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Damos PT. On formal limitations of causal ecological networks. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230170. [PMID: 39034692 PMCID: PMC11293863 DOI: 10.1098/rstb.2023.0170] [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: 08/05/2023] [Revised: 12/02/2023] [Accepted: 02/22/2024] [Indexed: 07/23/2024] Open
Abstract
Causal multivariate time-series analysis, combined with network theory, provide a powerful tool for studying complex ecological interactions. However, these methods have limitations often underestimated when used in graphical modelling of ecological systems. In this opinion article, I examine the relationship between formal logic methods used to describe causal networks and their inherent statistical and epistemological limitations. I argue that while these methods offer valuable insights, they are restricted by axiomatic assumptions, statistical constraints and the incompleteness of our knowledge. To prove that, I first consider causal networks as formal systems, define causality and formalize their axioms in terms of modal logic and use ecological counterexamples to question the axioms. I also highlight the statistical limitations when using multivariate time-series analysis and Granger causality to develop ecological networks, including the potential for spurious correlations among other data characteristics. Finally, I draw upon Gödel's incompleteness theorems to highlight the inherent limits of fully understanding complex networks as formal systems and conclude that causal ecological networks are subject to initial rules and data characteristics and, as any formal system, will never fully capture the intricate complexities of the systems they represent. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.
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Affiliation(s)
- Petros T. Damos
- Minstry of Education, Religious and Sports, Directorate of Secondary Education Veroia, Ergohori59132, Greece
- Department of Agriculture, School of Agricultural Studies, University of Western Macedonia, Florina, 53100, Greece
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, Kozani50100, Greece
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2
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Sysoev YI, Okovityi SV. Prospects of Electrocorticography in Neuropharmacological Studies in Small Laboratory Animals. Brain Sci 2024; 14:772. [PMID: 39199466 PMCID: PMC11353129 DOI: 10.3390/brainsci14080772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 07/24/2024] [Accepted: 07/29/2024] [Indexed: 09/01/2024] Open
Abstract
Electrophysiological methods of research are widely used in neurobiology. To assess the bioelectrical activity of the brain in small laboratory animals, electrocorticography (ECoG) is most often used, which allows the recording of signals directly from the cerebral cortex. To date, a number of methodological approaches to the manufacture and implantation of ECoG electrodes have been proposed, the complexity of which is determined by experimental tasks and logistical capabilities. Existing methods for analyzing bioelectrical signals are used to assess the functional state of the nervous system in test animals, as well as to identify correlates of pathological changes or pharmacological effects. The review presents current areas of applications of ECoG in neuropharmacological studies in small laboratory animals. Traditionally, this method is actively used to study the antiepileptic activity of new molecules. However, the possibility of using ECoG to assess the neuroprotective activity of drugs in models of traumatic, vascular, metabolic, or neurodegenerative CNS damage remains clearly underestimated. Despite the fact that ECoG has a number of disadvantages and methodological difficulties, the recorded data can be a useful addition to traditional molecular and behavioral research methods. An analysis of the works in recent years indicates a growing interest in the method as a tool for assessing the pharmacological activity of psychoactive drugs, especially in combination with classification and prediction algorithms.
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Affiliation(s)
- Yuriy I. Sysoev
- Pavlov Institute of Physiology, Russian Academy of Sciences (RAS), Saint Petersburg 199034, Russia
- Department of Neuroscience, Sirius University of Science and Technology, Sirius Federal Territory 354340, Russia
- Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg 199034, Russia
| | - Sergey V. Okovityi
- Department of Pharmacology and Clinical Pharmacology, Saint Petersburg State Chemical Pharmaceutical University, Saint Petersburg 197022, Russia;
- N.P. Bechtereva Institute of the Human Brain, Saint Petersburg 197022, Russia
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3
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Harrington EG, Kissack P, Terry JR, Woldman W, Junges L. Treatment effects in epilepsy: a mathematical framework for understanding response over time. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1308501. [PMID: 38988793 PMCID: PMC11233745 DOI: 10.3389/fnetp.2024.1308501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 05/30/2024] [Indexed: 07/12/2024]
Abstract
Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom.
