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Rajaraman RR, Smith RJ, Oana S, Daida A, Shrey DW, Nariai H, Lopour BA, Hussain SA. Computational EEG attributes predict response to therapy for epileptic spasms. Clin Neurophysiol 2024; 163:39-46. [PMID: 38703698 DOI: 10.1016/j.clinph.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 03/10/2024] [Accepted: 03/28/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE We set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes. METHODS We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively. RESULTS After statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044). CONCLUSION This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse. SIGNIFICANCE This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.
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Affiliation(s)
- Rajsekar R Rajaraman
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shingo Oana
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel W Shrey
- Division of Pediatric Neurology, University of California, Irvine, Irvine, CA, USA; Department of Neurology, Children's Hospital of Orange County, Orange, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Shaun A Hussain
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA.
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Hu W, Bian G, Huang L, Pi Y, Zhang X, Zhang X, de Albuquerque VHC, Wu W. Constructing Bodily Emotion Maps Based on High-Density Body Surface Potentials for Psychophysiological Computing. IEEE J Biomed Health Inform 2024; 28:2500-2511. [PMID: 38051611 DOI: 10.1109/jbhi.2023.3339382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system. In this study, a body surface potential mapping (BSPM) system was constructed, and an experiment was designed to induce emotions and obtain high-density body surface potential information under negative and non-negative emotions. Then, by constructing and analyzing the functional connectivity network of BSPs, the high-density electrophysiological characteristics are obtained and visualized as bodily emotion maps. The results showed that the functional connectivity network of BSPs under negative emotions had denser connections, and emotion maps based on local clustering coefficient (LCC) are consistent with BSMs under negative emotions. in addition, our features can classify negative and non-negative emotions with the highest classification accuracy of 80.77%. In conclusion, this study constructs an emotion map based on high-density BSPs, which offers a novel approach to psychophysiological computing.
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Lin J, Smith GC, Gliske SV, Zochowski M, Shedden K, Stacey WC. High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery. Brain Commun 2024; 6:fcae032. [PMID: 38384998 PMCID: PMC10881100 DOI: 10.1093/braincomms/fcae032] [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: 07/07/2023] [Revised: 12/28/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.
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Affiliation(s)
- Jack Lin
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Garnett C Smith
- Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Michal Zochowski
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Physics and Biophysics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kerby Shedden
- Department of Statistics and Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - William C Stacey
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Division of Neurology, Ann Arbor VA Health System, Ann Arbor, MI 48109, USA
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Samfira IMA, Galanopoulou AS, Nariai H, Gursky JM, Moshé SL, Bardakjian BL. EEG-based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms. Epilepsia Open 2024; 9:122-137. [PMID: 37743321 PMCID: PMC10839371 DOI: 10.1002/epi4.12831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/17/2023] [Indexed: 09/26/2023] Open
Abstract
OBJECTIVE Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls. METHODS We retrospectively analyzed peri-ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age-matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency- and time-dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel-based closeness centrality (CC), over frequency and time. RESULTS GE in the fast ripple band (251-400 Hz) was significantly greater following the onset of spasms in all patients (P < 0.05). Fast ripple networks during the first 10s from spasm onset show enhanced anteroposterior gradient in connectivity (posterior > central > anterior, Kruskal-Wallis P < 0.001), with maximum CC over the centroparietal channels in 10/14 patients. Additionally, this anteroposterior gradient in CC connectivity is observed during spasms but not during the interictal awake or asleep states of infants with IS. In controls, anteroposterior gradient in fast ripple CC was noted during arousals and wakefulness but not during sleep. There was also a simultaneous decrease in GE in the 5-8 Hz range after the onset of spasms (P < 0.05), of unclear biological significance. SIGNIFICANCE We identified an anteroposterior gradient in the CC connectivity of fast ripple hubs during spasms. This anteroposterior gradient observed during spasms is similar to the anteroposterior gradient in the CC connectivity observed in wakefulness or arousals in controls, suggesting that this state change is related to arousal networks.
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Affiliation(s)
- Ioana M. A. Samfira
- Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoOntarioCanada
| | - Aristea S. Galanopoulou
- Saul R. Korey Department of Neurology and Comprehensive Einstein/Montefiore Epilepsy CenterAlbert Einstein College of MedicineBronxNew YorkUSA
- Isabelle Rapin Division of Child NeurologyAlbert Einstein College of MedicineBronxNew YorkUSA
- Dominick P. Purpura Department of NeuroscienceAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Hiroki Nariai
- Department of PediatricsUCLA Mattel Children's HospitalLos AngelesCaliforniaUSA
| | - Jonathan M. Gursky
- Saul R. Korey Department of Neurology and Comprehensive Einstein/Montefiore Epilepsy CenterAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Solomon L. Moshé
- Saul R. Korey Department of Neurology and Comprehensive Einstein/Montefiore Epilepsy CenterAlbert Einstein College of MedicineBronxNew YorkUSA
- Isabelle Rapin Division of Child NeurologyAlbert Einstein College of MedicineBronxNew YorkUSA
- Dominick P. Purpura Department of NeuroscienceAlbert Einstein College of MedicineBronxNew YorkUSA
- Department of PediatricsEinstein College of MedicineBronxNew YorkUSA
| | - Berj L. Bardakjian
- Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoOntarioCanada
- Institute of Biomedical EngineeringUniversity of TorontoTorontoOntarioCanada
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Lainscsek C, Salami P, Carvalho VR, Mendes EMAM, Fan M, Cash SS, Sejnowski TJ. Network-motif delay differential analysis of brain activity during seizures. CHAOS (WOODBURY, N.Y.) 2023; 33:123136. [PMID: 38156987 PMCID: PMC10757649 DOI: 10.1063/5.0165904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024]
Abstract
Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.
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Affiliation(s)
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | | | - Eduardo M. A. M. Mendes
- Laboratório de Modelagem, Análise e Controle de Sistemas Não Lineares, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil
| | - Miaolin Fan
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
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Alreshidi SM. Compassion fatigue prevalence and risk factors among Saudi psychiatric nurses: A cross-sectional study. Medicine (Baltimore) 2023; 102:e35975. [PMID: 37960724 PMCID: PMC10637429 DOI: 10.1097/md.0000000000035975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023] Open
Abstract
In recent times, compassion fatigue is increasingly being recognized as a damaging outcome associated with the stress experienced by psychiatric nurses. In addition to affecting their job performance work-related stress undermines nurses' physical and emotional well-being. However, there is a lack of research on compassion fatigue that specifically focuses on psychiatric nurses working in Saudi Arabia. This cross-sectional study investigated the prevalence and factors associated with compassion fatigue among Saudi psychiatric nurses. The study participants were asked to complete a demographic questionnaire and the Arabic form of the Professional Quality of Life Scale. Statistical analyses, including one-way ANOVA, t-tests, the Levene test, and multiple linear regression, were employed to assess variables related to compassion fatigue. The survey spanned 158 psychiatric nurses from the Mental Health Complex located in Riyadh City. The mean scores for compassion satisfaction, burnout, and secondary traumatic stress were 39.72 ± 6.881, 24.29 ± 5.386, and 26.94 ± 6.973, respectively. The analysis revealed that variables such as age range (36-55 years) and an associated degree or lower explained 5.2% of the variance in compassion satisfaction. Age range (18-25 years), exercise frequency, and years of nursing experience collectively accounted for 8.4% of the variables contributing to burnout. The age range (25 to 35 years) and working night shifts also explained 5% of the variance in secondary traumatic stress. The findings indicated that compassion satisfaction, burnout, and secondary traumatic stress among the population of psychiatric nurses working in Saudi Arabia were at a moderate level. A higher frequency of healthy lifestyle practices, such as regular exercise, and being in an older age range, were positively associated with compassion satisfaction and negatively associated with burnout and secondary traumatic stress.
