2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)最新文献_第8页

BGCN: An EEG-based Graphical Classification Method for Parkinson's Disease Diagnosis with Heuristic Functional Connectivity Speculation BGCN:一种基于脑电图的启发式功能连接推测帕金森病诊断的图形分类方法

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123796

Tian Lyu, Haotian Guo

{"title":"BGCN: An EEG-based Graphical Classification Method for Parkinson's Disease Diagnosis with Heuristic Functional Connectivity Speculation","authors":"Tian Lyu, Haotian Guo","doi":"10.1109/NER52421.2023.10123796","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123796","url":null,"abstract":"As the population ages, the prevalence of Parkinson's Disease (PD), a neurodegeneration disorder that deeply hinders one's daily intellectual and physical activities, has increased rapidly over the past years. However, finding an effective modifiable treatment for PD remains stagnant to date, elevating the significance of the accurate diagnosis. Meanwhile, studies on functional connectivity could provide insights into the neurophysiological mechanisms underlying PD. Hence, this study intends to provide a unified framework, Brain Graph Convolutional Networks (BGCN), incorporating the non-Euclidean heuristic-based brain functional connectivity into a graph-based deep learning model (GCN) for PD diagnosis. The graph representation of Electroencephalography (EEG) data priors in retaining the spatial interdependence among the EEG channels and facilitating the formulation of the functional connectivity construction problem. With the GCN, we modeled neural information exchange with convolutions between nodes along functional connectivity. In this work, functional connectivity was attained by solving a Minimum Spanning Tree (MST) problem with a heuristic search algorithm. As a result, the obtained functional connectivity corresponded to existing MRI studies in terms of the affected regions and hub shift. To evaluate the efficacy of the proposed framework, we compared the heuristic functional connectivity speculation with random/uniform connectivity generated by K-nearest neighbors(k-NN). The proposed framework has achieved excellent precision (95.59%) and learning robustness.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"39 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133108240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Closed-Loop Control of Grasp Force With Biorealistic Hand Prosthesis 仿生假肢抓取力的闭环控制

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123762

Zhuozhi Zhang, Chih-hong Chou, N. Lan

{"title":"Closed-Loop Control of Grasp Force With Biorealistic Hand Prosthesis","authors":"Zhuozhi Zhang, Chih-hong Chou, N. Lan","doi":"10.1109/NER52421.2023.10123762","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123762","url":null,"abstract":"Virtual environments are often used in pre-wearing training and assessment of prosthetic control abilities. Here, we developed a virtual prosthetic hand training platform for evaluation of closed-loop control of grasp force. Biorealistic controllers emulated a pair of antagonistic muscles that actuated the thumb and index fingers of the hand. Surface electromyographic (sEMG) signals from a pair of antagonistic residual muscles drove the biorealistic controllers. Tactile forces from fingertip sensors were conveyed to amputees through evoked tactile sensations (ETS) elicited at the projected finger map (PFM) areas of the stump. A forearm amputee subject participated in force tracking or holding tasks using the virtual hand with residual muscle EMGs, or the contralateral intact hand. Root-mean-square error (RMSE) was used as outcome measure of motor performance. Results in this subject showed that the biorealistic controller enabled the virtual hand to track and maintain grasping forces. The best performance in both tasks was achieved by the contralateral intact hand with visual feedback. The roles of visual or tactile feedback in force tracking or maintaining were also assessed with the virtual hand. For force holding task, hybrid tactile and visual feedback with biorealistic control had a better performance than single visual or tactile feedback in terms of RMSE, success rate, and force variability. While in the force pursuing task, tactile feedback did not seem to add visual feedback in following the target force. The study suggests that training may be required for a novel virtual hand user to perceive and integrate multiple modalities of feedback information, so as to optimize the closed-loop control ability.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"414 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133549353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A pilot study for active muscles decoding using functional near-infrared spectroscopy 使用功能性近红外光谱对活动肌肉进行解码的初步研究

