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Riddle J, Schooler JW. Hierarchical consciousness: the Nested Observer Windows model. Neurosci Conscious 2024; 2024:niae010. [PMID: 38504828 PMCID: PMC10949963 DOI: 10.1093/nc/niae010] [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: 11/08/2023] [Revised: 01/31/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024] Open
Abstract
Foremost in our experience is the intuition that we possess a unified conscious experience. However, many observations run counter to this intuition: we experience paralyzing indecision when faced with two appealing behavioral choices, we simultaneously hold contradictory beliefs, and the content of our thought is often characterized by an internal debate. Here, we propose the Nested Observer Windows (NOW) Model, a framework for hierarchical consciousness wherein information processed across many spatiotemporal scales of the brain feeds into subjective experience. The model likens the mind to a hierarchy of nested mosaic tiles-where an image is composed of mosaic tiles, and each of these tiles is itself an image composed of mosaic tiles. Unitary consciousness exists at the apex of this nested hierarchy where perceptual constructs become fully integrated and complex behaviors are initiated via abstract commands. We define an observer window as a spatially and temporally constrained system within which information is integrated, e.g. in functional brain regions and neurons. Three principles from the signal analysis of electrical activity describe the nested hierarchy and generate testable predictions. First, nested observer windows disseminate information across spatiotemporal scales with cross-frequency coupling. Second, observer windows are characterized by a high degree of internal synchrony (with zero phase lag). Third, observer windows at the same spatiotemporal level share information with each other through coherence (with non-zero phase lag). The theoretical framework of the NOW Model accounts for a wide range of subjective experiences and a novel approach for integrating prominent theories of consciousness.
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Affiliation(s)
- Justin Riddle
- Department of Psychology, Florida State University, 1107 W Call St, Tallahassee, FL 32304, USA
| | - Jonathan W Schooler
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, Psychological & Brain Sciences, Santa Barbara, CA 93106, USA
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Miao Y, Iimura Y, Sugano H, Fukumori K, Tanaka T. Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram. Cogn Neurodyn 2023; 17:1591-1607. [PMID: 37969944 PMCID: PMC10640557 DOI: 10.1007/s11571-022-09915-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/26/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Automatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC serving as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low-frequency rhythms (0.5-24 Hz) and high frequency oscillations (HFOs) (80-560 Hz). We used statistical measures to test the significant difference in PAC between the SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to learn automatically the characteristics of the obtained PAC features and classify them to identify the SOZ. Besides, to handle imbalanced dataset classification, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed a time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only showed that a significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ, but also indicated the effectiveness of the PAC features and the proposed models in achieving better classification performance .
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Affiliation(s)
- Yao Miao
- Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Yasushi Iimura
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Hidenori Sugano
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Kosuke Fukumori
- Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Toshihisa Tanaka
- Tokyo University of Agriculture and Technology, Tokyo, Japan
- Department of Neurosurgery, Juntendo University School of Medicine, Tokyo, Japan
- RIKEN Center for Brain Science, Saitama, Japan
- RIKEN Center for Advanced Intelligent Project, Tokyo, Japan
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Jiang X, Liu X, Liu Y, Wang Q, Li B, Zhang L. Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis. Front Neurosci 2023; 17:1191683. [PMID: 37260846 PMCID: PMC10228742 DOI: 10.3389/fnins.2023.1191683] [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: 03/22/2023] [Accepted: 04/14/2023] [Indexed: 06/02/2023] Open
Abstract
Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.
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Affiliation(s)
- Ximiao Jiang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Xiaotong Liu
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Youjun Liu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
| | - Bao Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Liyuan Zhang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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Neurophysiological assessment of cortical activity in DEPDC5- and NPRL3-related epileptic mTORopathies. Orphanet J Rare Dis 2023; 18:11. [PMID: 36639812 PMCID: PMC9840333 DOI: 10.1186/s13023-022-02600-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 12/19/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Mutations in the GATOR1 complex genes, DEPDC5 and NPRL3, play a major role in the development of lesional and non-lesional focal epilepsy through increased mTORC1 signalling. We aimed to assess the effects of mTORC1 hyperactivation on GABAergic inhibitory circuits, in 3 and 5 individuals carrying DEPDC5 and NPRL3 mutations respectively using a multimodal approach including transcranial magnetic stimulation (TMS), magnetic resonance spectroscopy (MRS), and electroencephalography (EEG). RESULTS Inhibitory functions probed by TMS and MRS showed no effect of mutations on cortical GABAergic receptor-mediated inhibition and GABA concentration, in both cortical and subcortical regions. However, stronger EEG theta oscillations and stronger and more synchronous gamma oscillations were observed in DEPDC5 and NPRL3 mutations carriers. CONCLUSIONS These results suggest that DEPDC5 and NPRL3-related epileptic mTORopathies may not directly modulate GABAergic functions but are nonetheless characterized by a stronger neural entrainment that may be reflective of a cortical hyperexcitability mediated by increased mTORC1 signaling.