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Affiliation(s)
- Salman M. Alreshidi
- Community and Psychiatric Mental Health Nursing Department, College of Nursing, King Saud University, Riyadh City, Saudi Arabia
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Dong Y, Jin L, Li M, Lian R, Wu G, Xu R, Zhang X, Du K, Jia T, Wang H, Zhao S. Crucial involvement of fast waves and Delta band in the brain network attributes of infantile epileptic spasms syndrome. Front Pediatr 2023; 11:1249789. [PMID: 37928352 PMCID: PMC10623136 DOI: 10.3389/fped.2023.1249789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/28/2023] [Indexed: 11/07/2023] Open
Abstract
Objective This study aims to describe the characteristics of the brain network attributes in children diagnosed with Infantile Epileptic Spasms Syndrome (IESS) and to determine the influence exerted by adrenocorticotrophic hormone (ACTH) or methylprednisolone (MP) on network attributes. Methods In this retrospective cohort study, we recruited 19 infants diagnosed with IESS and 10 healthy subjects as the control from the Pediatric Neurology Department at the Third Affiliated Hospital of Zhengzhou University between October 2019 and December 2020. The first thirty-minute processed electroencephalograms (EEGs) were clipped and filtered into EEG frequency bands (2 s each). A comparative assessment was conducted between the IESS group and the controls as well as the pre- and post-treatment in the IESS group. Mutual information values for each EEG channel were collected and compared including characteristic path length (CPL), node degree (ND), clustering coefficient (CC), and betweenness centrality (BC), based on graph theory. Results Comparing the control group, in the IESS group, there was an increase in CPL of the Delta band, and a decrease in ND and CC of the Delta band during the waking period, contrary to those during the sleeping period (P < 0.05), a decreased in CPL of the fast waves and an increase in ND and CC (P < 0.05) in the sleep-wake cycle, and a decrease in ND and CC of the Theta band in the waking phase. Post-treatment compared with the pre-treatment, during the waking ictal phase, there was a noted decrease in CPL in the Delta band and fast waves, while an increase was observed in ND and CC (P < 0.05). Conclusions The Delta band and fast waves are crucial components of the network attributes in IESS. Significance This investigation provides a precise characterization of the brain network in children afflicted with IESS, and lays the groundwork for predicting the prognosis using graph theory.
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Affiliation(s)
- Yan Dong
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Third Affiliated Hospital and Institute of Neuroscience of Zhengzhou University, Zhengzhou, China
| | - Liang Jin
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Mengchun Li
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Ruofei Lian
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Gongao Wu
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Ruijuan Xu
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
- Department of Pediatrics, Zhumadian Central Hospital, Zhumadian, China
| | - Xiaoli Zhang
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Kaixian Du
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Tianming Jia
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Haiyan Wang
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Shichao Zhao
- Department of Pediatrics, The Third Affiliated Hospital of Zheng Zhou University, Zhengzhou, China
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Dallmer-Zerbe I, Jiruska P, Hlinka J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. Epilepsia 2023; 64:2221-2238. [PMID: 37340565 DOI: 10.1111/epi.17690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, Negi AS, Gummadavelli A, Kang H, Reddy SB, Naftel RP, Bick SK, Williams Roberson S, Dawant BM, Wallace MT, Englot DJ. The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence. Brain 2023; 146:2828-2845. [PMID: 36722219 PMCID: PMC10316780 DOI: 10.1093/brain/awad016] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/24/2022] [Accepted: 01/08/2023] [Indexed: 02/02/2023] Open
Abstract
Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.
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Affiliation(s)
- Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Danika L Paulo
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Jared S Shless
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Aarushi S Negi
- Department of Neuroscience, Vanderbilt University, Nashville, TN 37232, USA
| | - Abhijeet Gummadavelli
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA
| | - Shilpa B Reddy
- Department of Pediatrics, Vanderbilt Children’s Hospital, Nashville, TN 37232, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sarah K Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Mark T Wallace
- Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychology, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
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10
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Rijal S, Corona L, Perry MS, Tamilia E, Madsen JR, Stone SSD, Bolton J, Pearl PL, Papadelis C. Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy. Sci Rep 2023; 13:9622. [PMID: 37316544 PMCID: PMC10267141 DOI: 10.1038/s41598-023-36551-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.
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Affiliation(s)
- Sakar Rijal
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - Ludovica Corona
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA.
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA.
- School of Medicine, Texas Christian University, Fort Worth, TX, 76129, USA.
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11
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Bernabei JM, Li A, Revell AY, Smith RJ, Gunnarsdottir KM, Ong IZ, Davis KA, Sinha N, Sarma S, Litt B. Quantitative approaches to guide epilepsy surgery from intracranial EEG. Brain 2023; 146:2248-2258. [PMID: 36623936 PMCID: PMC10232272 DOI: 10.1093/brain/awad007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/11/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Li
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Andrew Y Revell
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Neuroengineering Program, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Kristin M Gunnarsdottir
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sridevi Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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12
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Nogales A, García-Tejedor ÁJ, Chazarra P, Ugalde-Canitrot A. Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107427. [PMID: 36870168 DOI: 10.1016/j.cmpb.2023.107427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of brain connectivity using artificial intelligence and network analysis techniques since their study requires large amounts of data over large spatial and temporal scales. For example, to discriminate states that would otherwise be indistinguishable from the human eye. This paper aims to identify the different brain states that appear concerning the intriguing seizure type of epileptic spasms. Once these states have been differentiated, an attempt is made to understand their corresponding brain activity. METHODS The representation of brain connectivity can be done by graphing the topology and intensity of brain activations. Graph images from different instants within and outside the actual seizure are used as input to a deep learning model for classification purposes. This work uses convolutional neural networks to discriminate the different states of the epileptic brain based on the appearance of these graphs at different times. Next, we apply several graph metrics as an aid to interpret what happens in the brain regions during and around the seizure. RESULTS Results show that the model consistently finds distinctive brain states in children with epilepsy with focal onset epileptic spasms that are indistinguishable under the expert visual inspection of EEG traces. Furthermore, differences are found in brain connectivity and network measures in each of the different states. CONCLUSIONS Computer-assisted discrimination using this model can detect subtle differences in the various brain states of children with epileptic spasms. The research reveals previously undisclosed information regarding brain connectivity and networks, allowing for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. From our data, we speculate that the prefrontal, premotor, and motor cortices could be more involved in a hypersynchronized state occurring in the few seconds immediately preceding the visually evident EEG and clinical ictal features of the first spasm in a cluster. On the other hand, a disconnection in centro-parietal areas seems a relevant feature in the predisposition and repetitive generation of epileptic spasms within clusters.
