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Joshi RB, Duckrow RB, Goncharova II, Hirsch LJ, Spencer DD, Godwin DW, Zaveri HP. Stability of infraslow correlation structure in time-shifted intracranial EEG signals. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1441294. [PMID: 39258030 PMCID: PMC11384574 DOI: 10.3389/fnetp.2024.1441294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 08/19/2024] [Indexed: 09/12/2024]
Abstract
It is increasingly understood that the epilepsies are characterized by network pathology that can span multiple spatial and temporal scales. Recent work indicates that infraslow (<0.2 Hz) envelope correlations may form a basis for distant spatial coupling in the brain. We speculated that infraslow correlation structure may be preserved even with some time lag between signals. To this end, we studied intracranial EEG (icEEG) data collected from 22 medically refractory epilepsy patients. For each patient, we selected hour-long background, awake icEEG epochs before and after antiseizure medication (ASM) taper. For each epoch, we selected 5,000 random electrode contact pairs and estimated magnitude-squared coherence (MSC) below 0.15 Hz of band power time-series in the traditional EEG frequency bands. Using these same contact pairs, we shifted one signal of the pair by random durations in 15-s increments between 0 and 300 s. We aggregated these data across all patients to determine how infraslow MSC varies with duration of lag. We further examined the effect of ASM taper on infraslow correlation structure. We also used surrogate data to empirically characterize MSC estimator and to set optimal parameters for estimation specifically for the study of infraslow activity. Our empirical analysis of the MSC estimator showed that hour-long segments with MSC computed using 3-min windows with 50% overlap was sufficient to capture infraslow envelope correlations while minimizing estimator bias and variance. The mean MSC decreased monotonically with increasing time lag until 105 s of lag, then plateaued between 106 and 300 s. Significantly nonzero infraslow envelope MSC was preserved in all frequency bands until about 1 min of time lag, both pre- and post-ASM taper. We also saw a slight, but significant increase in infraslow MSC post-ASM taper, consistent with prior work. These results provide evidence for the feasibility of examining infraslow activity via its modulation of higher-frequency activity in the absence of DC-coupled recordings. The use of surrogate data also provides a general methodology for benchmarking measures used in network neuroscience studies. Finally, our study points to the clinical relevance of infraslow activity in assessing seizure risk.
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Affiliation(s)
- Rasesh B Joshi
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Yale Clinical Neuroscience Neuroanalytics, Yale University, New Haven, CT, United States
| | - Robert B Duckrow
- Yale Clinical Neuroscience Neuroanalytics, Yale University, New Haven, CT, United States
- Department of Neurology, Yale University, New Haven, CT, United States
| | - Irina I Goncharova
- Yale Clinical Neuroscience Neuroanalytics, Yale University, New Haven, CT, United States
- Department of Neurology, Yale University, New Haven, CT, United States
| | - Lawrence J Hirsch
- Department of Neurology, Yale University, New Haven, CT, United States
| | - Dennis D Spencer
- Yale Clinical Neuroscience Neuroanalytics, Yale University, New Haven, CT, United States
- Department of Neurosurgery, Yale University, New Haven, CT, United States
| | - Dwayne W Godwin
- Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Hitten P Zaveri
- Yale Clinical Neuroscience Neuroanalytics, Yale University, New Haven, CT, United States
- Department of Neurology, Yale University, New Haven, CT, United States
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Raghavan M, Pilet J, Carlson C, Anderson CT, Mueller W, Lew S, Ustine C, Shah-Basak P, Youssofzadeh V, Beardsley SA. Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy. Sci Rep 2024; 14:17736. [PMID: 39085280 PMCID: PMC11291981 DOI: 10.1038/s41598-024-67120-8] [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: 04/23/2024] [Accepted: 07/08/2024] [Indexed: 08/02/2024] Open
Abstract
Methods to quantify cortical hyperexcitability are of enormous interest for mapping epileptic networks in patients with focal epilepsy. We hypothesize that, in the resting state, cortical hyperexcitability increases firing-rate correlations between neuronal populations within seizure onset zones (SOZs). This hypothesis predicts that in the gamma frequency band (40-200 Hz), amplitude envelope correlations (AECs), a relatively straightforward measure of functional connectivity, should be elevated within SOZs compared to other areas. To test this prediction, we analyzed archived samples of interictal electrocorticographic (ECoG) signals recorded from patients who became seizure-free after surgery targeting SOZs identified by multiday intracranial recordings. We show that in the gamma band, AECs between nodes within SOZs are markedly elevated relative to those elsewhere. AEC-based node strength, eigencentrality, and clustering coefficient are also robustly increased within the SOZ with maxima in the low-gamma band (permutation test Z-scores > 8) and yield moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73). By contrast to AECs, phase locking values (PLVs), a measure of narrow-band phase coupling across sites, and PLV-based graph metrics discriminate the seizure onset nodes weakly. Our results suggest that gamma band AECs may provide a clinically useful marker of cortical hyperexcitability in focal epilepsy.