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Li C, Liu S, Wang Z, Yuan G. Classifying epileptic phase-amplitude coupling in SEEG using complex-valued convolutional neural network. Front Physiol 2023; 13:1085530. [PMID: 36685186 PMCID: PMC9849379 DOI: 10.3389/fphys.2022.1085530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/20/2022] [Indexed: 01/06/2023] Open
Abstract
EEG phase-amplitude coupling (PAC), the amplitude of high-frequency oscillations modulated by the phase of low-frequency oscillations (LFOs), is a useful biomarker to localize epileptogenic tissue. It is commonly represented in a comodulogram of coupling strength but without coupled phase information. The phase-amplitude coupling is also found in the normal brain, and it is difficult to discriminate pathological phase-amplitude couplings from normal ones. This study proposes a novel approach based on complex-valued phase-amplitude coupling (CV-PAC) for classifying epileptic phase-amplitude coupling. The CV-PAC combines both the coupling strengths and the coupled phases of low-frequency oscillations. The complex-valued convolutional neural network (CV-CNN) is then used to classify epileptic CV-PAC. Stereo-electroencephalography (SEEG) recordings from nine intractable epilepsy patients were analyzed. The leave-one-out cross-validation is performed, and the area-under-curve (AUC) value is used as the indicator of the performance of different measures. Our result shows that the area-under-curve value is .92 for classifying epileptic CV-PAC using CV-CNN. The area-under-curve value decreases to .89, .80, and .88 while using traditional convolutional neural networks, support vector machine, and random forest, respectively. The phases of delta (1-4 Hz) and alpha (8-10 Hz) bands are different between epileptic and normal CV-PAC. The phase information of CV-PAC is important for improving classification performance. The proposed approach of CV-PAC/CV-CNN promises to identify more accurate epileptic brain activities for potential surgical intervention.
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Affiliation(s)
- Chunsheng Li
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China,*Correspondence: Chunsheng Li,
| | - Shiyue Liu
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
| | - Zeyu Wang
- Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang, China,Department of Electrical Engineering and Information Systems, University of Pannonia, Veszprem, Hungary
| | - Guanqian Yuan
- Department of Neurosurgery, General Hospital of Northern Theater Command, Shenyang, China
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Wang Y, Yang Y, Cao G, Guo J, Wei P, Feng T, Dai Y, Huang J, Kang G, Zhao G. SEEG-Net: An explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy. Comput Biol Med 2022; 148:105703. [PMID: 35791972 DOI: 10.1016/j.compbiomed.2022.105703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/16/2022] [Accepted: 06/04/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Precise preoperative evaluation of drug-resistant epilepsy (DRE) requires accurate analysis of invasive stereoelectroencephalography (SEEG). With the tremendous breakthrough of Artificial intelligence (AI), previous studies can help clinical experts to identify pathological activities automatically. However, they still face limitations when applied in real-world clinical DRE scenarios, such as sample imbalance, cross-subject domain shift, and poor interpretability. Our objective is to propose a model that can address the above problems and realizes high-sensitivity SEEG pathological activity detection based on two real clinical datasets. METHODS Our proposed innovative and effective SEEG-Net introduces a multiscale convolutional neural network (MSCNN) to increase the receptive field of the model, and to learn SEEG multiple frequency domain features, local and global features. Moreover, we designed a novel focal domain generalization loss (FDG-loss) function to enhance the target sample weight and to learn domain consistency features. Furthermore, to enhance the interpretability and flexibility of SEEG-Net, we explain SEEG-Net from multiple perspectives, such as significantly different features, interpretable models, and model learning process interpretation by Grad-CAM++. RESULTS The performance of our proposed method is verified on a public benchmark multicenter SEEG dataset and a private clinical SEEG dataset for a robust comparison. The experimental results demonstrate that the SEEG-Net model achieves the highest sensitivity and is state-of-the-art on cross-subject (for different patients) evaluation, and well deal with the known problems. Besides, we provide an SEEG processing and database construction flow, by maintaining consistency with the real-world clinical scenarios. SIGNIFICANCE According to the results, SEEG-Net is constructed to increase the sensitivity of SEEG pathological activity detection. Simultaneously, we settled certain problems about AI assistance in clinical DRE, built a bridge between AI algorithm application and clinical practice.
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Affiliation(s)
- Yiping Wang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Yanfeng Yang
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Gongpeng Cao
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Jinjie Guo
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Penghu Wei
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Tao Feng
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Yang Dai
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Jinguo Huang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Guixia Kang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
| | - Guoguang Zhao
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
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