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Affiliation(s)
- Alberto Nogales
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain.
| | - Álvaro J García-Tejedor
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain
| | - Pedro Chazarra
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain
| | - Arturo Ugalde-Canitrot
- School of Medicine. Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800. Pozuelo de Alarcón 28223, Spain; Epilepsy Unit, Neurology and Clinical Neurophysiology Service, Hospital Universitario La Paz, Paseo de la Castellana, 261, Madrid 28046, Spain
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13
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Li W, Ding S, Zhao G. Static and dynamic topological organization of brain functional connectome in acute mild traumatic brain injury. Acta Radiol 2023; 64:1175-1183. [PMID: 35765198 DOI: 10.1177/02841851221109897] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Prior studies have detected topological changes of brain functional networks in patients with acute mild traumatic brain injury (mTBI). However, the alterations of dynamic topological characteristics in mTBI have been scarcely elucidated. PURPOSE To evaluate static and dynamic functional connectivity topological networks in patients with acute mTBI using resting-state functional magnetic resonance imaging (fMRI). MATERIAL AND METHODS A total of 55 patients with acute mTBI and 55 age-, sex-, and education-matched healthy controls (HCs) were enrolled in this study. All participants underwent resting-state fMRI scans, and data were analyzed using graph-theory methods and a sliding window approach. Post-traumatic cognitive performance and resting-state fMRI data were collected within one week after injury. Static and dynamic functional connectivity patterns were determined by independent component analysis. Spearman's correlation analysis was further performed between fMRI changes and Montreal cognitive assessment (MoCA) scores. RESULTS Global efficiency was lower (P = 0.02), and local efficiency (P < 0.001) and mean Cp (P < 0.001) were higher in patients with acute mTBI than in HCs. Local efficiency was correlated with visuospatial/executive performance (r = -0.421; P = 0.002) in patients with acute mTBI. Significant differences in nodal efficiency and node degree centrality (P < 0.01) were found between the mTBI and HC groups. For dynamic properties, patients with mTBI showed higher variance (P = 0.016) in global efficiency than HCs. CONCLUSIONS The present study shows that patients with mTBI have abnormal brain functional connectome topology, especially the dynamic graph theory characteristics, which provide new insights into the role of topological network properties in patients with acute mTBI.
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Affiliation(s)
- Weigang Li
- Department of Radiology, Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
| | - Shaohua Ding
- Department of Radiology, Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
| | - Guoqian Zhao
- Department of Radiology, Chinese Traditional Medicine Hospital of Danyang, Danyang, Jiangsu, PR China
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14
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Conrad EC, Bernabei JM, Sinha N, Ghosn NJ, Stein JM, Shinohara RT, Litt B. Addressing spatial bias in intracranial EEG functional connectivity analyses for epilepsy surgical planning. J Neural Eng 2022; 19:056019. [PMID: 36084621 PMCID: PMC9590099 DOI: 10.1088/1741-2552/ac90ed] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/26/2022] [Accepted: 09/09/2022] [Indexed: 01/25/2023]
Abstract
Objective.To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and the ability of functional connectivity to localize the seizure onset zone (SOZ), controlling for spatial biases.Approach.We analyzed intracranial EEG data from patients with drug-resistant epilepsy admitted for pre-surgical planning. We calculated intracranial EEG functional networks and determined whether changes in functional connectivity lateralized the SOZ using a spatial subsampling method to control for spatial bias. We developed a 'spatial null model' to localize the SOZ electrode using only spatial sampling information, ignoring EEG data. We compared the performance of this spatial null model against models incorporating EEG functional connectivity and interictal spike rates.Main results.About 110 patients were included in the study, although the number of patients differed across analyses. Controlling for spatial sampling, the average connectivity was lower in the SOZ region relative to the same anatomic region in the contralateral hemisphere. A model using intra-hemispheric connectivity accurately lateralized the SOZ (average accuracy 75.5%). A spatial null model incorporating spatial sampling information alone achieved moderate accuracy in classifying SOZ electrodes (mean AUC = 0.70, 95% CI 0.63-0.77). A model incorporating intracranial EEG functional connectivity and spike rate data further outperformed this spatial null model (AUC 0.78,p= 0.002 compared to spatial null model). However, a model incorporating functional connectivity without spike rate data did not significantly outperform the null model (AUC 0.72,p= 0.38).Significance.Intracranial EEG functional connectivity is reduced in the SOZ region, and interictal data predict SOZ electrode localization and laterality, however a predictive model incorporating functional connectivity without interictal spike rates did not significantly outperform a spatial null model. We propose constructing a spatial null model to provide an estimate of the pre-implant hypothesis of the SOZ, and to serve as a benchmark for further machine learning algorithms in order to avoid overestimating model performance because of electrode sampling alone.
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Affiliation(s)
- Erin C Conrad
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - John M Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Nishant Sinha
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Nina J Ghosn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Joel M Stein
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States of America
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Brian Litt
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
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15
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Romero Milà B, Remakanthakurup Sindhu K, Mytinger JR, Shrey DW, Lopour BA. EEG biomarkers for the diagnosis and treatment of infantile spasms. Front Neurol 2022; 13:960454. [PMID: 35968272 PMCID: PMC9366674 DOI: 10.3389/fneur.2022.960454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.