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Affiliation(s)
- Elanor G. Harrington
- School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Peter Kissack
- School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - John R. Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
- Neuronostics Ltd, Engine Shed, Station Approach, Bristol, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
- Neuronostics Ltd, Engine Shed, Station Approach, Bristol, United Kingdom
| | - Leandro Junges
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
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Maeda K, Ogawa T, Kayama T, Sasaki T, Tainaka K, Murakami M, Haseyama M. Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis. Bioengineering (Basel) 2024; 11:523. [PMID: 38927759 PMCID: PMC11200460 DOI: 10.3390/bioengineering11060523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/26/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024] Open
Abstract
This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens.
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Affiliation(s)
- Keisuke Maeda
- Data-Driven Interdisciplinary Research Emergence Department, Hokkaido University, N-13, W-10, Kita-ku, Sapporo 060-0813, Japan;
| | - Takahiro Ogawa
- Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Japan;
| | - Tasuku Kayama
- Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Tohoku University, 6-3 Aramaki-Aoba, Aoba-ku, Sendai 980-8578, Japan; (T.K.); (T.S.)
| | - Takuya Sasaki
- Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Tohoku University, 6-3 Aramaki-Aoba, Aoba-ku, Sendai 980-8578, Japan; (T.K.); (T.S.)
- Department of Neuropharmacology, Tohoku University School of Medicine, 4-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan
| | - Kazuki Tainaka
- Department of System Pathology for Neurological Disorders, Brain Research Institute, Niigata University, 1-757 Asahimachi-dori, Chuo-ku, Niigata 951-8585, Japan;
| | - Masaaki Murakami
- Division of Molecular Psychoimmunology, Institute for Genetic Medicine and Graduate School of Medicine, Hokkaido University, Kita-15, Nishi-7, Kita-ku, Sapporo 060-0815, Japan;
- Division of Molecular Neuroimmunology, National Institute for Physiological Sciences, Myodaiji, Okazaki 444-8585, Japan
- Group of Quantum Immunology, National Institute for Quantum and Radiological Science and Technology (QST), 4-9-1 Anagawa, Inage 263-8555, Japan
- Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Kita-21, Nishi-11, Kita-ku, Sapporo 001-0021, Japan
| | - Miki Haseyama
- Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Japan;
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Caravati E, Barbeni F, Chiarion G, Raggi M, Mesin L. Closed-Loop Transcranial Electrical Neurostimulation for Sustained Attention Enhancement: A Pilot Study towards Personalized Intervention Strategies. Bioengineering (Basel) 2024; 11:467. [PMID: 38790334 PMCID: PMC11118513 DOI: 10.3390/bioengineering11050467] [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: 04/06/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Sustained attention is pivotal for tasks like studying and working for which focus and low distractions are necessary for peak productivity. This study explores the effectiveness of adaptive transcranial direct current stimulation (tDCS) in either the frontal or parietal region to enhance sustained attention. The research involved ten healthy university students performing the Continuous Performance Task-AX (AX-CPT) while receiving either frontal or parietal tDCS. The study comprised three phases. First, we acquired the electroencephalography (EEG) signal to identify the most suitable metrics related to attention states. Among different spectral and complexity metrics computed on 3 s epochs of EEG, the Fuzzy Entropy and Multiscale Sample Entropy Index of frontal channels were selected. Secondly, we assessed how tDCS at a fixed 1.0 mA current affects attentional performance. Finally, a real-time experiment involving continuous metric monitoring allowed personalized dynamic optimization of the current amplitude and stimulation site (frontal or parietal). The findings reveal statistically significant improvements in mean accuracy (94.04 vs. 90.82%) and reaction times (262.93 vs. 302.03 ms) with the adaptive tDCS compared to a non-stimulation condition. Average reaction times were statistically shorter during adaptive stimulation compared to a fixed current amplitude condition (262.93 vs. 283.56 ms), while mean accuracy stayed similar (94.04 vs. 93.36%, improvement not statistically significant). Despite the limited number of subjects, this work points out the promising potential of adaptive tDCS as a tailored treatment for enhancing sustained attention.
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Affiliation(s)
| | | | | | | | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy; (E.C.); (F.B.); (G.C.); (M.R.)
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Wang D, Zhang X, Xin C, Wang C, Yue S, Guo D, Wang W, Zhang Y, Xu F. Electroencephalography-based biological and functional characteristics of spinal cord injury patients with neuropathic pain and numbness. Front Neurosci 2024; 18:1356858. [PMID: 38751860 PMCID: PMC11094546 DOI: 10.3389/fnins.2024.1356858] [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: 12/16/2023] [Accepted: 04/08/2024] [Indexed: 05/18/2024] Open
Abstract
Objectives To identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI patients with NP or numbness. Participants and methods The EEG signals during rest, as well as left- and right-hand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the θ (4-8 Hz), α (8-12 Hz), and β (13-30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation. Results The specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower θ and α bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all p < 0.05), but higher β band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all p < 0.05). During left-hand and feet MI, in the lower frequency bands (θ and α bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (β band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group. Conclusion The differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness.
