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Charlebois CM, Anderson DN, Smith EH, Davis TS, Newman BJ, Peters AY, Arain AM, Dorval AD, Rolston JD, Butson CR. Circadian changes in aperiodic activity are correlated with seizure reduction in patients with mesial temporal lobe epilepsy treated with responsive neurostimulation. Epilepsia 2024; 65:1360-1373. [PMID: 38517356 PMCID: PMC11138949 DOI: 10.1111/epi.17938] [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: 07/28/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVES Responsive neurostimulation (RNS) is an established therapy for drug-resistant epilepsy that delivers direct electrical brain stimulation in response to detected epileptiform activity. However, despite an overall reduction in seizure frequency, clinical outcomes are variable, and few patients become seizure-free. The aim of this retrospective study was to evaluate aperiodic electrophysiological activity, associated with excitation/inhibition balance, as a novel electrographic biomarker of seizure reduction to aid early prognostication of the clinical response to RNS. METHODS We identified patients with intractable mesial temporal lobe epilepsy who were implanted with the RNS System between 2015 and 2021 at the University of Utah. We parameterized the neural power spectra from intracranial RNS System recordings during the first 3 months following implantation into aperiodic and periodic components. We then correlated circadian changes in aperiodic and periodic parameters of baseline neural recordings with seizure reduction at the most recent follow-up. RESULTS Seizure reduction was correlated significantly with a patient's average change in the day/night aperiodic exponent (r = .50, p = .016, n = 23 patients) and oscillatory alpha power (r = .45, p = .042, n = 23 patients) across patients for baseline neural recordings. The aperiodic exponent reached its maximum during nighttime hours (12 a.m. to 6 a.m.) for most responders (i.e., patients with at least a 50% reduction in seizures). SIGNIFICANCE These findings suggest that circadian modulation of baseline broadband activity is a biomarker of response to RNS early during therapy. This marker has the potential to identify patients who are likely to respond to mesial temporal RNS. Furthermore, we propose that less day/night modulation of the aperiodic exponent may be related to dysfunction in excitation/inhibition balance and its interconnected role in epilepsy, sleep, and memory.
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Affiliation(s)
- Chantel M. Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
- Department of Pharmacology & Toxicology, University of Utah, Salt Lake City, Utah, USA
- School of Biomedical Engineering, University of Sydney, Darlington, NSW, Australia
| | - Elliot H. Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Tyler S. Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Blake J. Newman
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Angela Y. Peters
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Amir M. Arain
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Alan D. Dorval
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - John D. Rolston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Department of Neurosurgery, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher R. Butson
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA
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Bódizs R, Schneider B, Ujma PP, Horváth CG, Dresler M, Rosenblum Y. Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics. Prog Neurobiol 2024; 234:102589. [PMID: 38458483 DOI: 10.1016/j.pneurobio.2024.102589] [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: 08/19/2023] [Revised: 01/26/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024]
Abstract
Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations - rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.
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Affiliation(s)
- Róbert Bódizs
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.
| | - Bence Schneider
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Péter P Ujma
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Csenge G Horváth
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Martin Dresler
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
| | - Yevgenia Rosenblum
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
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Schneider B, Szalárdy O, Ujma PP, Simor P, Gombos F, Kovács I, Dresler M, Bódizs R. Scale-free and oscillatory spectral measures of sleep stages in humans. Front Neuroinform 2022; 16:989262. [PMID: 36262840 PMCID: PMC9574340 DOI: 10.3389/fninf.2022.989262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
Power spectra of sleep electroencephalograms (EEG) comprise two main components: a decaying power-law corresponding to the aperiodic neural background activity, and spectral peaks present due to neural oscillations. “Traditional” band-based spectral methods ignore this fundamental structure of the EEG spectra and thus are susceptible to misrepresenting the underlying phenomena. A fitting method that attempts to separate and parameterize the aperiodic and periodic spectral components called “fitting oscillations and one over f” (FOOOF) was applied to a set of annotated whole-night sleep EEG recordings of 251 subjects from a wide age range (4–69 years). Most of the extracted parameters exhibited sleep stage sensitivity; significant main effects and interactions of sleep stage, age, sex, and brain region were found. The spectral slope (describing the steepness of the aperiodic component) showed especially large and consistent variability between sleep stages (and low variability between subjects), making it a candidate indicator of sleep states. The limitations and arisen problems of the FOOOF method are also discussed, possible solutions for some of them are suggested.