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Affiliation(s)
- Blanca Romero Milà
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain
| | | | - John R. Mytinger
- Division of Pediatric Neurology, Department of Pediatrics, Nationwide Children's Hospital, The Ohio State University, Columbus, OH, United States
| | - Daniel W. Shrey
- Division of Neurology, Children's Hospital Orange County, Orange, CA, United States
- Department of Pediatrics, University of California, Irvine, Irvine, CA, United States
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- *Correspondence: Beth A. Lopour
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16
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Albaladejo-Belmonte M, Prats-Boluda G, Ye Lin Y, Garfield RE, Garcia-Casado J. Uterine slow wave: directionality and changes with imminent delivery. Physiol Meas 2022; 43. [PMID: 35896091 DOI: 10.1088/1361-6579/ac84c0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/27/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The slow wave (SW) of the electrohysterogram (EHG) may contain relevant information on the electrophysiological condition of the uterus throughout pregnancy and labor. Our aim was to assess differences in the SW as regards the imminence of labor and the directionality of uterine myoelectrical activity. APPROACH The SW of the EHG was extracted from the signals of the Icelandic 16-electrode EHG database in the bandwidth [5, 30] mHz and its power, spectral content, complexity and synchronization between the horizontal (X) and vertical (Y) directions were characterized by the root mean square (RMS), dominant frequency (domF), sample entropy (SampEn) and maximum cross-correlation (CCmax) of the signals, respectively. Significant differences between parameters at time-to-delivery (TTD) ≤7 vs. >7 days and between the horizontal vs. vertical directions were assessed. MAIN RESULTS The SW power significantly increased in both directions as labor approached (TTD≤7d vs. >7d (mean±SD): x= 0.12±0.10 vs. 0.08±0.06mV; y= 0.12±0.09 vs. 0.08±0.05mV), as well as the dominant frequency in the horizontal direction (= 9.1±1.3 vs. 8.5±1.2mHz) and the synchronization between both directions (= 0.44±0.16 vs. 0.36±0.14). Furthermore, its complexity decreased in the vertical direction (= 6.13·10-2±8.7·10-3 vs. 6.50·10-2±8.3·10-3), suggesting a higher cell-to-cell electrical coupling. Whereas there were no differences between the SW features in both directions in the general population, statistically significant differences were obtained between them in individuals in many cases. SIGNIFICANCE Our results suggest that the SW of the EHG is related to bioelectrical events in the uterus and could provide objective information to clinicians in challenging obstetric scenarios.
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Affiliation(s)
- Monica Albaladejo-Belmonte
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, Valencia, Valencia, 46022, SPAIN
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, Valencia, Valencia, 46022, SPAIN
| | - Yiyao Ye Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, Valencia, Valencia, 46022, SPAIN
| | - Robert Edward Garfield
- The University of Arizona College of Medicine Tucson, 1501 N Campbell Ave, Tucson, AZ 85724, USA, Tucson, Arizona, 85724-5018, UNITED STATES
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Edif. 8B, Camino de Vera SN, Valencia, Valencia, 46022, SPAIN
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17
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Zheng R, Feng Y, Wang T, Cao J, Wu D, Jiang T, Gao F. Scalp EEG functional connection and brain network in infants with West syndrome. Neural Netw 2022; 153:76-86. [DOI: 10.1016/j.neunet.2022.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/21/2022] [Accepted: 05/31/2022] [Indexed: 10/18/2022]
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18
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Sinha N, Johnson GW, Davis KA, Englot DJ. Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps. Epilepsy Curr 2022; 22:272-278. [PMID: 36285209 PMCID: PMC9549227 DOI: 10.1177/15357597221101271] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Drug resistant epilepsy is a disorder involving widespread brain network
alterations. Recently, many groups have reported neuroimaging and
electrophysiology network analysis techniques to aid medical
management, support presurgical planning, and understand postsurgical
seizure persistence. While these approaches may supplement standard
tests to improve care, they are not yet used clinically or influencing
medical or surgical decisions. When will this change? Which approaches
have shown the most promise? What are the barriers to translating them
into clinical use? How do we facilitate this transition? In this
review, we will discuss progress, barriers, and next steps regarding
the integration of brain network analysis into the medical and
presurgical pipeline.
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Affiliation(s)
- Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Graham W. Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science at Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kathryn A. Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Dario J. Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science at Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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19
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Drug-resistant focal epilepsy in children is associated with increased modal controllability of the whole brain and epileptogenic regions. Commun Biol 2022; 5:394. [PMID: 35484213 PMCID: PMC9050895 DOI: 10.1038/s42003-022-03342-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 04/06/2022] [Indexed: 02/06/2023] Open
Abstract
Network control theory provides a framework by which neurophysiological dynamics of the brain can be modelled as a function of the structural connectome constructed from diffusion MRI. Average controllability describes the ability of a region to drive the brain to easy-to-reach neurophysiological states whilst modal controllability describes the ability of a region to drive the brain to difficult-to-reach states. In this study, we identify increases in mean average and modal controllability in children with drug-resistant epilepsy compared to healthy controls. Using simulations, we purport that these changes may be a result of increased thalamocortical connectivity. At the node level, we demonstrate decreased modal controllability in the thalamus and posterior cingulate regions. In those undergoing resective surgery, we also demonstrate increased modal controllability of the resected parcels, a finding specific to patients who were rendered seizure free following surgery. Changes in controllability are a manifestation of brain network dysfunction in epilepsy and may be a useful construct to understand the pathophysiology of this archetypical network disease. Understanding the mechanisms underlying these controllability changes may also facilitate the design of network-focussed interventions that seek to normalise network structure and function.
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20
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Sinha N, Joshi RB, Sandhu MRS, Netoff TI, Zaveri HP, Lehnertz K. Perspectives on Understanding Aberrant Brain Networks in Epilepsy. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:868092. [PMID: 36926081 PMCID: PMC10013006 DOI: 10.3389/fnetp.2022.868092] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/14/2022] [Indexed: 01/21/2023]
Abstract
Epilepsy is a neurological disorder affecting approximately 70 million people worldwide. It is characterized by seizures that are complex aberrant dynamical events typically treated with drugs and surgery. Unfortunately, not all patients become seizure-free, and there is an opportunity for novel approaches to treat epilepsy using a network view of the brain. The traditional seizure focus theory presumed that seizures originated within a discrete cortical area with subsequent recruitment of adjacent cortices with seizure progression. However, a more recent view challenges this concept, suggesting that epilepsy is a network disease, and both focal and generalized seizures arise from aberrant activity in a distributed network. Changes in the anatomical configuration or widespread neural activities spanning lobes and hemispheres could make the brain more susceptible to seizures. In this perspective paper, we summarize the current state of knowledge, address several important challenges that could further improve our understanding of the human brain in epilepsy, and invite novel studies addressing these challenges.
