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Minbay M, Khan A, Ghasemi AR, Ingram KK, Ay AA. Sex-specific associations between circadian-related genes and depression in UK Biobank participants highlight links to glucose metabolism, inflammation and neuroplasticity pathways. Psychiatry Res 2024; 337:115948. [PMID: 38788553 DOI: 10.1016/j.psychres.2024.115948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/18/2024] [Accepted: 05/01/2024] [Indexed: 05/26/2024]
Abstract
Depressive disorders have increased in global prevalence, making improved management of these disorders a public health priority. Prior research has linked circadian clock genes to depression, either through direct interactions with mood-related pathways in the brain or by modulating the phase of circadian rhythms. Using machine learning and statistical techniques, we explored associations between 157,347 SNP variants from 51 circadian-related genes and depression scores from the patient health questionnaire 9 (PHQ-9) in 99,939 UK Biobank participants. Our results highlight multiple pathways linking the circadian system to mood, including metabolic, monoamine, immune, and stress-related pathways. Notably, genes regulating glucose metabolism and inflammation (GSK3B, LEP, RORA, and NOCT) were prominent factors in females, in addition to DELEC1 and USP46, two genes of unknown function. In contrast, FBXL3 and DRD4 emerged as significant risk factors for male depression. We also found epistatic interactions involving RORA, NFIL3, and ZBTB20 as either risk or protective factors for depression, underscoring the importance of transcription factors (ZBTB20, NFIL3) and hormone receptors (RORA) in depression etiology. Understanding the complex, sex-specific links between circadian genes and mood disorders will facilitate the development of therapeutic interventions and enhance the efficacy of multi-target treatments for depression.
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Affiliation(s)
- Mete Minbay
- Department of Computer Science, Colgate University, Hamilton, NY, USA
| | - Ayub Khan
- Department of Computer Science, Colgate University, Hamilton, NY, USA; Department of Biology, Colgate University, Hamilton, NY, USA
| | - Ali R Ghasemi
- Department of Computer Science, Colgate University, Hamilton, NY, USA
| | - Krista K Ingram
- Department of Biology, Colgate University, Hamilton, NY, USA.
| | - Ahmet A Ay
- Department of Biology, Colgate University, Hamilton, NY, USA
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2
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D’Andrea A, Croce P, O’Byrne J, Jerbi K, Pascarella A, Raffone A, Pizzella V, Marzetti L. Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity. Front Neurosci 2024; 18:1295615. [PMID: 38370436 PMCID: PMC10869546 DOI: 10.3389/fnins.2024.1295615] [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: 09/16/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Background The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates. Methods Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed. Results Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM. Discussion Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics.
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Affiliation(s)
- Antea D’Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Jordan O’Byrne
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Karim Jerbi
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Annalisa Pascarella
- Institute for the Applications of Calculus “M. Picone”, National Research Council, Rome, Lazio, Italy
| | - Antonino Raffone
- Department of Psychology, Sapienza University of Rome, Rome, Lazio, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
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3
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Armonaite K, Conti L, Tecchio F. Fractal Neurodynamics. ADVANCES IN NEUROBIOLOGY 2024; 36:659-675. [PMID: 38468057 DOI: 10.1007/978-3-031-47606-8_33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The neuronal ongoing electrical activity in the brain network, the neurodynamics, reflects the structure and functionality of generating neuronal pools. The activity of neurons due to their excitatory and inhibitory projections is associated with specific brain functions. Here, the purpose was to investigate if the local ongoing electrical activity exhibits its characteristic spectral and fractal features in wakefulness and sleep across and within subjects. Moreover, we aimed to show that measures typical of complex systems catch physiological features missed by linear spectral analyses. For this study, we concentrated on the evaluation of the power spectral density (PSD) and Higuchi fractal dimension (HFD) measures. Relevant clinical impact of the specific features of neurodynamics identification stands primarily in the potential of classifying cortical parcels according to their neurodynamics as well as enhancing the effectiveness of neuromodulation interventions to cure symptoms secondary to neuronal activity unbalances.
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Affiliation(s)
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Rome, Italy
| | - Franca Tecchio
- Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche, Rome, Italy.
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Olejarczyk E, Cukic M, Porcaro C, Zappasodi F, Tecchio F. Clinical Sensitivity of Fractal Neurodynamics. ADVANCES IN NEUROBIOLOGY 2024; 36:285-312. [PMID: 38468039 DOI: 10.1007/978-3-031-47606-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland.