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Affiliation(s)
- Bence Schneider
- Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary
- Institute of Behavioural Sciences, Semmelweis University Budapest, Budapest, Hungary
- *Correspondence: Bence Schneider
| | - Orsolya Szalárdy
- Institute of Behavioural Sciences, Semmelweis University Budapest, Budapest, Hungary
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Péter P. Ujma
- Institute of Behavioural Sciences, Semmelweis University Budapest, Budapest, Hungary
| | - Péter Simor
- Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary
| | - Ferenc Gombos
- Department of General Psychology, Pázmány Péter Catholic University, Budapest, Hungary
- MTA—PPKE Adolescent Development Research Group, Budapest, Hungary
| | - Ilona Kovács
- Department of General Psychology, Pázmány Péter Catholic University, Budapest, Hungary
| | - Martin Dresler
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Róbert Bódizs
- Institute of Behavioural Sciences, Semmelweis University Budapest, Budapest, Hungary
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Zhang H, Xu M, Liu M, Song X, He F, Chen S, Ming D. Biological current source imaging method based on acoustoelectric effect: A systematic review. Front Neurosci 2022; 16:807376. [PMID: 35924223 PMCID: PMC9339687 DOI: 10.3389/fnins.2022.807376] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging can help reveal the spatial and temporal diversity of neural activity, which is of utmost importance for understanding the brain. However, conventional non-invasive neuroimaging methods do not have the advantage of high temporal and spatial resolution, which greatly hinders clinical and basic research. The acoustoelectric (AE) effect is a fundamental physical phenomenon based on the change of dielectric conductivity that has recently received much attention in the field of biomedical imaging. Based on the AE effect, a new imaging method for the biological current source has been proposed, combining the advantages of high temporal resolution of electrical measurements and high spatial resolution of focused ultrasound. This paper first describes the mechanism of the AE effect and the principle of the current source imaging method based on the AE effect. The second part summarizes the research progress of this current source imaging method in brain neurons, guided brain therapy, and heart. Finally, we discuss the problems and future directions of this biological current source imaging method. This review explores the relevant research literature and provides an informative reference for this potential non-invasive neuroimaging method.
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Affiliation(s)
- Hao Zhang
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin International Joint Research Centre for Neural Engineering, Tianjin University, Tianjin, China
| | - Miao Liu
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin International Joint Research Centre for Neural Engineering, Tianjin University, Tianjin, China
| | - Xizi Song
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin International Joint Research Centre for Neural Engineering, Tianjin University, Tianjin, China
| | - Feng He
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin International Joint Research Centre for Neural Engineering, Tianjin University, Tianjin, China
| | - Shanguang Chen
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin International Joint Research Centre for Neural Engineering, Tianjin University, Tianjin, China
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin International Joint Research Centre for Neural Engineering, Tianjin University, Tianjin, China
- *Correspondence: Dong Ming
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Chen Y, Gong C, Hao H, Guo Y, Xu S, Zhang Y, Yin G, Cao X, Yang A, Meng F, Ye J, Liu H, Zhang J, Sui Y, Li L. Automatic Sleep Stage Classification Based on Subthalamic Local Field Potentials. IEEE Trans Neural Syst Rehabil Eng 2019; 27:118-128. [PMID: 30605104 PMCID: PMC6544463 DOI: 10.1109/tnsre.2018.2890272] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Deep brain stimulation (DBS) is an established treatment for patients with Parkinson's disease (PD). Sleep disorders are common complications of PD and affected by subthalamic DBS treatment. To achieve more precise neuromodulation, chronicsleepmonitoringand closed-loop DBS toward sleep-wake cycles could potentially be utilized. Local field potential (LFP) signals that are sensed by the DBS electrode could be processed as primary feedback signals. This is the first study to systematically investigate the sleep-stage classification based on LFPs in subthalamic nucleus (STN). With our newly developed recording and transmission system, STN-LFPs were collected from 12 PD patients during wakefulness and nocturnal polysomnography sleep monitoring at one month after DBS implantation. Automatic sleep-stage classificationmodels were built with robust and interpretable machine learning methods (support vector machine and decision tree). The accuracy, sensitivity, selectivity, and specificity of the classification reached high values (above90% at most measures) at group and individual levels. Features extracted in alpha (8-13 Hz), beta (13-35 Hz), and gamma (35-50 Hz) bandswere found to contribute the most to the classification. These results will directly guide the engineering development of implantable sleepmonitoring and closed-loopDBS and pave the way for a better understanding of the STN-LFP sleep patterns.