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Affiliation(s)
- Nishant Sinha
- Department of Neurology, Penn Epilepsy Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
| | - Rasesh B. Joshi
- Boston Children’s Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Theoden I. Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Hitten P. Zaveri
- Department of Neurology, Yale University, New Haven, CT, United States
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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21
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Johnson GW, Doss DJ, Englot DJ. Network dysfunction in pre and postsurgical epilepsy: connectomics as a tool and not a destination. Curr Opin Neurol 2022; 35:196-201. [PMID: 34799514 PMCID: PMC8891078 DOI: 10.1097/wco.0000000000001008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Patients with focal drug-resistant epilepsy (DRE) sometimes continue to have seizures after surgery. Recently, there is increasing interest in using advanced network analyses (connectomics) to better understand this problem. Connectomics has changed the way researchers and clinicians view DRE, but it must be applied carefully in a hypothesis-driven manner to avoid spurious results. This review will focus on studies published in the last 18 months that have thoughtfully used connectomics to advance our fundamental understanding of network dysfunction in DRE - hopefully for the eventual direct benefit to patient care. RECENT FINDINGS Impactful recent findings have centered on using patient-specific differences in network dysfunction to predict surgical outcome. These works span functional and structural connectivity and include the modalities of functional and diffusion magnetic resonance imaging (MRI) and electrophysiology. Using functional MRI, many groups have described an increased functional segregation outside of the surgical resection zone in patients who fail surgery. Using electrophysiology, groups have reported network characteristics of resected tissue that suggest whether a patient will respond favorably to surgery. SUMMARY If we can develop accurate models to outline functional and structural network characteristics that predict failure of standard surgical approaches, then we can not only improve current clinical decision-making; we can also begin developing alternative treatments including network approaches to improve surgical success rates.
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Affiliation(s)
- Graham W. Johnson
- Department of Biomedical Engineering at Vanderbilt University
- Vanderbilt University Institute of Imaging Science at Vanderbilt University Medical Center
| | - Derek J. Doss
- Department of Biomedical Engineering at Vanderbilt University
- Vanderbilt University Institute of Imaging Science at Vanderbilt University Medical Center
| | - Dario J. Englot
- Department of Biomedical Engineering at Vanderbilt University
- Vanderbilt University Institute of Imaging Science at Vanderbilt University Medical Center
- Department of Neurological Surgery
- Department of Neurology
- Department of Radiology and Radiological Sciences at Vanderbilt University Medical Center
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22
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Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm. Sci Rep 2022; 12:4420. [PMID: 35292691 PMCID: PMC8924190 DOI: 10.1038/s41598-022-08322-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/22/2022] [Indexed: 11/28/2022] Open
Abstract
Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm’s decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 (\documentclass[12pt]{minimal}
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\begin{document}$$\approx$$\end{document}≈44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients.
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23
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Dong Y, Xu R, Zhang Y, Shi Y, Du K, Jia T, Wang J, Wang F. Different Frequency Bands in Various Regions of the Brain Play Different Roles in the Onset and Wake-Sleep Stages of Infantile Spasms. Front Pediatr 2022; 10:878099. [PMID: 35633963 PMCID: PMC9135356 DOI: 10.3389/fped.2022.878099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE The study aimed to identify the signatures of brain networks using electroencephalogram (EEG) in patients with infantile spasms (IS). METHODS Scalp EEGs of subjects with IS were prospectively collected in the first year of life (n = 8; age range 4-8 months; 3 males, 5 females). Ten minutes of ictal and interictal EEGs were clipped and filtered into different EEG frequency bands. The values of each pair of EEG channels were directly compared between ictal with interictal onsets and the sleep-wake phase to calculate IS brain network attributes: characteristic path length (CPL), node degree (ND), clustering coefficient (CC), and betweenness centrality (BC). RESULTS CPL, ND, and CC of the fast waves decreased while BC increased. CPL and BC of the slow waves decreased, while ND and CC increased during the IS ictal onset (P < 0.05). CPL of the alpha decreased, and BC increased during the waking time (P < 0.05). CONCLUSION The transmission capability of the fast waves, the local connectivity, and the defense capability of the slow waves during the IS ictal onset were enhanced. The alpha band played the most important role in both the global and local networks during the waking time. These may represent the brain network signatures of IS.
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Affiliation(s)
- Yan Dong
- Henan Provincial Key Laboratory of Child Brain Injury, Department of Pediatrics, Third Associated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Ruijuan Xu
- Henan Provincial Key Laboratory of Child Brain Injury, Department of Pediatrics, Third Associated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Yaodong Zhang
- Henan Key Laboratory of Children's Genetics and Metabolic Diseases, Henan Neurodevelopment Engineering Research Center for Children, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yali Shi
- Henan Provincial Key Laboratory of Child Brain Injury, Department of Pediatrics, Third Associated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Kaixian Du
- Henan Provincial Key Laboratory of Child Brain Injury, Department of Pediatrics, Third Associated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Tianming Jia
- Henan Provincial Key Laboratory of Child Brain Injury, Department of Pediatrics, Third Associated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Jun Wang
- Department of Children's Rehabilitation, Third Associated Hospital of Zheng Zhou University, Zhengzhou, China
| | - Fang Wang
- Department of Medical Record Management, Third Associated Hospital of Zheng Zhou University, Zhengzhou, China
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24
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Fan JM, Lee AT, Kudo K, Ranasinghe KG, Morise H, Findlay AM, Kirsch HE, Chang EF, Nagarajan SS, Rao VR. Network connectivity predicts effectiveness of responsive neurostimulation in focal epilepsy. Brain Commun 2022; 4:fcac104. [PMID: 35611310 PMCID: PMC9123848 DOI: 10.1093/braincomms/fcac104] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/23/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Responsive neurostimulation is a promising treatment for drug-resistant focal epilepsy; however, clinical outcomes are highly variable across individuals. The therapeutic mechanism of responsive neurostimulation likely involves modulatory effects on brain networks; however, with no known biomarkers that predict clinical response, patient selection remains empiric. This study aimed to determine whether functional brain connectivity measured non-invasively prior to device implantation predicts clinical response to responsive neurostimulation therapy. Resting-state magnetoencephalography was obtained in 31 participants with subsequent responsive neurostimulation device implantation between 15 August 2014 and 1 October 2020. Functional connectivity was computed across multiple spatial scales (global, hemispheric, and lobar) using pre-implantation magnetoencephalography and normalized to maps of healthy controls. Normalized functional connectivity was investigated as a predictor of clinical response, defined as percent change in self-reported seizure frequency in the most recent year of clinic visits relative to pre-responsive neurostimulation baseline. Area under the receiver operating characteristic curve quantified the performance of functional connectivity in predicting responders (≥50% reduction in seizure frequency) and non-responders (<50%). Leave-one-out cross-validation was furthermore performed to characterize model performance. The relationship between seizure frequency reduction and frequency-specific functional connectivity was further assessed as a continuous measure. Across participants, stimulation was enabled for a median duration of 52.2 (interquartile range, 27.0-62.3) months. Demographics, seizure characteristics, and responsive neurostimulation lead configurations were matched across 22 responders and 9 non-responders. Global functional connectivity in the alpha and beta bands were lower in non-responders as compared with responders (alpha, pfdr < 0.001; beta, pfdr < 0.001). The classification of responsive neurostimulation outcome was improved by combining feature inputs; the best model incorporated four features (i.e. mean and dispersion of alpha and beta bands) and yielded an area under the receiver operating characteristic curve of 0.970 (0.919-1.00). The leave-one-out cross-validation analysis of this four-feature model yielded a sensitivity of 86.3%, specificity of 77.8%, positive predictive value of 90.5%, and negative predictive value of 70%. Global functional connectivity in alpha band correlated with seizure frequency reduction (alpha, P = 0.010). Global functional connectivity predicted responder status more strongly, as compared with hemispheric predictors. Lobar functional connectivity was not a predictor. These findings suggest that non-invasive functional connectivity may be a candidate personalized biomarker that has the potential to predict responsive neurostimulation effectiveness and to identify patients most likely to benefit from responsive neurostimulation therapy. Follow-up large-cohort, prospective studies are required to validate this biomarker. These findings furthermore support an emerging view that the therapeutic mechanism of responsive neurostimulation involves network-level effects in the brain.