| | - Milena Cukic
- Department of Biomimetic Membranes and Textiles, EMPA Material Science and Technology, St. Gallen, Switzerland
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Filippo Zappasodi
- Department of Neuroscienze, Imaging and Clinical Sciences, Gabriele D'annunzio University, Chieti, Italy
| | - Franca Tecchio
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
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Hao C, Xie T, Peng Y, Li M, Luo W, Ma N. Effect of homeostatic pressure on daytime vigilance performance: Evidence from behaviour and resting-state EEG. J Sleep Res 2023; 32:e13890. [PMID: 36948509 DOI: 10.1111/jsr.13890] [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: 12/08/2022] [Revised: 02/25/2023] [Accepted: 03/06/2023] [Indexed: 03/24/2023]
Abstract
Vigilance is highly sensitive to the time-of-day effect and goes through the daytime trough during the period of the post-noon dip. A midday nap could maintain individuals' vigilance at an optimal level. Thus, homeostatic sleep pressure is one of the main reasons for the post-noon dip in daytime vigilance. The current study focussed on the role of homeostatic sleep pressure in the diurnal variation of vigilance performance with normal circadian rhythms and the corresponding neural basis. With 34 healthy adults, we recorded the resting-state electroencephalogram activities and the following vigilance performance measured by psychomotor vigilance test in the morning, the no-nap mid afternoon, and the nap mid afternoon. The circadian process was controlled by measuring vigilance and resting-state electroencephalogram activities at the same time point in the nap and no-nap conditions. Homeostatic sleep pressure accumulated from morning to mid afternoon induced the declined vigilance performance and a global increase in resting-state delta, theta, alpha, and beta1 bands power, and a local increase in beta2 band power in the central region. Furthermore, the more the spontaneous beta2 power increased, the less vigilance declined from morning to mid afternoon. The current findings suggest that homeostatic sleep pressure increased cortical excitability but decreased cortical communication efficiency from morning to mid afternoon. In addition, the activity of the high beta waves probably reflected the compensatory effort to counteract the negative impact of the low arousal state on the following vigilance task by performing more action preparation in the no-nap afternoon.
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Affiliation(s)
- Chao Hao
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, Ministry of Education, South China Normal University, 510631, Guangzhou, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, 510631, Guangzhou, China
| | - Tian Xie
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, Ministry of Education, South China Normal University, 510631, Guangzhou, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, 510631, Guangzhou, China
| | - Yudi Peng
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, Ministry of Education, South China Normal University, 510631, Guangzhou, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, 510631, Guangzhou, China
| | - Mingzhu Li
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, Ministry of Education, South China Normal University, 510631, Guangzhou, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, 510631, Guangzhou, China
| | - Wei Luo
- School of Architecture and Urban Planning, Shenzhen University, 518060, Shenzhen, China
| | - Ning Ma
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents, Ministry of Education, South China Normal University, 510631, Guangzhou, China
- Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, 510631, Guangzhou, China
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Masuda K, Katsuda Y, Niwa Y, Sakurai T, Hirano A. Analysis of circadian rhythm components in EEG/EMG data of aged mice. Front Neurosci 2023; 17:1173537. [PMID: 37250413 PMCID: PMC10213445 DOI: 10.3389/fnins.2023.1173537] [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: 02/24/2023] [Accepted: 04/21/2023] [Indexed: 05/31/2023] Open
Abstract
Aging disrupts circadian clocks, as evidenced by a reduction in the amplitude of circadian rhythms. Because the circadian clock strongly influences sleep-wake behavior in mammals, age-related alterations in sleep-wake patterns may be attributable, at least partly, to functional changes in the circadian clock. However, the effect of aging on the circadian characteristics of sleep architecture has not been well assessed, as circadian behaviors are usually evaluated through long-term behavioral recording with wheel-running or infrared sensors. In this study, we examined age-related changes in circadian sleep-wake behavior using circadian components extracted from electroencephalography (EEG) and electromyography (EMG) data. EEG and EMG were recorded from 12 to 17-week-old and 78 to 83-week-old mice for 3 days under light/dark and constant dark conditions. We analyzed time-dependent changes in the duration of sleep. Rapid eye movement (REM) and non-REM (NREM) sleep significantly increased during the night phase in old mice, whereas no significant change was observed during the light phase. The circadian components were then extracted from the EEG data for each sleep-wake stage, revealing that the circadian rhythm in the power of delta waves during NREM sleep was attenuated and delayed in old mice. Furthermore, we used machine learning to evaluate the phase of the circadian rhythm, with EEG data serving as the input and the phase of the sleep-wake rhythm (environmental time) as the output. The results indicated that the output time for the old mice data tended to be delayed, specifically at night. These results indicate that the aging process significantly impacts the circadian rhythm in the EEG power spectrum despite the circadian rhythm in the amounts of sleep and wake attenuated but still remaining in old mice. Moreover, EEG/EMG analysis is useful not only for evaluating sleep-wake stages but also for circadian rhythms in the brain.
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Affiliation(s)
- Kosaku Masuda
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yoko Katsuda
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yasutaka Niwa
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
- Graduate School of Medicine, Hirosaki University, Hirosaki, Aomori, Japan
| | - Takeshi Sakurai
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Arisa Hirano
- Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
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7
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Arif S, Munawar S, Ali H. Driving drowsiness detection using spectral signatures of EEG-based neurophysiology. Front Physiol 2023; 14:1153268. [PMID: 37064914 PMCID: PMC10097971 DOI: 10.3389/fphys.2023.1153268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 03/09/2023] [Indexed: 03/31/2023] Open
Abstract
Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks.Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and β rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics.Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen’s kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods.Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.