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Boussen S, Spiegler A, Benar C, Carrère M, Bartolomei F, Metellus P, Voituriez R, Velly L, Bruder N, Trébuchon A. Time rescaling reproduces EEG behavior during transition from propofol anesthesia-induced unconsciousness to consciousness. Sci Rep 2018; 8:6015. [PMID: 29662089 PMCID: PMC5902625 DOI: 10.1038/s41598-018-24405-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 04/03/2018] [Indexed: 02/02/2023] Open
Abstract
General anesthesia (GA) is a reversible manipulation of consciousness whose mechanism is mysterious at the level of neural networks leaving space for several competing hypotheses. We recorded electrocorticography (ECoG) signals in patients who underwent intracranial monitoring during awake surgery for the treatment of cerebral tumors in functional areas of the brain. Therefore, we recorded the transition from unconsciousness to consciousness directly on the brain surface. Using frequency resolved interferometry; we studied the intermediate ECoG frequencies (4-40 Hz). In the theoretical study, we used a computational Jansen and Rit neuron model to simulate recovery of consciousness (ROC). During ROC, we found that f increased by a factor equal to 1.62 ± 0.09, and δf varied by the same factor (1.61 ± 0.09) suggesting the existence of a scaling factor. We accelerated the time course of an unconscious EEG trace by an approximate factor 1.6 and we showed that the resulting EEG trace match the conscious state. Using the theoretical model, we successfully reproduced this behavior. We show that the recovery of consciousness corresponds to a transition in the frequency (f, δf) space, which is exactly reproduced by a simple time rescaling. These findings may perhaps be applied to other altered consciousness states.
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Affiliation(s)
- S Boussen
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France.
- Aix Marseille Université, IFSTTAR, LBA UMR_T 24, 13916, Marseille, France.
| | - A Spiegler
- Institut de Neurosciences des Systèmes - Inserm UMR1106 - Aix-Marseille Université - Faculté de Médecine, 27, Boulevard Jean Moulin, 13005, Marseille, France
| | - C Benar
- Institut de Neurosciences des Systèmes - Inserm UMR1106 - Aix-Marseille Université - Faculté de Médecine, 27, Boulevard Jean Moulin, 13005, Marseille, France
| | - M Carrère
- Institut de Neurosciences des Systèmes - Inserm UMR1106 - Aix-Marseille Université - Faculté de Médecine, 27, Boulevard Jean Moulin, 13005, Marseille, France
| | - F Bartolomei
- Institut de Neurosciences des Systèmes - Inserm UMR1106 - Aix-Marseille Université - Faculté de Médecine, 27, Boulevard Jean Moulin, 13005, Marseille, France
- Clinical Electrophysiology Department, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - P Metellus
- Neurosurgery Department, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - R Voituriez
- Laboratoire Jean Perrin-UMR 8237 CNRS Université Pierre et Marie Curie, 75005, Paris, France
| | - L Velly
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
- Institut des Neurciences de la Timone, CNRS UMR1106 - Aix-Marseille Université - Faculté de Médecine, 27, Boulevard Jean Moulin, 13005, Marseille, France
| | - N Bruder
- Department of Anesthesiology and Intensive Care, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
| | - A Trébuchon
- Institut de Neurosciences des Systèmes - Inserm UMR1106 - Aix-Marseille Université - Faculté de Médecine, 27, Boulevard Jean Moulin, 13005, Marseille, France
- Clinical Electrophysiology Department, CHU Timone, Assistance Publique Hôpitaux de Marseille, Aix Marseille Université, 264 rue Saint-Pierre, 13005, Marseille, France
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Association between Scale-Free Brain Dynamics and Behavioral Performance: Functional MRI Study in Resting State and Face Processing Task. Behav Neurol 2018; 2017:2824615. [PMID: 29430081 PMCID: PMC5752971 DOI: 10.1155/2017/2824615] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 10/23/2017] [Accepted: 11/01/2017] [Indexed: 12/20/2022] Open
Abstract
The scale-free dynamics of human brain activity, characterized by an elaborate temporal structure with scale-free properties, can be quantified using the power-law exponent (PLE) as an index. Power laws are well documented in nature in general, particularly in the brain. Some previous fMRI studies have demonstrated a lower PLE during cognitive-task-evoked activity than during resting state activity. However, PLE modulation during cognitive-task-evoked activity and its relationship with an associated behavior remain unclear. In this functional fMRI study in the resting state and face processing + control task, we investigated PLE during both the resting state and task-evoked activities, as well as its relationship with behavior measured using mean reaction time (mRT) during the task. We found that (1) face discrimination-induced BOLD signal changes in the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), amygdala, and fusiform face area; (2) PLE significantly decreased during task-evoked activity specifically in mPFC compared with resting state activity; (3) most importantly, in mPFC, mRT significantly negatively correlated with both resting state PLE and the resting-task PLE difference. These results may lead to a better understanding of the associations between task performance parameters (e.g., mRT) and the scale-free dynamics of spontaneous and task-evoked brain activities.