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Affiliation(s)
- Joline M Fan
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Anthony T Lee
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Kiwamu Kudo
- Medical Imaging Center, Ricoh Company, Ltd., Kanazawa, Japan.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Kamalini G Ranasinghe
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Hirofumi Morise
- Medical Imaging Center, Ricoh Company, Ltd., Kanazawa, Japan.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Anne M Findlay
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Heidi E Kirsch
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Edward F Chang
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
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25
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Gunnarsdottir KM, Gonzalez-Martinez J, Wing S, Sarma SV. Sources and Sinks in Interictal iEEG Networks: An iEEG Marker of the Epileptogenic Zone. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6558-6561. [PMID: 34892611 DOI: 10.1109/embc46164.2021.9630035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Around 30% of epilepsy patients have seizures that cannot be controlled with medication. The most effective treatments for medically resistant epilepsy are interventions that surgically remove the epileptogenic zone (EZ), the regions of the brain that initiate seizure activity. A precise identification of the EZ is essential for surgical success but unfortunately, current success rates range from 20-80%. Localization of the EZ requires visual inspection of intracranial EEG (iEEG) recordings during seizure events. The need for seizure occurrence makes the process both costly and time-consuming and in the end, less than 1% of the data captured is used to assist in EZ localization. In this study, we aim to leverage interictal (between seizures) data to localize the EZ. We develop and test the source-sink index as an interictal iEEG marker by identifying two groups of network nodes from a patient's interictal iEEG network: those that inhibit a set of their neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, we i) estimate patient-specific dynamical network models from interictal iEEG data and ii) compute a source-sink index for every network node (iEEG channel) to identify pathological nodes that correspond to the EZ. Our results suggest that in patients with successful surgical outcomes, the source-sink index clearly separates the clinically identified EZ (CA-EZ) channels from other channels whereas in patients with failed outcomes CA-EZ channels cannot be distinguished from the rest of the network.
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26
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Wing S, Gunnarsdottir KM, Gonzalez-Martinez J, Sarma SV. Transfer Entropy between Intracranial EEG Nodes Highlights Network Dynamics that Cause and Stop Epileptic Seizures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6121-6125. [PMID: 34892513 DOI: 10.1109/embc46164.2021.9629793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Transfer entropy (TE) is used to examine the connectivity between nodes and the roles of nodes in epileptic neural networks during rest, moments before seizure, during seizure, and moments after seizure. There is a set of nodes that dominate information flow to epileptogenic zone (EZ) nodes, regions that trigger seizure, and non-EZ nodes during rest. The TE from the dominant to the EZ nodes decreases shortly before a seizure event and reaches a minimum during seizure. During the seizure, the dominant nodes cease or only weakly interact with the EZ nodes. This supports the hypothesis that seizure occurs when some nodes stop inhibiting the EZ nodes. The TE from the dominant to the EZ nodes peaks immediately after seizure, suggesting that seizure may stop when the brain exerts the highest level of information flow/activation/communication to the EZ nodes. The information flow from the dominant to EZ nodes is different from that to non-EZ nodes. This TE dynamics entering and exiting seizures may identify more accurately the EZ nodes, which may improve surgical planning.
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27
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Cho KH, Park KM, Lee HJ, Cho H, Lee DA, Heo K, Kim SE. Metabolic network is related to surgical outcome in temporal lobe epilepsy with hippocampal sclerosis: A brain FDG-PET study. J Neuroimaging 2021; 32:300-313. [PMID: 34679233 DOI: 10.1111/jon.12941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/15/2021] [Accepted: 10/03/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE The aim of this study was to investigate differences in metabolic networks based on preoperative fluorodeoxyglucose (FDG)-positron emission tomography (PET) in temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS) between patients with complete seizure-free (SF) and those with noncomplete seizure-free (non-SF) after anterior temporal lobectomy. METHODS This study was retrospectively performed at a tertiary hospital. We recruited pathologically confirmed 75 TLE patients with HS who underwent preoperative FDG-PET. All patients underwent a standard anterior temporal lobectomy. The surgical outcome was evaluated at least 12 months after surgery, and we divided the subjects into patients with SF (International League Against Epilepsy [ILAE] class I) and those with non-SF (ILAE class II-VI). We evaluated the metabolic network using graph theoretical analysis based on FDG-PET. We investigated the differences in network measures between the two groups. RESULTS Of the 75 TLE patients with HS, 32 patients (42.6%) had SF, whereas 43 patients (57.3%) had non-SF. There were significant differences in global metabolic networks according to surgical outcomes. The patients with SF had a lower assortative coefficient than those with non-SF (-0.020 vs. -0.009, p = .044). We also found widespread regional differences in local metabolic networks according to surgical outcomes. CONCLUSION Our study demonstrates significant differences in preoperative metabolic networks based on FDG-PET in TLE patients with HS according to surgical outcomes. This work introduces a metabolic network based on FDG-PET and can be used as a potential tool for predicting surgical outcome in TLE patients with HS.
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Affiliation(s)
- Kyoo Ho Cho
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.,Department of Neurology, Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Hojin Cho
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Kyoung Heo
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Eun Kim
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
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28
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Dharan AL, Bowden SC, Lai A, Peterson ADH, Cheung MWL, Woldman W, D'Souza WJ. Resting-state functional connectivity in the idiopathic generalized epilepsies: A systematic review and meta-analysis of EEG and MEG studies. Epilepsy Behav 2021; 124:108336. [PMID: 34607215 DOI: 10.1016/j.yebeh.2021.108336] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/09/2021] [Accepted: 09/12/2021] [Indexed: 11/20/2022]
Abstract
For idiopathic generalized epilepsies (IGE), brain network analysis is emerging as a biomarker for potential use in clinical care. To determine whether people with IGE show alterations in resting-state brain connectivity compared to healthy controls, and to quantify these differences, we conducted a systematic review and meta-analysis of EEG and magnetoencephalography (MEG) functional connectivity and network studies. The review was conducted according to PRISMA guidelines. Twenty-two studies were eligible for inclusion. Outcomes from individual studies supported hypotheses for interictal, resting-state brain connectivity alterations in IGE patients compared to healthy controls. In contrast, meta-analysis from six studies of common network metrics clustering coefficient, path length, mean degree and nodal strength showed no significant differences between IGE and control groups (effect sizes ranged from -0.151 -1.78). The null findings of the meta-analysis and the heterogeneity of the included studies highlights the importance of developing standardized, validated methodologies for future research. Network neuroscience has significant potential as both a diagnostic and prognostic biomarker in epilepsy, though individual variability in network dynamics needs to be better understood and accounted for.
