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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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Wang Z, Feng Z, Yuan Y, Guo Z, Cui J, Jiang T. Dynamics of neuronal firing modulated by high-frequency electrical pulse stimulations at axons in rat hippocampus. J Neural Eng 2024; 21:026025. [PMID: 38530299 DOI: 10.1088/1741-2552/ad37da] [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: 09/12/2023] [Accepted: 03/26/2024] [Indexed: 03/27/2024]
Abstract
Objective. The development of electrical pulse stimulations in brain, including deep brain stimulation, is promising for treating various brain diseases. However, the mechanisms of brain stimulations are not yet fully understood. Previous studies have shown that the commonly used high-frequency stimulation (HFS) can increase the firing of neurons and modulate the pattern of neuronal firing. Because the generation of neuronal firing in brain is a nonlinear process, investigating the characteristics of nonlinear dynamics induced by HFS could be helpful to reveal more mechanisms of brain stimulations. The aim of present study is to investigate the fractal properties in the neuronal firing generated by HFS.Approach. HFS pulse sequences with a constant frequency 100 Hz were applied in the afferent fiber tracts of rat hippocampal CA1 region. Unit spikes of both the pyramidal cells and the interneurons in the downstream area of stimulations were recorded. Two fractal indexes-the Fano factor and Hurst exponent were calculated to evaluate the changes of long-range temporal correlations (LRTCs), a typical characteristic of fractal process, in spike sequences of neuronal firing.Mainresults. Neuronal firing at both baseline and during HFS exhibited LRTCs over multiple time scales. In addition, the LRTCs significantly increased during HFS, which was confirmed by simulation data of both randomly shuffled sequences and surrogate sequences.Conclusion. The purely periodic stimulation of HFS pulses, a non-fractal process without LRTCs, can increase rather than decrease the LRTCs in neuronal firing.Significance. The finding provides new nonlinear mechanisms of brain stimulation and suggests that LRTCs could be a new biomarker to evaluate the nonlinear effects of HFS.
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Affiliation(s)
- Zhaoxiang Wang
- Zhejiang Lab, Hangzhou, People's Republic of China
- Key Lab of Biomedical Engineering for Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Zhouyan Feng
- Key Lab of Biomedical Engineering for Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yue Yuan
- Zhejiang Lab, Hangzhou, People's Republic of China
- Key Lab of Biomedical Engineering for Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Zheshan Guo
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, Hainan, People's Republic of China
| | - Jian Cui
- Zhejiang Lab, Hangzhou, People's Republic of China
| | - Tianzi Jiang
- Zhejiang Lab, Hangzhou, People's Republic of China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
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3
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Myrov V, Siebenhühner F, Juvonen JJ, Arnulfo G, Palva S, Palva JM. Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture. Commun Biol 2024; 7:405. [PMID: 38570628 PMCID: PMC10991572 DOI: 10.1038/s42003-024-06083-y] [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: 09/05/2023] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for the direct quantification of rhythmicity. We applied pACF to human intracerebral stereoelectroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.
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Affiliation(s)
- Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki, Finland
| | - Joonas J Juvonen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, Genoa, Italy
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
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4
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Fink L, Simola J, Tavano A, Lange E, Wallot S, Laeng B. From pre-processing to advanced dynamic modeling of pupil data. Behav Res Methods 2024; 56:1376-1412. [PMID: 37351785 PMCID: PMC10991010 DOI: 10.3758/s13428-023-02098-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/20/2023] [Indexed: 06/24/2023]
Abstract
The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.
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Affiliation(s)
- Lauren Fink
- Department of Music, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, 60322, Frankfurt am Main, Germany.
- Department of Psychology, Neuroscience & Behavior, McMaster University, 1280 Main St. West, Hamilton, Ontario, L8S 4L8, Canada.
| | - Jaana Simola
- Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
- Department of Education, University of Helsinki, Helsinki, Finland
| | - Alessandro Tavano
- Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
| | - Elke Lange
- Department of Music, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, 60322, Frankfurt am Main, Germany
| | - Sebastian Wallot
- Department of Literature, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Institute for Sustainability Education and Psychologyy, Leuphana University, Lüneburg, Germany
| | - Bruno Laeng
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary studies in Rhythm, Time, and Motion, University of Oslo, Oslo, Norway
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Mariani B, Nicoletti G, Barzon G, Ortiz Barajas MC, Shukla M, Guevara R, Suweis SS, Gervain J. Prenatal experience with language shapes the brain. SCIENCE ADVANCES 2023; 9:eadj3524. [PMID: 37992161 PMCID: PMC10664997 DOI: 10.1126/sciadv.adj3524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/20/2023] [Indexed: 11/24/2023]
Abstract
Human infants acquire language with notable ease compared to adults, but the neural basis of their remarkable brain plasticity for language remains little understood. Applying a scaling analysis of neural oscillations to address this question, we show that newborns' electrophysiological activity exhibits increased long-range temporal correlations after stimulation with speech, particularly in the prenatally heard language, indicating the early emergence of brain specialization for the native language.
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Affiliation(s)
- Benedetta Mariani
- Department of Physics and Astronomy, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Giorgio Nicoletti
- Department of Physics and Astronomy, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of Mathematics, University of Padua, Padua, Italy
| | - Giacomo Barzon
- Department of Physics and Astronomy, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
| | | | - Mohinish Shukla
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
- Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Ramón Guevara
- Department of Physics and Astronomy, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
| | - Samir Simon Suweis
- Department of Physics and Astronomy, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Judit Gervain
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Integrative Neuroscience and Cognition Center, CNRS and Université Paris Cité, Paris, France
- Department of Developmental and Social Psychology, University of Padua, Padua, Italy
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6
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Wang SH, Siebenhühner F, Arnulfo G, Myrov V, Nobili L, Breakspear M, Palva S, Palva JM. Critical-like Brain Dynamics in a Continuum from Second- to First-Order Phase Transition. J Neurosci 2023; 43:7642-7656. [PMID: 37816599 PMCID: PMC10634584 DOI: 10.1523/jneurosci.1889-22.2023] [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: 10/05/2022] [Revised: 06/07/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Abstract
The classic brain criticality hypothesis postulates that the brain benefits from operating near a continuous second-order phase transition. Slow feedback regulation of neuronal activity could, however, lead to a discontinuous first-order transition and thereby bistable activity. Observations of bistability in awake brain activity have nonetheless remained scarce and its functional significance unclear. Moreover, there is no empirical evidence to support the hypothesis that the human brain could flexibly operate near either a first- or second-order phase transition despite such a continuum being common in models. Here, using computational modeling, we found bistable synchronization dynamics to emerge through elevated positive feedback and occur exclusively in a regimen of critical-like dynamics. We then assessed bistability in vivo with resting-state MEG in healthy adults (7 females, 11 males) and stereo-electroencephalography in epilepsy patients (28 females, 36 males). This analysis revealed that a large fraction of the neocortices exhibited varying degrees of bistability in neuronal oscillations from 3 to 200 Hz. In line with our modeling results, the neuronal bistability was positively correlated with classic assessment of brain criticality across narrow-band frequencies. Excessive bistability was predictive of epileptic pathophysiology in the patients, whereas moderate bistability was positively correlated with task performance in the healthy subjects. These empirical findings thus reveal the human brain as a one-of-a-kind complex system that exhibits critical-like dynamics in a continuum between continuous and discontinuous phase transitions.SIGNIFICANCE STATEMENT In the model, while synchrony per se was controlled by connectivity, increasing positive local feedback led to gradually emerging bistable synchrony with scale-free dynamics, suggesting a continuum between second- and first-order phase transitions in synchrony dynamics inside a critical-like regimen. In resting-state MEG and SEEG, bistability of ongoing neuronal oscillations was pervasive across brain areas and frequency bands and was observed only with concurring critical-like dynamics as the modeling predicted. As evidence for functional relevance, moderate bistability was positively correlated with executive functioning in the healthy subjects, and excessive bistability was associated with epileptic pathophysiology. These findings show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.
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Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Doctoral Programme Brain & Mind, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, 16136 Genoa, Italy
| | - Vladislav Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Lino Nobili
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Children's Sciences, University of Genoa, 16136 Genoa, Italy
- Child Neuropsychiatry Unit, Istituto Di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, 16147 Genoa, Italy
- Centre of Epilepsy Surgery "C. Munari," Department of Neuroscience, Niguarda Hospital, 20162 Milan, Italy
| | - Michael Breakspear
- College of Engineering, Science and Environment, College of Health and Medicine, University of Newcastle, Callaghan, 2308 Australia
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
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7
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Sysoeva O, Maximenko V, Kuc A, Voinova V, Martynova O, Hramov A. Abnormal spectral and scale-free properties of resting-state EEG in girls with Rett syndrome. Sci Rep 2023; 13:12932. [PMID: 37558701 PMCID: PMC10412611 DOI: 10.1038/s41598-023-39398-7] [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: 11/14/2022] [Accepted: 07/25/2023] [Indexed: 08/11/2023] Open
Abstract
Spontaneous EEG contains important information about neuronal network properties that is valuable for understanding different neurological and psychiatric conditions. Rett syndrome (RTT) is a rare neurodevelopmental disorder, caused by mutation in the MECP2 gene. RTT is characterized by severe motor impairments that prevent adequate assessment of cognitive functions. Here we probe EEG parameters obtained in no visual input condition from a 28-channels system in 23 patients with Rett Syndrome and 38 their typically developing peers aged 3-17 years old. Confirming previous results, RTT showed a fronto-central theta power (4-6.25 Hz) increase that correlates with a progression of the disease. Alpha power (6.75-11.75 Hz) across multiple regions was, on the contrary, decreased in RTT, also corresponding to general background slowing reported previously. Among novel results we found an increase in gamma power (31-39.5 Hz) across frontal, central and temporal electrodes, suggesting elevated excitation/inhibition ratio. Long-range temporal correlation measured by detrended fluctuation analysis within 6-13 Hz was also increased, pointing to a more predictable oscillation pattern in RTT. Overall measured EEG parameters allow to differentiate groups with high accuracy, ROC AUC value of 0.92 ± 0.08, indicating clinical relevance.
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Affiliation(s)
- Olga Sysoeva
- Center for Cognitive Sciences, Sirius University of Science and Technology, Sochi, Russia, 354340.
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Butlerova St. 5a, Moscow, Russia, 117485.
| | - Vladimir Maximenko
- Artificial Intelligence and Neurotechnology Lab, Privolzhsky Research Medical University, Nizhny Novgorod, Russia, 603950
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016
| | - Alexander Kuc
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016
| | - Victoria Voinova
- Veltischev Research and Clinical Institute for Pediatrics of the Pirogov, Russian National Research Medical University, Ministry of Health of Russian Federation, Moscow, Russia, 125412
- Mental Health Research Center, Moscow, Russia, 117152
| | - Olga Martynova
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Butlerova St. 5a, Moscow, Russia, 117485
| | - Alexander Hramov
- Artificial Intelligence and Neurotechnology Lab, Privolzhsky Research Medical University, Nizhny Novgorod, Russia, 603950
- Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016
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Fuscà M, Siebenhühner F, Wang SH, Myrov V, Arnulfo G, Nobili L, Palva JM, Palva S. Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data. Nat Commun 2023; 14:4736. [PMID: 37550300 PMCID: PMC10406818 DOI: 10.1038/s41467-023-40056-9] [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: 12/07/2022] [Accepted: 07/10/2023] [Indexed: 08/09/2023] Open
Abstract
Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.