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Alpha Oscillations Reduce Temporal Long-Range Dependence in Spontaneous Human Brain Activity. J Neurosci 2017; 38:755-764. [PMID: 29167403 DOI: 10.1523/jneurosci.0831-17.2017] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 10/18/2017] [Accepted: 11/12/2017] [Indexed: 01/26/2023] Open
Abstract
Ongoing neural dynamics comprise both frequency-specific oscillations and broadband-features, such as long-range dependence (LRD). Despite both being behaviorally relevant, little is known about their potential interactions. In humans, 8-12 Hz α oscillations constitute the strongest deviation from 1/f power-law scaling, the signature of LRD. We postulated that α oscillations, believed to exert active inhibitory gating, downmodulate the temporal width of LRD in slower ongoing brain activity. In two independent "resting-state" datasets (electroencephalography surface recordings and magnetoencephalography source reconstructions), both across space and dynamically over time, power of α activity covaried with the power slope <5 Hz (i.e., greater α activity shortened LRD). Causality of α activity dynamics was implied by its temporal precedence over changes of slope. A model where power-law fluctuations of the α envelope inhibit baseline activity closely replicated our results. Thus, α oscillations may provide an active control mechanism to adaptively regulate LRD of brain activity at slow temporal scales, thereby shaping internal states and cognitive processes.SIGNIFICANCE STATEMENT The two prominent features of ongoing brain activity are oscillations and temporal long-range dependence. Both shape behavioral performance, but little is known about their interaction. Here, we demonstrate such an interaction in EEG and MEG recordings of task-free human brain activity. Specifically, we show that spontaneous dynamics in alpha activity explain ensuing variations of dependence in the low and ultra-low-frequency range. In modeling, two features of alpha oscillations are critical to account for the observed effects on long-range dependence, scale-free properties of alpha oscillations themselves, and a modulation of baseline levels, presumably inhibitory. Both these properties have been observed empirically, and our study hence establishes alpha oscillations as a regulatory mechanism governing long-range dependence or "memory" in slow ongoing brain activity.
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Kremen V, Duque JJ, Brinkmann BH, Berry BM, Kucewicz MT, Khadjevand F, Van Gompel J, Stead M, St Louis EK, Worrell GA. Behavioral state classification in epileptic brain using intracranial electrophysiology. J Neural Eng 2017; 14:026001. [PMID: 28050973 PMCID: PMC5460075 DOI: 10.1088/1741-2552/aa5688] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. APPROACH Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. MAIN RESULTS Classification accuracy of 97.8 ± 0.3% (normal tissue) and 89.4 ± 0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8 ± 0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1 ± 1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy ⩾90% using a single electrode contact and single spectral feature. SIGNIFICANCE Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.