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Affiliation(s)
- Anita L Dharan
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia; Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Alan Lai
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Andre D H Peterson
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Mike W-L Cheung
- Department of Psychology, National University of Singapore, Singapore
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Edgbaston, United Kingdom
| | - Wendyl J D'Souza
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
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29
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Schmid W, Fan Y, Chi T, Golanov E, Regnier-Golanov AS, Austerman RJ, Podell K, Cherukuri P, Bentley T, Steele CT, Schodrof S, Aazhang B, Britz GW. Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries. J Neural Eng 2021; 18. [PMID: 34330120 DOI: 10.1088/1741-2552/ac1982] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/30/2021] [Indexed: 12/16/2022]
Abstract
Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Yingying Fan
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Taiyun Chi
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Eugene Golanov
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | | | - Ryan J Austerman
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | - Kenneth Podell
- Department of Neurology, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | - Paul Cherukuri
- Institute of Biosciences and Bioengineering (IBB), Rice University, Houston, TX 77005, United States of America
| | - Timothy Bentley
- Office of Naval Research, Arlington, VA 22203, United States of America
| | - Christopher T Steele
- Military Operational Medicine Research Program, US Army Medical Research and Development Command, Fort Detrick, MD 21702, United States of America
| | - Sarah Schodrof
- Department of Athletics-Sports Medicine, Rice University, Houston, TX 77005, United States of America
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Gavin W Britz
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
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30
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Lee DA, Lee HJ, Kim HC, Park KM. Temporal lobe epilepsy with or without hippocampal sclerosis: Structural and functional connectivity using advanced MRI techniques. J Neuroimaging 2021; 31:973-980. [PMID: 34110654 DOI: 10.1111/jon.12898] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/18/2021] [Accepted: 05/27/2021] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND AND PURPOSE The aim of this study was to investigate the differences in structural connectivity based on diffusion tensor imaging (DTI) and functional connectivity based on arterial spin labeling (ASL) MRI between temporal lobe epilepsy (TLE) patients with and without hippocampal sclerosis (HS). METHODS We enrolled 50 patients with TLE, including 25 patients with HS and 25 patients without HS, who underwent brain MRI, including DTI and ASL. We calculated the network parameters of structural connectivity based on DTI and functional connectivity based on ASL using a graph theoretical analysis. The parameters included global network measures (radius, diameter, characteristic path length, global efficiency, local efficiency, mean clustering coefficient, transitivity, assortative coefficient, and small-worldness index) and a local network measure (betweenness centrality). RESULTS The global and local network measures of structural connectivity were not different between TLE patients with and without HS. However, significant differences in functional connectivity existed between the two groups. The radius and diameter of the global network measures in the TLE patients with HS were significantly increased compared with those without HS (4.140 vs. 3.140, p = 0.045; 6.812 vs. 5.132, p = 0.049; respectively). No differences were detected between other global network measures of functional connectivity and local network measure. CONCLUSIONS Significant differences in global network measures of functional connectivity based on ASL existed between TLE patients with and without HS. These findings suggest that TLE patients with HS exhibit a more disconnected functional brain network than those without HS.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology and Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Hyung Chan Kim
- Department of Neurology and Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology and Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
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31
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Li L, He L, Harris N, Zhou Y, Engel J, Bragin A. Topographical reorganization of brain functional connectivity during an early period of epileptogenesis. Epilepsia 2021; 62:1231-1243. [PMID: 33720411 DOI: 10.1111/epi.16863] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVE The current study aims to investigate functional brain network representations during the early period of epileptogenesis. METHODS Eighteen rats with the intrahippocampal kainate model of mesial temporal lobe epilepsy were used for this experiment. Functional magnetic resonance imaging (fMRI) measurements were made 1 week after status epilepticus, followed by 2-4-month electrophysiological and video monitoring. Animals were identified as having (1) developed epilepsy (E+, n = 9) or (2) not developed epilepsy (E-, n = 6). Nine additional animals served as controls. Graph theory analysis was performed on the fMRI data to quantify the functional brain networks in all animals prior to the development of epilepsy. Spectrum clustering with the network features was performed to estimate their predictability in epileptogenesis. RESULTS Our data indicated that E+ animals showed an overall increase in functional connectivity strength compared to E- and control animals. Global network features and small-worldness of E- rats were similar to controls, whereas E+ rats demonstrated increased small-worldness, including increased reorganization degree, clustering coefficient, and global efficiency, with reduced shortest pathlength. A notable classification of the combined brain network parameters was found in E+ and E- animals. For the local network parameters, the E- rats showed increased hubs in sensorimotor cortex, and decreased hubness in hippocampus. The E+ rats showed a complete loss of hippocampal hubs, and the appearance of new hubs in the prefrontal cortex. We also observed that lesion severity was not related to epileptogenesis. SIGNIFICANCE Our data provide a view of the reorganization of topographical functional brain networks in the early period of epileptogenesis and how it can significantly predict the development of epilepsy. The differences from E- animals offer a potential means for applying noninvasive neuroimaging tools for the early prediction of epilepsy.
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Affiliation(s)
- Lin Li
- Department of Neurology, University of California, Los Angeles, Los Angeles, California, USA.,Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA
| | - Lingna He
- Department of Computer Science, Zhejiang University of Technology, Zhejiang, China
| | - Neil Harris
- Department of Neurosurgery, UCLA Brain Injury Research Center, University of California, Los Angeles,, Los Angeles, California, USA.,Brain Research Institute, University of California, Los Angeles, Los Angeles, California, USA.,Semel Institute for Neuroscience and Human Behavior, Intellectual Development and Disorders Research Center, University of California, Los Angeles, Los Angeles, California, USA
| | - Yufeng Zhou
- Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA
| | - Jerome Engel
- Department of Neurology, University of California, Los Angeles, Los Angeles, California, USA.,Brain Research Institute, University of California, Los Angeles, Los Angeles, California, USA.,Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Anatol Bragin
- Department of Neurology, University of California, Los Angeles, Los Angeles, California, USA.,Brain Research Institute, University of California, Los Angeles, Los Angeles, California, USA
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Frusque G, Borgnat P, Gonçalves P, Jung J. Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures. Front Neurol 2020; 11:579725. [PMID: 33362688 PMCID: PMC7759641 DOI: 10.3389/fneur.2020.579725] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 09/08/2020] [Indexed: 11/24/2022] Open
Abstract
Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. Second, we compare the activated subgraphs obtained by the BTND method with visual interpretation of SEEG signals recorded in 27 seizures from nine different patients. As a whole, we found that activated subgraphs corresponded to brain regions involved during the course of the seizures and their time course was highly consistent with classical visual interpretation. We believe that the proposed method can complement the visual analysis of SEEG signals recorded during seizures by highlighting and characterizing the most significant parts of epileptic networks with their activation dynamics.
