustness of the present method. Results demonstrate that the multiple morphometric features can be applied to form a rational reproducible individual-based morphological brain network.
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http://dx.doi.org/10.3389/fnana.2017.00034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5403938PMC
April 2017
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