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Affiliation(s)
- Marco Fuscà
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Felix Siebenhühner
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Sheng H Wang
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- CEA, NeuroSpin, Gif-sur-Yvette, France
- MIND team, Inria, Université Paris-Saclay, Bures-sur-Yvette, France
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Dept. of Informatics, Bioengineering, Robotics and System engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS, Istituto G. Gaslini, Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - J Matias Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Satu Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
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9
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Gervais C, Boucher LP, Villar GM, Lee U, Duclos C. A scoping review for building a criticality-based conceptual framework of altered states of consciousness. Front Syst Neurosci 2023; 17:1085902. [PMID: 37304151 PMCID: PMC10248073 DOI: 10.3389/fnsys.2023.1085902] [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: 10/31/2022] [Accepted: 05/08/2023] [Indexed: 06/13/2023] Open
Abstract
The healthy conscious brain is thought to operate near a critical state, reflecting optimal information processing and high susceptibility to external stimuli. Conversely, deviations from the critical state are hypothesized to give rise to altered states of consciousness (ASC). Measures of criticality could therefore be an effective way of establishing the conscious state of an individual. Furthermore, characterizing the direction of a deviation from criticality may enable the development of treatment strategies for pathological ASC. The aim of this scoping review is to assess the current evidence supporting the criticality hypothesis, and the use of criticality as a conceptual framework for ASC. Using the PRISMA guidelines, Web of Science and PubMed were searched from inception to February 7th 2022 to find articles relating to measures of criticality across ASC. N = 427 independent papers were initially found on the subject. N = 378 were excluded because they were either: not related to criticality; not related to consciousness; not presenting results from a primary study; presenting model data. N = 49 independent papers were included in the present research, separated in 7 sub-categories of ASC: disorders of consciousness (DOC) (n = 5); sleep (n = 13); anesthesia (n = 18); epilepsy (n = 12); psychedelics and shamanic state of consciousness (n = 4); delirium (n = 1); meditative state (n = 2). Each category included articles suggesting a deviation of the critical state. While most studies were only able to identify a deviation from criticality without being certain of its direction, the preliminary consensus arising from the literature is that non-rapid eye movement (NREM) sleep reflects a subcritical state, epileptic seizures reflect a supercritical state, and psychedelics are closer to the critical state than normal consciousness. This scoping review suggests that, though the literature is limited and methodologically inhomogeneous, ASC are characterized by a deviation from criticality, though its direction is not clearly reported in a majority of studies. Criticality could become, with more extensive research, an effective and objective way to characterize ASC, and help identify therapeutic avenues to improve criticality in pathological brain states. Furthermore, we suggest how anesthesia and psychedelics could potentially be used as neuromodulation techniques to restore criticality in DOC.
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Affiliation(s)
- Charles Gervais
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
- Centre for Advanced Research in Sleep Medicine & Integrated Trauma Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montréal, QC, Canada
| | - Louis-Philippe Boucher
- Centre for Advanced Research in Sleep Medicine & Integrated Trauma Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montréal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montréal, QC, Canada
| | - Guillermo Martinez Villar
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
- Centre for Advanced Research in Sleep Medicine & Integrated Trauma Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montréal, QC, Canada
- Department of Biomedical Sciences, Université de Montréal, Montréal, QC, Canada
| | - UnCheol Lee
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Catherine Duclos
- Centre for Advanced Research in Sleep Medicine & Integrated Trauma Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montréal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montréal, QC, Canada
- Department of Anesthesiology and Pain Medicine, Université de Montréal, Montréal, QC, Canada
- CIFAR Azrieli Global Scholars Program, Toronto, ON, Canada
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10
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van Nifterick AM, Mulder D, Duineveld DJ, Diachenko M, Scheltens P, Stam CJ, van Kesteren RE, Linkenkaer-Hansen K, Hillebrand A, Gouw AA. Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer's disease patients. Sci Rep 2023; 13:7419. [PMID: 37150756 PMCID: PMC10164744 DOI: 10.1038/s41598-023-33973-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023] Open
Abstract
An early disruption of neuronal excitation-inhibition (E-I) balance in preclinical animal models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E-I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space. E-I balance was investigated by detrended fluctuation analysis (DFA), a functional E/I (fE/I) algorithm, and the aperiodic exponent of the power spectrum. We found a disrupted E-I ratio in AD dementia patients specifically, by a lower DFA, and a shift towards higher excitation, by a higher fE/I and a lower aperiodic exponent. Healthy subjects showed lower fE/I ratios (< 1.0) than reported in previous literature, not explained by age or choice of an arbitrary threshold parameter, which warrants caution in interpretation of fE/I results. Correlation analyses showed that a lower DFA (E-I imbalance) and a lower aperiodic exponent (more excitation) was associated with a worse cognitive score in AD dementia patients. In contrast, a higher DFA in the hippocampi of MCI patients was associated with a worse cognitive score. This MEG-study showed E-I imbalance, likely due to increased excitation, in AD dementia, but not in early stage AD patients. To accurately determine the direction of shift in E-I balance, validations of the currently used markers and additional in vivo markers of E-I are required.
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Affiliation(s)
- Anne M van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands.
| | - Danique Mulder
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Denise J Duineveld
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Marina Diachenko
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Ronald E van Kesteren
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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11
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Gunnarsdottir KM, Li A, Smith RJ, Kang JY, Korzeniewska A, Crone NE, Rouse AG, Cheng JJ, Kinsman MJ, Landazuri P, Uysal U, Ulloa CM, Cameron N, Cajigas I, Jagid J, Kanner A, Elarjani T, Bicchi MM, Inati S, Zaghloul KA, Boerwinkle VL, Wyckoff S, Barot N, Gonzalez-Martinez J, Sarma SV. Source-sink connectivity: a novel interictal EEG marker for seizure localization. Brain 2022; 145:3901-3915. [PMID: 36412516 PMCID: PMC10200292 DOI: 10.1093/brain/awac300] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 07/05/2022] [Accepted: 08/01/2022] [Indexed: 07/26/2023] Open
Abstract
Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.
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Affiliation(s)
| | - Adam Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Rachel J Smith
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Joon-Yi Kang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Anna Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Adam G Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Jennifer J Cheng
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Michael J Kinsman
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Patrick Landazuri
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Utku Uysal
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Carol M Ulloa
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Nathaniel Cameron
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Iahn Cajigas
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Jonathan Jagid
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Andres Kanner
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Turki Elarjani
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Manuel Melo Bicchi
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sara Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Varina L Boerwinkle
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Sarah Wyckoff
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Niravkumar Barot
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | | | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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12
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From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans. Transl Psychiatry 2022; 12:467. [PMID: 36344497 PMCID: PMC9640647 DOI: 10.1038/s41398-022-02218-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/22/2022] [Accepted: 10/06/2022] [Indexed: 11/09/2022] Open
Abstract
Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially underlie a spectrum of neuropsychiatric conditions (e.g., Autism, Schizophrenia, Downs' Syndrome, intellectual disability). Hypotheses related to E/I dysfunction have the potential to provide cross-diagnostic explanations and to combine genetic and neurological evidence that exists within and between psychiatric conditions. However, the hypothesis has been difficult to test because: (1) it lacks specificity-an E/I dysfunction could pertain to any level in the neural system- neurotransmitters, single neurons/receptors, local networks of neurons, or global brain balance - most researchers do not define the level at which they are examining E/I function; (2) We lack validated methods for assessing E/I function at any of these neural levels in humans. As a result, it has not been possible to reliably or robustly test the E/I hypothesis of psychiatric disorders in a large cohort or longitudinal patient studies. Currently available, in vivo markers of E/I in humans either carry significant risks (e.g., deep brain electrode recordings or using Positron Emission Tomography (PET) with radioactive tracers) and/or are highly restrictive (e.g., limited spatial extent for Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Spectroscopy (MRS). More recently, a range of novel Electroencephalography (EEG) features has been described, which could serve as proxy markers for E/I at a given level of inference. Thus, in this perspective review, we survey the theories and experimental evidence underlying 6 novel EEG markers and their biological underpinnings at a specific neural level. These cheap-to-record and scalable proxy markers may offer clinical utility for identifying subgroups within and between diagnostic categories, thus directing more tailored sub-grouping and, therefore, treatment strategies. However, we argue that studies in clinical populations are premature. To maximize the potential of prospective EEG markers, we first need to understand the link between underlying E/I mechanisms and measurement techniques.
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13
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Juarez-Martinez EL, van Andel DM, Sprengers JJ, Avramiea AE, Oranje B, Scheepers FE, Jansen FE, Mansvelder HD, Linkenkaer-Hansen K, Bruining H. Bumetanide Effects on Resting-State EEG in Tuberous Sclerosis Complex in Relation to Clinical Outcome: An Open-Label Study. Front Neurosci 2022; 16:879451. [PMID: 35645706 PMCID: PMC9134117 DOI: 10.3389/fnins.2022.879451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 04/15/2022] [Indexed: 12/05/2022] Open
Abstract
Neuronal excitation-inhibition (E/I) imbalances are considered an important pathophysiological mechanism in neurodevelopmental disorders. Preclinical studies on tuberous sclerosis complex (TSC), suggest that altered chloride homeostasis may impair GABAergic inhibition and thereby E/I-balance regulation. Correction of chloride homeostasis may thus constitute a treatment target to alleviate behavioral symptoms. Recently, we showed that bumetanide-a chloride-regulating agent-improved behavioral symptoms in the open-label study Bumetanide to Ameliorate Tuberous Sclerosis Complex Hyperexcitable Behaviors trial (BATSCH trial; Eudra-CT: 2016-002408-13). Here, we present resting-state EEG as secondary analysis of BATSCH to investigate associations between EEG measures sensitive to network-level changes in E/I balance and clinical response to bumetanide. EEGs of 10 participants with TSC (aged 8-21 years) were available. Spectral power, long-range temporal correlations (LRTC), and functional E/I ratio (fE/I) in the alpha-frequency band were compared before and after 91 days of treatment. Pre-treatment measures were compared against 29 typically developing children (TDC). EEG measures were correlated with the Aberrant Behavioral Checklist-Irritability subscale (ABC-I), the Social Responsiveness Scale-2 (SRS-2), and the Repetitive Behavior Scale-Revised (RBS-R). At baseline, TSC showed lower alpha-band absolute power and fE/I than TDC. Absolute power increased through bumetanide treatment, which showed a moderate, albeit non-significant, correlation with improvement in RBS-R. Interestingly, correlations between baseline EEG measures and clinical outcomes suggest that most responsiveness might be expected in children with network characteristics around the E/I balance point. In sum, E/I imbalances pointing toward an inhibition-dominated network are present in TSC. We established neurophysiological effects of bumetanide although with an inconclusive relationship with clinical improvement. Nonetheless, our results further indicate that baseline network characteristics might influence treatment response. These findings highlight the possible utility of E/I-sensitive EEG measures to accompany new treatment interventions for TSC. Clinical Trial Registration EU Clinical Trial Register, EudraCT 2016-002408-13 (www.clinicaltrialsregister.eu/ctr-search/trial/2016-002408-13/NL). Registered 25 July 2016.
