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Pascovich C, Serantes D, Rodriguez A, Mateos D, González J, Gallo D, Rivas M, Devera A, Lagos P, Rubido N, Torterolo P. Dorsal and median raphe neuronal firing dynamics characterized by nonlinear measures. PLoS Comput Biol 2024; 20:e1012111. [PMID: 38805554 PMCID: PMC11161118 DOI: 10.1371/journal.pcbi.1012111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 06/07/2024] [Accepted: 04/25/2024] [Indexed: 05/30/2024] Open
Abstract
The dorsal (DRN) and median (MRN) raphe are important nuclei involved in similar functions, including mood and sleep, but playing distinct roles. These nuclei have a different composition of neuronal types and set of neuronal connections, which among other factors, determine their neuronal dynamics. Most works characterize the neuronal dynamics using classic measures, such as using the average spiking frequency (FR), the coefficient of variation (CV), and action potential duration (APD). In the current study, to refine the characterization of neuronal firing profiles, we examined the neurons within the raphe nuclei. Through the utilization of nonlinear measures, our objective was to discern the redundancy and complementarity of these measures, particularly in comparison with classic methods. To do this, we analyzed the neuronal basal firing profile in both nuclei of urethane-anesthetized rats using the Shannon entropy (Bins Entropy) of the inter-spike intervals, permutation entropy of ordinal patterns (OP Entropy), and Permutation Lempel-Ziv Complexity (PLZC). Firstly, we found that classic (i.e., FR, CV, and APD) and nonlinear measures fail to distinguish between the dynamics of DRN and MRN neurons, except for the OP Entropy. We also found strong relationships between measures, including the CV with FR, CV with Bins entropy, and FR with PLZC, which imply redundant information. However, APD and OP Entropy have either a weak or no relationship with the rest of the measures tested, suggesting that they provide complementary information to the characterization of the neuronal firing profiles. Secondly, we studied how these measures are affected by the oscillatory properties of the firing patterns, including rhythmicity, bursting patterns, and clock-like behavior. We found that all measures are sensitive to rhythmicity, except for the OP Entropy. Overall, our work highlights OP Entropy as a powerful and useful quantity for the characterization of neuronal discharge patterns.
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Affiliation(s)
- Claudia Pascovich
- Laboratory of Sleep Neurobiology, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
- Consciousness and Cognition Laboratory, Department of Psychology, King’s College, University of Cambridge, Cambridge, United Kingdom
| | - Diego Serantes
- Laboratory of Sleep Neurobiology, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Alejo Rodriguez
- Laboratory of Sleep Neurobiology, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Diego Mateos
- Achucarro Basque Center for Neuroscience, Leioa (Bizkaia), Spain
- Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL), Santa Fé, Argentina
- Universidad Autónoma de Entre Ríos (UADER), Oro Verde, Entre Ríos, Argentina
| | - Joaquín González
- Laboratory of Sleep Neurobiology, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Diego Gallo
- Laboratory of Sleep Neurobiology, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Mayda Rivas
- Laboratory of Sleep Neurobiology, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Andrea Devera
- Laboratory of Sleep Neurobiology, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Patricia Lagos
- Laboratory of Neuropeptide Transmission, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Nicolás Rubido
- Institute for Complex Systems and Mathematical Biology, King’s College, University of Aberdeen, Aberdeen, United Kingdom
| | - Pablo Torterolo
- Laboratory of Sleep Neurobiology, Department of Physiology, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
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Williams RS, Adams NE, Hughes LE, Rouse MA, Murley AG, Naessens M, Street D, Holland N, Rowe JB. Syndromes associated with frontotemporal lobar degeneration change response patterns on visual analogue scales. Sci Rep 2023; 13:8939. [PMID: 37268659 DOI: 10.1038/s41598-023-35758-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/23/2023] [Indexed: 06/04/2023] Open
Abstract
Self-report scales are widely used in cognitive neuroscience and psychology. However, they rest on the central assumption that respondents engage meaningfully. We hypothesise that this assumption does not hold for many patients, especially those with syndromes associated with frontotemporal lobar degeneration. In this study we investigated differences in response patterns on a visual analogue scale between people with frontotemporal degeneration and controls. We found that people with syndromes associated with frontotemporal lobar degeneration respond with more invariance and less internal consistency than controls, with Bayes Factors = 15.2 and 14.5 respectively indicating strong evidence for a group difference. There was also evidence that patient responses feature lower entropy. These results have important implications for the interpretation of self-report data in clinical populations. Meta-response markers related to response patterns, rather than the values reported on individual items, may be an informative addition to future research and clinical practise.
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Affiliation(s)
- Rebecca S Williams
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Natalie E Adams
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Laura E Hughes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Matthew A Rouse
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Alexander G Murley
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Michelle Naessens
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Duncan Street
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Negin Holland
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - James B Rowe
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
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Multiscale Permutation Lempel-Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals. ENTROPY 2021; 23:e23070832. [PMID: 34210034 PMCID: PMC8307896 DOI: 10.3390/e23070832] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/23/2021] [Accepted: 06/25/2021] [Indexed: 11/18/2022]
Abstract
This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel–Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel–Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n = 100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann–Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p < 0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%.
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Alexeenko V, Fraser JA, Dolgoborodov A, Bowen M, Huang CLH, Marr CM, Jeevaratnam K. The application of Lempel-Ziv and Titchener complexity analysis for equine telemetric electrocardiographic recordings. Sci Rep 2019; 9:2619. [PMID: 30796330 PMCID: PMC6385502 DOI: 10.1038/s41598-019-38935-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 12/28/2018] [Indexed: 12/19/2022] Open
Abstract
The analysis of equine electrocardiographic (ECG) recordings is complicated by the absence of agreed abnormality classification criteria. We explore the applicability of several complexity analysis methods for characterization of non-linear aspects of electrocardiographic recordings. We here show that complexity estimates provided by Lempel-Ziv ’76, Titchener’s T-complexity and Lempel-Ziv ’78 analysis of ECG recordings of healthy Thoroughbred horses are highly dependent on the duration of analysed ECG fragments and the heart rate. The results provide a methodological basis and a feasible reference point for the complexity analysis of equine telemetric ECG recordings that might be applied to automate detection of equine arrhythmias in equine clinical practice.
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Affiliation(s)
- Vadim Alexeenko
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, United Kingdom.,Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom
| | - James A Fraser
- Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom
| | | | - Mark Bowen
- Faculty of Medicine & Health Sciences, University of Nottingham, Nottingham, NG7 2UH, United Kingdom
| | - Christopher L-H Huang
- Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom.,Division of Cardiovascular Biology, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, United Kingdom
| | - Celia M Marr
- Rossdales Equine Hospital and Diagnostic Centre, Exning, CB8 7NN, Suffolk, United Kingdom
| | - Kamalan Jeevaratnam
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, United Kingdom. .,Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom.
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