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Affiliation(s)
- Saad Arif
- Department of Mechanical Engineering, HITEC University Taxila, Taxila Cantt, Pakistan
| | - Saba Munawar
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Hashim Ali
- Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
- *Correspondence: Hashim Ali,
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8
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Armonaite K, Nobili L, Paulon L, Balsi M, Conti L, Tecchio F. Local neurodynamics as a signature of cortical areas: new insights from sleep. Cereb Cortex 2023; 33:3284-3292. [PMID: 35858209 DOI: 10.1093/cercor/bhac274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/02/2022] [Accepted: 06/04/2022] [Indexed: 11/13/2022] Open
Abstract
Sleep crucial for the animal survival is accompanied by huge changes in neuronal electrical activity over time, the neurodynamics. Here, drawing on intracranial stereo-electroencephalographic (sEEG) recordings from the Montreal Neurological Institute (MNI), we analyzed local neurodynamics in the waking state at rest and during the N2, N3, and rapid eye movement (REM) sleep phases. Higuchi fractal dimension (HFD)-a measure of signal complexity-was studied as a feature of the local neurodynamics of the primary motor (M1), somatosensory (S1), and auditory (A1) cortices. The key working hypothesis, that the relationships between local neurodynamics preserve in all sleep phases despite the neurodynamics complexity reduces in sleep compared with wakefulness, was supported by the results. In fact, while HFD awake > REM > N2 > N3 (P < 0.001 consistently), HFD in M1 > S1 > A1 in awake and all sleep stages (P < 0.05 consistently). Also power spectral density was studied for consistency with previous investigations. Meaningfully, we found a local specificity of neurodynamics, well quantified by the fractal dimension, expressed in wakefulness and during sleep. We reinforce the idea that neurodynamic may become a new criterion for cortical parcellation, prospectively improving the understanding and ability of compensatory interventions for behavioral disorders.
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Affiliation(s)
- Karolina Armonaite
- Faculty of Psychology, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
| | - Lino Nobili
- Child Neurology and Psychiatry, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, n. 5, 16147, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health (DINOGMI), University of Genoa, Largo Paolo Daneo, n. 3, 16132, Genoa, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
| | - Marco Balsi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University, Via Eudossiana, n. 18, 00184, Rome
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
- INFN - Istituto Nazionale di Fisica Nucleare, Sezione Roma Tor Vergata, Via della Ricerca Scientifica, n.1, 00133, Rome, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
- Faculty of Psychology, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
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Patchitt J, Porffy LA, Whomersley G, Szentgyorgyi T, Brett J, Mouchlianitis E, Mehta MA, Nottage JF, Shergill SS. Alpha3/alpha2 power ratios relate to performance on a virtual reality shopping task in ageing adults. Front Aging Neurosci 2022; 14:876832. [PMID: 36212034 PMCID: PMC9540381 DOI: 10.3389/fnagi.2022.876832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
Background Aspects of cognitive function decline with age. This phenomenon is referred to as age-related cognitive decline (ARCD). Improving the understanding of these changes that occur as part of the ageing process can serve to enhance the detection of the more incapacitating neurodegenerative disorders such as Alzheimer’s disease (AD). In this study, we employ novel methods to assess ARCD by exploring the utility of the alpha3/alpha2 electroencephalogram (EEG) power ratio – a marker of AD, and a novel virtual reality (VR) functional cognition task – VStore, in discriminating between young and ageing healthy adults. Materials and methods Twenty young individuals aged 20–30, and 20 older adults aged 60–70 took part in the study. Participants underwent resting-state EEG and completed VStore and the Cogstate Computerised Cognitive Battery. The difference in alpha3/alpha2 power ratios between the age groups was tested using t-test. In addition, the discriminatory accuracy of VStore and Cogstate were compared using logistic regression and overlying receiver operating characteristic (ROC) curves. Youden’s J statistic was used to establish the optimal threshold for sensitivity and specificity and model performance was evaluated with the DeLong’s test. Finally, alpha3/alpha2 power ratios were correlated with VStote and Cogstate performance. Results The difference in alpha3/alpha2 power ratios between age cohorts was not statistically significant. On the other hand, VStore discriminated between age groups with high sensitivity (94%) and specificity (95%) The Cogstate Pre-clinical Alzheimer’s Battery achieved a sensitivity of 89% and specificity of 60%, and Cogstate Composite Score achieved a sensitivity of 83% and specificity of 85%. The differences between the discriminatory accuracy of VStore and Cogstate models were statistically significant. Finally, high alpha3/alpha2 power ratios correlated strongly with VStore (r = 0.73), the Cogstate Pre-clinical Alzheimer’s Battery (r = -0.67), and Cogstate Composite Score (r = -0.76). Conclusion While we did not find evidence that the alpha3/alpha2 power ratio is elevated in healthy ageing individuals compared to young individuals, we demonstrated that VStore can classify age cohorts with high accuracy, supporting its utility in the assessment of ARCD. In addition, we found preliminary evidence that elevated alpha3/alpha2 power ratio may be linked to lower cognitive performance.