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Affiliation(s)
- Erika L. Juarez-Martinez
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
- Child and Adolescent Psychiatry and Psychosocial Care, Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Dorinde M. van Andel
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Jan J. Sprengers
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Arthur-Ervin Avramiea
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Bob Oranje
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Floortje E. Scheepers
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Floor E. Jansen
- Department of Pediatric Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Huibert D. Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Hilgo Bruining
- Child and Adolescent Psychiatry and Psychosocial Care, Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht, Netherlands
- N=You Neurodevelopmental Precision Center, Amsterdam Neuroscience, Amsterdam Reproduction and Development, Amsterdam UMC, Amsterdam, Netherlands
- Levvel, Academic Center for Child and Adolescent Psychiatry, Amsterdam, Netherlands
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14
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Houtman SJ, Lammertse HCA, van Berkel AA, Balagura G, Gardella E, Ramautar JR, Reale C, Møller RS, Zara F, Striano P, Misra-Isrie M, van Haelst MM, Engelen M, van Zuijen TL, Mansvelder HD, Verhage M, Bruining H, Linkenkaer-Hansen K. STXBP1 Syndrome Is Characterized by Inhibition-Dominated Dynamics of Resting-State EEG. Front Physiol 2022; 12:775172. [PMID: 35002760 PMCID: PMC8733612 DOI: 10.3389/fphys.2021.775172] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/23/2021] [Indexed: 11/19/2022] Open
Abstract
STXBP1 syndrome is a rare neurodevelopmental disorder caused by heterozygous variants in the STXBP1 gene and is characterized by psychomotor delay, early-onset developmental delay, and epileptic encephalopathy. Pathogenic STXBP1 variants are thought to alter excitation-inhibition (E/I) balance at the synaptic level, which could impact neuronal network dynamics; however, this has not been investigated yet. Here, we present the first EEG study of patients with STXBP1 syndrome to quantify the impact of the synaptic E/I dysregulation on ongoing brain activity. We used high-frequency-resolution analyses of classical and recently developed methods known to be sensitive to E/I balance. EEG was recorded during eyes-open rest in children with STXBP1 syndrome (n = 14) and age-matched typically developing children (n = 50). Brain-wide abnormalities were observed in each of the four resting-state measures assessed here: (i) slowing of activity and increased low-frequency power in the range 1.75–4.63 Hz, (ii) increased long-range temporal correlations in the 11–18 Hz range, (iii) a decrease of our recently introduced measure of functional E/I ratio in a similar frequency range (12–24 Hz), and (iv) a larger exponent of the 1/f-like aperiodic component of the power spectrum. Overall, these findings indicate that large-scale brain activity in STXBP1 syndrome exhibits inhibition-dominated dynamics, which may be compensatory to counteract local circuitry imbalances expected to shift E/I balance toward excitation, as observed in preclinical models. We argue that quantitative EEG investigations in STXBP1 and other neurodevelopmental disorders are a crucial step to understand large-scale functional consequences of synaptic E/I perturbations.
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Affiliation(s)
- Simon J Houtman
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Hanna C A Lammertse
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.,Department of Human Genetics, Amsterdam UMC, Amsterdam, Netherlands
| | - Annemiek A van Berkel
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.,Department of Human Genetics, Amsterdam UMC, Amsterdam, Netherlands
| | - Ganna Balagura
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.,IRCCS Istituto Giannina Gaslini, Genova, Italy.,Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Elena Gardella
- Department of Epilepsy Genetics and Personalized Treatment, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Regional Health Research, University of Southern Denmark, Odense, Denmark.,Member of the ERN EpiCARE
| | - Jennifer R Ramautar
- Child and Adolescent Psychiatry and Psychosocial Care, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Chiara Reale
- Department of Epilepsy Genetics and Personalized Treatment, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Clinical and Experimental Medicine, Epilepsy Center, University Hospital of Messina, Messina, Italy
| | - Rikke S Møller
- Department of Epilepsy Genetics and Personalized Treatment, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Regional Health Research, University of Southern Denmark, Odense, Denmark.,Member of the ERN EpiCARE
| | - Federico Zara
- IRCCS Istituto Giannina Gaslini, Genova, Italy.,Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Pasquale Striano
- IRCCS Istituto Giannina Gaslini, Genova, Italy.,Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Mala Misra-Isrie
- Department of Human Genetics, Amsterdam UMC, Amsterdam, Netherlands
| | | | - Marc Engelen
- Department of Pediatric Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Titia L van Zuijen
- Research Institute of Child Development and Education, University of Amsterdam, Amsterdam, Netherlands
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Matthijs Verhage
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.,Department of Human Genetics, Amsterdam UMC, Amsterdam, Netherlands
| | - Hilgo Bruining
- Child and Adolescent Psychiatry and Psychosocial Care, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,N=You Neurodevelopmental Precision Center, Amsterdam Neuroscience, Amsterdam Reproduction and Development, Amsterdam UMC, Amsterdam, Netherlands.,Levvel, Center for Child and Adolescent Psychiatry, Amsterdam, Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
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15
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Tong X, Wang J, Qin L, Zhou J, Guan Y, Zhai F, Teng P, Wang M, Li T, Wang X, Luan G. Analysis of power spectrum and phase lag index changes following deep brain stimulation of the anterior nucleus of the thalamus in patients with drug-resistant epilepsy: A retrospective study. Seizure 2022; 96:6-12. [PMID: 35042005 DOI: 10.1016/j.seizure.2022.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/18/2021] [Accepted: 01/07/2022] [Indexed: 11/26/2022] Open
Abstract
OBJECTIVES The mechanisms underlying the anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) for the treatment of drug-resistant epilepsy (DRE) have not been fully explored. The present study aimed to measure the changes in whole-brain activity generated by ANT DBS using interictal electroencephalography (EEG). MATERIALS AND METHODS Interictal EEG signals were retrospectively collected in 20 DRE patients who underwent ANT DBS surgery. Patients were classified as responders or non-responders depending on their response to ANT DBS treatment. The power spectrum (PS) and Phase Lag Index (PLI) were determined and data analyzed using a paired sample t-test to evaluate activity differences between pre-and-post-treatment on different frequency categories. Student's t-test, Mann-Whitney test (non-parametric test) and Fisher exact test were used to compare groups in terms of clinical variables and EEG metrics. P values < 0.05 were considered statistically significant, and FDR-corrected values were used for multiple testing. RESULTS PS analysis revealed that whole-brain spectral power had a significant decrease in the beta (p = 0.005) and gamma (p = 0.037) bands following ANT DBS treatment in responders. The analysis of scalp topographic images of all patients showed that ANT DBS decreases PS in the beta band at the F3, F7 and Cz electrode sites. The findings indicated a decrease in PS in the gamma band at the Fp2, F3, Cz, T3, T5 and Oz electrode sites. After ANT DBS treatment, PLI analysis showed a significant decrease in PLI between Fp1 and T3 in the gamma band in responders. CONCLUSION The findings showed that ANT DBS induces a decrease in power in the left frontal lobe, left temporal lobe and midline areas in the beta and gamma bands. Lower whole-brain power in the beta and gamma bands can be used as biomarkers for a favorable therapeutic response to ANT DBS, and decreased synchronization between the left frontal pole and temporal lobe in the gamma band can also be used as a biomarker for effective clinical stimulation to guide postoperative programming.
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Affiliation(s)
- Xuezhi Tong
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Jing Wang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Lang Qin
- McGovern Institute for Brain Research, Peking University, Beijing 100093, China; Center for MRI Research, Peking University, Beijing 100093, China
| | - Jian Zhou
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Yuguang Guan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Feng Zhai
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Pengfei Teng
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Mengyang Wang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China
| | - Tianfu Li
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China; Beijing Key Laboratory of Epilepsy, Beijing 100093, China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China; Beijing Key Laboratory of Epilepsy, Beijing 100093, China; Epilepsy Institute, Beijing Institute for Brain Disorders, Beijing 100093, China
| | - Guoming Luan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing 100093, China; Beijing Key Laboratory of Epilepsy, Beijing 100093, China; Epilepsy Institute, Beijing Institute for Brain Disorders, Beijing 100093, China
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16
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Huang J, Ahlers E, Bogatsch H, Böhme P, Ethofer T, Fallgatter AJ, Gallinat J, Hegerl U, Heuser I, Hoffmann K, Kittel-Schneider S, Reif A, Schöttle D, Unterecker S, Gärtner M, Strauß M. The role of comorbid depressive symptoms on long-range temporal correlations in resting EEG in adults with ADHD. Eur Arch Psychiatry Clin Neurosci 2022; 272:1421-1435. [PMID: 35781841 PMCID: PMC9653316 DOI: 10.1007/s00406-022-01452-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/12/2022] [Indexed: 11/23/2022]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder, characterized by core symptoms of inattention, hyperactivity and impulsivity. Comorbid depression is commonly observed in ADHD-patients. Psychostimulants are recommended as first-line treatment for ADHD. Aberrant long-range temporal correlations (LRTCs) of neuronal activities in resting-state are known to be associated with disorganized thinking and concentrating difficulties (typical in ADHD) and with maladaptive thinking (typical in depression). It has yet to be examined whether (1) LRTC occur in ADHD-patients, and if so, (2) whether LRTC might be a competent biomarker in ADHD comorbid with current depression and (3) how depression affects psychostimulant therapy of ADHD symptoms. The present study registered and compared LRTCs in different EEG frequency bands in 85 adults with ADHD between groups with (n = 28) and without (n = 57) additional depressive symptoms at baseline. Treatment-related changes in ADHD, depressive symptoms and LRTC were investigated in the whole population and within each group. Our results revealed significant LRTCs existed in all investigated frequency bands. There were, however, no significant LRTC-differences between ADHD-patients with and without depressive symptoms at baseline and no LRTC-changes following treatment. However, depressed ADHD patients did seem to benefit more from the therapy with psychostimulant based on self-report.
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Affiliation(s)
- Jue Huang
- Department of Psychiatry and Psychotherapy, University of Leipzig, 04103, Leipzig, Germany.
