1
|
Zhu X, Shappell H, Kramer MA, Chu CJ, Kolaczyk ED. Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures. PLoS Comput Biol 2023; 19:e1011188. [PMID: 37327238 PMCID: PMC10310035 DOI: 10.1371/journal.pcbi.1011188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/29/2023] [Accepted: 05/17/2023] [Indexed: 06/18/2023] Open
Abstract
In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks-in which edges indicate strong enough coupling between brain regions-are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy.
Collapse
Affiliation(s)
- Xiaojing Zhu
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
| | - Heather Shappell
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Mark A. Kramer
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
| | - Catherine J. Chu
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Eric D. Kolaczyk
- Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
2
|
Jiang L, Fan Q, Ren J, Dong F, Jiang T, Liu J. An improved BECT spike detection method with functional brain network features based on PLV. Front Neurosci 2023; 17:1150668. [PMID: 37008227 PMCID: PMC10060895 DOI: 10.3389/fnins.2023.1150668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
BackgroundChildren with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging.PurposeThis paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning.MethodsTo obtain high detection effect, this method uses a specific template matching method and the ‘peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes.ResultsBased on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.
Collapse
Affiliation(s)
- Lurong Jiang
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China
| | - Qikai Fan
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China
| | - Juntao Ren
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China
| | - Fang Dong
- College of Information and Electric Engineering, Zhejiang University City College, Hangzhou, China
| | - Tiejia Jiang
- Department of Neurology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Junbiao Liu
- Digital Culture Innovation Research Institute, Zhejiang University City College, Hangzhou, China
- *Correspondence: Junbiao Liu
| |
Collapse
|
3
|
Guo L, Liu D, Wu Y, Xu G. Comparison of spiking neural networks with different topologies based on anti-disturbance ability under external noise. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
4
|
Wang M, Xu B, Hou X, Shi Q, Zhao H, Gui Q, Wu G, Dong X, Xu Q, Shen M, Cheng Q, Feng H. Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment. Front Aging Neurosci 2023; 15:1153496. [PMID: 37122379 PMCID: PMC10140296 DOI: 10.3389/fnagi.2023.1153496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 03/28/2023] [Indexed: 05/02/2023] Open
Abstract
Objective Accumulating evidence shows that cognitive impairment (CI) in chronic heart failure (CHF) patients is related to brain network dysfunction. This study investigated brain network structure and rich-club organization in chronic heart failure patients with cognitive impairment based on graph analysis of diffusion tensor imaging data. Methods The brain structure networks of 30 CHF patients without CI and 30 CHF patients with CI were constructed. Using graph theory analysis and rich-club analysis, changes in global and local characteristics of the subjects' brain network and rich-club organization were quantitatively calculated, and the correlation with cognitive function was analyzed. Results Compared to the CHF patients in the group without CI group, the CHF patients in the group with CI group had lower global efficiency, local efficiency, clustering coefficient, the small-world attribute, and increased shortest path length. The CHF patients with CI group showed lower nodal degree centrality in the fusiform gyrus on the right (FFG.R) and nodal efficiency in the orbital superior frontal gyrus on the left (ORB sup. L), the orbital inferior frontal gyrus on the left (ORB inf. L), and the posterior cingulate gyrus on the right (PCG.R) compared with CHF patients without CI group. The CHF patients with CI group showed a smaller fiber number of edges in specific regions. In CHF patients with CI, global efficiency, local efficiency and the connected edge of the orbital superior frontal gyrus on the right (ORB sup. R) to the orbital middle frontal gyrus on the right (ORB mid. R) were positively correlated with Visuospatial/Executive function. The connected edge of the orbital superior frontal gyrus on the right to the orbital inferior frontal gyrus on the right (ORB inf. R) is positively correlated to attention/calculation. Compared with the CHF patients without CI group, the connection strength of feeder connection and local connection in CHF patients with CI group was significantly reduced, although the strength of rich-club connection in CHF patients complicated with CI group was decreased compared with the control, there was no statistical difference. In addition, the rich-club connection strength was related to the orientation (direction force) of the Montreal cognitive assessment (MoCA) scale, and the feeder and local connection strength was related to Visuospatial/Executive function of MoCA scale in the CHF patients with CI. Conclusion Chronic heart failure patients with CI exhibited lower global and local brain network properties, reduced white matter fiber connectivity, as well as a decreased strength in local and feeder connections in key brain regions. The disrupted brain network characteristics and connectivity was associated with cognitive impairment in CHF patients. Our findings suggest that impaired brain network properties and decreased connectivity, a feature of progressive disruption of brain networks, predict the development of cognitive impairment in patients with chronic heart failure.