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Affiliation(s)
- Joel Patchitt
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Trafford Centre for Medical Research, University of Sussex, Brighton, United Kingdom
| | - Lilla A. Porffy
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- *Correspondence: Lilla A. Porffy,
| | - Gabriella Whomersley
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Timea Szentgyorgyi
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Jack Brett
- Faculty of Media and Communications, Bournemouth University, Poole, United Kingdom
| | - Elias Mouchlianitis
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- School of Psychology, University of East London, London, United Kingdom
| | - Mitul A. Mehta
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Judith F. Nottage
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Sukhi S. Shergill
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Kent and Medway Medical School, Canterbury, United Kingdom
- Kent and Medway National Health Service and Social Care Partnership Trust, Kent, United Kingdom
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10
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Zou H, Zhou H, Yan R, Yao Z, Lu Q. Chronotype, circadian rhythm, and psychiatric disorders: Recent evidence and potential mechanisms. Front Neurosci 2022; 16:811771. [PMID: 36033630 PMCID: PMC9399511 DOI: 10.3389/fnins.2022.811771] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 06/21/2022] [Indexed: 12/27/2022] Open
Abstract
The circadian rhythm is crucial for physiological and behavioral functions. Chronotype, which represents individual preferences for activity and performance, is associated with human health issues, particularly psychiatric disorders. This narrative review, which focuses on the relationship between chronotype and mental disorders, provides an insight into the potential mechanism. Recent evidence indicates that (1) the evening chronotype is a risk factor for depressive disorders and substance use disorders, whereas the morning chronotype is a protective factor. (2) Evening chronotype individuals with bipolar disorder tend to have more severe symptoms and comorbidities. (3) The evening chronotype is only related to anxiety symptoms. (4) The relationship between chronotype and schizophrenia remains unclear, despite increasing evidence on their link. (5) The evening chronotype is significantly associated with eating disorders, with the majority of studies have focused on binge eating disorders. Furthermore, the underlying mechanisms or influence factors are described in detail, including clock genes, brain characteristics, neuroendocrinology, the light/dark cycle, social factors, psychological factors, and sleep disorders. These findings provide the latest evidence on chronotypes and psychiatric disorders and serve as a valuable reference for researchers.
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Affiliation(s)
- Haowen Zou
- Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hongliang Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Rui Yan
- Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zhijian Yao
- Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
- *Correspondence: Zhijian Yao,
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
- Qing Lu,
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11
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Izadi Laybidi M, Rasoulzadeh Y, Dianat I, Samavati M, Asghari Jafarabadi M, Nazari MA. Cognitive performance and electroencephalographic variations in air traffic controllers under various mental workload and time of day. Physiol Behav 2022; 252:113842. [PMID: 35561808 DOI: 10.1016/j.physbeh.2022.113842] [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: 09/29/2021] [Revised: 03/12/2022] [Accepted: 05/09/2022] [Indexed: 11/19/2022]
Abstract
The aim of this study was to investigate the effects of mental workload (MWL) and time of day on cognitive performance and electroencephalographic (EEG) parameters of air traffic controllers. EEG signals recorded while 20 professional air traffic controllers performed cognitive tasks [A-X Continuous Performance Test (AX-CPT) and 3-back working memory task] after they were exposed to two levels of task difficulty (high and low MWL) in the morning and afternoon. Significant decreases in cognitive performance were found when the levels of task difficulty increased in both tasks. The results confirmed the sensitivity of the theta and beta activities to levels of task difficulty in the 3-back task, while they were not affected in the AX-CPT. Theta and beta activities were influenced by time of day in the AX-CPT. The findings provide guidance for application of changes in EEG parameters when MWL level is manipulated during the day that could be implemented in future for the development of real-time monitoring systems to improve aviation safety.
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Affiliation(s)
- Marzieh Izadi Laybidi
- Department of Occupational Health and Ergonomics, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran; Student Research Committee, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yahya Rasoulzadeh
- Department of Occupational Health and Ergonomics, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran; Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Iman Dianat
- Department of Occupational Health and Ergonomics, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mehdi Samavati
- Research Center for Biomedical Technologies & Robotics, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Asghari Jafarabadi
- Center for the Development of Interdisciplinary Research in Islamic Sciences and Health Sciences, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Statistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Mohammad Ali Nazari
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
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12
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Iinuma Y, Nobukawa S, Nishimura H, Takahashi T. Dynamic Characteristics of State Transitions Composed of Neural Activity in the Brain by Circadian Rhythms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:152-157. [PMID: 36085992 DOI: 10.1109/embc48229.2022.9871057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In recent years, as a treatment for mental disorders in addition to drug treatment, a non-drug treatment called chronotherapy has been attracting attention. However, the achievement of optimized chronotherapy for each subject's condition requires that the disturbance of the patient's circadian rhythm must be captured over a long duration. Therefore, it is necessary to develop biomarkers that are easy to measure, quantitative, and continuously measured. Complexity analysis of electroencephalograms revealed specific patterns related to circadian rhythms. However, such complexity analysis cannot capture variability in spatial patterns, although moment-to-moment temporal dynamic characteristics can be captured. Therefore, it is necessary to evaluate the dynamic characteristics of the interaction of neural activity throughout the brain. To evaluate the dynamic whole-brain interaction, we proposed a new microstate approach based on the instantaneous frequency distribution. In this context, we hypothesized that it would be possible to detect circadian rhythms using the microstate approach. In this study, to clarify the dynamic interactions of the entire neural network of the brain by circadian rhythms, we measured EEG data at day and night, and detected dynamic state transitions based on the instantaneous frequency distribution of the whole brain from EEG. The results showed the probability of transition among region-specific phase-leading states related to circadian rhythms. This finding might be widely utilized to detect circadian rhythms in healthy and pathological conditions.