| | - Eike Ahlers
- grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin, 10117 Berlin, Germany
| | - Holger Bogatsch
- grid.9647.c0000 0004 7669 9786Clinical Trial Centre Leipzig, Faculty of Medicine, University of Leipzig, 04107 Leipzig, Germany
| | - Pierre Böhme
- grid.411091.cDepartment of Psychiatry Psychotherapy and Preventive Medicine, University Hospital of Bochum, 44791 Bochum, Germany
| | - Thomas Ethofer
- grid.411544.10000 0001 0196 8249Department of Biomedical Magnetic Resonance, University Hospital of Tübingen, 72076 Tübingen, Germany ,grid.10392.390000 0001 2190 1447Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen, 72076 Tübingen, Germany
| | - Andreas J. Fallgatter
- grid.10392.390000 0001 2190 1447Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen, 72076 Tübingen, Germany
| | - Jürgen Gallinat
- grid.13648.380000 0001 2180 3484Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Ulrich Hegerl
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital of Frankfurt – Goethe University, 60528 Frankfurt am Main, Germany
| | - Isabella Heuser
- grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin, 10117 Berlin, Germany
| | - Knut Hoffmann
- grid.411091.cDepartment of Psychiatry Psychotherapy and Preventive Medicine, University Hospital of Bochum, 44791 Bochum, Germany
| | - Sarah Kittel-Schneider
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital of Frankfurt – Goethe University, 60528 Frankfurt am Main, Germany ,grid.411760.50000 0001 1378 7891Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, 97080 Würzburg, Germany
| | - Andreas Reif
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital of Frankfurt – Goethe University, 60528 Frankfurt am Main, Germany
| | - Daniel Schöttle
- grid.13648.380000 0001 2180 3484Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Stefan Unterecker
- grid.411760.50000 0001 1378 7891Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, 97080 Würzburg, Germany
| | - Matti Gärtner
- grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin, 10117 Berlin, Germany ,grid.466457.20000 0004 1794 7698MSB Medical School Berlin, 14179 Berlin, Germany
| | - Maria Strauß
- grid.9647.c0000 0004 7669 9786Department of Psychiatry and Psychotherapy, University of Leipzig, 04103 Leipzig, Germany
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17
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Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.05.126] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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18
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Smith RJ, Hu DK, Shrey DW, Rajaraman R, Hussain SA, Lopour BA. Computational characteristics of interictal EEG as objective markers of epileptic spasms. Epilepsy Res 2021; 176:106704. [PMID: 34218209 DOI: 10.1016/j.eplepsyres.2021.106704] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/26/2021] [Accepted: 06/23/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE Favorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five categories of computational electroencephalographic (EEG) measures as markers of ES. METHODS We measured 1) amplitude, 2) power spectra, 3) Shannon entropy and permutation entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity using cross-correlation and phase lag index (PLI). EEG data were analyzed from ES patients (n = 40 patients) and healthy controls (n = 20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient. RESULTS In ES patients, EEG amplitude was significantly higher in all electrodes when compared to controls. Shannon and permutation entropy were lower in ES patients than control subjects. The DFA intercept values in ES patients were significantly higher than control subjects, while DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls when based on both cross-correlation and PLI. Significance for all statistical tests was p < 0.05, adjusted for multiple comparisons using the Benjamini-Hochberg procedure as appropriate. Finally, using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96). CONCLUSIONS Computational EEG features successfully distinguish ES patients from controls in a large, blinded study. SIGNIFICANCE These objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.
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Affiliation(s)
- Rachel J Smith
- Department of Biomedical Engineering, University of California, Irvine, CA, United States
| | - Derek K Hu
- Department of Biomedical Engineering, University of California, Irvine, CA, United States
| | - Daniel W Shrey
- Division of Neurology, Children's Hospital of Orange County, Orange, CA, United States; Department of Pediatrics, University of California, Irvine, CA, United States
| | - Rajsekar Rajaraman
- Division of Pediatric Neurology, University of California, Los Angeles, CA, United States
| | - Shaun A Hussain
- Division of Pediatric Neurology, University of California, Los Angeles, CA, United States
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, CA, United States.
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19
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Cruz G, Grent-'t-Jong T, Krishnadas R, Palva JM, Palva S, Uhlhaas PJ. Long range temporal correlations (LRTCs) in MEG-data during emerging psychosis: Relationship to symptoms, medication-status and clinical trajectory. Neuroimage Clin 2021; 31:102722. [PMID: 34130193 PMCID: PMC8209846 DOI: 10.1016/j.nicl.2021.102722] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/30/2021] [Accepted: 06/04/2021] [Indexed: 12/24/2022]
Abstract
Long-Range Temporal Correlations (LRTCs) index the capacity of the brain to optimally process information. Previous research has shown that patients with chronic schizophrenia present altered LRTCs at alpha and beta oscillations. However, it is currently unclear at which stage of schizophrenia aberrant LRTCs emerge. To address this question, we investigated LRTCs in resting-state magnetoencephalographic (MEG) recordings obtained from patients with affective disorders and substance abuse (clinically at low-risk of psychosis, CHR-N), patients at clinical high-risk of psychosis (CHR-P) (n = 115), as well as patients with a first episode (FEP) (n = 25). Matched healthy controls (n = 47) served as comparison group. LRTCs were obtained for frequencies from 4 to 40 Hz and correlated with clinical and neuropsychological data. In addition, we examined the relationship between LRTCs and transition to psychosis in CHR-P participants, and the relationship between LRTC and antipsychotic medication in FEP participants. Our results show that participants from the clinical groups have similar LRTCs to controls. In addition, LRTCs did not correlate with clinical and neurocognitive variables across participants nor did LRTCs predict transition to psychosis. Therefore, impaired LRTCs do not reflect a feature in the clinical trajectory of psychosis. Nevertheless, reduced LRTCs in the beta-band over posterior sensors of medicated FEP participants indicate that altered LRTCs may appear at the onset of the illness. Future studies are needed to elucidate the role of anti-psychotic medication in altered LRTCs.
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Affiliation(s)
- Gabriela Cruz
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
| | - Tineke Grent-'t-Jong
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Rajeev Krishnadas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
| | - J Matias Palva
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Neuroscience Centre, Helsinki Institute of Life Science, University of Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland
| | - Satu Palva
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Neuroscience Centre, Helsinki Institute of Life Science, University of Helsinki, Finland
| | - Peter J Uhlhaas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
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20
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Differential classification of states of consciousness using envelope- and phase-based functional connectivity. Neuroimage 2021; 237:118171. [PMID: 34000405 DOI: 10.1016/j.neuroimage.2021.118171] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/06/2021] [Accepted: 05/09/2021] [Indexed: 12/14/2022] Open
Abstract
The development of sophisticated computational tools to quantify changes in the brain's oscillatory dynamics across states of consciousness have included both envelope- and phase-based measures of functional connectivity (FC), but there are very few direct comparisons of these techniques using the same dataset. The goal of this study was to compare an envelope-based (i.e. Amplitude Envelope Correlation, AEC) and a phase-based (i.e. weighted Phase Lag Index, wPLI) measure of FC in their classification of states of consciousness. Nine healthy participants underwent a three-hour experimental anesthetic protocol with propofol induction and isoflurane maintenance, in which five minutes of 128-channel electroencephalography were recorded before, during, and after anesthetic-induced unconsciousness, at the following time points: Baseline; light sedation with propofol (Light Sedation); deep unconsciousness following three hours of surgical levels of anesthesia with isoflurane (Unconscious); five minutes prior to the recovery of consciousness (Pre-ROC); and three hours following the recovery of consciousness (Recovery). Support vector machine classification was applied to the source-localized EEG in the alpha (8-13 Hz) frequency band in order to investigate the ability of AEC and wPLI (separately and together) to discriminate i) the four states from Baseline; ii) Unconscious ("deep" unconsciousness) vs. Pre-ROC ("light" unconsciousness); and iii) responsiveness (Baseline, Light Sedation, Recovery) vs. unresponsiveness (Unconscious, Pre-ROC). AEC and wPLI yielded different patterns of global connectivity across states of consciousness, with AEC showing the strongest network connectivity during the Unconscious epoch, and wPLI showing the strongest connectivity during full consciousness (i.e., Baseline and Recovery). Both measures also demonstrated differential predictive contributions across participants and used different brain regions for classification. AEC showed higher classification accuracy overall, particularly for distinguishing anesthetic-induced unconsciousness from Baseline (83.7 ± 0.8%). AEC also showed stronger classification accuracy than wPLI when distinguishing Unconscious from Pre-ROC (i.e., "deep" from "light" unconsciousness) (AEC: 66.3 ± 1.2%; wPLI: 56.2 ± 1.3%), and when distinguishing between responsiveness and unresponsiveness (AEC: 76.0 ± 1.3%; wPLI: 63.6 ± 1.8%). Classification accuracy was not improved compared to AEC when both AEC and wPLI were combined. This analysis of source-localized EEG data demonstrates that envelope- and phase-based FC provide different information about states of consciousness but that, on a group level, AEC is better able to detect relative alterations in brain FC across levels of anesthetic-induced unconsciousness compared to wPLI.
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21
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Auno S, Lauronen L, Wilenius J, Peltola M, Vanhatalo S, Palva JM. Detrended fluctuation analysis in the presurgical evaluation of parietal lobe epilepsy patients. Clin Neurophysiol 2021; 132:1515-1525. [PMID: 34030053 DOI: 10.1016/j.clinph.2021.03.041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 02/22/2021] [Accepted: 03/02/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To examine the usability of long-range temporal correlations (LRTCs) in non-invasive localization of the epileptogenic zone (EZ) in refractory parietal lobe epilepsy (RPLE) patients. METHODS We analyzed 10 RPLE patients who had presurgical MEG and underwent epilepsy surgery. We quantified LRTCs with detrended fluctuation analysis (DFA) at four frequency bands for 200 cortical regions estimated using individual source models. We correlated individually the DFA maps to the distance from the resection area and from cortical locations of interictal epileptiform discharges (IEDs). Additionally, three clinical experts inspected the DFA maps to visually assess the most likely EZ locations. RESULTS The DFA maps correlated with the distance to resection area in patients with type II focal cortical dysplasia (FCD) (p<0.05), but not in other etiologies. Similarly, the DFA maps correlated with the IED locations only in the FCD II patients. Visual analysis of the DFA maps showed high interobserver agreement and accuracy in FCD patients in assigning the affected hemisphere and lobe. CONCLUSIONS Aberrant LRTCs correlate with the resection areas and IED locations. SIGNIFICANCE This methodological pilot study demonstrates the feasibility of approximating cortical LRTCs from MEG that may aid in the EZ localization and provide new non-invasive insight into the presurgical evaluation of epilepsy.
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Affiliation(s)
- Sami Auno
- Epilepsia Helsinki, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Clinical Neurophysiology and BABA center, Children's Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital (HUH), Helsinki, Finland; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
| | - Leena Lauronen
- Epilepsia Helsinki, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Clinical Neurophysiology and BABA center, Children's Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital (HUH), Helsinki, Finland
| | - Juha Wilenius
- Epilepsia Helsinki, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Clinical Neurophysiology and BABA center, Children's Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital (HUH), Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital(HUH), Helsinki, Finland
| | - Maria Peltola
- Epilepsia Helsinki, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Clinical Neurophysiology and BABA center, Children's Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital (HUH), Helsinki, Finland
| | - Sampsa Vanhatalo
- Department of Clinical Neurophysiology and BABA center, Children's Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital (HUH), Helsinki, Finland; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, Finland; Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, United Kingdom; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
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22
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Herzog ND, Steinfath TP, Tarrasch R. Critical Dynamics in Spontaneous Resting-State Oscillations Are Associated With the Attention-Related P300 ERP in a Go/Nogo Task. Front Neurosci 2021; 15:632922. [PMID: 33828446 PMCID: PMC8019703 DOI: 10.3389/fnins.2021.632922] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/26/2021] [Indexed: 11/13/2022] Open
Abstract
Sustained attention is the ability to continually concentrate on task-relevant information, even in the presence of distraction. Understanding the neural mechanisms underlying this ability is critical for comprehending attentional processes as well as neuropsychiatric disorders characterized by attentional deficits, such as attention deficit hyperactivity disorder (ADHD). In this study, we aimed to investigate how trait-like critical oscillations during rest relate to the P300 evoked potential-a biomarker commonly used to assess attentional deficits. We measured long-range temporal correlations (LRTC) in resting-state EEG oscillations as index for criticality of the signal. In addition, the attentional performance of the subjects was assessed as reaction time variability (RTV) in a continuous performance task following an oddball paradigm. P300 amplitude and latencies were obtained from EEG recordings during this task. We found that, after controlling for individual variability in task performance, LRTC were positively associated with P300 amplitudes but not latencies. In line with previous findings, good performance in the sustained attention task was related to higher P300 amplitudes and earlier peak latencies. Unexpectedly, we observed a positive relationship between LRTC in ongoing oscillations during rest and RTV, indicating that greater criticality in brain oscillations during rest relates to worse task performance. In summary, our results show that resting-state neuronal activity, which operates near a critical state, relates to the generation of higher P300 amplitudes. Brain dynamics close to criticality potentially foster a computationally advantageous state which promotes the ability to generate higher event-related potential (ERP) amplitudes.