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Affiliation(s)
- Vaclav Kremen
- Department of Neurology, Mayo Systems Electrophysiology Laboratory, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Zikova street 1903/4, 166 36 Prague 6, Czech Republic. Department of Physiology and Biomedical Engineering, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
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Huang Z, Obara N, Davis HH, Pokorny J, Northoff G. The temporal structure of resting-state brain activity in the medial prefrontal cortex predicts self-consciousness. Neuropsychologia 2016; 82:161-170. [PMID: 26805557 DOI: 10.1016/j.neuropsychologia.2016.01.025] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 12/23/2015] [Accepted: 01/20/2016] [Indexed: 02/06/2023]
Abstract
Recent studies have demonstrated an overlap between the neural substrate of resting-state activity and self-related processing in the cortical midline structures (CMS). However, the neural and psychological mechanisms mediating this so-called "rest-self overlap" remain unclear. To investigate the neural mechanisms, we estimated the temporal structure of spontaneous/resting-state activity, e.g. its long-range temporal correlations or self-affinity across time as indexed by the power-law exponent (PLE). The PLE was obtained in resting-state activity in the medial prefrontal cortex (MPFC) and the posterior cingulate cortex (PCC) in 47 healthy subjects by functional magnetic resonance imaging (fMRI). We performed correlation analyses of the PLE and Revised Self-Consciousness Scale (SCSR) scores, which enabled us to access different dimensions of self-consciousness and specified rest-self overlap in a psychological regard. The PLE in the MPFC's resting-state activity correlated with private self-consciousness scores from the SCSR. Conversely, we found no correlation between the PLE and the other subscales of the SCSR (public, social) or between other resting-state measures, including functional connectivity, and the SCSR subscales. This is the first evidence for the association between the scale-free dynamics of resting-state activity in the CMS and the private dimension of self-consciousness. This finding implies the relationship of especially the private dimension of self with the temporal structure of resting-state activity.
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Affiliation(s)
- Zirui Huang
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada K1Z 7K4.
| | - Natsuho Obara
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada K1Z 7K4; Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada K1H 8M5
| | | | - Johanna Pokorny
- Department of Anthropology, University of Toronto, Toronto, ON, Canada M5S 2S2
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada K1Z 7K4; Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 311121, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou 310015, PR China; Taipei Medical University, Graduate Institute of Humanities in Medicine, Taipei, Taiwan; Taipei Medical University-Shuang Ho Hospital, Brain and Consciousness Research Center, New Taipei City, Taiwan
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11
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Pahwa M, Kusner M, Hacker CD, Bundy DT, Weinberger KQ, Leuthardt EC. Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications. PLoS One 2015; 10:e0142947. [PMID: 26562013 PMCID: PMC4643046 DOI: 10.1371/journal.pone.0142947] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 10/28/2015] [Indexed: 11/18/2022] Open
Abstract
Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70-110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.
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Affiliation(s)
- Mrinal Pahwa
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, United States of America
- * E-mail:
| | - Matthew Kusner
- Department of Computer Science and Engineering, Washington University, St. Louis, Missouri, United States of America
| | - Carl D. Hacker
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, United States of America
- School of Medicine, Washington University, St. Louis, Missouri, United States of America
| | - David T. Bundy
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, United States of America
| | - Kilian Q. Weinberger
- Department of Computer Science and Engineering, Washington University, St. Louis, Missouri, United States of America
| | - Eric C. Leuthardt
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, United States of America
- School of Medicine, Washington University, St. Louis, Missouri, United States of America
- Department of Neurological Surgery, Washington University, St. Louis, Missouri, United States of America
- Center for Innovation in Neuroscience and Technology, Washington University, St. Louis, Missouri, United States of America
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12
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Allegrini P, Paradisi P, Menicucci D, Laurino M, Piarulli A, Gemignani A. Self-organized dynamical complexity in human wakefulness and sleep: different critical brain-activity feedback for conscious and unconscious states. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 92:032808. [PMID: 26465529 PMCID: PMC4909144 DOI: 10.1103/physreve.92.032808] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Indexed: 06/05/2023]
Abstract
Criticality reportedly describes brain dynamics. The main critical feature is the presence of scale-free neural avalanches, whose auto-organization is determined by a critical branching ratio of neural-excitation spreading. Other features, directly associated to second-order phase transitions, are: (i) scale-free-network topology of functional connectivity, stemming from suprathreshold pairwise correlations, superimposable, in waking brain activity, with that of ferromagnets at Curie temperature; (ii) temporal long-range memory associated to renewal intermittency driven by abrupt fluctuations in the order parameters, detectable in human brain via spatially distributed phase or amplitude changes in EEG activity. Herein we study intermittent events, extracted from 29 night EEG recordings, including presleep wakefulness and all phases of sleep, where different levels of mentation and consciousness are present. We show that while critical avalanching is unchanged, at least qualitatively, intermittency and functional connectivity, present during conscious phases (wakefulness and REM sleep), break down during both shallow and deep non-REM sleep. We provide a theory for fragmentation-induced intermittency breakdown and suggest that the main difference between conscious and unconscious states resides in the backwards causation, namely on the constraints that the emerging properties at large scale induce to the lower scales. In particular, while in conscious states this backwards causation induces a critical slowing down, preserving spatiotemporal correlations, in dreamless sleep we see a self-organized maintenance of moduli working in parallel. Critical avalanches are still present, and establish transient auto-organization, whose enhanced fluctuations are able to trigger sleep-protecting mechanisms that reinstate parallel activity. The plausible role of critical avalanches in dreamless sleep is to provide a rapid recovery of consciousness, if stimuli are highly arousing.