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13
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Olejarczyk E, Gotman J, Frauscher B. Region-specific complexity of the intracranial EEG in the sleeping human brain. Sci Rep 2022; 12:451. [PMID: 35013431 PMCID: PMC8748934 DOI: 10.1038/s41598-021-04213-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 12/13/2021] [Indexed: 11/18/2022] Open
Abstract
As the brain is a complex system with occurrence of self-similarity at different levels, a dedicated analysis of the complexity of brain signals is of interest to elucidate the functional role of various brain regions across the various stages of vigilance. We exploited intracranial electroencephalogram data from 38 cortical regions using the Higuchi fractal dimension (HFD) as measure to assess brain complexity, on a dataset of 1772 electrode locations. HFD values depended on sleep stage and topography. HFD increased with higher levels of vigilance, being highest during wakefulness in the frontal lobe. HFD did not change from wake to stage N2 in temporo-occipital regions. The transverse temporal gyrus was the only area in which the HFD did not differ between any two vigilance stages. Interestingly, HFD of wakefulness and stage R were different mainly in the precentral gyrus, possibly reflecting motor inhibition in stage R. The fusiform and parahippocampal gyri were the only areas showing no difference between wakefulness and N2. Stages R and N2 were similar only for the postcentral gyrus. Topographical analysis of brain complexity revealed that sleep stages are clearly differentiated in fronto-central brain regions, but that temporo-occipital regions sleep differently.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Trojdena 4 Str., 02-109, Warsaw, Poland.
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
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14
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Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5040078] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.
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15
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Tecchio F, Bertoli M, Gianni E, L'Abbate T, Paulon L, Zappasodi F. To Be Is To Become. Fractal Neurodynamics of the Body-Brain Control System. Front Physiol 2021; 11:609768. [PMID: 33384616 PMCID: PMC7770125 DOI: 10.3389/fphys.2020.609768] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 11/25/2020] [Indexed: 11/28/2022] Open
Affiliation(s)
- Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Massimo Bertoli
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy.,Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy
| | - Eugenia Gianni
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy.,Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Teresa L'Abbate
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy
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16
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Lehnertz K, Rings T, Bröhl T. Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:755016. [PMID: 36925573 PMCID: PMC10013076 DOI: 10.3389/fnetp.2021.755016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022]
Abstract
Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.
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Affiliation(s)
- 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
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
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17
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Dierick F, Vandevoorde C, Chantraine F, White O, Buisseret F. Benefits of nonlinear analysis indices of walking stride interval in the evaluation of neurodegenerative diseases. Hum Mov Sci 2020; 75:102741. [PMID: 33310379 DOI: 10.1016/j.humov.2020.102741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 11/26/2020] [Accepted: 11/28/2020] [Indexed: 11/30/2022]
Abstract
Indices characterising the long-range temporal structure of walking stride interval (SI) variability such as Hurst exponent (H) and fractal dimension (D) may be used in addition to indices measuring the amount of variability like the coefficient of variation (CV). We assess the added value of the former indices in a clinical neurological context. Our aim is to demonstrate that they provide a clinical significance in aging and in frequent neurodegenerative diseases such as Parkinson's disease, Huntington, and amyotrophic lateral sclerosis. Indices assessing the temporal structure of variability are mainly dependent on SI time series length and algorithms used, making quantitative comparisons between different studies difficult or even impossible. Here, we recompute these indices from available SI time series, either from our lab or from online databases. More precisely, we recompute CV, H, and D in a unified way. The average SI is also added to the measured parameters. We confirm that variability indices are relevant indicators of aging process and neurodegenerative diseases. While CV is sensitive to aging process and pathology, it does not discriminate between specific neurodegenerative diseases. H, which measures predictability of SI, significantly decreases with age but increases in patients suffering from amyotrophic lateral sclerosis. D, catching complexity of SI, is correlated with total functional capacity in patients with Huntington's disease. We conclude that the computation of H complements the clinical diagnosis of walking in patients with neurodegenerative diseases and we recommend it as a relevant supplement to classical CV or averaged SI. Since H and D indices did not lead to the same observations, suggesting the multi-fractal nature of SI dynamics, we recommend to open clinical gait analysis to the evaluation of more parameters.
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Affiliation(s)
- Frédéric Dierick
- Centre National de Rééducation Fonctionnelle et de Réadaptation - Rehazenter, Laboratoire d'Analyse du Mouvement et de la Posture (LAMP), 2674 Luxembourg, Luxembourg; Centre de recherche et de formation (CeREF Technique), Haute Ecole Louvain en Hainaut, 7000 Mons, Belgium; Faculté des Sciences de la Motricité, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium.