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Affiliation(s)
- Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
| | - Jared Pilet
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA
| | - Chad Carlson
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | | | - Wade Mueller
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Sean Lew
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Priyanka Shah-Basak
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Vahab Youssofzadeh
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Scott A Beardsley
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA
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Tajmirriahi M, Rabbani H. A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data. JOURNAL OF MEDICAL SIGNALS & SENSORS 2024; 14:19. [PMID: 39234592 PMCID: PMC11373807 DOI: 10.4103/jmss.jmss_11_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/07/2024] [Accepted: 05/24/2024] [Indexed: 09/06/2024]
Abstract
Unexpected seizures significantly decrease the quality of life in epileptic patients. Seizure attacks are caused by hyperexcitability and anatomical lesions of special regions of the brain, and cognitive impairments and memory deficits are their most common concomitant effects. In addition to seizure reduction treatments, medical rehabilitation involving brain-computer interfaces and neurofeedback can improve cognition and quality of life in patients with focal epilepsy in most cases, in particular when resective epilepsy surgery has been considered treatment in drug-resistant epilepsy. Source estimation and precise localization of epileptic foci can improve such rehabilitation and treatment. Electroencephalography (EEG) monitoring and multimodal noninvasive neuroimaging techniques such as ictal/interictal single-photon emission computerized tomography (SPECT) imaging and structural magnetic resonance imaging are common practices for the localization of epileptic foci and have been studied in several kinds of researches. In this article, we review the most recent research on EEG-based localization of seizure foci and discuss various methods, their advantages, limitations, and challenges with a focus on model-based data processing and machine learning algorithms. In addition, we survey whether combined analysis of EEG monitoring and neuroimaging techniques, which is known as multimodal brain data fusion, can potentially increase the precision of the seizure foci localization. To this end, we further review and summarize the key parameters and challenges of processing, fusion, and analysis of multiple source data, in the framework of model-based signal processing, for the development of a multimodal brain data analyzing system. This article has the potential to be used as a valuable resource for neuroscience researchers for the development of EEG-based rehabilitation systems based on multimodal data analysis related to focal epilepsy.
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Affiliation(s)
- Mahnoosh Tajmirriahi
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Rabbani
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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Lucasius C, Grigorovsky V, Nariai H, Galanopoulou AS, Gursky J, Moshe SL, Bardakjian BL. Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction. IEEE Trans Biomed Eng 2024; 71:1056-1067. [PMID: 37851549 PMCID: PMC10979638 DOI: 10.1109/tbme.2023.3325762] [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] [Indexed: 10/20/2023]
Abstract
OBJECTIVE In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models. METHODS Our proposed model incorporates modular Volterra kernel convolutional networks and bidirectional recurrent networks in combination with the phase amplitude cross-frequency coupling features derived from scalp EEG. They are applied to the standard CHB-MIT dataset containing focal epilepsy episodes as well as two other datasets from the Montefiore Medical Center and the University of California Los Angeles that provide data of patients experiencing infantile spasm (IS) syndrome. RESULTS Overall, in this study, the networks can produce accurate predictions (100%) and significant detection latencies (10 min). Furthermore, the biomimetic network outperforms conventional ones by producing no false positives. SIGNIFICANCE Biomimetic neural networks utilize extensive knowledge about processing and learning in the electrical networks of the brain. Predicting seizures in adults can improve their quality of life. Epileptic spasms in infants are part of a particular seizure type that needs identifying when suspicious behaviors are noticed in babies. Predicting epileptic spasms within a given time frame (the prediction horizon) suggests their existence and allows an epileptologist to flag an EEG trace for future review.