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Affiliation(s)
- Nadine D Herzog
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,School of Education and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Tim P Steinfath
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ricardo Tarrasch
- School of Education and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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23
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Pu L, Lion KM, Todorovic M, Moyle W. Portable EEG monitoring for older adults with dementia and chronic pain - A feasibility study. Geriatr Nurs 2021; 42:124-128. [PMID: 33385771 DOI: 10.1016/j.gerinurse.2020.12.008] [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: 09/01/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 01/02/2023]
Abstract
Given the reduced ability of people with dementia to self-report pain, this study examined the feasibility of using a portable electroencephalography (EEG) headband (MUSE 2) as a pain measurement tool for long-term care residents with dementia. Ten minutes of resting-state EEG was acquired by MUSE 2 from people with dementia experiencing ongoing pain (n = 3) and without current pain (n = 1) over three days. The MUSE 2 was acceptable and feasible for use in people with dementia while challenges regarding software, data collection and analysis in using this device are reported. Compared to the resident not experiencing pain, EEG signals of residents with ongoing pain showed different EEG patterns, and this could be a potential biomarker to support pain measurement in people with dementia. Further research with larger sample size is warranted to verify study results.
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Affiliation(s)
- Lihui Pu
- School of Nursing and Midwifery, Griffith University, QLD, Australia; Menzies Health Institute Queensland, Griffith University, 2.10 Health Sciences (N48), 170 Kessel Rd, Nathan, Brisbane, QLD 4111, Australia
| | - Katarzyna Malgorzata Lion
- Menzies Health Institute Queensland, Griffith University, 2.10 Health Sciences (N48), 170 Kessel Rd, Nathan, Brisbane, QLD 4111, Australia
| | - Michael Todorovic
- School of Nursing and Midwifery, Griffith University, QLD, Australia; Menzies Health Institute Queensland, Griffith University, 2.10 Health Sciences (N48), 170 Kessel Rd, Nathan, Brisbane, QLD 4111, Australia
| | - Wendy Moyle
- School of Nursing and Midwifery, Griffith University, QLD, Australia; Menzies Health Institute Queensland, Griffith University, 2.10 Health Sciences (N48), 170 Kessel Rd, Nathan, Brisbane, QLD 4111, Australia.
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24
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Chou P, Wang GH, Hsueh SW, Yang YC, Kuo CC. Delta-Frequency Augmentation and Synchronization in Seizure Discharges and Telencephalic Transmission. iScience 2020; 23:101666. [PMID: 33134896 PMCID: PMC7586134 DOI: 10.1016/j.isci.2020.101666] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/09/2020] [Accepted: 10/07/2020] [Indexed: 02/06/2023] Open
Abstract
Epileptic seizures constitute a common neurological disease primarily diagnosed by characteristic rhythms or waves in the local field potentials (LFPs) of cerebral cortices or electroencephalograms. With a basolateral amygdala (BLA) kindling model, we found that the dominant frequency of BLA oscillations is in the delta range (1-5 Hz) in both normal and seizure conditions. Multi-unit discharges are increased with higher seizure staging but remain phase-locked to the delta waves in LFPs. Also, the change in synchrony precedes and outlasts the changes in discharging units as well as behavioral seizures. One short train of stimuli readily drives the pyramidal-inhibitory neuronal networks in BLA slices into prolonged reverberating activities, where the burst and interburst intervals may concurrently set a "natural wavelength" for delta frequencies. Seizures thus could be viewed as erroneous temporospatial continuums to normal oscillations in a system with a built-in synchronizing and resonating nature for information relay.
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Affiliation(s)
- Ping Chou
- Department of Physiology, National Taiwan University College of Medicine, 1 Jen-Ai Road, 1st Section, Taipei 100, Taiwan
| | - Guan-Hsun Wang
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, 259 Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan
- School of Medicine, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
- Department of Medical Education, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
| | - Shu-Wei Hsueh
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Ya-Chin Yang
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, 259 Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
- Neuroscience Research Center, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
| | - Chung-Chin Kuo
- Department of Physiology, National Taiwan University College of Medicine, 1 Jen-Ai Road, 1st Section, Taipei 100, Taiwan
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
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25
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Zimmern V. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Front Neural Circuits 2020; 14:54. [PMID: 32982698 PMCID: PMC7479292 DOI: 10.3389/fncir.2020.00054] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 07/23/2020] [Indexed: 12/13/2022] Open
Abstract
The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains - including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry - are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered.
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Affiliation(s)
- Vincent Zimmern
- Division of Child Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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26
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Duncan NW, Hsu TY, Cheng PZ, Wang HY, Lee HC, Lane TJ. Intrinsic activity temporal structure reactivity to behavioural state change is correlated with depressive symptoms. Eur J Neurosci 2020; 52:4840-4850. [PMID: 32524682 DOI: 10.1111/ejn.14858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 06/01/2020] [Accepted: 06/02/2020] [Indexed: 01/24/2023]
Abstract
The brain's intrinsic activity plays a fundamental role in its function. In normal conditions this activity is responsive to behavioural context, changing as an individual switches between directed tasks and task-free conditions. A key feature of such changes is the movement of the brain between corresponding critical and sub-critical states, with these dynamics supporting efficient cognitive processing. Breakdowns in processing efficiency can occur, however, in brain disorders such as depression. It was therefore hypothesised that depressive symptoms would be related to reduced intrinsic activity responsiveness to changes in behavioural state. This was tested in a mixed group of major depressive disorder patients (n = 26) and healthy participants (n = 37) by measuring intrinsic EEG activity temporal structure, quantified with detrended fluctuation analysis (DFA), in eyes-closed (EC) and eyes-open task-free states and contrasting between the conditions. The degree to which DFA values changed between the states was found to correlate negatively with depressive symptoms. DFA values did not differ between states in those with higher symptom levels, meaning that the brain remained in a less flexible sub-critical condition. This sub-critical condition in the EC state was further found to correlate with levels of maladaptive rumination. This may reflect a general cognitive inflexibility resulting from a lack in neural activity reactivity that may predispose people to overly engage in self-directed attention. These results provide an initial link between intrinsic activity reactivity and psychological features found in psychiatric disorders.
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Affiliation(s)
- Niall W Duncan
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Tzu-Yu Hsu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Paul Z Cheng
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Hsin-Yi Wang
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Department of Psychiatry, TMU Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Timothy J Lane
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City, Taiwan.,College of Humanities and Social Science, Taipei Medical University, Taipei, Taiwan
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27
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Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy. J Neurosci 2020; 40:5572-5588. [PMID: 32513827 PMCID: PMC7363471 DOI: 10.1523/jneurosci.0905-19.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 05/31/2020] [Accepted: 06/02/2020] [Indexed: 12/18/2022] Open
Abstract
Drug-resistant focal epilepsy is a large-scale brain networks disorder characterized by altered spatiotemporal patterns of functional connectivity (FC), even during interictal resting state (RS). Although RS-FC-based metrics can detect these changes, results from RS functional magnetic resonance imaging (RS-fMRI) studies are unclear and difficult to interpret, and the underlying dynamical mechanisms are still largely unknown. To better capture the RS dynamics, we phenomenologically extended the neural mass model of partial seizures, the Epileptor, by including two neuron subpopulations of epileptogenic and nonepileptogenic type, making it capable of producing physiological oscillations in addition to the epileptiform activity. Using the neuroinformatics platform The Virtual Brain, we reconstructed 14 epileptic and 5 healthy human (of either sex) brain network models (BNMs), based on individual anatomical connectivity and clinically defined epileptogenic heatmaps. Through systematic parameter exploration and fitting to neuroimaging data, we demonstrated that epileptic brains during interictal RS are associated with lower global excitability induced by a shift in the working point of the model, indicating that epileptic brains operate closer to a stable equilibrium point than healthy brains. Moreover, we showed that functional networks are unaffected by interictal spikes, corroborating previous experimental findings; additionally, we observed higher excitability in epileptogenic regions, in agreement with the data. We shed light on new dynamical mechanisms responsible for altered RS-FC in epilepsy, involving the following two key factors: (1) a shift of excitability of the whole brain leading to increased stability; and (2) a locally increased excitability in the epileptogenic regions supporting the mixture of hyperconnectivity and hypoconnectivity in these areas. SIGNIFICANCE STATEMENT Advances in functional neuroimaging provide compelling evidence for epilepsy-related brain network alterations, even during the interictal resting state (RS). However, the dynamical mechanisms underlying these changes are still elusive. To identify local and network processes behind the RS-functional connectivity (FC) spatiotemporal patterns, we systematically manipulated the local excitability and the global coupling in the virtual human epileptic patient brain network models (BNMs), complemented by the analysis of the impact of interictal spikes and fitting to the neuroimaging data. Our results suggest that a global shift of the dynamic working point of the brain model, coupled with locally hyperexcitable node dynamics of the epileptogenic networks, provides a mechanistic explanation of the epileptic processes during the interictal RS period. These, in turn, are associated with the changes in FC.
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28
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Smith RJ, Ombao HC, Shrey DW, Lopour BA. Inference on Long-Range Temporal Correlations in Human EEG Data. IEEE J Biomed Health Inform 2020; 24:1070-1079. [DOI: 10.1109/jbhi.2019.2936326] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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29
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Fatigue-Mediated Loss of Complexity is Contraction-Type Dependent in Vastus Lateralis Electromyographic Signals. Sports (Basel) 2019; 7:sports7040078. [PMID: 30986944 PMCID: PMC6524352 DOI: 10.3390/sports7040078] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 03/28/2019] [Accepted: 03/29/2019] [Indexed: 11/23/2022] Open
Abstract
The purpose of this study was to investigate the effect of fatigue status and contraction type on complexity of the surface electromyographic (sEMG) signal. Twelve females (mean age ± SD = 21.1 ± 1.4 years) performed three fatigue-inducing protocols that involved maximal concentric, eccentric, or isometric knee-extensor contractions over three non-consecutive sessions. Pre- and post-fatigue assessments were also completed each session and consisted of three maximal efforts for each type of contraction. Complexity of sEMG signals from the vastus lateralis was assessed using Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA) as expressed using the scaling exponent α. The results showed that fatigue decreased (p < 0.05) sEMG complexity as indicated by decreased SampEn (non-fatigued: 1.57 ± 0.22 > fatigued: 1.46 ± 0.25) and increased DFA α (non-fatigued: 1.27 ± 0.26 < fatigued: 1.32 ± 0.23). In addition, sEMG complexity was different among contraction types as indicated by SampEn (concentric: 1.58 ± 0.22 > eccentric: 1.47 ± 0.27 and isometric: 1.50 ± 0.21) and DFA α (concentric: 1.27 ± 0.18 < isometric: 1.32 ± 0.18). Thus, these findings suggested sEMG complexity is affected by fatigue status and contraction type, with the degree of fatigue-mediated loss of complexity dependent on the type of contraction used to elicit fatigue.