| | - Charlotte Vandevoorde
- Laboratoire Forme et Fonctionnement Humain (FFH), Haute Ecole Louvain en Hainaut, 6061 Montignies-sur-Sambre, Belgium
| | - Frédéric Chantraine
- Centre National de Rééducation Fonctionnelle et de Réadaptation - Rehazenter, Laboratoire d'Analyse du Mouvement et de la Posture (LAMP), 2674 Luxembourg, Luxembourg
| | - Olivier White
- Université de Bourgogne INSERM-U1093 Cognition, Action, and Sensorimotor Plasticity, Campus Universitaire, BP 27877, 21078 Dijon, France
| | - Fabien Buisseret
- Centre de recherche et de formation (CeREF Technique), Haute Ecole Louvain en Hainaut, 7000 Mons, Belgium; Laboratoire Forme et Fonctionnement Humain (FFH), Haute Ecole Louvain en Hainaut, 6061 Montignies-sur-Sambre, Belgium; Service de Physique Nucléaire et Subnucléaire, Université de Mons, UMONS Research Institute for Complex Systems, 7000 Mons, Belgium
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18
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Doho H, Nobukawa S, Nishimura H, Wagatsuma N, Takahashi T. Transition of Neural Activity From the Chaotic Bipolar-Disorder State to the Periodic Healthy State Using External Feedback Signals. Front Comput Neurosci 2020; 14:76. [PMID: 32982709 PMCID: PMC7484049 DOI: 10.3389/fncom.2020.00076] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 07/20/2020] [Indexed: 12/21/2022] Open
Abstract
Chronotherapy is a treatment for mood disorders, including major depressive disorder, mania, and bipolar disorder (BD). Neurotransmitters associated with the pathology of mood disorders exhibit circadian rhythms. A functional deficit in the neural circuits related to mood disorders disturbs the circadian rhythm; chronotherapy is an intervention that helps resynchronize the patient's biological clock with the periodic daily cycle, leading to amelioration of symptoms. In previous reports, Hadaeghi et al. proposed a non-linear dynamic model composed of the frontal and sensory cortical neural networks and the hypothalamus to explain the relationship between deficits in neural function in the frontal cortex and the disturbed circadian rhythm/mood transitions in BD (hereinafter referred to as the Hadaeghi model). In this model, neural activity in the frontal and sensory lobes exhibits periodic behavior in the healthy state; while in BD, this neural activity is in a state of chaos-chaos intermittency; this temporal departure from the healthy periodic state disturbs the circadian pacemaker in the hypothalamus. In this study, we propose an intervention based on a feedback method called the “reduced region of orbit” (RRO) method to facilitate the transition of the disturbed frontal cortical neural activity underlying BD to healthy periodic activity. Our simulation was based on the Hadaeghi model. We used an RRO feedback signal based on the return-map structure of the simulated frontal and sensory lobes to induce synchronization with a relatively weak periodic signal corresponding to the healthy condition by applying feedback of appropriate strength. The RRO feedback signal induces chaotic resonance, which facilitates the transition to healthy, periodic frontal neural activity, although this synchronization is restricted to a relatively low frequency of the periodic input signal. Additionally, applying an appropriate strength of the RRO feedback signal lowered the amplitude of the periodic input signal required to induce a synchronous state compared with the periodic signal applied alone. In conclusion, through a chaotic-resonance effect induced by the RRO feedback method, the state of the disturbed frontal neural activity characteristic of BD was transformed into a state close to healthy periodic activity by relatively weak periodic perturbations. Thus, RRO feedback-modulated chronotherapy might be an innovative new type of minimally invasive chronotherapy.
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Affiliation(s)
- Hirotaka Doho
- Faculty of Education, Teacher Training Division, Kochi University, Kochi, Japan.,Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, Kobe, Japan
| | - Nobuhiko Wagatsuma
- Department of Information Science, Faculty of Science, Toho University, Funabashi, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.,Department of Neuropsychiatry, University of Fukui, Yoshida, Japan
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19
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Venkat N, Sinha M, Sinha R, Ghate J, Pande B. Neuro-Cognitive Profile of Morning and Evening Chronotypes at Different Times of Day. Ann Neurosci 2020; 27:257-265. [PMID: 34556966 PMCID: PMC8455015 DOI: 10.1177/0972753121990280] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Chronotype is the circadian time preference for sleep-wake timings. However, its impact on cognitive performance is least explored. OBJECTIVE The present study investigated the effect of chronotype (morning "M" vs. evening "E") on cognitive measures as a function of time of the day. In addition, the correlation between electroencephalogram (EEG) waves and subjective/objective cognitive measures were investigated. METHOD Cognitive status of 28 adult male subjects (15 "M" and 13 "E") was assessed objectively through event-related potential (ERP) by administering visual odd ball paradigm test and subjectively through Montreal Cognitive Assessment questionnaire. In addition, 20 to 30 min of resting EEG was recorded. Recordings were done from 8 to 10 am and from 4 to 6 pm on a single day. Power spectral analysis of EEG for alpha and beta waves at PZ and FZ cortical sites was done after subjecting selected epochs to fast Fourier transformation. Also, latency and amplitude of P300 potential from event-related potential record were measured. Appropriate statistical tests were applied for analysis. RESULTS Higher alpha and beta power was observed in "E" at PZ in the evening. "M" showed increased P300 latency and amplitude during evening session for frequent and rare stimuli and vice versa in "E."' Significant negative correlation was seen between latency of rare stimuli and alpha and beta power at FZ site during evening in "E" chronotype only. CONCLUSION Result indicates better attention and alertness during evening hours in evening chronotypes and vice versa in morning chronotypes. The findings could be implemented to schedule the mental performance/cognitive load according to individual chronotype.