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Affiliation(s)
- Dezheng Wang
- Rehabilitation and Physical Therapy Department, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xinting Zhang
- Department of Pediatrics, Qilu Hospital of Shandong University, Jinan, China
| | - Chen Xin
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Chongfeng Wang
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Shouwei Yue
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Dongju Guo
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Wei Wang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Yang Zhang
- Rehabilitation and Physical Therapy Department, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
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Mokhtarinejad E, Tavakoli M, Ghaderi AH. Exploring the correlation and causation between alpha oscillations and one-second time perception through EEG and tACS. Sci Rep 2024; 14:8035. [PMID: 38580671 PMCID: PMC10997657 DOI: 10.1038/s41598-024-57715-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 03/21/2024] [Indexed: 04/07/2024] Open
Abstract
Alpha oscillations have been implicated in time perception, yet a consensus on their precise role remains elusive. This study directly investigates this relationship by examining the impact of alpha oscillations on time perception. Resting-state EEG recordings were used to extract peak alpha frequency (PAF) and peak alpha power (PAP) characteristics. Participants then performed a time generalization task under transcranial alternating current stimulation (tACS) at frequencies of PAF-2, PAF, and PAF+2, as well as a sham condition. Results revealed a significant correlation between PAP and accuracy, and between PAF and precision of one-second time perception in the sham condition. This suggests that alpha oscillations may influence one-second time perception by modulating their frequency and power. Interestingly, these correlations weakened with real tACS stimulations, particularly at higher frequencies. A second analysis aimed to establish a causal relationship between alpha peak modulation by tACS and time perception using repeated measures ANOVAs, but no significant effect was observed. Results were interpreted according to the state-dependent networks and internal clock model.
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Affiliation(s)
- Ehsan Mokhtarinejad
- Department of Psychology, Faculty of Education and Psychology, University of Isfahan, Isfahan, Iran
| | - Mahgol Tavakoli
- Department of Psychology, Faculty of Education and Psychology, University of Isfahan, Isfahan, Iran.
| | - Amir Hossein Ghaderi
- Department of Psychology, Faculty of Education and Psychology, University of Isfahan, Isfahan, Iran
- Center for Affective Neuroscience, Development, Learning and Education, University of Southern California (USC), Los Angeles, USA
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Candia‐Rivera D, Vidailhet M, Chavez M, De Vico Fallani F. A framework for quantifying the coupling between brain connectivity and heartbeat dynamics: Insights into the disrupted network physiology in Parkinson's disease. Hum Brain Mapp 2024; 45:e26668. [PMID: 38520378 PMCID: PMC10960553 DOI: 10.1002/hbm.26668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/07/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024] Open
Abstract
Parkinson's disease (PD) often shows disrupted brain connectivity and autonomic dysfunctions, progressing alongside with motor and cognitive decline. Recently, PD has been linked to a reduced sensitivity to cardiac inputs, that is, cardiac interoception. Altogether, those signs suggest that PD causes an altered brain-heart connection whose mechanisms remain unclear. Our study aimed to explore the large-scale network disruptions and the neurophysiology of disrupted interoceptive mechanisms in PD. We focused on examining the alterations in brain-heart coupling in PD and their potential connection to motor symptoms. We developed a proof-of-concept method to quantify relationships between the co-fluctuations of brain connectivity and cardiac sympathetic and parasympathetic activities. We quantified the brain-heart couplings from electroencephalogram and electrocardiogram recordings from PD patients on and off dopaminergic medication, as well as in healthy individuals at rest. Our results show that the couplings of fluctuating alpha and gamma connectivity with cardiac sympathetic dynamics are reduced in PD patients, as compared to healthy individuals. Furthermore, we show that PD patients under dopamine medication recover part of the brain-heart coupling, in proportion with the reduced motor symptoms. Our proposal offers a promising approach to unveil the physiopathology of PD and promoting the development of new evaluation methods for the early stages of the disease.