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123845

Ruisen Huang, K. Hong, Fei Gao

{"title":"A pilot study for active muscles decoding using functional near-infrared spectroscopy","authors":"Ruisen Huang, K. Hong, Fei Gao","doi":"10.1109/NER52421.2023.10123845","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123845","url":null,"abstract":"This study is a preliminary step toward gait identification using a non-invasive brain-computer interface. We investigated the feasibility of decoding different active muscles from brain activation using functional near-infrared spectroscopy (fNIRS). A two-section experiment was designed to alternately activate the subjects' hamstring and quadriceps. Nine right-handed subjects, aged $28.1pm 3.5$, were recruited for the experiment. The measured optical intensities were converted to optical density changes and filtered by targeted principal component analysis (tPCA), a lowpass filter, and a highpass filter sequentially. Six features (slope, skewness, kurtosis, peak-to-peak, standard deviation, and entropy) were extracted from the filtered signals and fed to a linear discriminant analysis (LDA) classifier in pairs. Results showed that using the feature pair of slope-standard deviation, we could achieve a classification rate of more than 80% for all four categories (sitting extension, sitting flexion, standing extension, and standing flexion). The maximum classification accuracy was 85.34% for training validation and 92.22% for the testing dataset. Subsequently, an ANOVA test found significant decoding differences among feature combinations. Additionally, no significant difference is found among slope-included feature pairs, skewness-standard deviation, and standard deviation-entropy. The results proved that decoding different muscles related to gait is possible using fNIRS in the future.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116759113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Orderly Motor Unit Activation Using Sinusoidal Low Frequency Alternating Current Stimulation 使用正弦低频交流电刺激的有序运动单元激活

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123723

Awadh Alhawwash, M. R. Horn, N. Lazorchak, Ken Yoshida

{"title":"Orderly Motor Unit Activation Using Sinusoidal Low Frequency Alternating Current Stimulation","authors":"Awadh Alhawwash, M. R. Horn, N. Lazorchak, Ken Yoshida","doi":"10.1109/NER52421.2023.10123723","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123723","url":null,"abstract":"Traditionally, electrical stimulation uses short duration (<1ms) charge balanced rectangular impulses to initiate action potentials. Although pulse stimulation provides an efficient means to activate nerve fibers, the fibers are activated synchronously to the depolarizing edge of the pulse and favors large caliber fibers resulting in an inverse order of recruitment. This study describes the use of low frequency alternating current (LFAC) stimulation using a pure tone continuous sinusoidal waveform to evoke motor nerve activation. In-silico and in-vivo experiments were conducted where LFAC was applied to the rat sciatic nerve via bipolar cuff electrode. The in-vivo model responses were quantified by measuring the electromyogram (EMG) responses of the lateral gastrocnemius and soleus muscles and the combined twitch force. These measures were made in comparison to standard rectangular pulse stimulation. These preliminary results indicate that the response to LFAC were en masse phase locked to the sinusoidal cycle, but of two different modes: 1) Burst mode, and 2) Unitary mode. These results are in agreement with the in-silico predictions. The LFAC activation threshold of the soleus muscle was lower than the lateral gastrocnemius's, suggesting a normal (small-large caliber) physiological order of recruitment. In contrast, the rectangular pulse stimulation produced an inverted order of recruitment.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"19 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125781200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-frequency SSVEP stimuli with 20%-pixel density can induce larger EEG and fNIRS responses 20%像素密度的低频SSVEP刺激可诱导较大的EEG和fNIRS反应

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123852

Jiayuan Meng, Hongzhan Zhou, Jin Yue, Hui Liu, Xiaoyu Li, Minpeng Xu, Dong Ming

{"title":"Low-frequency SSVEP stimuli with 20%-pixel density can induce larger EEG and fNIRS responses","authors":"Jiayuan Meng, Hongzhan Zhou, Jin Yue, Hui Liu, Xiaoyu Li, Minpeng Xu, Dong Ming","doi":"10.1109/NER52421.2023.10123852","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123852","url":null,"abstract":"The brain-computer interface based on steady-state visual evoked potential (SSVEP) has received increasing attention due to its high information transfer rate and low subject variation. A major challenge of current SSVEP-BCI is the uncomfortableness and fatigue induced by the strong visual flicker. Thus, it is of vital importance to optimize SSVEP stimuli for a better user experience. Reducing the pixel density of stimuli is a promising method to improve SSVEP. However, it remains unknown how the neural responses would be when faced with low-pixel density stimuli, and it is also unclear whether the corresponding decoding accuracy can be improved or not. Hereto, this study investigated neural responses induced by the stimuli with distinct pixel densities (1%, 10%, 20%, 60%, 100%) under both low (8Hz, 15Hz) and high frequencies (33Hz, 40Hz, 60Hz), responses from parietal-occipital area were recorded by functional near-infrared spectroscopy (fNIRS) and electroencephalo- gram (EEG) concurrently, aiming to have a better understanding of low-pixel-density-related responses. As a result, the behavioral performance showed that the comfort index inclined as the pixel density became lower. EEG and fNIRS signal analysis indicated that 20%-pixel induced larger EEG and fNIRS response than most densities in the low-frequency band. As to classification, comparing to the 100%, classification accuracy of 20%-pixel density classifies significantly better in low-frequency and high-frequency bands, whether in EEG, fNIRS, or hybrid. The maximum classification accuracy of 20%-density can reach 97.66% in hybrid binary classification, with 3.77% more than 100% density. This research provides a theoretical and technical basis for developing user-friendly SSVEP-BCI.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128854469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of Neural Biomarkers of Major Depressive Disorder Symptom Severity Using Computerized Linguistic Analysis 用计算机语言分析识别重度抑郁症症状严重程度的神经生物标志物