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Affiliation(s)
- Nanditha Venkat
- MBBS Intern, All India Institute of Medical Sciences, Tatibandh, Raipur, Chhattisgarh, India
| | - Meenakshi Sinha
- Department of Physiology, All India Institute of Medical Sciences, Tatibandh, Raipur (C.G), India
| | - Ramanjan Sinha
- Department of Physiology, All India Institute of Medical Sciences, Tatibandh, Raipur (C.G), India
| | - Jayshri Ghate
- Department of Physiology, All India Institute of Medical Sciences, Tatibandh, Raipur (C.G), India
| | - Babita Pande
- Department of Physiology, All India Institute of Medical Sciences, Tatibandh, Raipur (C.G), India
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20
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Phinyomark A, Larracy R, Scheme E. Fractal Analysis of Human Gait Variability via Stride Interval Time Series. Front Physiol 2020; 11:333. [PMID: 32351405 PMCID: PMC7174763 DOI: 10.3389/fphys.2020.00333] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 03/23/2020] [Indexed: 11/13/2022] Open
Abstract
Fractal analysis of stride interval time series is a useful tool in human gait research which could be used as a marker for gait adaptability, gait disorder, and fall risk among patients with movement disorders. This study is designed to systematically and comprehensively investigate two practical aspects of fractal analysis which significantly affect the outcome: the series length and the parameters used in the algorithm. The Hurst exponent, scaling exponent, and/or fractal dimension are computed from both simulated and experimental data using three fractal methods, namely detrended fluctuation analysis, box-counting dimension, and Higuchi's fractal dimension. The advantages and drawbacks of each method are discussed, in terms of biases and variability. The results demonstrate that a careful selection of fractal analysis methods and their parameters is required, which is dependent on the aim of study (either analyzing differences between experimental groups or estimating an accurate determination of fractal features). A set of guidelines for the selection of the fractal methods and the length of stride interval time series is provided, along with the optimal parameters for a robust implementation for each method.
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Affiliation(s)
- Angkoon Phinyomark
- Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada
| | - Robyn Larracy
- Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada.,Department of Electrical and Computer Engineering, Faculty of Engineering, University of New Brunswick, Fredericton, NB, Canada
| | - Erik Scheme
- Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada.,Department of Electrical and Computer Engineering, Faculty of Engineering, University of New Brunswick, Fredericton, NB, Canada
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21
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Suh YA, Yim MS. A Worker’s Fitness-for-Duty Status Identification Based on Biosignals to Reduce Human Error in Nuclear Power Plants. NUCL TECHNOL 2020. [DOI: 10.1080/00295450.2020.1731405] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Young A Suh
- Korea Advanced Institute of Science and Technology, Department of Nuclear and Quantum Engineering, Nuclear Environment and Nuclear Security Laboratory, Daejeon, Korea
| | - Man-Sung Yim
- Korea Advanced Institute of Science and Technology, Department of Nuclear and Quantum Engineering, Nuclear Environment and Nuclear Security Laboratory, Daejeon, Korea
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22
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González J, Cavelli M, Mondino A, Pascovich C, Castro-Zaballa S, Torterolo P, Rubido N. Decreased electrocortical temporal complexity distinguishes sleep from wakefulness. Sci Rep 2019; 9:18457. [PMID: 31804569 PMCID: PMC6895088 DOI: 10.1038/s41598-019-54788-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 11/06/2019] [Indexed: 11/09/2022] Open
Abstract
In most mammals, the sleep-wake cycle is constituted by three behavioral states: wakefulness (W), non-REM (NREM) sleep, and REM sleep. These states are associated with drastic changes in cognitive capacities, mostly determined by the function of the thalamo-cortical system. The intra-cranial electroencephalogram or electocorticogram (ECoG), is an important tool for measuring the changes in the thalamo-cortical activity during W and sleep. In the present study we analyzed broad-band ECoG recordings of the rat by means of a time-series complexity measure that is easy to implement and robust to noise: the Permutation Entropy (PeEn). We found that PeEn is maximal during W and decreases during sleep. These results bring to light the different thalamo-cortical dynamics emerging during sleep-wake states, which are associated with the well-known spectral changes that occur when passing from W to sleep. Moreover, the PeEn analysis allows us to determine behavioral states independently of the electrodes' cortical location, which points to an underlying global pattern in the signal that differs among the cycle states that is missed by classical methods. Consequently, our data suggest that PeEn analysis of a single EEG channel could allow for cheap, easy, and efficient sleep monitoring.
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Affiliation(s)
- Joaquín González
- Universidad de la República, Departamento de Fisiología de Facultad de Medicina, Av. Gral. Flores 2125, 11800, Montevideo, Uruguay
| | - Matias Cavelli
- Universidad de la República, Departamento de Fisiología de Facultad de Medicina, Av. Gral. Flores 2125, 11800, Montevideo, Uruguay
| | - Alejandra Mondino
- Universidad de la República, Departamento de Fisiología de Facultad de Medicina, Av. Gral. Flores 2125, 11800, Montevideo, Uruguay
| | - Claudia Pascovich
- Universidad de la República, Departamento de Fisiología de Facultad de Medicina, Av. Gral. Flores 2125, 11800, Montevideo, Uruguay
| | - Santiago Castro-Zaballa
- Universidad de la República, Departamento de Fisiología de Facultad de Medicina, Av. Gral. Flores 2125, 11800, Montevideo, Uruguay
| | - Pablo Torterolo
- Universidad de la República, Departamento de Fisiología de Facultad de Medicina, Av. Gral. Flores 2125, 11800, Montevideo, Uruguay.