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Affiliation(s)
- Gaëtan Frusque
- Univ Lyon, Inria, CNRS, ENS de Lyon, UCB Lyon 1, LIP UMR 5668, Lyon, France
| | - Pierre Borgnat
- Univ Lyon, CNRS, ENS de Lyon, UCB Lyon 1, Laboratoire de Physique, UMR 5672, Lyon, France
| | - Paulo Gonçalves
- Univ Lyon, Inria, CNRS, ENS de Lyon, UCB Lyon 1, LIP UMR 5668, Lyon, France
| | - Julien Jung
- National Institute of Health and Medical Research U1028/National Center for Scientific Research, Mixed Unit of Research 5292, Lyon Neuroscience Research Center, Lyon, France.,Department of Functional Neurology and Epileptology, Member of the ERN EpiCARE Lyon University Hospital and Lyon 1 University, Lyon, France
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33
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Saggio ML, Crisp D, Scott JM, Karoly P, Kuhlmann L, Nakatani M, Murai T, Dümpelmann M, Schulze-Bonhage A, Ikeda A, Cook M, Gliske SV, Lin J, Bernard C, Jirsa V, Stacey WC. A taxonomy of seizure dynamotypes. eLife 2020; 9:55632. [PMID: 32691734 PMCID: PMC7375810 DOI: 10.7554/elife.55632] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/12/2020] [Indexed: 01/02/2023] Open
Abstract
Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties. Epileptic seizures have been recognized for centuries. But it was only in the 1930s that it was realized that seizures are the result of out-of-control electrical activity in the brain. By placing electrodes on the scalp, doctors can identify when and where in the brain a seizure begins. But they cannot tell much about how the seizure behaves, that is, how it starts, stops or spreads to other areas. This makes it difficult to control and prevent seizures. It also helps explain why almost a third of patients with epilepsy continue to have seizures despite being on medication. Saggio, Crisp et al. have now approached this problem from a new angle using methods adapted from physics and engineering. In these fields, “dynamics research” has been used with great success to predict and control the behavior of complex systems like electrical power grids. Saggio, Crisp et al. reasoned that applying the same approach to the brain would reveal the dynamics of seizures and that such information could then be used to categorize seizures into groups with similar properties. This would in effect create for seizures what the periodic table is for the elements. Applying the dynamics research method to seizure data from more than a hundred patients from across the world revealed 16 types of seizure dynamics. These “dynamotypes” had distinct characteristics. Some were more common than others, and some tended to occur together. Individual patients showed different dynamotypes over time. By constructing a way to classify seizures based on the relationships between the dynamotypes, Saggio, Crisp et al. provide a new tool for clinicians and researchers studying epilepsy. Previous clinical tools have focused on the physical symptoms of a seizure (referred to as the phenotype) or its potential genetic causes (genotype). The current approach complements these tools by adding the dynamotype: how seizures start, spread and stop in the brain. This approach has the potential to lead to new branches of research and better understanding and treatment of seizures.
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Affiliation(s)
- Maria Luisa Saggio
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Dakota Crisp
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Jared M Scott
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Philippa Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Levin Kuhlmann
- Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Faculty of Information Technology, Monash University, Clayton, Australia
| | - Mitsuyoshi Nakatani
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Tomohiko Murai
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Mark Cook
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia.,Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Stephen V Gliske
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Jack Lin
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Christophe Bernard
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - William C Stacey
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States.,Department of Neurology, University of Michigan, Ann Arbor, United States
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Xie W, Wang J, Okoli CTC, He H, Feng F, Zhuang L, Tang P, Zeng L, Jin M. Prevalence and factors of compassion fatigue among Chinese psychiatric nurses: A cross-sectional study. Medicine (Baltimore) 2020; 99:e21083. [PMID: 32702852 PMCID: PMC7373503 DOI: 10.1097/md.0000000000021083] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Compassion fatigue has emerged as a detrimental consequence of experiencing work-related stress among psychiatric nurses, and affected the job performance, emotional and physical health of psychiatric nurses. However, researches on Chinese psychiatric nurses' compassion fatigue are dearth. This cross-sectional study aimed to investigate the prevalence and factors of compassion fatigue among Chinese psychiatric nurses.All participants completed the demographic questionnaire and the Chinese version of Professional Quality of Life Scale (ProQOL-CN). One-way ANOVA, t-tests, Levene test and multiple linear regression analysis were conducted to evaluate factors associated with compassion fatigue.A total of 352 psychiatric nurses in 9 psychiatric hospitals from the Chengdu, Wuhan, and Hefei were surveyed. The mean scores of compassion satisfaction, burnout and secondary traumatic stress were 32.59 ± 7.124, 26.92 ± 6.003 and 25.97 ± 5.365, respectively. Four variables of job satisfaction, exercise, had children, and age range from 36 to 50 years explained 30.7% of the variance in compassion satisfaction. Job satisfaction, sleeping quality, and marital status accounted for 40.4% variables in burnout. Furthermore, job satisfaction, average sleeping quality, and years of nursing experience remained significantly associated with secondary trauma stress, explaining 10.9% of the variance.Compassion satisfaction, burnout and secondary traumatic stress among Chinese psychiatric nurses were at the level of moderate. The higher job satisfaction, healthy lifestyle (high sleep quality and regular exercise), and family support (children, stable and harmonious marital status) positively influenced compassion satisfaction and negatively associated with burnout or secondary traumatic stress.
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Affiliation(s)
- Wanqing Xie
- College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Jialin Wang
- College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | | | - Huijuan He
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, Hubei Province
| | - Fen Feng
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Linli Zhuang
- College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Ping Tang
- College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Li Zeng
- College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Man Jin
- College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
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Abstract
While numerous studies have suggested that large natural, biological, social, and technological networks are fragile, convincing theories are still lacking to explain why natural evolution and human design have failed to optimize networks and avoid fragility. In this paper we provide analytical and numerical evidence that a tradeoff exists in networks with linear dynamics, according to which general measures of robustness and performance are in fact competitive features that cannot be simultaneously optimized. Our findings show that large networks can either be robust to variations of their weights and parameters, or efficient in responding to external stimuli, processing noise, or transmitting information across long distances. As illustrated in our numerical studies, this performance tradeoff seems agnostic to the specific application domain, and in fact it applies to simplified models of ecological, neuronal, and traffic networks.
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