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Affiliation(s)
- Diego Candia‐Rivera
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Marie Vidailhet
- Sorbonne Université, Paris Brain Institute (ICM)—Team “Movement Investigations and Therapeutics” (MOV'IT), CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Mario Chavez
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Fabrizio De Vico Fallani
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Karittevlis C, Papadopoulos M, Lima V, Orphanides GA, Tiwari S, Antonakakis M, Papadopoulou Lesta V, Ioannides AA. First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation. Front Syst Neurosci 2024; 17:1305022. [PMID: 38250330 PMCID: PMC10797085 DOI: 10.3389/fnsys.2023.1305022] [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: 09/30/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation. Methods We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory. Results Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters. Discussion It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.
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Affiliation(s)
- Christodoulos Karittevlis
- AAI Scientific Cultural Services Ltd., Nicosia, Cyprus
- Department of Computer Science, European University Cyprus, Nicosia, Cyprus
| | | | - Vinicius Lima
- Aix Marseille Université, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Gregoris A. Orphanides
- AAI Scientific Cultural Services Ltd., Nicosia, Cyprus
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Shubham Tiwari
- Department of Geography, Durham University, Durham, United Kingdom
| | - Marios Antonakakis
- School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
- Institute for Biomagnetism and Biosignal Analysis, Medicine Faculty, University of Münster, Münster, Germany
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11
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Liu W, Li G, Huang Z, Jiang W, Luo X, Xu X. Enhancing generalized anxiety disorder diagnosis precision: MSTCNN model utilizing high-frequency EEG signals. Front Psychiatry 2023; 14:1310323. [PMID: 38179243 PMCID: PMC10764566 DOI: 10.3389/fpsyt.2023.1310323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/01/2023] [Indexed: 01/06/2024] Open
Abstract
Generalized Anxiety Disorder (GAD) is a prevalent mental disorder on the rise in modern society. It is crucial to achieve precise diagnosis of GAD for improving the treatments and averting exacerbation. Although a growing number of researchers beginning to explore the deep learning algorithms for detecting mental disorders, there is a dearth of reports concerning precise GAD diagnosis. This study proposes a multi-scale spatial-temporal local sequential and global parallel convolutional model, named MSTCNN, which designed to achieve highly accurate GAD diagnosis using high-frequency electroencephalogram (EEG) signals. To this end, 10-min resting EEG data were collected from 45 GAD patients and 36 healthy controls (HC). Various frequency bands were extracted from the EEG data as the inputs of the MSTCNN. The results demonstrate that the proposed MSTCNN, combined with the attention mechanism of Squeeze-and-Excitation Networks, achieves outstanding classification performance for GAD detection, with an accuracy of 99.48% within the 4-30 Hz EEG data, which is competitively related to state-of-art methods in terms of GAD classification. Furthermore, our research unveils an intriguing revelation regarding the pivotal role of high-frequency band in GAD diagnosis. As the frequency band increases, diagnostic accuracy improves. Notably, high-frequency EEG data ranging from 10-30 Hz exhibited an accuracy rate of 99.47%, paralleling the performance of the broader 4-30 Hz band. In summary, these findings move a step forward towards the practical application of automatic diagnosis of GAD and provide basic theory and technical support for the development of future clinical diagnosis system.
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Affiliation(s)
- Wei Liu
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Ziyi Huang
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Weixiong Jiang
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | | | - Xingjuan Xu
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
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Pitetzis D, Frantzidis C, Psoma E, Ketseridou SN, Deretzi G, Kalogera-Fountzila A, Bamidis PD, Spilioti M. The Pre-Interictal Network State in Idiopathic Generalized Epilepsies. Brain Sci 2023; 13:1671. [PMID: 38137119 PMCID: PMC10741409 DOI: 10.3390/brainsci13121671] [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: 10/29/2023] [Revised: 11/24/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Generalized spike wave discharges (GSWDs) are the typical electroencephalographic findings of Idiopathic Generalized Epilepsies (IGEs). These discharges are either interictal or ictal and recent evidence suggests differences in their pathogenesis. The aim of this study is to investigate, through functional connectivity analysis, the pre-interictal network state in IGEs, which precedes the formation of the interictal GSWDs. A high-density electroencephalogram (HD-EEG) was recorded in twenty-one patients with IGEs, and cortical connectivity was analyzed based on lagged coherence and individual anatomy. Graph theory analysis was used to estimate network features, assessed using the characteristic path length and clustering coefficient. The functional connectivity analysis identified two distinct networks during the pre-interictal state. These networks exhibited reversed connectivity attributes, reflecting synchronized activity at 3-4 Hz (delta2), and desynchronized activity at 8-10.5 Hz (alpha1). The delta2 network exhibited a statistically significant (p < 0.001) decrease in characteristic path length and an increase in the mean clustering coefficient. In contrast, the alpha1 network showed opposite trends in these features. The nodes influencing this state were primarily localized in the default mode network (DMN), dorsal attention network (DAN), visual network (VIS), and thalami. In conclusion, the coupling of two networks defined the pre-interictal state in IGEs. This state might be considered as a favorable condition for the generation of interictal GSWDs.