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30
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Moran JK, Michail G, Heinz A, Keil J, Senkowski D. Long-Range Temporal Correlations in Resting State Beta Oscillations are Reduced in Schizophrenia. Front Psychiatry 2019; 10:517. [PMID: 31379629 PMCID: PMC6659128 DOI: 10.3389/fpsyt.2019.00517] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 07/01/2019] [Indexed: 01/26/2023] Open
Abstract
Symptoms of schizophrenia (SCZ) are likely to be generated by genetically mediated synaptic dysfunction, which contribute to large-scale functional neural dysconnectivity. Recent electrophysiological studies suggest that this dysconnectivity is present not only at a spatial level but also at a temporal level, operationalized as long-range temporal correlations (LRTCs). Previous research suggests that alpha and beta frequency bands have weaker temporal stability in people with SCZ. This study sought to replicate these findings with high-density electroencephalography (EEG), enabling a spatially more accurate analysis of LRTC differences, and to test associations with characteristic SCZ symptoms and cognitive deficits. A 128-channel EEG was used to record eyes-open resting state brain activity of 23 people with SCZ and 24 matched healthy controls (HCs). LRTCs were derived for alpha (8-12 Hz) and beta (13-25 Hz) frequency bands. As an exploratory analysis, LRTC was source projected using sLoreta. People with SCZ showed an area of significantly reduced beta-band LRTC compared with HCs over bilateral posterior regions. There were no between-group differences in alpha-band activity. Individual symptoms of SCZ were not related to LRTC values nor were cognitive deficits. The study confirms that people with SCZ have reduced temporal stability in the beta frequency band. The absence of group differences in the alpha band may be attributed to the fact that people had, in contrast to previous studies, their eyes open in the current study. Taken together, our study confirms the utility of LRTC as a marker of network instability in people with SCZ and provides a novel empirical perspective for future examinations of network dysfunction salience in SCZ research.
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Affiliation(s)
- James K Moran
- Department of Psychiatry and Psychotherapy, St. Hedwig Hospital, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Georgios Michail
- Department of Psychiatry and Psychotherapy, St. Hedwig Hospital, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, St. Hedwig Hospital, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Julian Keil
- Biological Psychology, Christian-Albrechts University Kiel, Kiel, Germany
| | - Daniel Senkowski
- Department of Psychiatry and Psychotherapy, St. Hedwig Hospital, Charité-Universitätsmedizin Berlin, Berlin, Germany
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31
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Irrmischer M, Poil S, Mansvelder HD, Intra FS, Linkenkaer‐Hansen K. Strong long-range temporal correlations of beta/gamma oscillations are associated with poor sustained visual attention performance. Eur J Neurosci 2018; 48:2674-2683. [PMID: 28858404 PMCID: PMC6221163 DOI: 10.1111/ejn.13672] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 07/27/2017] [Accepted: 08/25/2017] [Indexed: 12/11/2022]
Abstract
Neuronal oscillations exhibit complex amplitude fluctuations with autocorrelations that persist over thousands of oscillatory cycles. Such long-range temporal correlations (LRTC) are thought to reflect neuronal systems poised near a critical state, which would render them capable of quick reorganization and responsive to changing processing demands. When we concentrate, however, the influence of internal and external sources of distraction is better reduced, suggesting that neuronal systems involved with sustained attention could benefit from a shift toward the less volatile sub-critical state. To test these ideas, we recorded electroencephalography (EEG) from healthy volunteers during eyes-closed rest and during a sustained attention task requiring a speeded response to images deviating in their presentation duration. We show that for oscillations recorded during rest, high levels of alpha-band LRTC in the sensorimotor region predicted good reaction-time performance in the attention task. During task execution, however, fast reaction times were associated with high-amplitude beta and gamma oscillations with low LRTC. Finally, we show that reduced LRTC during the attention task compared to the rest condition correlates with better performance, while increased LRTC of oscillations from rest to attention is associated with reduced performance. To our knowledge, this is the first empirical evidence that 'resting-state criticality' of neuronal networks predicts swift behavioral responses in a sensorimotor task, and that steady attentive processing of visual stimuli requires brain dynamics with suppressed temporal complexity.
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Affiliation(s)
- Mona Irrmischer
- Department of Integrative NeurophysiologyCenter for Neurogenomics and Cognitive Research (CNCR)Amsterdam NeuroscienceVU University Amsterdam1081 HVAmsterdamThe Netherlands
| | - Simon‐Shlomo Poil
- Department of Integrative NeurophysiologyCenter for Neurogenomics and Cognitive Research (CNCR)Amsterdam NeuroscienceVU University Amsterdam1081 HVAmsterdamThe Netherlands
- NBT Analytics BVAmsterdamThe Netherlands
| | - Huibert D. Mansvelder
- Department of Integrative NeurophysiologyCenter for Neurogenomics and Cognitive Research (CNCR)Amsterdam NeuroscienceVU University Amsterdam1081 HVAmsterdamThe Netherlands
| | - Francesca Sangiuliano Intra
- Department of Integrative NeurophysiologyCenter for Neurogenomics and Cognitive Research (CNCR)Amsterdam NeuroscienceVU University Amsterdam1081 HVAmsterdamThe Netherlands
- IRCCSDon Gnocchi FoundationMilanItaly
| | - Klaus Linkenkaer‐Hansen
- Department of Integrative NeurophysiologyCenter for Neurogenomics and Cognitive Research (CNCR)Amsterdam NeuroscienceVU University Amsterdam1081 HVAmsterdamThe Netherlands
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32
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Ros T, Frewen P, Théberge J, Michela A, Kluetsch R, Mueller A, Candrian G, Jetly R, Vuilleumier P, Lanius RA. Neurofeedback Tunes Scale-Free Dynamics in Spontaneous Brain Activity. Cereb Cortex 2018; 27:4911-4922. [PMID: 27620975 DOI: 10.1093/cercor/bhw285] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 08/19/2016] [Indexed: 11/13/2022] Open
Abstract
Brain oscillations exhibit long-range temporal correlations (LRTCs), which reflect the regularity of their fluctuations: low values representing more random (decorrelated) while high values more persistent (correlated) dynamics. LRTCs constitute supporting evidence that the brain operates near criticality, a state where neuronal activities are balanced between order and randomness. Here, healthy adults used closed-loop brain training (neurofeedback, NFB) to reduce the amplitude of alpha oscillations, producing a significant increase in spontaneous LRTCs post-training. This effect was reproduced in patients with post-traumatic stress disorder, where abnormally random dynamics were reversed by NFB, correlating with significant improvements in hyperarousal. Notably, regions manifesting abnormally low LRTCs (i.e., excessive randomness) normalized toward healthy population levels, consistent with theoretical predictions about self-organized criticality. Hence, when exposed to appropriate training, spontaneous cortical activity reveals a residual capacity for "self-tuning" its own temporal complexity, despite manifesting the abnormal dynamics seen in individuals with psychiatric disorder. Lastly, we observed an inverse-U relationship between strength of LRTC and oscillation amplitude, suggesting a breakdown of long-range dependence at high/low synchronization extremes, in line with recent computational models. Together, our findings offer a broader mechanistic framework for motivating research and clinical applications of NFB, encompassing disorders with perturbed LRTCs.
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Affiliation(s)
- T Ros
- Geneva Neuroscience Center, Department of Neuroscience, University of Geneva, CH-1202 Geneva, Switzerland
| | - P Frewen
- Department of Psychiatry, Western University, London N6A 5A5, Ontario, Canada
| | - J Théberge
- Department of Medical Imaging, Lawson Health Research Institute, London N6C 2R5, Ontario, Canada
| | - A Michela
- Geneva Neuroscience Center, Department of Neuroscience, University of Geneva, CH-1202 Geneva, Switzerland
| | - R Kluetsch
- Department of Psychosomatic Medicine and Psychotherapy, Mannheim-Heidelberg University, 68159 Mannheim, Germany
| | - A Mueller
- Brain and Trauma Foundation, CH-7000 Chur, Switzerland
| | - G Candrian
- Brain and Trauma Foundation, CH-7000 Chur, Switzerland
| | - R Jetly
- Directorate of Mental Health, Canadian Forces Health Services, Ottawa K1A 0K6, Canada
| | - P Vuilleumier
- Geneva Neuroscience Center, Department of Neuroscience, University of Geneva, CH-1202 Geneva, Switzerland
| | - R A Lanius
- Department of Psychiatry, Western University, London N6A 5A5, Ontario, Canada
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33
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Thiery T, Lajnef T, Combrisson E, Dehgan A, Rainville P, Mashour GA, Blain-Moraes S, Jerbi K. Long-range temporal correlations in the brain distinguish conscious wakefulness from induced unconsciousness. Neuroimage 2018; 179:30-39. [PMID: 29885482 DOI: 10.1016/j.neuroimage.2018.05.069] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 04/18/2018] [Accepted: 05/29/2018] [Indexed: 12/20/2022] Open
Abstract
Rhythmic neuronal synchronization across large-scale networks is thought to play a key role in the regulation of conscious states. Changes in neuronal oscillation amplitude across states of consciousness have been widely reported, but little is known about possible changes in the temporal dynamics of these oscillations. The temporal structure of brain oscillations may provide novel insights into the neural mechanisms underlying consciousness. To address this question, we examined long-range temporal correlations (LRTC) of EEG oscillation amplitudes recorded during both wakefulness and anesthetic-induced unconsciousness. Importantly, the time-varying EEG oscillation envelopes were assessed over the course of a sevoflurane sedation protocol during which the participants alternated between states of consciousness and unconsciousness. Both spectral power and LRTC in oscillation amplitude were computed across multiple frequency bands. State-dependent differences in these features were assessed using non-parametric tests and supervised machine learning. We found that periods of unconsciousness were associated with increases in LRTC in beta (15-30Hz) amplitude over frontocentral channels and with a suppression of alpha (8-13Hz) amplitude over occipitoparietal electrodes. Moreover, classifiers trained to predict states of consciousness on single epochs demonstrated that the combination of beta LRTC with alpha amplitude provided the highest classification accuracy (above 80%). These results suggest that loss of consciousness is accompanied by an augmentation of temporal persistence in neuronal oscillation amplitude, which may reflect an increase in regularity and a decrease in network repertoire compared to the brain's activity during resting-state consciousness.