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Affiliation(s)
- Paolo Allegrini
- Istituto di Scienze della Vita, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 7, 56127 Pisa, Italy
- Istituto di Fisiologia Clinica (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy
| | - Paolo Paradisi
- Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" (ISTI-CNR), Via Moruzzi 1, 56124 Pisa, Italy
| | - Danilo Menicucci
- Istituto di Fisiologia Clinica (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy
- Dipartimento di Ricerca Traslazionale e delle Nuove Tecnologie in Medicina e Chirurgia, Via Savi 10, 56126 Pisa, Italy
| | - Marco Laurino
- Istituto di Scienze della Vita, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 7, 56127 Pisa, Italy
- Istituto di Fisiologia Clinica (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy
| | - Andrea Piarulli
- PERCRO laboratory, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 7, 56127 Pisa, Italy
| | - Angelo Gemignani
- Istituto di Scienze della Vita, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 7, 56127 Pisa, Italy
- Istituto di Fisiologia Clinica (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy
- Dipartimento di Patologia Chirurgica, Medica, Molecolare e dell'Area Critica, Università di Pisa, Via Savi 10, 56126 Pisa, Italy
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Power laws from linear neuronal cable theory: power spectral densities of the soma potential, soma membrane current and single-neuron contribution to the EEG. PLoS Comput Biol 2014; 10:e1003928. [PMID: 25393030 PMCID: PMC4230751 DOI: 10.1371/journal.pcbi.1003928] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Accepted: 09/19/2014] [Indexed: 02/04/2023] Open
Abstract
Power laws, that is, power spectral densities (PSDs) exhibiting behavior for large frequencies f, have been observed both in microscopic (neural membrane potentials and currents) and macroscopic (electroencephalography; EEG) recordings. While complex network behavior has been suggested to be at the root of this phenomenon, we here demonstrate a possible origin of such power laws in the biophysical properties of single neurons described by the standard cable equation. Taking advantage of the analytical tractability of the so called ball and stick neuron model, we derive general expressions for the PSD transfer functions for a set of measures of neuronal activity: the soma membrane current, the current-dipole moment (corresponding to the single-neuron EEG contribution), and the soma membrane potential. These PSD transfer functions relate the PSDs of the respective measurements to the PSDs of the noisy input currents. With homogeneously distributed input currents across the neuronal membrane we find that all PSD transfer functions express asymptotic high-frequency power laws with power-law exponents analytically identified as for the soma membrane current, for the current-dipole moment, and for the soma membrane potential. Comparison with available data suggests that the apparent power laws observed in the high-frequency end of the PSD spectra may stem from uncorrelated current sources which are homogeneously distributed across the neural membranes and themselves exhibit pink () noise distributions. While the PSD noise spectra at low frequencies may be dominated by synaptic noise, our findings suggest that the high-frequency power laws may originate in noise from intrinsic ion channels. The significance of this finding goes beyond neuroscience as it demonstrates how power laws with a wide range of values for the power-law exponent α may arise from a simple, linear partial differential equation. The common observation of power laws in nature and society, that is, quantities or probabilities that follow distributions, has for long intrigued scientists. In the brain, power laws in the power spectral density (PSD) have been reported in electrophysiological recordings, both at the microscopic (single-neuron recordings) and macroscopic (EEG) levels. We here demonstrate a possible origin of such power laws in the basic biophysical properties of neurons, that is, in the standard cable-equation description of neuronal membranes. Taking advantage of the mathematical tractability of the so called ball and stick neuron model, we demonstrate analytically that high-frequency power laws in key experimental neural measures will arise naturally when the noise sources are evenly distributed across the neuronal membrane. Comparison with available data further suggests that the apparent high-frequency power laws observed in experiments may stem from uncorrelated current sources, presumably intrinsic ion channels, which are homogeneously distributed across the neural membranes and themselves exhibit pink () noise distributions. The significance of this finding goes beyond neuroscience as it demonstrates how power laws power-law exponents α may arise from a simple, linear physics equation.