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Affiliation(s)
- Dimitrios Pitetzis
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece;
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Christos Frantzidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
| | - Elizabeth Psoma
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (E.P.); (A.K.-F.)
| | - Smaranda Nafsika Ketseridou
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Georgia Deretzi
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece;
| | - Anna Kalogera-Fountzila
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (E.P.); (A.K.-F.)
| | - Panagiotis D. Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Martha Spilioti
- 1st Department of Neurology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece;
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Siviero I, Bonfanti D, Menegaz G, Savazzi S, Mazzi C, Storti SF. Graph Analysis of TMS-EEG Connectivity Reveals Hemispheric Differences following Occipital Stimulation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8833. [PMID: 37960532 PMCID: PMC10650175 DOI: 10.3390/s23218833] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/23/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023]
Abstract
(1) Background: Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) provides a unique opportunity to investigate brain connectivity. However, possible hemispheric asymmetries in signal propagation dynamics following occipital TMS have not been investigated. (2) Methods: Eighteen healthy participants underwent occipital single-pulse TMS at two different EEG sites, corresponding to early visual areas. We used a state-of-the-art Bayesian estimation approach to accurately estimate TMS-evoked potentials (TEPs) from EEG data, which has not been previously used in this context. To capture the rapid dynamics of information flow patterns, we implemented a self-tuning optimized Kalman (STOK) filter in conjunction with the information partial directed coherence (iPDC) measure, enabling us to derive time-varying connectivity matrices. Subsequently, graph analysis was conducted to assess key network properties, providing insight into the overall network organization of the brain network. (3) Results: Our findings revealed distinct lateralized effects on effective brain connectivity and graph networks after TMS stimulation, with left stimulation facilitating enhanced communication between contralateral frontal regions and right stimulation promoting increased intra-hemispheric ipsilateral connectivity, as evidenced by statistical test (p < 0.001). (4) Conclusions: The identified hemispheric differences in terms of connectivity provide novel insights into brain networks involved in visual information processing, revealing the hemispheric specificity of neural responses to occipital stimulation.
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Affiliation(s)
- Ilaria Siviero
- Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Davide Bonfanti
- Perception and Awareness (PandA) Lab., Department of Neuroscience, Biomedicine and Movement Science, University of Verona, Piazzale Ludovico Antonio Scuro 10, 37124 Verona, Italy; (D.B.); (S.S.); (C.M.)
| | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Silvia Savazzi
- Perception and Awareness (PandA) Lab., Department of Neuroscience, Biomedicine and Movement Science, University of Verona, Piazzale Ludovico Antonio Scuro 10, 37124 Verona, Italy; (D.B.); (S.S.); (C.M.)
| | - Chiara Mazzi
- Perception and Awareness (PandA) Lab., Department of Neuroscience, Biomedicine and Movement Science, University of Verona, Piazzale Ludovico Antonio Scuro 10, 37124 Verona, Italy; (D.B.); (S.S.); (C.M.)