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Affiliation(s)
- Thomas Thiery
- Psychology Department, University of Montreal, QC, Canada.
| | - Tarek Lajnef
- Psychology Department, University of Montreal, QC, Canada
| | - Etienne Combrisson
- Psychology Department, University of Montreal, QC, Canada; Center of Research and Innovation in Sport, Mental Processes and Motor Performance, University Claude Bernard Lyon I, University of Lyon, Villeurbanne, France; Brain Dynamics and Cognition, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University of Lyon, Villeurbanne, France
| | - Arthur Dehgan
- Psychology Department, University of Montreal, QC, Canada
| | | | - George A Mashour
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan, USA
| | - Stefanie Blain-Moraes
- School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada
| | - Karim Jerbi
- Psychology Department, University of Montreal, QC, Canada
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34
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Park SC, Chung CK. Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy. J Neurophysiol 2018. [PMID: 29513147 DOI: 10.1152/jn.00225.2017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathological activities in neocortical epilepsy. Electrocorticographies for 39 patients with medically intractable neocortical epilepsy were analyzed. We separately analyzed 38 frequencies from 0.9 to 800 Hz including both high-frequency activities and low-frequency activities to select bands related to seizure outcome. An automatic detector using amplitude-duration-number thresholds was used. Interictal electrocorticography data sets of 8 min for each patient were selected. In the first training data set of 20 patients, the automatic detector was optimized to best differentiate the seizure-free group from not-seizure-free-group based on ranks of resection percentages of activities detected using a genetic algorithm. The optimization was validated in a different data set of 19 patients. There were 16 (41%) seizure-free patients. The mean follow-up duration was 21 ± 11 mo (range, 13-44 mo). After validation, frequencies significantly related to seizure outcome were 5.8, 8.4-25, 30, 36, 52, and 75 among low-frequency activities and 108 and 800 Hz among high-frequency activities. Resection for 5.8, 8.4-25, 108, and 800 Hz activities consistently improved seizure outcome. Resection effects of 17-36, 52, and 75 Hz activities on seizure outcome were variable according to thresholds. We developed and validated an automated detector for monitoring interictal pathological and inhibitory/physiological activities in neocortical epilepsy using a data-driven approach through outcome-guided machine learning. NEW & NOTEWORTHY Outcome-guided machine learning based on seizure outcome was used to improve detections for interictal electrocorticographic low- and high-frequency activities. This method resulted in better separation of seizure outcome groups than others reported in the literature. The automatic detector can be trained without human intervention and no prior information. It is based only on objective seizure outcome data without relying on an expert's manual annotations. Using the method, we could find and characterize pathological and inhibitory activities.
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Affiliation(s)
- Seong-Cheol Park
- Department of Neurosurgery, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea.,Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chun Kee Chung
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea.,Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul, Republic of Korea.,Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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35
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Munia TTK, Haider A, Schneider C, Romanick M, Fazel-Rezai R. A Novel EEG Based Spectral Analysis of Persistent Brain Function Alteration in Athletes with Concussion History. Sci Rep 2017; 7:17221. [PMID: 29222477 PMCID: PMC5722818 DOI: 10.1038/s41598-017-17414-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 11/21/2017] [Indexed: 11/09/2022] Open
Abstract
The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Detecting deficits are vital in making a decision about the treatment plan as it can persist one year or more following a brain injury. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits combining EEG analysis with three standard post-concussive assessment tools. Data were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency (Hjorth Parameters) and nonlinear features (approximate entropy and Hurst exponent) for the first time to explore post-concussive deficits. Besides traditional frequency-band analysis, we also presented a new individual frequency-based approach for EEG assessment. While EEG analysis exhibited significant discrepancies between the groups, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlights that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management.
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Affiliation(s)
- Tamanna T K Munia
- Department of Electrical Engineering, University of North Dakota, Grand Forks, 58202, USA
| | - Ali Haider
- Department of Electrical Engineering, University of North Dakota, Grand Forks, 58202, USA
| | - Charles Schneider
- Department of Electrical Engineering, University of North Dakota, Grand Forks, 58202, USA
| | - Mark Romanick
- Department of Physical Therapy, University of North Dakota, Grand Forks, 58202, USA
| | - Reza Fazel-Rezai
- Department of Electrical Engineering, University of North Dakota, Grand Forks, 58202, USA.
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36
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Guay S, De Beaumont L, Drisdelle BL, Lina JM, Jolicoeur P. Electrophysiological impact of multiple concussions in asymptomatic athletes: A re-analysis based on alpha activity during a visual-spatial attention task. Neuropsychologia 2017; 108:42-49. [PMID: 29162458 DOI: 10.1016/j.neuropsychologia.2017.11.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Revised: 10/31/2017] [Accepted: 11/16/2017] [Indexed: 01/02/2023]
Abstract
Most EEG studies used event-related potentials to assess long-term and cumulative effects of sport-related concussions on brain activity. Time-frequency methods provide another approach that allows the detection of subtle shifts in types and patterns of brain oscillations. We sought to discover whether event-related alpha activity would be significantly affected in asymptomatic multi-concussed athletes. We measured the amplitude of alpha activity (8-12Hz) from the EEG recorded during a visual-spatial attention task to compare event-related alpha perturbations in 13 multi-concussed athletes and 14 age-equivalent, non-concussed teammates. Relative to non-concussed athletes, multi-concussed athletes showed significantly less event-related perturbations time-locked to stimulus presentation. Alpha activity alterations were closely related to the number of concussions sustained. Event-related alpha activity differed in asymptomatic multi-concussed athletes when compared to controls. Our study suggests that low-level neurophysiological underpinnings of the deployment of visual-spatial attention are affected in multi-concussed athletes even though their last concussion occurred on average 30 months prior to testing.
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Affiliation(s)
- Samuel Guay
- Department of Psychology, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada; Centre de recherche de l'Hôpital du Sacré-Coeur de Montréal, Montreal QC, Canada
| | - Louis De Beaumont
- Centre de recherche de l'Hôpital du Sacré-Coeur de Montréal, Montreal QC, Canada; Department of Surgery, Université de Montréal, Montreal, QC, Canada
| | - Brandi Lee Drisdelle
- Department of Psychology, Université de Montréal, Montreal, QC, Canada; Centre de recherche en neuropsychologie et cognition (CERNEC), Université de Montréal, Montreal, QC, Canada
| | - Jean-Marc Lina
- Centre de recherche de l'Hôpital du Sacré-Coeur de Montréal, Montreal QC, Canada; Montréal Polytechnique, Montreal, QC, Canada
| | - Pierre Jolicoeur
- Department of Psychology, Université de Montréal, Montreal, QC, Canada; Centre de recherche en neuropsychologie et cognition (CERNEC), Université de Montréal, Montreal, QC, Canada; Centre de recherche de l'Institut universitaire de gériatrie de Montreal, Montreal, QC, Canada.
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37
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Attenuation of temporal correlations of neuronal oscillations in patients with mild spastic diplegia. Sci Rep 2017; 7:14966. [PMID: 29097718 PMCID: PMC5668314 DOI: 10.1038/s41598-017-14879-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 10/19/2017] [Indexed: 11/08/2022] Open
Abstract
The aim of this study was to investigate the temporal correlations of neuronal oscillations in patients with mild spastic diplegia (MSD). Resting-state electroencephalography (EEG) was recorded from 15 male adolescent and young adult patients with MSD and 15 healthy controls. We characterized the temporal correlations of neuronal oscillations, both on long temporal scale (i.e., >1 second) and short-to-intermediate temporal scale (i.e., <≈1 second) using detrended fluctuation analysis (DFA) and an analysis of the life- and waiting-time statistics of oscillation bursts respectively. The DFA exponents at alpha and beta bands, the life-time biomarker of alpha oscillation, and the life- and waiting-time biomarkers of beta oscillation were significantly attenuated in the patients compared with controls. Moreover, altered scalp distributions of some temporal correlation measures were found at alpha and beta bands in these patients. All these findings suggest that MSD is associated with highly volatile neuronal states of alpha and beta oscillations on short-to-intermediate and much longer time scales, which may be related to cognitive dysfunction in patients with MSD.
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38
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Smith RJ, Sugijoto A, Rismanchi N, Hussain SA, Shrey DW, Lopour BA. Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms. Brain Topogr 2017; 30:810-821. [PMID: 28905146 PMCID: PMC6058722 DOI: 10.1007/s10548-017-0588-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 09/06/2017] [Indexed: 10/18/2022]
Abstract
Infantile spasms syndrome is an epileptic encephalopathy in which prompt diagnosis and treatment initiation are critical to therapeutic response. Diagnosis of the disease heavily depends on the identification of characteristic electroencephalographic (EEG) patterns, including hypsarrhythmia. However, visual assessment of the presence and characteristics of hypsarrhythmia is challenging because multiple variants of the pattern exist, leading to poor inter-rater reliability. We investigated whether a quantitative measurement of the control of neural synchrony in the EEGs of infantile spasms patients could be used to reliably distinguish the presence of hypsarrhythmia and indicate successful treatment outcomes. We used autocorrelation and Detrended Fluctuation Analysis (DFA) to measure the strength of long-range temporal correlations in 21 infantile spasms patients before and after treatment and 21 control subjects. The strength of long-range temporal correlations was significantly lower in patients with hypsarrhythmia than control patients, indicating decreased control of neural synchrony. There was no difference between patients without hypsarrhythmia and control patients. Further, the presence of hypsarrhythmia could be classified based on the DFA exponent and intercept with 92% accuracy using a support vector machine. Successful treatment was marked by a larger increase in the DFA exponent compared to those in which spasms persisted. These results suggest that the strength of long-range temporal correlations is a marker of pathological cortical activity that correlates with treatment response. Combined with current clinical measures, this quantitative tool has the potential to aid objective identification of hypsarrhythmia and assessment of treatment efficacy to inform clinical decision-making.
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Affiliation(s)
- Rachel J Smith
- Department of Biomedical Engineering, The Henry Samueli School of Engineering, University of California, Irvine, CA, USA
| | - Amanda Sugijoto
- Department of Biomedical Engineering, The Henry Samueli School of Engineering, University of California, Irvine, CA, USA
| | - Neggy Rismanchi
- Division of Neurology, Children's Hospital Orange County, Orange, CA, USA
| | - Shaun A Hussain
- Division of Pediatric Neurology, University of California, Los Angeles, CA, USA
| | - Daniel W Shrey
- Division of Neurology, Children's Hospital Orange County, Orange, CA, USA
- Department of Pediatrics, University of California, Irvine, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, The Henry Samueli School of Engineering, University of California, Irvine, CA, USA.
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39
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Breakdown of long-range temporal correlations in brain oscillations during general anesthesia. Neuroimage 2017; 159:146-158. [DOI: 10.1016/j.neuroimage.2017.07.047] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 07/13/2017] [Accepted: 07/22/2017] [Indexed: 01/19/2023] Open
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40
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Meisel C, Bailey K, Achermann P, Plenz D. Decline of long-range temporal correlations in the human brain during sustained wakefulness. Sci Rep 2017; 7:11825. [PMID: 28928479 PMCID: PMC5605531 DOI: 10.1038/s41598-017-12140-w] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 09/05/2017] [Indexed: 02/05/2023] Open
Abstract
Sleep is crucial for daytime functioning, cognitive performance and general well-being. These aspects of daily life are known to be impaired after extended wake, yet, the underlying neuronal correlates have been difficult to identify. Accumulating evidence suggests that normal functioning of the brain is characterized by long-range temporal correlations (LRTCs) in cortex, which are supportive for decision-making and working memory tasks. Here we assess LRTCs in resting state human EEG data during a 40-hour sleep deprivation experiment by evaluating the decay in autocorrelation and the scaling exponent of the detrended fluctuation analysis from EEG amplitude fluctuations. We find with both measures that LRTCs decline as sleep deprivation progresses. This decline becomes evident when taking changes in signal power into appropriate consideration. In contrast, the presence of strong signal power increases in some frequency bands over the course of sleep deprivation may falsely indicate LRTC changes that do not reflect the underlying long-range temporal correlation structure. Our results demonstrate the importance of sleep to maintain LRTCs in the human brain. In complex networks, LRTCs naturally emerge in the vicinity of a critical state. The observation of declining LRTCs during wake thus provides additional support for our hypothesis that sleep reorganizes cortical networks towards critical dynamics for optimal functioning.