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Ilyas A, Alamoudi OA, Riley KO, Pati S. Pro-Ictal State in Human Temporal Lobe Epilepsy. NEJM EVIDENCE 2023; 2:EVIDoa2200187. [PMID: 38320014 DOI: 10.1056/evidoa2200187] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
BACKGROUND: Studies of continuous electroencephalography (EEG) suggest that seizures in individuals with focal-onset epilepsies preferentially occur during periods of heightened risk, typified by pathologic brain activities, termed pro-ictal states; however, the presence of (pathologic) pro-ictal states among a plethora of otherwise physiologic (e.g., sleep–wake cycle) states has not been established. METHODS: We studied a prospective, consecutive series of 15 patients with temporal lobe epilepsy who underwent limbic thalamic recordings in addition to routine (cortical) intracranial EEG for seizure localization. For each participant, pro-ictal (45 minutes before seizure onset) and interictal (4 hours removed from all seizures) EEG segments were divided into 10-minute, nonoverlapping windows, which were randomly distributed into training and validation cohorts in a 1:1 ratio. A deep neural classifier was applied to distinguish pro-ictal from interictal brain activities in a patient-specific fashion. RESULTS: We analyzed 1800 patient-hours of continuous thalamocortical EEG. Distinct pro-ictal states were detected in each participant. The median area under the receiver-operating characteristic curve of the classifier was 0.92 (interquartile range, 0.90–0.96). Pro-ictal states were distinguished at least 45 minutes before seizure onset in 13 of 15 participants; in 2 of 15 participants, they were distinguished up to 35 minutes prior. CONCLUSIONS: On the basis of thalamocortical EEG, pro-ictal states — pathologic brain activities during periods of heightened seizure risk — could be identified in patients with temporal lobe epilepsy and were detected, in our small sample, more than one half hour before seizure onset.
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Affiliation(s)
- Adeel Ilyas
- Department of Neurological Surgery, University of Alabama at Birmingham, Birmingham, AL
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School at UTHealth Houston, Houston
- Texas Institute for Restorative Neurotechnologies, UTHealth Houston, Houston
| | - Omar A Alamoudi
- Texas Institute for Restorative Neurotechnologies, UTHealth Houston, Houston
- Department of Neurology, McGovern Medical School at UTHealth Houston, Houston
- Department of Biomedical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Kristen O Riley
- Department of Neurological Surgery, University of Alabama at Birmingham, Birmingham, AL
| | - Sandipan Pati
- Texas Institute for Restorative Neurotechnologies, UTHealth Houston, Houston
- Department of Neurology, McGovern Medical School at UTHealth Houston, Houston
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Kropotov JD. The enigma of infra-slow fluctuations in the human EEG. Front Hum Neurosci 2022; 16:928410. [PMID: 35982689 PMCID: PMC9378968 DOI: 10.3389/fnhum.2022.928410] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
Spontaneous Infra-Slow Fluctuations (ISFs) of the human EEG (EEG-ISFs) were discovered 60 years ago when appropriate amplifiers for their recordings were designed. To avoid skin-related artifacts the recording of EEG-ISFs required puncturing the skin under the electrode. In the beginning of the 21st century the interest in EEG-ISFs was renewed with the appearance of commercially available DC-coupled amplified and by observation of ISFs of the blood oxygen level–dependent (BOLD) functional magnetic resonance imaging signal at a similar frequency. The independent components of irregular EEG-ISFs were shown to correlate with BOLD signals which in turn were driven by changes in arousal level measured by galvanic skin response (GSR), pupil size and HRV. There is no consensus regarding the temporal organization of EEG-ISFs: some studies emphasize the absence of peaks on EEG-ISFs spectra, some studies report prominent oscillations with frequency around 0.1 or 0.02 Hz, while some emphasize multiple discrete infraslow oscillations. No studies used parameters of EEG-ISFs as neuromarkers to discriminate psychiatric patients from healthy controls. Finally, a set of working hypotheses is suggested that must be tested in future research to solve the enigma of EEG-ISFs.
