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Sun Y, Song Y, Ren H, Zhu H, Wang Y, Li X, Yan W, Wang Y. Synchronization clusters located on epileptic onset zones in neocortical epilepsy. ACTA EPILEPTOLOGICA 2022. [DOI: 10.1186/s42494-022-00113-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Abstract
Background
Brain function is thought to rely on complex interactions of dynamic neural systems, which depend on the integrity of structural and functional networks. Focal epilepsy is considered to result from excessive focal synchronization in the network. Synchronization analysis of multichannel electrocorticography (ECoG) contributes to the understanding of and orientation of epilepsy. The aim of this study was to explore the synchronization in multichannel ECoG recordings from patients with neocortical epilepsy and characterize neural activity inside and outside the onset zone.
Methods
Four patients with neocortical epilepsy, who became seizure-free for more than 1 year after surgery guided by ECoG monitoring, were included in this study. ECoG data recorded during pre-surgical evaluation were analyzed. Synchronizations in phase and amplitude of different frequency bands between ECoG channels was analyzed using MATLAB. We generated 100 surrogate data from the original ECoG data using Amplitude Adjusted Fourier Transform to calculate the enhanced synchronization. The relationship between synchronization characteristics and seizure onset zone was analyzed.
Results
We found synchronization clusters in the 14–30 Hz and 30–80 Hz bands around the onset areas during both interictal and the beginning of ictal periods in all four patients.
Conclusions
The enhanced-synchronization clusters play a central role in epilepsy, and may activate the onset areas and contribute to the spreading of epileptiform activity.
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Ma X, Wang XH, Li L. Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony. Neurosci Lett 2020; 742:135519. [PMID: 33246027 DOI: 10.1016/j.neulet.2020.135519] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/03/2020] [Accepted: 11/19/2020] [Indexed: 11/29/2022]
Abstract
Autism spectrum disorder (ASD) is a brain disorder that develops during an early stage of childhood. Previous neuroimaging-based diagnostic models for ASD were based on static functional connectivity (FC). The nonlinear complexity of brain connectivity remains unexplored for ASD diagnosis. This study aimed to build intelligent discriminative models for ASD based on phase synchrony (PS). To this end, data from 49 patients with ASD and 41 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) project. PS between brain regions was determined using Hilbert transform. Principal component analysis (PCA) and support vector machines (SVMs) were used to build the discriminative models. PS-based models (AUC = 0.81) outperformed static FC-based models (AUC = 0.71). Furthermore, embedded functional biomarkers were discovered. Moreover, significant correlations were found between PCA-PS and the clinical severity of ASD. Together, intelligent discriminative models based on PS were established for ASD identification. The performance of the diagnostic models suggested the potential benefits of PS for clinical applications. The discriminative patterns indicated that PCA-PS features could be additional biomarkers for ASD research. Furthermore, the significant relationships between the PCA-PS features and clinical scores implied their potential use for personalized medication strategies.
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Affiliation(s)
- Xueke Ma
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China.
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Zhou Z, Cai B, Zhang G, Zhang A, Calhoun VD, Wang YP. Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI. Neuroimage 2020; 221:117190. [PMID: 32711063 DOI: 10.1016/j.neuroimage.2020.117190] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 12/15/2022] Open
Abstract
Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.
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Affiliation(s)
- Zhongxing Zhou
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States; Tianjin University, School of Precision Instruments and Optoelectronics Engineering, Tianjin, China
| | - Biao Cai
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Gemeng Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Aiying Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States.
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Lloyd D. The Musical Structure of Time in the Brain: Repetition, Rhythm, and Harmony in fMRI During Rest and Passive Movie Viewing. Front Comput Neurosci 2020; 13:98. [PMID: 32038214 PMCID: PMC6985279 DOI: 10.3389/fncom.2019.00098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 12/23/2019] [Indexed: 11/13/2022] Open
Abstract
Space generally overshadows time in the construction of theories in cognitive neuroscience. In this paper, we pivot from the spatial axes to the temporal, analyzing fMRI image series to reveal structures in time rather than space. To determine affinities among global brain patterns at different times, core concepts in network analysis (derived from graph theory) were applied temporally, as relations among brain images at every time point during an fMRI scanning epoch. To explore the temporal structures observed through this adaptation of network analysis, data from 180 subjects in the Human Connectome Project were examined, during two experimental conditions: passive movie viewing and rest. The temporal brain, like the spatial brain, exhibits a modular structure, where "modules" are intermittent (distributed in time). These temporal entities are here referred to as themes. Short sequences of themes - motifs - were studied in sequences from 4 to 11 s in length. Many motifs repeated at constant intervals, and are therefore rhythmic; rhythms, converted to frequencies, were often harmonic. We speculate that the structure and interaction of these global oscillations underwrites the capacity to experience and navigate a world which is both recognizably stable and noticeably changing at every moment - a temporal world. In its temporal structure, this brain-constituted world resembles music.
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Affiliation(s)
- Dan Lloyd
- Department of Philosophy and Program in Neuroscience, Trinity College, Hartford, CT, United States
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Lv H, Wang Z, Tong E, Williams LM, Zaharchuk G, Zeineh M, Goldstein-Piekarski AN, Ball TM, Liao C, Wintermark M. Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know. AJNR Am J Neuroradiol 2018; 39:1390-1399. [PMID: 29348136 DOI: 10.3174/ajnr.a5527] [Citation(s) in RCA: 162] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Resting-state fMRI was first described by Biswal et al in 1995 and has since then been widely used in both healthy subjects and patients with various neurologic, neurosurgical, and psychiatric disorders. As opposed to paradigm- or task-based functional MR imaging, resting-state fMRI does not require subjects to perform any specific task. The low-frequency oscillations of the resting-state fMRI signal have been shown to relate to the spontaneous neural activity. There are many ways to analyze resting-state fMRI data. In this review article, we will briefly describe a few of these and highlight the advantages and limitations of each. This description is to facilitate the adoption and use of resting-state fMRI in the clinical setting, helping neuroradiologists become familiar with these techniques and applying them for the care of patients with neurologic and psychiatric diseases.
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Affiliation(s)
- H Lv
- From the Department of Radiology (H.L., Z.W.), Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Radiology (H.L., G.Z., M.Z., M.W.), Neuroradiology Division
| | - Z Wang
- From the Department of Radiology (H.L., Z.W.), Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - E Tong
- Department of Radiology (E.T.), Neuroradiology Section, University of California, San Francisco, San Francisco, California
| | - L M Williams
- Department of Psychiatry and Behavioral Sciences (L.M.W., A.N.G.-P., T.M.B.), Stanford University, Stanford, California
| | - G Zaharchuk
- Department of Radiology (H.L., G.Z., M.Z., M.W.), Neuroradiology Division
| | - M Zeineh
- Department of Radiology (H.L., G.Z., M.Z., M.W.), Neuroradiology Division
| | - A N Goldstein-Piekarski
- Department of Psychiatry and Behavioral Sciences (L.M.W., A.N.G.-P., T.M.B.), Stanford University, Stanford, California
| | - T M Ball
- Department of Psychiatry and Behavioral Sciences (L.M.W., A.N.G.-P., T.M.B.), Stanford University, Stanford, California
| | - C Liao
- Department of Radiology (C.L.), Yunnan Tumor Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, Yunnan Province, China
| | - M Wintermark
- Department of Radiology (H.L., G.Z., M.Z., M.W.), Neuroradiology Division
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