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Affiliation(s)
- Christian Meisel
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, Maryland, 20892, USA. .,Department of Neurology, University Clinic Carl Gustav Carus, Fetscherstraße 74, 01307, Dresden, Germany.
| | - Kimberlyn Bailey
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, Maryland, 20892, USA
| | - Peter Achermann
- Institute of Pharmacology and Toxicology, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, Maryland, 20892, USA
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41
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Deviations from Critical Dynamics in Interictal Epileptiform Activity. J Neurosci 2017; 36:12276-12292. [PMID: 27903734 DOI: 10.1523/jneurosci.0809-16.2016] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 10/09/2016] [Accepted: 10/11/2016] [Indexed: 11/21/2022] Open
Abstract
The framework of criticality provides a unifying perspective on neuronal dynamics from in vitro cortical cultures to functioning human brains. Recent findings suggest that a healthy cortex displays critical dynamics, giving rise to scale-free spatiotemporal cascades of activity, termed neuronal avalanches. Pharmacological manipulations of the excitation-inhibition balance (EIB) in cortical cultures were previously shown to result in deviations from criticality and from the power law scaling of avalanche size distribution. To examine the sensitivity of neuronal avalanche metrics to altered EIB in humans, we focused on epilepsy, a neurological disorder characterized by hyperexcitable networks. Using magnetoencephalography, we quantitatively assessed deviations from criticality in the brain dynamics of patients with epilepsy during interictal (between-seizures) activity. Compared with healthy control subjects, epilepsy patients tended to exhibit a higher neural gain and larger avalanches, particularly during interictal epileptiform activity. Moreover, deviations from scale-free behavior were exclusively connected to brief intervals at epileptiform discharges, strengthening the association between deviations from criticality and the instantaneous changes in EIB. The avalanches collected during interictal epileptiform activity had not only a stereotypical size range but also involved particular spatial patterns of activations, as expected for periods of epileptic network dominance. Overall, the neuronal avalanche metrics provide a quantitative novel description of interictal brain activity of patients with epilepsy. SIGNIFICANCE STATEMENT Healthy brain dynamics requires a delicate balance between excitatory and inhibitory processes. Several brain disorders, such as epilepsy, are associated with altered excitation-inhibition balance, but assessing this balance using noninvasive tools is still challenging. In this study, we apply the framework of critical brain dynamics to data from epilepsy patients, which were recorded between seizures. We show that metrics of criticality provide a sensitive tool for noninvasive assessment of changes in the balance. Specifically, brain activity of epilepsy patients deviates from healthy critical brain dynamics, particularly during abnormal epileptiform activity. The study offers a novel quantitative perspective on epilepsy and its relation to healthy brain dynamics.
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42
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Quantitative assessments of extracellular EEG to classify specific features of main phases of seizure acquisition based on kindling model in Rat. Neurosci Lett 2017; 656:144-151. [DOI: 10.1016/j.neulet.2017.07.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/21/2017] [Accepted: 07/19/2017] [Indexed: 11/23/2022]
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Zhigalov A, Arnulfo G, Nobili L, Palva S, Palva JM. Modular co-organization of functional connectivity and scale-free dynamics in the human brain. Netw Neurosci 2017; 1:143-165. [PMID: 29911674 PMCID: PMC5988393 DOI: 10.1162/netn_a_00008] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 02/19/2017] [Indexed: 02/06/2023] Open
Abstract
Scale-free neuronal dynamics and interareal correlations are emergent characteristics of spontaneous brain activity. How such dynamics and the anatomical patterns of neuronal connectivity are mutually related in brain networks has, however, remained unclear. We addressed this relationship by quantifying the network colocalization of scale-free neuronal activity-both neuronal avalanches and long-range temporal correlations (LRTCs)-and functional connectivity (FC) by means of intracranial and noninvasive human resting-state electrophysiological recordings. We found frequency-specific colocalization of scale-free dynamics and FC so that the interareal couplings of LRTCs and the propagation of neuronal avalanches were most pronounced in the predominant pathways of FC. Several control analyses and the frequency specificity of network colocalization showed that the results were not trivial by-products of either brain dynamics or our analysis approach. Crucially, scale-free neuronal dynamics and connectivity also had colocalized modular structures at multiple levels of network organization, suggesting that modules of FC would be endowed with partially independent dynamic states. These findings thus suggest that FC and scale-free dynamics-hence, putatively, neuronal criticality as well-coemerge in a hierarchically modular structure in which the modules are characterized by dense connectivity, avalanche propagation, and shared dynamic states.
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Affiliation(s)
- Alexander Zhigalov
- Neuroscience Center, University of Helsinki, Finland.,BioMag laboratory, HUS Medical Imaging Center, Helsinki University Central Hospital, Finland.,Department of Computer Science, University of Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, University of Helsinki, Finland.,Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Italy
| | - Lino Nobili
- Claudio Munari Epilepsy Surgery Centre, Niguarda Hospital, Italy
| | - Satu Palva
- Neuroscience Center, University of Helsinki, Finland
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Scale-Free Neural and Physiological Dynamics in Naturalistic Stimuli Processing. eNeuro 2016; 3:eN-NWR-0191-16. [PMID: 27822495 PMCID: PMC5075946 DOI: 10.1523/eneuro.0191-16.2016] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 10/05/2016] [Accepted: 10/05/2016] [Indexed: 01/05/2023] Open
Abstract
Neural activity recorded at multiple spatiotemporal scales is dominated by arrhythmic fluctuations without a characteristic temporal periodicity. Such activity often exhibits a 1/f-type power spectrum, in which power falls off with increasing frequency following a power-law function: [Formula: see text], which is indicative of scale-free dynamics. Two extensively studied forms of scale-free neural dynamics in the human brain are slow cortical potentials (SCPs)-the low-frequency (<5 Hz) component of brain field potentials-and the amplitude fluctuations of α oscillations, both of which have been shown to carry important functional roles. In addition, scale-free dynamics characterize normal human physiology such as heartbeat dynamics. However, the exact relationships among these scale-free neural and physiological dynamics remain unclear. We recorded simultaneous magnetoencephalography and electrocardiography in healthy subjects in the resting state and while performing a discrimination task on scale-free dynamical auditory stimuli that followed different scale-free statistics. We observed that long-range temporal correlation (captured by the power-law exponent β) in SCPs positively correlated with that of heartbeat dynamics across time within an individual and negatively correlated with that of α-amplitude fluctuations across individuals. In addition, across individuals, long-range temporal correlation of both SCP and α-oscillation amplitude predicted subjects' discrimination performance in the auditory task, albeit through antagonistic relationships. These findings reveal interrelations among different scale-free neural and physiological dynamics and initial evidence for the involvement of scale-free neural dynamics in the processing of natural stimuli, which often exhibit scale-free dynamics.
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45
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Dickten H, Porz S, Elger CE, Lehnertz K. Weighted and directed interactions in evolving large-scale epileptic brain networks. Sci Rep 2016; 6:34824. [PMID: 27708381 PMCID: PMC5052583 DOI: 10.1038/srep34824] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 09/21/2016] [Indexed: 01/03/2023] Open
Abstract
Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess-with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics-both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only - in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.
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Affiliation(s)
- Henning Dickten
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany.,Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany.,Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
| | - Stephan Porz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany.,Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
| | - Christian E Elger
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany.,Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany.,Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
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46
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Classifying amygdala kindling stages using quantitative assessments of extracellular recording of EEG in rats. Brain Res Bull 2016; 127:148-155. [PMID: 27659238 DOI: 10.1016/j.brainresbull.2016.09.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 09/13/2016] [Accepted: 09/19/2016] [Indexed: 11/23/2022]
Abstract
PURPOSE Determining different seizure stage specific features in a kindling model is a crucial step in developing efficient objective techniques for early prediction and treatment of seizures. This study identified and categorized kindling stages based on their electrophysiological features through processing extracellular field potentials of Amygdala rapid kindling. METHODS Thirteen Wistar rats (200±10g) were divided into 2 groups including kindle (n=7) and sham (n=6) and respectively underwent an amygdala rapid kindling and placebo stimulation. EEG signals in each stage were classified into 7 bands: delta (0-4Hz), theta (4-8Hz), alpha (8-12Hz), low beta (12-16Hz), mid beta (16-20Hz), high beta (20-28Hz) and gamma (28-40Hz). Spectral power and power of sub bands of stage 3 (localized seizure stage (SS)) and stages 4 and 5 (generalized SSs) were compared between kindling and sham groups. RESULT Spectral analyses showed larger spikes in delta and theta subbands in the stages of 3, 4, and 5 of kindling, compared with sham animals. Generalized SSs contained more spikes than the localized SS in the kindling. Kindling process was accompanied by reduction in high beta and gamma oscillations and increase in delta sub band power which were significant in the generalized SSs. The theta/alpha ratio in the localized SS was higher than the generalized SSs and sham group, but the difference with the sham group was statistically significant. CONCLUSION Our results showed that reduced high beta and gamma and increased delta oscillations power are associated with behavioral seizure progression.
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47
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Zhigalov A, Kaplan A, Palva JM. Modulation of critical brain dynamics using closed-loop neurofeedback stimulation. Clin Neurophysiol 2016; 127:2882-2889. [DOI: 10.1016/j.clinph.2016.04.028] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 04/05/2016] [Accepted: 04/30/2016] [Indexed: 11/16/2022]
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48
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Dimitriadis SI, Linden D. Modulation of brain criticality via suppression of EEG long-range temporal correlations (LRTCs) in a closed-loop neurofeedback stimulation. Clin Neurophysiol 2016; 127:2878-2881. [PMID: 27323657 DOI: 10.1016/j.clinph.2016.05.359] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 05/28/2016] [Indexed: 12/29/2022]
Affiliation(s)
- Stavros I Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF24 4HQ, UK; Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK; Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece; NeuroInformatics Group, AUTH, Thessaloniki, Greece.
| | - David Linden
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF24 4HQ, UK; Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
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49
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Euler MJ, Wiltshire TJ, Niermeyer MA, Butner JE. Working memory performance inversely predicts spontaneous delta and theta-band scaling relations. Brain Res 2016; 1637:22-33. [DOI: 10.1016/j.brainres.2016.02.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 01/05/2016] [Accepted: 02/02/2016] [Indexed: 10/22/2022]
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50
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Nedic S, Stufflebeam SM, Rondinoni C, Velasco TR, dos Santos AC, Leite JP, Gargaro AC, Mujica-Parodi LR, Ide JS. Using network dynamic fMRI for detection of epileptogenic foci. BMC Neurol 2015; 15:262. [PMID: 26689596 PMCID: PMC4687299 DOI: 10.1186/s12883-015-0514-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 12/04/2015] [Indexed: 01/21/2023] Open
Abstract
Background Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. Methods In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients’ cognitive performance using a delayed verbal memory recall task. Results ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal – posterior cingulate cortex connectivity). Conclusions Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions.
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Affiliation(s)
- Sanja Nedic
- Department of Biomedical Engineering, Stony Brook University School of Medicine, Stony Brook, NY, 11794, USA. .,Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
| | - Steven M Stufflebeam
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
| | - Carlo Rondinoni
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Tonicarlo R Velasco
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Antonio C dos Santos
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Joao P Leite
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Ana C Gargaro
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Lilianne R Mujica-Parodi
- Department of Biomedical Engineering, Stony Brook University School of Medicine, Stony Brook, NY, 11794, USA. .,Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
| | - Jaime S Ide
- Department of Biomedical Engineering, Stony Brook University School of Medicine, Stony Brook, NY, 11794, USA. .,Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA. .,Department of Science and Technology, Federal University of Sao Paulo, Sao Jose dos Campos, SP, 12231, Brazil.
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