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123766

Daniela A. Astudillo Maya, K. Sellers, Noah Stapper, A. Khambhati, Catherine Henderson, Joline M. Fan, V. Rao, K. Scangos, E. Chang, A. Krystal

{"title":"Identification of Neural Biomarkers of Major Depressive Disorder Symptom Severity Using Computerized Linguistic Analysis","authors":"Daniela A. Astudillo Maya, K. Sellers, Noah Stapper, A. Khambhati, Catherine Henderson, Joline M. Fan, V. Rao, K. Scangos, E. Chang, A. Krystal","doi":"10.1109/NER52421.2023.10123766","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123766","url":null,"abstract":"Although numerous treatments are available for major depressive disorder (MDD), patients can be refractory to sequential treatment regimens. Experimental studies have demonstrated promising results implementing deep brain stimulation (DBS) as a therapy for treatment resistant MDD. However, optimization of this technique requires repeated assessments of the clinical effects of treatment in each patient and the ability to reliably capture the complexity and dynamics of depression symptoms. In our initial studies evaluating the feasibility and preliminary efficacy of a novel closed-loop DBS (CL-DBS) approach, we have observed that repeated self-rated MDD metrics can be burdensome to complete and may not provide accurate measures of symptom severity fluctuations over time, making the identification of neural biomarkers of MDD a challenge. To address this, we evaluated if text analysis could identify linguistic indicators of depression, including providing insights into symptom severity. Using the Linguistic Inquiry and Word Count software, we analyzed written symptom reports from one patient in clinical trial for CL-DBS. We found significant linguistic predictors of depression symptoms that were associated with the same frequency- and region- specific spectral power correlates found when assessing symptoms captured by self-rated depression metrics. These preliminary findings suggest that the close association between language use and symptom strength could be utilized to detect neural biomarkers of depression and potentially to assess treatment outcome.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129920693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One-class classifier based on Riemannian Geometry Distances for Outlier Detection in Motor Imagery* 基于黎曼几何距离的一类分类器在运动图像异常点检测中的应用*

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123715

Kyle Kilcrease, H. Cecotti

{"title":"One-class classifier based on Riemannian Geometry Distances for Outlier Detection in Motor Imagery*","authors":"Kyle Kilcrease, H. Cecotti","doi":"10.1109/NER52421.2023.10123715","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123715","url":null,"abstract":"The classification of motor imagery in non-invasive brain-computer interface (BCI) is a challenge due to the high variation of brain evoked responses across users and the non-stationarity properties of the electroencephalography (EEG) signal. With different sessions from the same user, it is possible to find substantial differences that require the BCI system to be recalibrated. In clinical settings, it is therefore necessary to know when a system should be recalibrated or when the system should adapt itself to deal with the shifts in the signal, i.e., the covariate shift, and/or catch artefacts that deviate substantially from the original data distribution. In this paper, we propose to use density based one-class classifiers using distances based on the Riemannian geometry framework for assessing the distribution of the EEG signal in motor imagery BCI. We assess the performance of the algorithms with a database of 14 participants. The results show that sessions from the same person can be reliably detected using the proposed approach. We also assess how the one-class classifiers can be used to determine if it is necessary to run domain adaptation in the test phase. The results support the conclusion that the accuracy improves as the system is adapted to shifting domains in signals.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126448530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of neck proprioceptive modulation on pallidal network connectivity in dystonia 颈本体感觉调节对肌张力障碍患者苍白质网络连通性的影响

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123779

Prajakta Joshi, A. Sedov, U. Semenova, S. Usova, A. Gamaleya, A. Tomskiy, H. A. Jinnah, A. Shaikh