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Ramon C, Holmes MD. Spatiotemporal phase clusters and phase synchronization patterns derived from high density EEG and ECoG recordings. Curr Opin Neurobiol 2014; 31:127-32. [PMID: 25460068 DOI: 10.1016/j.conb.2014.10.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Revised: 08/12/2014] [Accepted: 10/01/2014] [Indexed: 10/24/2022]
Abstract
High density scalp EEG and subdural ECoG recordings provide an opportunity to map the electrical activity of the cortex with high spatial resolution. The spatial power spectral densities conform to a power law distribution with some nonlinear variations. The spatiotemporal patterns of phase derived from these data sets have unique features, such as, amplitude and phase modulation waves and also exhibited formation of spatial phase cluster patterns. These unique features represent different cognitive states and are different between normal and diseased states. Reported results show that the rate of formation of phase cluster patterns derived from the seizure-free interictal EEG data are higher in epileptogenic zones as compared with nearby normal areas of the brain.
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Affiliation(s)
- Ceon Ramon
- Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA; Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
| | - Mark D Holmes
- Department of Neurology, University of Washington, Seattle, WA 98195, USA
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15
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Ochab JK, Tyburczyk J, Beldzik E, Chialvo DR, Domagalik A, Fafrowicz M, Gudowska-Nowak E, Marek T, Nowak MA, Oginska H, Szwed J. Scale-free fluctuations in behavioral performance: delineating changes in spontaneous behavior of humans with induced sleep deficiency. PLoS One 2014; 9:e107542. [PMID: 25222128 PMCID: PMC4164638 DOI: 10.1371/journal.pone.0107542] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 08/19/2014] [Indexed: 02/05/2023] Open
Abstract
The timing and dynamics of many diverse behaviors of mammals, e.g., patterns of animal foraging or human communication in social networks exhibit complex self-similar properties reproducible over multiple time scales. In this paper, we analyze spontaneous locomotor activity of healthy individuals recorded in two different conditions: during a week of regular sleep and a week of chronic partial sleep deprivation. After separating activity from rest with a pre-defined activity threshold, we have detected distinct statistical features of duration times of these two states. The cumulative distributions of activity periods follow a stretched exponential shape, and remain similar for both control and sleep deprived individuals. In contrast, rest periods, which follow power-law statistics over two orders of magnitude, have significantly distinct distributions for these two groups and the difference emerges already after the first night of shortened sleep. We have found steeper distributions for sleep deprived individuals, which indicates fewer long rest periods and more turbulent behavior. This separation of power-law exponents is the main result of our investigations, and might constitute an objective measure demonstrating the severity of sleep deprivation and the effects of sleep disorders.
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Affiliation(s)
- Jeremi K. Ochab
- M. Kac Complex Systems Research Center and M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
- * E-mail:
| | - Jacek Tyburczyk
- M. Kac Complex Systems Research Center and M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| | - Ewa Beldzik
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, Poland
- Neurobiology Department, Małopolska Center of Biotechnology, Jagiellonian University, Kraków, Poland
| | | | - Aleksandra Domagalik
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, Poland
- Neurobiology Department, Małopolska Center of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, Poland
- Neurobiology Department, Małopolska Center of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Ewa Gudowska-Nowak
- M. Kac Complex Systems Research Center and M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
- Biocomplexity Department, Małopolska Center of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, Poland
- Neurobiology Department, Małopolska Center of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Maciej A. Nowak
- M. Kac Complex Systems Research Center and M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| | - Halszka Oginska
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, Poland
| | - Jerzy Szwed
- M. Kac Complex Systems Research Center and M. Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
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Kelsey M, Politte D, Verner R, Zempel JM, Nolan T, Babajani-Feremi A, Prior F, Larson-Prior LJ. Determination of neural state classification metrics from the power spectrum of human ECoG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:4336-40. [PMID: 23366887 DOI: 10.1109/embc.2012.6346926] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Brain electrical activity exhibits scale-free dynamics that follow power law scaling. Previous works have shown that broadband spectral power exhibits state-dependent scaling with a log frequency exponent that systematically varies with neural state. However, the frequency ranges which best characterize biological state are not consistent across brain location or subject. An adaptive piecewise linear fitting solution was developed to extract features for classification of brain state. Performance was evaluated by comparison to an a posteriori based feature search method. This analysis, using the 1/ƒ characteristics of the human ECoG signal, demonstrates utility in advancing the ability to perform automated brain state discrimination.
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Affiliation(s)
- Matthew Kelsey
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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