| | - Nicolás Rubido
- Universidad de la República, Instituto de Física de Facultad de Ciencias, Iguá 4225, 11400, Montevideo, Uruguay
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23
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Exploring Shopper's Browsing Behavior and Attention Level with an EEG Biosensor Cap. Brain Sci 2019; 9:brainsci9110301. [PMID: 31683586 PMCID: PMC6895988 DOI: 10.3390/brainsci9110301] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 10/27/2019] [Accepted: 10/29/2019] [Indexed: 12/01/2022] Open
Abstract
The online shopping market is developing rapidly, meaning that it is important for retailers and manufacturers to understand how consumers behave online compared to when in brick-and-mortar stores. Retailers want consumers to spend time shopping, browsing, and searching for products in the hope a purchase is made. On the other hand, consumers may want to restrict their duration of stay on websites due to perceived risk of loss of time or convenience. This phenomenon underlies the need to reduce the duration of consumer stay (namely, time pressure) on websites. In this paper, the browsing behavior and attention span of shoppers engaging in online shopping under time pressure were investigated. The attention and meditation level are measured by an electroencephalogram (EEG) biosensor cap. The results indicated that when under time pressure shoppers engaging in online shopping are less attentive. Thus, marketers may need to find strategies to increase a shopper’s attention. Shoppers unfamiliar with product catalogs on shopping websites are less attentive, therefore marketers should adopt an interesting style for product catalogs to hold a shopper’s attention. We discuss our findings and outline their business implications.
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Jin Y, Choi J, Lee S, Kim JW, Hong Y. Pathogenetical and Neurophysiological Features of Patients with Autism Spectrum Disorder: Phenomena and Diagnoses. J Clin Med 2019; 8:E1588. [PMID: 31581672 PMCID: PMC6832208 DOI: 10.3390/jcm8101588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/17/2019] [Accepted: 09/30/2019] [Indexed: 12/29/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is accompanied by social deficits, repetitive and restricted interests, and altered brain development. The majority of ASD patients suffer not only from ASD itself but also from its neuropsychiatric comorbidities. Alterations in brain structure, synaptic development, and misregulation of neuroinflammation are considered risk factors for ASD and neuropsychiatric comorbidities. Electroencephalography has been developed to quantitatively explore effects of these neuronal changes of the brain in ASD. The pineal neurohormone melatonin is able to contribute to neural development. Also, this hormone has an inflammation-regulatory role and acts as a circadian key regulator to normalize sleep. These functions of melatonin may play crucial roles in the alleviation of ASD and its neuropsychiatric comorbidities. In this context, this article focuses on the presumable role of melatonin and suggests that this hormone could be a therapeutic agent for ASD and its related neuropsychiatric disorders.
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Affiliation(s)
- Yunho Jin
- Department of Rehabilitation Science, Graduate School of Inje University, Gimhae 50834, Korea.
- Ubiquitous Healthcare & Anti-aging Research Center (u-HARC), Inje University, Gimhae 50834, Korea.
- Biohealth Products Research Center (BPRC), Inje University, Gimhae 50834, Korea.
- Department of Physical Therapy, College of Healthcare Medical Science & Engineering, Inje University, Gimhae 50834, Korea.
| | - Jeonghyun Choi
- Department of Rehabilitation Science, Graduate School of Inje University, Gimhae 50834, Korea.
- Ubiquitous Healthcare & Anti-aging Research Center (u-HARC), Inje University, Gimhae 50834, Korea.
- Biohealth Products Research Center (BPRC), Inje University, Gimhae 50834, Korea.
- Department of Physical Therapy, College of Healthcare Medical Science & Engineering, Inje University, Gimhae 50834, Korea.
| | - Seunghoon Lee
- Gimhae Industry Promotion & Biomedical Foundation, Gimhae 50969, Korea.
| | - Jong Won Kim
- Department of Healthcare Information Technology, College of Bio-Nano Information Technology, Inje University, Gimhae 50834, Korea.
| | - Yonggeun Hong
- Department of Rehabilitation Science, Graduate School of Inje University, Gimhae 50834, Korea.
- Ubiquitous Healthcare & Anti-aging Research Center (u-HARC), Inje University, Gimhae 50834, Korea.
- Biohealth Products Research Center (BPRC), Inje University, Gimhae 50834, Korea.
- Department of Physical Therapy, College of Healthcare Medical Science & Engineering, Inje University, Gimhae 50834, Korea.
- Department of Medicine, Division of Hematology/Oncology, Harvard Medical School-Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
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Korolj A, Wu HT, Radisic M. A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems. Biomaterials 2019; 219:119363. [PMID: 31376747 PMCID: PMC6759375 DOI: 10.1016/j.biomaterials.2019.119363] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 07/09/2019] [Accepted: 07/14/2019] [Indexed: 12/18/2022]
Abstract
Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.
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Affiliation(s)
- Anastasia Korolj
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada
| | - Hau-Tieng Wu
- Department of Statistical Science, Duke University, Durham, NC, USA; Department of Mathematics, Duke University, Durham, NC, USA; Mathematics Division, National Center for Theoretical Sciences, Taipei, Taiwan
| | - Milica Radisic
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada; Toronto General Research Institute, University Health Network, Toronto, Canada; The Heart and Stroke/Richard Lewar Center of Excellence, Toronto, Canada.
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