{"title":"Effects of neck proprioceptive modulation on pallidal network connectivity in dystonia","authors":"Prajakta Joshi, A. Sedov, U. Semenova, S. Usova, A. Gamaleya, A. Tomskiy, H. A. Jinnah, A. Shaikh","doi":"10.1109/NER52421.2023.10123779","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123779","url":null,"abstract":"Cervical dystonia (CD) is the third most common movement disorder affecting 1 million people worldwide. Proprioceptive modulation is the hallmark of contemporary therapies for dystonia, but the mechanism for this intervention is unclear. We studied proprioceptive influence on CD by measuring the spontaneous single-neuron responses and local field potentials (LFP) from the globus pallidus interna (GPi) in 17 CD patients (9 isolated CD and 8 with CD as a feature of generalized dystonia). The goal was to examine how high-frequency neck vibration, a putative modulator of neck proprioception changes pallidal physiology. We found that the neck vibration instantaneously alters the pallidal single neuron activity. We also found that neck vibration modulates pallido-cerebellar connectivity by changing alpha band in LFP recordings. The effects were more robust in those with isolated CD. The vibration also affects pallido-hippocampal connectivity by modulating theta-band power. These effects were more robust in CD with generalized dystonia. Vibration changed LFP only in select pallidal regions. Regions where LFP power was substantially modulated had a prominent proportion of burst subtypes of neurons, compared to pause or tonic subtypes. Such disparity in subtype was absent in regions where the LFP power was not modulated or subtly reduced with neck vibration. When changes in the theta, alpha and beta bands of the LFP recordings were compared against each other in response to vibration, high correlation was observed.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117186905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Robust High-Density EMG Pattern Recognition using Generative Adversarial Network and Convolutional Neural Network 基于生成对抗网络和卷积神经网络的高密度肌电模式识别

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123910

Zhenyu Lin, Philip Liang, Xiaorong Zhang, Zhuwei Qin

{"title":"Toward Robust High-Density EMG Pattern Recognition using Generative Adversarial Network and Convolutional Neural Network","authors":"Zhenyu Lin, Philip Liang, Xiaorong Zhang, Zhuwei Qin","doi":"10.1109/NER52421.2023.10123910","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123910","url":null,"abstract":"High-density electromyography (HD EMG)-based Pattern Recognition (PR) has attracted increasing interest in real-time Neural-Machine Interface (NMI) applications because HD EMG can capture neuromuscular information from one temporal and two spatial dimensions, and it does not require anatomically targeted electrode placements. In recent years, deep learning methods such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid CNN-RNN methods have shown great potential in HD EMG PR. Due to the high-density and multi-channel characteristics of HD EMG, the use of HD EMG-based NMIs in practice may be challenged by the unreliability of HD EMG recordings over time. So far, few studies have investigated the robustness of deep learning methods on HD EMG PR when noises and disturbances such as motion artifacts and bad contacts are present in the HD EMG signals. In this paper, we have developed RoHDE - a Robust deep learning-based HD EMG PR framework by introducing a Generative Adversarial Network (GAN) that can generate synthetic HD EMG signals to simulate recording conditions affected by disturbances. The generated synthetic HD EMG signals can be utilized to train robust deep learning models against real HD EMG signal disturbances. Experimental results have shown that our proposed RoHDE framework can improve the classification accuracy against disturbances such as contact artifacts and loose contacts from 64% to 99%. To the best of our knowledge, this work is the first to address the intrinsic robustness issue of deep learning-based HD EMG PR.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Observing Brain Most Visited Common Band Connectivity States from fMRI Resting State Studies 从功能磁共振成像静息状态研究观察大脑最常访问的共带连接状态

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123853

Janerra Allen, Sravani Varanasi, Rong Chen, L. E. Hong, F. Choa

{"title":"Observing Brain Most Visited Common Band Connectivity States from fMRI Resting State Studies","authors":"Janerra Allen, Sravani Varanasi, Rong Chen, L. E. Hong, F. Choa","doi":"10.1109/NER52421.2023.10123853","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123853","url":null,"abstract":"Neuroscientists have been working for years on finding the neural codes that can correlate neuron firing spatial and/or temporal patterns with behaviors to comprehend the mechanism of brain functions, predict behaviors, and identify methods to treat disorders. Due to the high spatial-temporal resolution requirement of such an approach, invasive measurement methods usually will be required. The other approach to study the mechanistic functions of brain spatial dynamics is using the activation statistics that are correlated to different types of tasks. Here we present rest state activation statistic results as baselines for later more advanced studies including our finding on “common bands” of these most visited brain connectivity states and the possible meaning of these findings. We bundle the MRI voxels to the thalamus (THL), basal ganglia (BSL), and 7 other cortical networks and use energy landscape analysis to explore connectivity signatures of them. Two different data sets obtained from two different fMRI tools were utilized. One dataset consists of 23 young adult and 47 old adult subjects with normal cognitive function. The other data set contains 107 schizophrenic patients and 86 healthy controls. We found that there are common bands of connectivity states that have consistently low energies in all 4 different groups of subjects. These brain-most visited states inside these bands are one or two hamming distances away from each other and centered around the BSL-THL core and then extended to the control type of cortical brain networks as well as other sensory networks.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122731809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0