Collapse
|
5
|
Wang M, Cheng X, Shi Q, Xu B, Hou X, Zhao H, Gui Q, Wu G, Dong X, Xu Q, Shen M, Cheng Q, Xue S, Feng H, Ding Z. Brain diffusion tensor imaging reveals altered connections and networks in epilepsy patients. Front Hum Neurosci 2023; 17:1142408. [PMID: 37033907 PMCID: PMC10073437 DOI: 10.3389/fnhum.2023.1142408] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction Accumulating evidence shows that epilepsy is a disease caused by brain network dysfunction. This study explored changes in brain network structure in epilepsy patients based on graph analysis of diffusion tensor imaging data. Methods The brain structure networks of 42 healthy control individuals and 26 epilepsy patients were constructed. Using graph theory analysis, global and local network topology parameters of the brain structure network were calculated, and changes in global and local characteristics of the brain network in epilepsy patients were quantitatively analyzed. Results Compared with the healthy control group, the epilepsy patient group showed lower global efficiency, local efficiency, clustering coefficient, and a longer shortest path length. Both healthy control individuals and epilepsy patients showed small-world attributes, with no significant difference between groups. The epilepsy patient group showed lower nodal local efficiency and nodal clustering coefficient in the right olfactory cortex and right rectus and lower nodal degree centrality in the right olfactory cortex and the left paracentral lobular compared with the healthy control group. In addition, the epilepsy patient group showed a smaller fiber number of edges in specific regions of the frontal lobe, temporal lobe, and default mode network, indicating reduced connection strength. Discussion Epilepsy patients exhibited lower global and local brain network properties as well as reduced white matter fiber connectivity in key brain regions. These findings further support the idea that epilepsy is a brain network disorder.
Collapse
Affiliation(s)
- Meixia Wang
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Xiaoyu Cheng
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Qianru Shi
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Bo Xu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Xiaoxia Hou
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Huimin Zhao
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Qian Gui
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Guanhui Wu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Xiaofeng Dong
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Qinrong Xu
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Mingqiang Shen
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Qingzhang Cheng
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Shouru Xue
- Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongxuan Feng
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
- *Correspondence: Hongxuan Feng,
| | - Zhiliang Ding
- Department of Neurology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
- Zhiliang Ding,
| |
Collapse
|
6
|
Zhong L, He S, Yi F, Li X, Wei L, Zeng C, Huang Z, Li Z. Spatio-temporal evaluation of epileptic intracranial EEG based on entropy and synchronization: A phase transition idea. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103689] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
7
|
Orendáčová M, Kvašňák E. Possible Mechanisms Underlying Neurological Post-COVID Symptoms and Neurofeedback as a Potential Therapy. Front Hum Neurosci 2022; 16:837972. [PMID: 35431842 PMCID: PMC9010738 DOI: 10.3389/fnhum.2022.837972] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/26/2022] [Indexed: 12/13/2022] Open
Abstract
Theoretical considerations related to neurological post-COVID complications have become a serious issue in the COVID pandemic. We propose 3 theoretical hypotheses related to neurological post-COVID complications. First, pathophysiological processes responsible for long-term neurological complications caused by COVID-19 might have 2 phases: (1) Phase of acute Sars-CoV-2 infection linked with the pathogenesis responsible for the onset of COVID-19-related neurological complications and (2) the phase of post-acute Sars-CoV-2 infection linked with the pathogenesis responsible for long-lasting persistence of post-COVID neurological problems and/or exacerbation of another neurological pathologies. Second, post-COVID symptoms can be described and investigated from the perspective of dynamical system theory exploiting its fundamental concepts such as system parameters, attractors and criticality. Thirdly, neurofeedback may represent a promising therapy for neurological post-COVID complications. Based on the current knowledge related to neurofeedback and what is already known about neurological complications linked to acute COVID-19 and post-acute COVID-19 conditions, we propose that neurofeedback modalities, such as functional magnetic resonance-based neurofeedback, quantitative EEG-based neurofeedback, Othmer's method of rewarding individual optimal EEG frequency and heart rate variability-based biofeedback, represent a potential therapy for improvement of post-COVID symptoms.
Collapse
Affiliation(s)
- Mária Orendáčová
- Department of Medical Biophysics and Medical Informatics, Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Eugen Kvašňák
- Department of Medical Biophysics and Medical Informatics, Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
| |
Collapse
|