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Hashimoto H, Ming Khoo H, Yanagisawa T, Tani N, Oshino S, Hirata M, Kishima H. Frequency band coupling with high-frequency activities in tonic-clonic seizures shifts from θ to δ band. Clin Neurophysiol 2022; 137:122-131. [DOI: 10.1016/j.clinph.2022.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 01/24/2022] [Accepted: 02/15/2022] [Indexed: 11/25/2022]
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Wang GH, Chuang AY, Lai YC, Chen HI, Hsueh SW, Yang YC. Pre- and post-synaptic A-type K + channels regulate glutamatergic transmission and switch of the network into epileptiform oscillations. Br J Pharmacol 2022; 179:3754-3777. [PMID: 35170022 DOI: 10.1111/bph.15818] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/28/2021] [Accepted: 02/02/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Anticonvulsants targeting K+ channels have not been clinically available, although neuronal hyperexcitability in seizures could be suppressed by activation of K+ channels. Voltage-gated A-type K+ channel (A-channel) inhibitors may be prescribed for diseases of neuromuscular junction but could cause seizures. Consistently, genetic loss of function of A-channels may also cause seizures. It is unclear why inhibition of A-channels, if compared with the other types of K+ channels, is particularly prone to seizure induction. This hinders the development of relevant therapeutic interventions. EXPERIMENTAL APPROACH The epileptogenic mechanisms of A-channel inhibition and antiepileptic actions of A-channel activation were investigated in electrophysiological and behavioral seizures with pharmacological and optogenetic maneuvers. KEY RESULTS Presynaptic Kv1.4 and postsynaptic Kv4.3 A-channels act synergistically to gate glutamatergic transmission and control rhythmogenesis in the amygdala. The interconnected neurons set into the oscillatory mode by A-channel inhibition would reverberate with regular paces and the same top frequency, demonstrating a spatiotemporally well-orchestrated system with built-in oscillatory rhythms normally curbed by A-channels. Accordingly, selective over-excitation of glutamatergic neurons or inhibition of A-channels suffices to induce behavioral seizures, which are effectively ameliorated by A-channel activators such as NS-5806 or AMPA receptor antagonists such as perampanel. CONCLUSION AND IMPLICATIONS Transsynaptic voltage-dependent A-channels serve as a biophysical-biochemical transducer responsible for a novel form of synaptic plasticity. Such a network-level switch into and out of the oscillatory mode may underlie a wide-scope of telencephalic information processing, or to its extreme, epileptic seizures. A-channels thus constitute a potential target of antiepileptic therapy.
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Affiliation(s)
- Guan-Hsun Wang
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
| | - Ai-Yu Chuang
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Yi-Chen Lai
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Hsin-I Chen
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, 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, Tao-Yuan, 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.,Department of Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
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Jafarian A, Wykes RC. Impact of DC-Coupled Electrophysiological Recordings for Translational Neuroscience: Case Study of Tracking Neural Dynamics in Rodent Models of Seizures. Front Comput Neurosci 2022; 16:900063. [PMID: 35936824 PMCID: PMC9351053 DOI: 10.3389/fncom.2022.900063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/15/2022] [Indexed: 11/29/2022] Open
Abstract
We propose that to fully understand biological mechanisms underlying pathological brain activity with transitions (e.g., into and out of seizures), wide-bandwidth electrophysiological recordings are important. We demonstrate the importance of ultraslow potential shifts and infraslow oscillations for reliable tracking of synaptic physiology, within a neural mass model, from brain recordings that undergo pathological phase transitions. We use wide-bandwidth data (direct current (DC) to high-frequency activity), recorded using epidural and penetrating graphene micro-transistor arrays in a rodent model of acute seizures. Using this technological approach, we capture the dynamics of infraslow changes that contribute to seizure initiation (active pre-seizure DC shifts) and progression (passive DC shifts). By employing a continuous-discrete unscented Kalman filter, we track biological mechanisms from full-bandwidth data with and without active pre-seizure DC shifts during paroxysmal transitions. We then apply the same methodological approach for tracking the same parameters after application of high-pass-filtering >0.3Hz to both data sets. This approach reveals that ultraslow potential shifts play a fundamental role in the transition to seizure, and the use of high-pass-filtered data results in the loss of key information in regard to seizure onset and termination dynamics.
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Affiliation(s)
- Amirhossein Jafarian
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, United Kingdom
| | - Rob C Wykes
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Nanomedicine Lab, University of Manchester, Manchester, United Kingdom
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