| | - Silvia Francesca Storti
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
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14
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Sparacino L, Faes L, Mijatović G, Parla G, Lo Re V, Miraglia R, de Ville de Goyet J, Sparacia G. Statistical Approaches to Identify Pairwise and High-Order Brain Functional Connectivity Signatures on a Single-Subject Basis. Life (Basel) 2023; 13:2075. [PMID: 37895456 PMCID: PMC10608185 DOI: 10.3390/life13102075] [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: 07/12/2023] [Revised: 09/21/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Keeping up with the shift towards personalized neuroscience essentially requires the derivation of meaningful insights from individual brain signal recordings by analyzing the descriptive indexes of physio-pathological states through statistical methods that prioritize subject-specific differences under varying experimental conditions. Within this framework, the current study presents a methodology for assessing the value of the single-subject fingerprints of brain functional connectivity, assessed both by standard pairwise and novel high-order measures. Functional connectivity networks, which investigate the inter-relationships between pairs of brain regions, have long been a valuable tool for modeling the brain as a complex system. However, their usefulness is limited by their inability to detect high-order dependencies beyond pairwise correlations. In this study, by leveraging multivariate information theory, we confirm recent evidence suggesting that the brain contains a plethora of high-order, synergistic subsystems that would go unnoticed using a pairwise graph structure. The significance and variations across different conditions of functional pairwise and high-order interactions (HOIs) between groups of brain signals are statistically verified on an individual level through the utilization of surrogate and bootstrap data analyses. The approach is illustrated on the single-subject recordings of resting-state functional magnetic resonance imaging (rest-fMRI) signals acquired using a pediatric patient with hepatic encephalopathy associated with a portosystemic shunt and undergoing liver vascular shunt correction. Our results show that (i) the proposed single-subject analysis may have remarkable clinical relevance for subject-specific investigations and treatment planning, and (ii) the possibility of investigating brain connectivity and its post-treatment functional developments at a high-order level may be essential to fully capture the complexity and modalities of the recovery.
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Affiliation(s)
- Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.S.); (L.F.)
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.S.); (L.F.)
| | - Gorana Mijatović
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia;
| | - Giuseppe Parla
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
| | | | - Roberto Miraglia
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
| | - Jean de Ville de Goyet
- Department for the Treatment and Study of Pediatric Abdominal Diseases and Abdominal Transplantation, IRCCS-ISMETT, 90127 Palermo, Italy;
| | - Gianvincenzo Sparacia
- Radiology Service, IRCCS-ISMETT, 90127 Palermo, Italy; (G.P.); (R.M.)
- Radiology Service, BiND, University of Palermo, 90128 Palermo, Italy
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Zhai J, Ji J, Liu J. Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm. Bioengineering (Basel) 2023; 10:909. [PMID: 37627794 PMCID: PMC10451563 DOI: 10.3390/bioengineering10080909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/22/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.
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Affiliation(s)
| | | | - Jinduo Liu
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China (J.J.)
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16
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Yen C, Lin CL, Chiang MC. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life (Basel) 2023; 13:1472. [PMID: 37511847 PMCID: PMC10381462 DOI: 10.3390/life13071472] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/12/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.
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Affiliation(s)
- Chiahui Yen
- Department of International Business, Ming Chuan University, Taipei 111, Taiwan
| | - Chia-Li Lin
- Department of International Business, Ming Chuan University, Taipei 111, Taiwan
| | - Ming-Chang Chiang
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan
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17
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Paoli M, Antonacci Y, Albi A, Faes L, Haase A. Granger Causality Analysis of Transient Calcium Dynamics in the Honey Bee Antennal Lobe Network. INSECTS 2023; 14:539. [PMID: 37367355 DOI: 10.3390/insects14060539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/28/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
Odorant processing presents multiple parallels across animal species, and insects became relevant models for the study of olfactory coding because of the tractability of the underlying neural circuits. Within the insect brain, odorants are received by olfactory sensory neurons and processed by the antennal lobe network. Such a network comprises multiple nodes, named glomeruli, that receive sensory information and are interconnected by local interneurons participating in shaping the neural representation of an odorant. The study of functional connectivity between the nodes of a sensory network in vivo is a challenging task that requires simultaneous recording from multiple nodes at high temporal resolutions. Here, we followed the calcium dynamics of antennal lobe glomeruli and applied Granger causality analysis to assess the functional connectivity among network nodes in the presence and absence of an odorous stimulus. This approach revealed the existence of causal connectivity links between antennal lobe glomeruli in the absence of olfactory stimulation, while at odor arrival, the connectivity network's density increased and became stimulus-specific. Thus, such an analytical approach may provide a new tool for the investigation of neural network plasticity in vivo.
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Affiliation(s)
- Marco Paoli
- Research Center of Animal Cognition, Center for Integrative Biology, CNRS, University of Toulouse, 31400 Toulouse, France
| | - Yuri Antonacci
- Dipartimento di Ingegneria, Università di Palermo, 90128 Palermo, Italy
| | - Angela Albi
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, 78457 Konstanz, Germany
- Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - Luca Faes
- Dipartimento di Ingegneria, Università di Palermo, 90128 Palermo, Italy
| | - Albrecht Haase
- Center for Mind/Brain Science (CIMeC), University of Trento, 38068 Rovereto, Italy
- Department of Physics, University of Trento, 38123 Povo, Italy
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